WO2023019254A2

WO2023019254A2 - Method for classification of cancer

Info

Publication number: WO2023019254A2
Application number: PCT/US2022/074920
Authority: WO
Inventors: Martin Sill; Stefan Pfister; David Jones; Andreas Von Deimling; David Capper; Volker Hovestadt; Felix Sahm; Daniel Schrimpf
Original assignee: Deutsches Krebsforschungszentrum DKFZ; Universitaet Heidelberg
Current assignee: Deutsches Krebsforschungszentrum DKFZ; Universitaet Heidelberg
Priority date: 2021-08-12
Filing date: 2022-08-12
Publication date: 2023-02-16
Anticipated expiration: 2024-02-12
Also published as: JP2024537596A; CN118475939A; CA3228765A1; AU2022326568A1; US20260058010A1; WO2023019254A3; EP4384959A2; EP4384959A4; EP4134452A1

Abstract

The present disclosure pertains to an in vitro method for the diagnostic classification of cancer based on the biological state of specific genomic sites. The disclosure provides a method that allows for a classification of a tumour sample obtained from a patient by analysing a multitude, preferably genome wide, collection of gene sites, combining the biological state of the analysed gene sites into a biological state pattern and comparing with pre-determined biological state patterns pertaining to different cancer types or tumour species. The disclosure is in particular useful for classifying cancer e.g. of the central nervous system, such as brain tumour samples and tumours of the spinal cord, since these are characterized by a large variety of distinct tumour species which have different prognostic values and require a developed treatment regime for each species in the clinical context. However, other cancers could similarly profit from the disclosure, for example sarcomas.

Description

METHOD FOR CLASSIFICATION OF CANCER

FIELD OF THE DISCLOSURE

The present disclosure pertains to the classification of cancer, in particular to a computer- implemented method for the diagnostic classification of cancer and/or an in vitro method for classification of cancer based on the biological state of specific genomic DNA sites or transcripts. The disclosure provides a method that allows for a classification of a cancer sample, specifically a tumour sample obtained from a patient by analysing a multitude, preferably genome wide, of gene sites, combining the biological state of the analysed gene sites into a biological state pattern and comparing it directly and/or indirectly with pre-determined biological state patterns pertaining to different cancer types or tumour species. The disclosure is in particular useful for classifying cancer of the central nervous system, i.e. brain tumour samples and/or tumours of the spinal cord, since these need to be correctly identified from a large variety of distinct tumour species which have different prognostic values and require a developed treatment regime for each species in the clinical context. However, other cancers could similarly profit from the disclosure, for example sarcomas.

BACKGROUND

When looking at brain tumour entities alone, there are more than 100 different entities listed in the World Health Organisation classification. Many of these show complex patterns of potentially overlapping histological features. Moreover, even histologically identical tumours can belong to different molecular groups with very different treatment requirements and prognosis. The same is true for tumours of the spinal cord and tumours originating in tissues outside the central nervous system. Therefore, more advanced diagnostic tools are needed.

Epigenetic patterns, for example the epigenetic states of different gene sites, play a critical role in development, differentiation and pathogenesis of diseases such as multiple sclerosis, diabetes, schizophrenia, aging, and multiple forms of cancer including tumours of the central nervous system. Tumour entities originate from different precursor-cell populations which are transformed by genetic and epigenetic alterations. It is now recognized that many tumour entities, including the ones of the central nervous system, that are of distinct biological groups are not always distinguishable by their histology. Most tumour entities display varied histological spectra with no clear boundaries. Epigenetic modifications, such as methylation, preserve the information of the cell of origin, its original identity. Therefore, methylation data, for example DNA methylation patterns, have a great potential to identify molecular subgroups of tumours, such as tumours of the central nervous system. Similar results can be obtained by analysing the transcripts of the respective genes of interest.

Still, treatment planning and in particular treatment success in many cancers, and in particular in cancers of the central nervous system, is highly dependent on an early and accurate diagnosis and classification of the tumour. In view of the above, new methods that overcome at least some of the problems in the art are beneficial.

SUMMARY

The present disclosure seeks to provide a strategy and method for the diagnostic classification of cancer samples with higher efficiency, specificity and sensitivity.

This object of the present invention is solved by the features of the independent claims. Preferred embodiments are defined in the dependent claims. Any “aspect”, “example” and “embodiment” of the description not falling within the scope of the claims does not form part of the invention and is provided for illustrative purposes only.

According to an independent aspect of the present disclosure, a computer-implemented method for diagnostic classification of cancer is provided. The method includes classifying a cancer using a classification algorithm based on biological states or biological state patterns of a set of gene sites of a cancer sample.

The classification algorithm is trained using biological data derived from classified cancer types, such as pre-classified cancer types. In particular, the cancer types can be pre-classified and/or can be new cancer types which are identified using the classification algorithm. For example, the classification algorithm may classify a cancer sample as unknown, wherein such unknown cancer samples can then be further analysed to determine a cancer type thereof. The further analysis may be conducted by various means, such as software and/or medical personnel. The classification algorithm is trained using at least data pertaining to biological states of the gene sites in Table 1 (SEQ ID No. 1 to SEQ ID No. 688). By training the classification algorithm with the data of all gene sites in Table 1, an efficient and flexible classification tool can be provided.

In particular, a cancer sample can be classified using:

(i) cancer sample data of all 688 gene sites in Table 1, or

(ii) cancer sample data of a subset of the 688 gene sites in Table 1, such as at least 3 gene sites of the cancer sample genome.

In other words, the classification algorithm is trained with biological data pertaining to all 688 gene sites in Table 1, but for the classification of a cancer sample, it might not be necessary to provide cancer sample data of all 688 gene sites. The number of gene sites used to classify the cancer sample can be selected depending on circumstances, such as data available from the cancer sample (e.g., it could be that only data pertaining to a subset of the 688 gene sites are available for analysis), time constraints (the fewer the gene sites, the faster the analysis), sensitivity requirements (the higher the number of gene sites, the higher the accuracy of the analysis), and the like.

In view of the above, the computer-implemented method for the diagnostic classification of cancer may reduce the processing resources used by a GPU and/or reduce the power consumed by a GPU. Moreover, by using cancer sample data of a subset of the 688 gene sites in Table 1, such as at least 3 gene sites of the cancer sample genome, the performance, power consumption, and/or programming flexibility of a GPU that performs the method for the diagnostic classification of cancer may be improved.

Preferably, the set of gene sites comprises at least 3 gene sites of the cancer sample genome selected from a list consisting of the gene sites in Table 1 of this document.

Preferably, the biological states of the gene sites comprise the biological states of the gene sites as listed in Table 1 of this document and preferably up to 20 (or 15 or 12 kb) upstream and/or downstream of each of said gene sites. For example, the biological states of the gene sites comprise the biological states of the gene sites as listed in Table 1 and up to 10 kb, pref- erably up to 8 kb or up to 6 kb or up to 4 kb or up to 2 kb, upstream and/or downstream of the gene sites.

According to some embodiments, which can be combined with other embodiments described herein, the method further includes determining biological states pertaining to the at least 3 gene sites of the cancer sample genome.

Additionally, or alternatively, the method further includes determining a biological state pattern of the set of gene sites based on the determined biological state(s) of each of the at least 3 gene sites.

According to another independent aspect of the present disclosure, a method for diagnostic classification of cancer is provided.

According to some embodiments, which can be combined with other embodiments described herein, the method for diagnostic classification of cancer is an in-vitro method.

In a preferred embodiment, the method includes: providing a cancer sample, determining biological states pertaining to at least 3 gene sites of the cancer sample genome, wherein the gene sites are selected from a list consisting of the gene sites in Table 1, determining a biological state pattern based on the determined biological state(s) of each of the at least 3 gene sites, and classifying a cancer type based on the determined biological state pattern and predetermined biological state patterns pertaining to different cancer types.

Preferably, the biological states of the gene sites comprise the biological states of the gene sites as listed in Table 1 of this document and preferably up to 20 kb (or 15 or 12 kb) upstream and/or downstream of each of said gene sites. For example, the biological states of the gene sites comprise the biological states of the gene sites as listed in Table 1 and preferably up to 10 kb, preferably up to 8 kb or up to 6 kb or up to 4 kb or up to 2 kb, upstream and/or downstream of the gene sites. Preferably, the step of determining a biological state pattern comprises combining the biological state(s) of the gene sites into the biological state pattern.

Preferably, classifying a cancer type comprises comparing the biological state pattern of the set of gene sites with pre-determined biological state patterns derived from the biological state data pertaining to different cancer types.

Preferably, the cancer is classified as a specific cancer type if the biological state pattern of the set of gene sites differs from the biological state data derived from the pre-classified cancer type by at most 5 %, preferably at most 4 % or at most 3 % or at most 2 % or at most 1 %.

Preferably, the biological state is selected from a group including, or consisting of, epigenetic state, mutation state, copy number and RNA expression.

Preferably, the epigenetic state is a methylation state.

Preferably, the set of gene sites comprises at least 10, preferably at least 20 or at least 30 or at least 40 or at least 50 or at least 60 or at least 70 or at least 80 or at least 90 or at least 100 or all gene sites of the cancer sample genome in Table 1.

Preferably, the at least one of the at least 3 gene sites are the ones with the highest values of variable importance (imp_sum) in Tables 3 to 172 of this document. Most preferred, at least one of the at least 3 gene sites is selected from the group including (or consisting) of PTPRN2 (SEQ ID No. 491), PRDM16 (SEQ ID No.477), HDAC4 (SEQ ID No.249), PAX6 (SEQ ID No. 431) and MAD1L1 (SEQ ID No. 349).

Preferably, the biological states of the gene sites comprise exclusively the biological states of the gene sites as listed in Table 1 without any bases upstream and/or downstream of the gene sites.

Preferably, the biological state is a methylation state and/or the biological state pattern is a methylation state pattern. Preferably, the cancer is a cancer of the central nervous system or a sarcoma. However, the present disclosure is not limited thereto, and other cancer types, such as carcinomas, sarcomas, myelomas, neural crest lineage tumors (e.g., melanoma), leukaemia, lymphoma and mixed types can be classified using the method according to the present invention.

Preferably, the cancer is a cancer listed in Table 2.

Preferably, the method further includes determining a further (second) biological state different from the (first) biological state and pertaining to at least one of the gene sites pertaining to the cancer sample genome.

Preferably, the method further includes correlating the further (second) biological state of the at least one gene site pertaining to the cancer sample genome with the classified cancer type.

Preferably, the method further includes defining the at least one gene site with the determined further (second) biological state as an alternative or additional biomarker in the diagnosis of the classified cancer types.

Preferably, the further (second) biological state is selected from the group including, or consisting of, epigenetic state, mutation state, RNA expression and copy number.

According to another independent aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium has computer-executable instructions stored, that, when executed, cause a computer to perform the methods described herein.

The term “computer-readable storage medium” may refer to any storage device used for storing data accessible by a computer, as well as any other means for providing access to data by a computer. Examples of a storage device-type computer-readable medium include: a magnetic hard disk; a floppy disk; an optical disk, such as a CD-ROM and a DVD; a magnetic tape; a memory chip.

According to another independent aspect of the present disclosure, a system for diagnosing cancer is provided. The system includes one or more processors and a memory coupled to the one or more processors and comprising instructions executable by the one or more processors to implement the methods described herein.

The system may be a computer system. The term a “computer system” may refer to a system having a computer, where the computer comprises a computer-readable storage medium embodying software to operate the computer.

The term “software” is used interchangeably herein with “program” and refers to prescribed rules to operate a computer. Examples of software include: software; code segments; instructions; computer programs; and programmed logic.

The embodiments of the present disclosure provide a classification of cancer samples in cancer diagnosis using a classification algorithm, which is a machine learning (ML) algorithm.

The term “classification” refers to a procedure and/or algorithm in which individual items are placed into groups or classes based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, features, etc.) and based on a statistical model and/or a training set of previously labelled items. Specifically in the context of the present disclosure, classification preferably means determining which specific cancer type, for example determined by its epigenetic features, a cancer sample belongs to.

The term “machine learning algorithm” as used throughout the present application refers to an algorithm that builds a model based on training data, in order to make predictions or decisions without being explicitly programmed to do so. In particular, the term “classification” refers to a machine learning algorithm in which individual items are placed into groups or classes based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, features, etc.) and based on a statistical model and/or a training data set of previously labelled items. Specifically in the context of the present invention, classification preferably means determining which specific cancer type, for example determined by its epigenetic state pattern, a cancer sample belongs to.

The term “training data set” in context of the invention refers to a set of biological state data, such as genomic methylation data, of a multitude of tumours that were classified by prior art methods, and therefore are of known tumour species. The classification algorithm can be any appropriate algorithm for establishing a correlation between datasets, namely the biological state(s) or biological state pattern(s) of the cancer sample and the biological state data derived from pre-classified cancer types, which can be pre-determined biological state(s) or biological state patterns. Methods for establishing correlation between datasets include, but are not limited to, discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), Discriminant Functional Analysis (DFA), Kernel Methods (e.g., SVM), Multidimensional Scaling (MDS), Nonparametric Methods (e.g., k-Nearest- Neighbour Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods), Generalized Linear Models (e.g., Logistic Regression), Principal Components based Methods (e.g., SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based methods.

The person skilled on the art will have no problem in selecting an appropriate meth- od/algorithm to establish the correlation between the biological state(s) or biological state pattem(s) of the cancer sample and the biological state data derived from pre-classified cancer types of the present invention. In one embodiment, the method/algorithm used in a correlating the biological state(s) or biological state pattern(s) of the cancer sample and the biological state data derived from pre-classified cancer types of the present invention is selected from the group including (or consisting of) DA (e.g., Linear-, Quadratic-, Regularized Discriminant Analysis), DFA, Kernel Methods (e.g., SVM), MDS, Nonparametric Methods (e.g., k- Nearest-Neighbour Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting Methods), Generalized Linear Models (e.g., Logistic Regression), and Principal Components Analysis.

In an exemplary embodiment, the classification algorithm uses random forest analysis. As used herein the term “random forest analysis” refers to a computational method that is based on the idea of using multiple different decision trees to compute the overall most predicted class (the mode). In a specific application, the mode will be either tumour species or class based on how many decision trees predicted the samples to match a specific class. The class predicted by the majority is selected as the predicted class for the sample. The different decision trees used in this algorithm are trained on a randomly generated subset of the training data set and on a randomly selected set of the variables. This is why this algorithm relies on two hyperparameters: the number of random trees to use, and the number of random variables used to train the different trees.

The term “biological state” may refer to an epigenetic state, mutation state, RNA expression or copy number of a gene or gene site.

The term “epigenetic state” refers to a measure for epigenetic changes (or for functionally relevant changes of an upregulation and/or downregulation) of the gene activity of a particular gene site and/or gene in the genome of a cancer sample. The epigenetic state comprises an epigenetic downregulation and/or upregulation of the gene site’s activity in the cancer sample in comparison to that same gene site’s activity in physiological tissue. Such downregulation and/or upregulation can for example be due to DNA methylation, histone modification or other epigenetic effects.

The term “epigenetic state pattern” refers to a combination of the epigenetic state(s) of a plurality of gene sites and/or genes. It comprises an overview of the epigenetic state(s) of the gene sites and/or genes. An epigenetic state pattern can therefore in its simplest form comprise information about which of the gene sites and/or genes of the plurality in question have an activation which is epigenetically modified in comparison to the physiological state and which do not. The epigenetic state pattern could also comprise information about which of the gene sites and/or genes are epigenetically upregulated and/or downregulated, for example in terms of hypermethylation (resulting in downregulation) or hypomethylation (resulting in upregulation) when DNA methylation is used as measure for epigenetic influence on gene or gene site activity.

In some embodiments, the classification algorithm of the present disclosure can be trained using epigenetic data derived from classified cancer types, such as pre-classified cancer types. The epigenetic data may be provided in the form of a predetermined pattern or predetermined epigenetic state pattern. The term “predetermined pattern” or “predetermined epigenetic state pattern” refers to an epigenetic state pattern that has been determined beforehand and that is typical of a specific cancer type, for example one of the types mentioned in Table 2 (and, for example, Tables 3 to 172). The first iteration of predetermined patterns has been determined by the inventors and has been used to train the classification algorithm. In a preferred embodiment of the disclosure, the predetermined epigenetic state patterns pertain to the cancer types listed in Table 2 (and, for example, Tables 3 to 172, respectively). Furthermore, the predetermined epigenetic state pattern comprises essentially the same gene sites as the set of gene sites of the cancer sample being analysed. If, by determining the epigenetic state of the set of gene sites, as explained in more detail below, an epigenetic state pattern is obtained that corresponds to one of the predetermined patterns, the cancer pertaining to the sample can be classified as pertaining to this cancer type. The predetermined epigenetic state patterns are preferably determined by the classification algorithm. This means that the predetermined epigenetic state patterns are not in itself accessible by a user, but contained in the results of the classification algorithm, which, for example, employs machine learning and continually updates its own reference material. The predetermined epigenetic state patterns determined by the classification algorithm therefore change over time in an effort to increase sensitivity and specificity even further. It is therefore neither feasible nor useful to give an example of the predetermined patterns used in the invention as they are subject to continuous change. On the other hand, the skilled person is familiar with these aspects of machine learning and can easily provide for a classification algorithm to establish its own predetermined epigenetic state patterns as used herein.

Biological changes, such as epigenetic changes, in cancer tissue are known to be specific for certain cancer types or subtypes. The biological state(s) of a gene site can be determined using different methods known to the skilled person. For example, the biological state, such as the epigenetic state, of a gene site can be determined by assaying histone modifications, proteomics or transcriptomics. One approach could, for example, be based on an Assay for Trans- posase-Accessible Chromatin using sequencing (ATAC-seq). Another approach is assaying DNA methylation. As there are robust and reliable DNA methylation assays established and readily available, determining the epigenetic state of gene sites through determining methylation is the preferred approach used in the disclosure. However, it is not a single data point determined by any of the mentioned assays that determines the type of the cancer. The type of the cancer is determined by the epigenetic downregulation or upregulation of its gene sites, which in turn determines the metabolism and phenotype of the cancer. Gene site activation can, however, be determined by a number of different epigenetic approaches, as outlined above. To classify cancer types, it is therefore more prudent to determine the effect of the epigenetic changes on gene site activity rather than rely on the specific epigenetic changes measured by a specific type of assay. In theory, all of the epigenetic approaches should in the end imply the same gene sites as having a pathological activity, provided that all gene sites and their activity are equally accessible through the various assays. This pathological gene site activity is what makes and defines the cancer types.

In view of the above, the methods of the present disclosure classify a cancer based on the biological state of specific genomic DNA sites or transcripts.

In one embodiment of the disclosure, the inventors used DNA methylation to find gene sites with pathological activity within the cancer genome. The epigenetic state of these gene sites was then used to find patterns typical for different cancer types. Thus, the inventors found a set of gene sites having the biggest impact on differentiating between different cancer types.

To this end the inventors tested their approach using an Illumina methylation bead chip with which a multitude of classically classified tumour specimen were tested. Illumina's Hu- manMethylation450 (450k) BeadChip allows to assays DNA methylation at 482,421 CpG dinucleotides. The platform measures DNA methylation by genotyping sodium bisulfite treated DNA. To run the assay only a small amount of DNA is needed and it is possible to use both frozen and paraffin (FFPE) material. So far, approximately 90000 tumour samples have been profiled by the inventors and allowed the verification of the surprisingly superior approach of the herein disclosed disclosure.

As readily apparent to the skilled person, the classification according to the disclosure also means that a stratification and/or a diagnosis of the cancer is achieved. In the context of the present disclosure the term “stratification” refers to the classification or grouping of patients according to one or more predetermined criteria. In certain embodiments stratification is performed in a diagnostic setting in order to group a patient according to the prognosis of disease progression, either with or without treatment. In particular embodiments stratification is used in order to distribute patients enrolled for a clinical study according to their individual characteristics. In particular embodiments stratification is used in order to identify the best suitable treatment option for a patient.

The term “diagnosis” or “diagnostic” is used herein to refer to the identification or classification of a molecular or pathological state, disease or condition. For example, “diagnosis” may refer to identification of a particular type of cancer, e.g., a lung cancer. “Diagnosis” may also refer to the classification of a particular type of cancer, e.g., by histology (e.g., a non-small cell lung carcinoma), by molecular features (e.g., a lung cancer characterized by nucleotide and/or amino acid variation(s) in a particular gene or protein), or both. However, it is important to note that the disclosure is directed to a strictly in vitro method in all its embodiments. None of the method steps of any embodiment are performed on the human or animal body.

The term “cancer type”, “tumour species” or “tumour class” shall refer to a specific kind of a tumour or subcategory of a tumour that can be classified based on its tissue origin, genetic makeup, histology etc. In particular in the field of brain tumours various distinct tumour species or classes of the central nervous system exist that can be differentiated via for example histopathology (1. Acta Neuropathol. 2007 Aug; 114(2):97-109. Epub 2007 Jul 6. “The 2007 WHO classification of tumours of the central nervous system.” Louis DN(1), Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P.). Specifically, the disclosure pertains to the cancer types as listed in Table 2.

The term “cancer sample” or “tumour sample” as used herein refers to a sample obtained from a patient. The tumour sample can be obtained from the patient by routine measures known to the person skilled in the art, i.e., biopsy (taken by aspiration or punctuation, excision or by any other surgical method leading to biopsy or resected cellular material). For those areas not easily reached via an open biopsy a surgeon can, through a small hole made in the skull, use stereotaxic instrumentation to obtain a “closed” biopsy. Stereotaxic instrumentation allows the surgeon to precisely position a biopsy probe in three-dimensional space to allow access almost anywhere in the brain. Therefore, it is possible to obtain tissue for the diagnostic method of the present disclosure. The actual removal of the sample from the patient is, however, not part of the inventive method. “Providing a cancer sample” therefore merely pertains to making a sample available for laboratory use without the step of obtaining it from a patient in the first place.

The term “cancer” or “tumour” is not limited to any stage, grade, histomorphological feature, invasiveness, aggressiveness or malignancy of an affected tissue or cell aggregation. In particular stage 0 cancer, stage I cancer, stage II cancer, stage III cancer, stage IV cancer, grade I cancer, grade II cancer, grade III cancer, malignant cancer, primary carcinomas, and all other types of cancers, malignancies etc. are included. As used herein, the term “gene site” refers to a region of DNA comprising or consisting of a gene, particularly a gene or gene site as listed in Table 1. In particular, the term “gene site” refers to a DNA sequence with a genetic locus as defined in Table 1. A gene site may comprise additional base pairs upstream and/or downstream of a gene, for example up to 12 kb, preferably up to 10 kb up to 8 kb or up to 6 kb or up to 4 kb or up to 2 kb upstream and/or downstream. A biological state, such as an epigenetic state, of a gene site may therefore refer to the biological state of the gene itself and additionally to the biological state of the additional string of base pairs upstream and/or downstream of the gene. In preferred embodiments of the disclosure, the biological state of the gene sites in the set comprises the biological state of the gene sites as listed in Table 1 and up to 10 kb, preferably up to 8 kb or up to 6 kb or up to 4 kb or up to 2 kb, upstream and/or downstream of the genes. In a further preferred embodiment, the biological state of the gene sites comprises exclusively the biological state of the gene sites as listed in Table 1 without any bases upstream and/or downstream of the gene sites. In this case, only the biological state of the gene sites themselves are being used and the gene sites do not comprise any bases outside of the gene sites as listed in Table 1.

The term “set of gene sites” refers to a number of gene sites being grouped together. For example, it is the epigenetic state(s) of this set of gene sites that is being evaluated in the disclosure, then combined into a pattern and analysed by the classification algorithm.

As used herein, the term “CpG site” or “CpG position” refers to a region of DNA where a cytosine nucleotide occurs next to a guanine nucleotide in the linear sequence of bases along its length, the cytosine (C) being separated by only one phosphate (p) from the guanine (G). About 70% of human gene promoters have a high CpG content. Regions of the genome that have a higher concentration of CpG sites are known as “CpG islands”. Cytosines in CpG dinucleotides can be methylated to form 5 -methylcytosine. Methylation of (i.e., introduction of a methyl group in) the cytosines of CpG site within the promoters of genes can lead to gene silencing, a feature found in a number of human cancers. In contrast, the hypomethylation of CpG sites has generally been associated with the over-expression of oncogenes within cancer cells. The term “independent genomic CpG positions” shall in the context of the present disclosure mean that each CpG position of a group of genomic CpG positions can be probed separately for its methylation state. The term “methylation state”, as used herein describes the state of methylation of a CpG position, thus refers to the presence or absence of 5-methylcytosine at one CpG site within genomic DNA. When none of the DNA of an individual is methylated at one given CpG site, the position is 0% methylated. When all the DNA of the individual is methylated at that given CpG site, the position is 100% methylated. When only a portion, e.g., 50%, 75%, or 80%, of the DNA of the individual is methylated at that CpG site, then the CpG position is said to be 50%, 75%, or 80%, methylated, respectively. The term “methylation state” reflects any relative or absolute amount of methylation of a CpG position. Methylation of CpG positions can be assessed by any method used in the art. The terms “methylation” and “hypermethylation” are used herein interchangeably. When used in reference to a CpG positions, they refer to the methylation state corresponding to an increased presence of 5-methylcytosine at a CpG site within the DNA of a biological sample obtained from a patient, relative to the amount of 5- methylcytosine found at the CpG site within the same genomic position of a biological sample obtained from a healthy individual, or alternatively from an individual suffering from a tumour of a different class or species.

The term “biological sample” is used herein in its broadest sense. In the practice of the present disclosure, a biological sample is generally obtained from a subject. A sample may be any biological tissue or fluid with which the biological state(s) of gene sites of the present disclosure may be assayed. Frequently, a sample will be a “clinical sample” (i.e., a sample obtained or derived from a patient to be tested). The sample may also be an archival sample with known diagnosis, treatment, and/or outcome history. Examples of biological samples suitable for use in the practice of the present disclosure include, but are not limited to, bodily fluids, e.g., blood samples (e.g., blood smears), and cerebrospinal fluid, brain tissue samples, spinal cord tissue samples or bone marrow tissue samples such as tissue or fine needle biopsy samples. Biological samples may also include sections of tissues such as frozen sections taken for histological purposes. The term “biological sample” also encompasses any material derived by processing a biological sample. Derived materials include, but are not limited to, cells (or their progeny) isolated from the sample, as well as nucleic acid molecules (DNA and/or RNA) extracted from the sample. Processing of a biological sample may involve one or more of: filtration, distillation, extraction, concentration, inactivation of interfering components, addition of reagents, and the like. The method according to some embodiments of the present disclosure includes a step of “determining an epigenetic state” of a set of gene sites. This can be achieved through any means suitable to assay epigenetically modified activity of the gene sites. In a preferred embodiment of the disclosure the epigenetic state of a set of gene sites is determined by assessing the DNA methylation state of a multitude of independent genomic CpG positions, particularly CpG positions within the gene sites as mentioned above, preferably within the gene sites listed in Table 1, in a biological sample obtained from a patient. Determination of the methylation state may be performed using any method known in the art to be suitable for assessing the methylation of cytosine residues in DNA. Such methods are known in the art and have been described; and one skilled in the art will know how to select the most suitable method depending on the number of samples to be tested, the quantity of sample available, and the like.

Thus, the methylation state of a genomic CpG position or a combination of genomic CpG positions according to the disclosure can be determined using any of a wide variety of methods that are generally divided into strategies based on methylation- specific PCR (MSP), and strategies employing PCR performed under methylation-independent conditions (MIP). Methylation-independent PCR (MIP) primers are used in most of the available PCR-based methods. They are designed for proportional amplification of methylated and unmethylated DNA. In contrast, methylation- specific PCR (MSP) primers are designed for the amplification of methylated template only.

Examples of methylation-independent PCR based techniques include, but are not limited to, direct bisulfite direct sequencing (Frommer et al., PNAS USA, 1992, 89: 1827-1831), pyrosequencing (Collela et al., Biotechniques, 2003, 35: 146-150; Uhlmann et al., Electrophoresis, 2002, 23: 4072-4079; Tost et al., Biotechniques, 2003, 35: 152-156), Combined Bisulfite Restriction Analysis or “COBRA” (Xiong et al., Nucleic Acids Res., 1997, 25: 2532-2534), Methylation-Sensitive Single-Nucleotide Primer Extension or “MS-SnuPE” (Gonzalgo et al., Nucleic Acids Res., 1997, 25: 2529-2531), Methylation-Sensitive Melting Curve Analysis or “MS-MSA” (Worm et al., Clin. Chem., 2001, 47: 1183-1189), Methylation-Sensitive High- Resolution Melting or “MS-HRM” (Wojdacz et al., Nucleic Acids Res., 2007, 35:e41), MALDI-TOF mass spectrometry with base-specific cleavage and primer extension (Ehrich et al., PNAS USA, 2005, 102: 15785-15790), and HeavyMethyl (Cottrell et al., Nucleic Acids Res., 2004, 32: elO). Examples of methylation- specific PCR based techniques include for example methylation specific PCR or “MSP” (Herman et al., PNAS USA, 1996, 93: 9821-9826; Mackay et al., Hum. Genet., 2006, 120: 262-269; Mackay et al., Hum. Genet., 2005, 116: 255-261; Palmisano et al., Cancer Res., 2000, 60: 5954-5958; Voso et al., Blood, 2004, 103: 698-700), MethylLight (Eads et al., Nucleic Acids Res., 2000, 28:e32; Eads et al., Cancer Res., 1999, 59: 2302-2306; Lo et al., Cancer Res., 1999, 59: 3899-3903), Melting curve Methylation Specific PCR or “McMSP” (Akey et al., Genomics, 2002, 80: 376-384), Sensitive Melting Analysis after Real-Time MSP or “SMART-MSP” (Kristensen et al., Nucleic Acids Res., 2008, 36: e42), and Methylation- Specific Fluorescent Amplicon Generation or “MS-FLAG” (Bonanno et al., Clin. Chem., 2007, 53: 2119-2127).

Many of these methods rely on the prior treatment of DNA with sodium bisulphite. This treatment leads to the conversion of unmethylated cytosine to uracil, while methylated cytosine remains unchanged (Clark et al., Nucleic Acids Res., 1994, 22: 2990-2997). This change in the DNA sequence following bisulphite conversion can be detected using a variety of methods, including PCR amplification followed by DNA sequencing. It is safe to say that the use of bisulphite-converted DNA for DNA methylation analysis has surpassed almost every other methodology for DNA methylation analysis, thereby becoming the gold standard for detecting changes in DNA methylation. The protocol described by Frommer et al. (PNAS USA, 1992, 89: 1827-1831) has been widely used for sodium bisulphite treatment of DNA, and a variety of commercial kits are now available for this purpose.

Thus, in a method according to the disclosure, the step of determining the epigenetic state can be achieved by determining the methylation state of a gene promoter, or of a combination of gene promoters of the disclosure. It may be performed using any of the techniques described above or any combination of these techniques. One skilled in the art will recognized that when the methylation state of a combination of gene promoters has to be determined, the determinations may be performed using the same DNA methylation analysis technique or different DNA methylation analysis techniques. Other methods include oligonucleotide methylation tiling arrays, BeadChip assays, HPLC/MS methods, methylation-specific multiplex ligation-dependent probe amplification (MS-MPLA), bisulphite sequencing, and assays using antibodies to DNA methylation, i.e., ELISA assays. By using the statistical model as described herein, the inventors found that the gene sites comprising the gene sites listed in Table 1 are sufficient to classify cancer samples into a large number of different cancer types. While it may be possible to classify even more cancer types by analysing the named gene sites, this has been validated for the cancer types listed in Table 2. To classify a cancer type, according to the disclosure, it is therefore only necessary to determine the epigenetic state of these selected gene sites, in particular of at least 3 gene sites. A full analysis of the whole genome of the cancer type can therefore be avoided. For a sufficiently specific classification, only those gene sites listed in Table 1 must be analysed, resulting in quicker and less laborious diagnosis.

The inventors further found that a set of gene sites comprising at least 3 of the gene sites listed in Table 1 is sufficient for the classification of the cancer sample. However, larger sets provide more accuracy. In preferred embodiments of the disclosure, the set of gene sites thus comprises at least 10, preferably at least 20 or at least 30 or at least 40 or at least 50 or at least 60 or at least 70 or at least 80 or at least 90 or at least 100, genes of the sample genome of the cancer being classified. A set of gene sites preferably comprising 100 or less, 90 or less, 80 or less, 70 or less, 60 or less, 50 or less, 40 or less, 30 or less, 20 or less, or 10 or less gene sites provide for a good balance between accuracy and work necessary. The embodiments of the present disclosure are not limited thereto, and the set may comprise more than 100 gene sites or all gene sites listed in Table 1.

While all of the gene sites or genes listed in Table 1 could be used to classify the cancer types as described herein in Table 2, the inventors identified subsets of the genes with higher importance, meaning resulting in more accuracy, when used to classify specific cancer types. It is therefore preferred that the predetermined pattern for a cancer type as listed in Table 2 comprises at least 3 gene sites for that specific cancer type. It is further preferred that the predetermined pattern for a cancer type comprises at least 3 gene sites for that specific cancer type selected from the gene sites listed in Tables 3 to 172, respectively. In a preferred embodiment the set of gene sites of the cancer sample genome being analysed comprises the exact same gene sites or genes as the predetermined pattern.

In a preferred embodiment, the statistical model employed by the inventors provides for a measure of the variable importance of the gene sites for each cancer. As can be seen from Tables 3 to 172, the different gene sites have different importance for the classification. To improve the accuracy of the classification, it is therefore preferred that the epigenetic data for a cancer type comprises those gene sites listed in Tables 3 to 172 for that cancer type that are the ones with the highest values of variable importance for that cancer type.

As stated before, it is preferred that the set of gene sites of the cancer sample genome being analysed comprises the same genes or gene sites as the epigenetic data derived from preclassified cancer types (predetermined pattern). The set of gene sites of the cancer sample genome being analysed, and the epigenetic data derived from pre-classified cancer types therefore preferably also comprise the same number of genes or gene sites.

While analysing gene sites of a set of genes comprising 3 genes is advantageous for being less laborious, the accuracy of the classification increases with more genes being analysed per cancer type. It is therefore preferred that the predetermined pattern for a cancer type listed in Table 2 comprises at least 10, preferably at least 20 or at least 30 or at least 40 or at least 50 or at least 60 or at least 70 or at least 80 or at least 90 or at least 100, gene sites or genes listed in Table 1. The preferred gene sites or genes “for a cancer type” are the ones listed for each cancer type in in Tables 3 to 172, respectively. As explained above for the set, 80 to 100 genes provide for a good balance between accuracy and workload.

The classification may include a direct or indirect comparison of the epigenetic state pattern of the set of gene sites with predetermined epigenetic state patterns, e.g. by determining the overlap of the two patterns, i.e., how much the two patterns are similar to or different from each other. This may, for example, be statistically determined and may be represented as a numerical value. Specifically, the difference between the patterns may be represented by a percentage. The accuracy of the classification can be influenced by allowing patterns with higher or lower difference from a predetermined pattern to still be classified as the cancer type the predetermined pattern pertains to. For a suitable accuracy, it is preferred that the cancer is classified as the cancer to which the predetermined pattern pertains if the epigenetic state pattern of the set of gene sites differs from the predetermined pattern by at most 5 %, preferably at most 4 % or at most 3 % or at most 2 % or at most 1 %. These values are both useful in practice and achievable by the inventive method. As explained above, the predetermined epigenetic state patterns used for comparison have been determined by the inventors by analysing more than 90000 cancer samples from a range of different sources. As this process is also part of the present disclosure, it is explained in detail below.

In all embodiments the method of the disclosure is performed as an ex-vivo or in-vitro method.

In another preferred aspect of the present invention, the invention then relates to a method of treating cancer in a patient, comprising performing a method according to the present invention, and providing a suitable treatment to said patient, wherein said treatment is based, at least in part, on the results of the method according to the present invention.

In another preferred aspect of the present invention, the invention relates to a method of developing a treatment regime for the cancer (e.g., a tumour species) classified using the method according to the present invention. Preferably, the method further includes providing a suitable treatment to a patient based on the developed treatment regime.

“Treatment” shall mean a reduction and/or amelioration of the symptoms of the disease. An effective treatment achieves, for example, a shrinking of the mass of a tumor and the number of cancer cells. A treatment can also avoid (prevent) and reduce the spread of the cancer, such as, for example, affect metastases and/or the formation thereof. A treatment may be a naive treatment (before any other treatment of a disease had started), or a treatment after the first round of treatment (e.g. after surgery or after a relapse). The treatment can also be a combined treatment, involving, for example, chemotherapy, surgery, and/or radiation treatment. The treatment can also modulate auto-immune response, infection and inflammation.

Most preferably the methods according to the disclosure are used for the classification of tumours of the central nervous system, therefore, the tumour preferably is a brain tumour or a spinal cord tumour, and the tumour species is a brain tumour species or a spinal cord tumour species. As already noted herein before, these tumours are characterized by a huge epigenetic variety which has a significant impact on the development of treatment regimes in order to allow for the best treatment of the patient. If the tumour disease is a tumour of the central nervous system (CNS), it is preferred that said tumour species comprises at least 184 different classes of CNS tumours. Additionally, the disclosure is also applicable to sarcomas. In a preferred embodiment said CNS tumours are selected form the list of cancer types or tumour species of Table 2.

The determination of DNA methylation levels of the disclosure is performed preferably with a genomic array or chip comprising probes which are specific for the methylation of at least 1000 CpG positions. It is preferred to test as many positions as possible in order to allow for the generation of a highly specific classification. Genome-wide DNA methylation assays are therefore preferred, such as the HumanMethylation450k-chip (Illumina®).

The classification algorithm may be based on random forest (RF). The training of the RF- based classification algorithm according to some embodiments of the disclosure may comprise a preceding step of selecting CpG position which of all CpG positions used provide the purest splitting rules, and using said selected CpG positions as a training-data-optimization- set to train a classification rule.

In other embodiments of the disclosure the training of the RF-based classification algorithm may comprise a step of down-sampling for each tumour species the number of bootstrap samples to the minority class, the minority class being the lowest sample size of a tumour species in the training dataset.

Another embodiment of the disclosure provides the above method and comprises the further step of including the methylation data of the tumour sample as classified into the training- data-set to obtain an enhanced-training-data-set and computing an enhanced-classification- rule by random forest analysis based on the enhanced-training-dataset. Optionally the classification of said tumour sample may be repeated with the enhanced-classification-rule. This embodiment serves the continuous development and improvement of the original training data set. Each further classified tumour species will have a genomic DNA methylation profile or epigenetic state pattern that further enhances the classification for that tumour species and that can then be used as a predetermined epigenetic state pattern in the present disclosure. Therefore, the disclosure in one preferred embodiment provides a classification system characterized by a self-learning classification rule. In order to provide a classification rule with good specificity and sensitivity, the predetermined methylation data I epigenetic state pattern used in context of the present disclosure includes for each pre-classified cancer type the methylation state/levels at said CpG position of at least one, two, three, four, five, six or more independent samples.

Another aspect of the present disclosure then pertains to a method for stratifying the treatment of a tumour patient, comprising the classification of the tumour species I cancer type of the tumour of the patient according to the classification methods of the disclosure and stratifying the treatment of the patient in accordance with the diagnosed tumour species.

Yet a further aspect of the disclosure pertains to a computer-implemented method for generating a classification-rule for aiding the classification of tumour samples in cancer diagnosis, the method comprising providing DNA methylation data of a multitude of independent genomic CpG positions of genomes of a multitude of diverse pre-classified tumour species of the same tumour type (for example brain cancer, lung cancer, leukaemia, etc.); computing a random forest of binary decision trees from the DNA methylation data, wherein in each binary decision tree of said random forest each node is a CpG position, and each terminal leave a specific tumour species, and each binary splitting rule is a methylation state at said CpG position. This method can be used to create the predetermined epigenetic state patterns as explained above.

To learn a classification rule that allows predicting the class assignment of future diagnostic cases the inventor’s applied the machine learning algorithm RandomForest (RF; Breiman, 2001). The RF algorithm is a so-called ensemble method that combines the predictions of several 'weak' classifiers to achieve improved prediction accuracy. The RF uses binary classification trees (Classification and Regression Trees (CART); Breiman et al., 1983) as 'weak' classifiers. Each of these trees is a sequence of binary splitting rules that are learned by recursive binary splitting. The CART algorithm starts with all samples assigned to a 'root' node and tries to find the variable, e.g., a measured CpG probe, and a corresponding cut-off that results in the purest split into the different classes. To measure this gain in class 'purity' the Gini index, a classical statistical measure for inequality, may be used. To fit a tree the CART algorithm iteratively repeats these steps until no further improvements can be made, i.e., only samples of the same class are assigned to the final 'leaf node, or a pre-specified node size is achieved. To predict the class of a new diagnostic case the binary splitting rules are compared with the new data starting in the root node down to one of the leaf nodes. The tree then predicts or votes for the class dominating that leaf node.

Decision trees have the advantage that they are non-parametric and do not rely on any distributional assumptions. Moreover, trees allow to learn complex non-linear relationships and interactions, they are easy to interpret and can be efficiently fitted in large data sets. The main disadvantages of decision trees are that they often tend to overfit the data and that they have a weak prediction performance.

However, to improve the prediction accuracy of a single tree the RF algorithm combines thousands of trees by bootstrap aggregation (bagging). In brief, each tree is fitted using training data sets that are generated by drawing bootstrap samples, i.e., randomly selecting two- third of the data with replacement. In addition, at each node only a random subset of the available variables is used to find an optimal splitting rule. This additional source of randomization allows selecting variables with lower predictive value that would otherwise be ruled out by the most prominent variables. This feature guarantees that the resulting trees are decorrelated, i.e., they use different variables to find an optimal prediction rule. Taking the majority vote over thousands of bootstrap aggregated and decorrelated trees greatly improves the prediction accuracy of the RF. The majority vote, i.e., the proportion of trees voting for a class, can be used as empirical class probabilities or scores that turned out to be a very useful tool for diagnosis.

To validate the resulting RF classifier, a repeated five-fold cross-validation is applied. In each cross-validation the reference set is randomly split into five parts. Then four-fifth of the data is used to train the RF classifier and one-fifth is used for prediction. Currently the estimated test error of the classifier is around 3.1%.

Alternatively, the resulting RF classifier is validated by a repeated threefold cross-validation. In each cross validation the reference set is randomly split into three parts. Then two-third of the data is used to train the RF classifier and one-third is used for prediction. Currently the estimated test error of the classifier is around 4.9%.

The classification scores generated by the RF, i.e., the proportion of trees voting for a class, are typically unequally distributed between classes. Furthermore, if interpreted as class prob- abilities, the scores often fail to estimate the actual class probabilities and are thus said to be not well-calibrated. However, to judge the classification of a single case in the context of clinical diagnosis, the uncertainties associated with an individual prediction in terms of a confidence scores, or estimated class probability is needed. To receive recalibrated scores that are comparable between classes and that are improved estimates of the certainty of individual predictions, the inventors fit a calibration model to raw RF scores. This calibration model is a multinomial logistic regression model, which takes the tumour subclasses as response variable and the ‘raw’ RF scores as explanatory variables. In addition, this model is fitted by incorporating a small ridge-penalty on the likelihood to prevent the model from over fitting as well as to stabilize estimation in situations where classes are perfectly separable. The amount of this regularization, i.e., the penalization parameter, is determined by running a ten-fold cross- validation and choosing the value that minimizes the misclassification error. To fit this model independent, ‘raw’ RF scores are needed, i.e., the scores need to be generated by an RF classifier that was not trained using the same samples, otherwise the RF scores will be systematically biased and not comparable to scores of unseen cases. To generate such independent ‘raw’ scores, the inventors apply a three-fold cross validation.

To validate the class predictions generated by using the recalibrated scores of the calibration model a three-fold nested cross-validation is applied. In each cross validation the reference set is randomly split into three parts. Then two-third of the data is used to train the RF classifier and one-third is used for prediction. Within each of these three cross-validation runs a nested three-fold cross-validation is applied to generate independent RF scores, which are used to train a calibration model. The predicted RF scores resulting from the outer cross-validation loop are then recalibrated by using the suitable calibration model, i.e. a model that was fitted using the RF scores generated by using the other two-third of the data in the inner loop. Currently the estimated test error of the classifier when using the recalibrated scores for prediction is around 3.2%.

Some embodiments of the disclosure pertain to a method where the diverse tumour species are selected from metastatic tumours, tumours stemming from specific tissues, tumours in a specific stage, recurrent tumours, tumours having a specific genetic mutation, tumours of patients having different gender, age or genetic background. BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will now be further described in the following examples with reference to the accompanying figures and sequences, nevertheless, without being limited thereto. For the purposes of the present disclosure, all references as cited herein are incorporated by reference in their entireties. In the Figures:

Figure 1: Heatmap representation of the reference set. The colour code indicates the different tumour classes, FFPE and frozen samples as well as samples that are misclassified in the cross validation. The heatmap shows the methylation profile of 10,000 CpG probes most important for the classification (highest average gain in Gini purity).

Figure 2: Example of a binary decision tree. At each node a CpG probe and corresponding cut-off is used to make a binary decision. The final leaf nodes display the abbreviation of the tumour subclass, i.e., EPN_PFA means posterior fossa ependymoma subtype A.

Figure 3: Median test error estimated by three five-fold cross validation runs.

Figure 4: The left panel shows a symbolically the histology of a WNT medulloblastoma and a Group 3 medulloblastoma which are not distinguishable. The right panel shows a multidimensional scaling (MDS) analysis of 107 medulloblastoma samples of all molecular subtypes using the 21,092 most variable CpG probes. WNT medulloblastoma are coloured in blue, SHH medulloblastoma in red, Group 3 medulloblastoma in yellow and Group 4 medulloblastoma in green.

Figure 5: A shows the result of the histology of the patient. B shows the classifier scores. Highlighted is the highest score entry.

Figure 6: A and B show the result of the histology of the patient. C shows the classifier scores. Highlighted is the highest score entry. Figure 7: Schematic overview how the classifier is trained and validated by the three-fold nested-cross validation. In each outer cross validation run the training data is used for an inner three-fold cross-validation that generates independent RF scores. These scores are used to fit a calibration model which can then be applied to recalibrate the RF scores generated by predicting the test data in the outer loop. To fit a calibration model using all the data in the reference set, which is later used for new diagnostic cases, the RF scores generated in the outer loop can be used.

Figure 8: Genome plot showing the PTPRN2 gene, CpG sites and RF variable importance measure.

Figure 9: Heatmap showing the methylation values of 100 CpGs located on PAX6, PTPRN2 and OSTM1 with highest standard deviation across 75 ATRT samples. Rows and columns have been reordered by applying hierarchical clustering with Euclidean distance as distance metric and complete linkage as linkage method. The class annotation colour code shows the previously known molecular subtypes, the gene annotation indicates the gene on which the CpGs are located.

Figure 10A: 75 ATRT tumour samples projected on to the first two PCs resulting from PCA.

Figure 10B: 75 ATRT tumour samples projected on to coordinates calculated by tSNE analysis.

Figure 10C: CART tree with two sequential splitting rules.

Figure 10D: Scatterplot of 75 ATRT tumour samples, the x and y-axes are the methylation value of the two CpG sites selected by the CART tree. The corresponding splitting rule cut-offs are displayed as dashed lines.

Figure 11: tSNE of 1167 samples for which DNA-methylation as well as gene expression data is available. The tSNE coordinates were calculated on the gene expression data of the 688 most important genes or gene sites. The class labels and colours correspond to classes predicted by the methylation classifier.

Figures 12A and B: Confusion matrices that show the results of a 3 -fold cross validation to validate the RF and the multinomial logistic regression model. Like for classifiers trained on methylation data, most errors occur between closely related entities such as the MB group 3 and 4 subtypes.

Figure 13: Simulation study to investigate brain tumor classifier performance for classifiers trained using CpG-probes located on random subsets of signature genes and random hgl9 genes.

Figure 14: tSNE dimension reduction of DNA-methylation profiles of 9084 TCGA cases from 33 different projects where each project focused on specific tumor entity.

Figure 15: Left: confusion matrix which shows the result of the 3-fold cross-validatio; right: tSNE dimension reduction highlighting the samples that were falsely predicted in the cross-validation.

Figure 16: Confusion matrices for four different statistical or machine learning models trained on the TCGA cohort shown in Figure 14.

DETAILED DESCRIPTION OF EMBODIMENTS

Infinium Methylation Assay

Genome-wide screening of DNA methylation patterns was performed by using the Infinium HumanMethylation450 BeadChips (Illumina, San Diego, US), allowing the simultaneous quantitative measurement of the methylation state at 485,577 CpG sites. By combining Infinium I and Infinium II assay chemistry technologies, the BeadChip provides coverage of 99% of RefSeq genes and 96 % of CpG islands.

DNA concentrations were determined using PicoGreen (Life Technologies, Darmstadt, Germany). The quality of genomic DNA samples was checked by agarose-gel analysis, and samples with an average fragment size >3kb were selected for methylation analysis. For formalin- fixed paraffin-embedded (FFPE) DNA samples the quality was evaluated by real-time PCR analysis on Light Cycler 480 Real-Time PCR System (Roche, Mannheim, Germany) using the Infinium HD FFPE QC Kit (Illumina). The laboratory work was done in the Genomics and Proteomics Core Facility at the German Cancer Research Center, Heidelberg, Germany (DKFZ).

DNA (500 ng genomic DNA and 250 ng FFPE DNA, respectively) from each sample was bisulfite converted using the EZ-96 DNA Methylation Kit (Zymo Research Corporation, Orange, US) according to the manufacturer recommendations. Bisulfite treatment leads to the deamination of non-methylated cytosines to uracils, while methylated cytosines are refractory to the effects of bisulfite and remain cytosine. After bisulfite conversion, FFPE samples were treated with the Infinium HD DNA Restoration Kit (Illumina) according to the manufacturer recommendations. By using enzymatic reactions, degraded FFPE DNA is restored in preparation for the whole genome amplification.

Each sample was whole genome amplified and enzymatically fragmented following the instructions in the Illumina Infinium HD Assay Methylation Protocol Guide (genomic DNA) or Infinium HD FFPE Methylation Guide (FFPE DNA), respectively. The DNA was applied to Infinium HumanMethylation450 BeadChip and hybridization is performed for 16-24h at 48°C. During hybridization, the DNA molecules anneal to locus-specific DNA oligomers linked to individual bead types. One or two probes are used to interrogate CpG locus, depending on the probe design for a particular CpG site.

Allele- specific primer annealing is followed by single-base extension using DNP- and Biotin- labeled ddNTPs. For Infinium I assay design, both bead types (one each for the methylated and unmethylated states) for the same CpG locus incorporate the same type of labeled nucleotide, determined by the base preceding the interrogated “C” in the CpG locus, and therefore are detected in the same color channel. Infinium II uses only one bead type with a unique type of probe allowing detection of both alleles. The methylated and unmethylated signals are generated respectively in the green and the red channels.

After extension, the array is fluorescently stained, scanned, and the intensities at each CpGs were measured. Microarray scanning was done using an iScan array scanner (Illumina). DNA methylation values, described as beta values, are recorded for each locus in each sample. DNA methylation beta values are continuous variables between 0 and 1 , representing the percentage of methylation of a given cytosine corresponding to the ratio of the methylated signal over the sum of the methylated and unmethylated signals.

Data pre-processing

All data analysis was performed using the open-source statistical programming language R (R Core Team, 2014). Raw data files generated by the iScan array scanner were read and pre- processed using the capabilities of the Bioconductor package minfi (Aryee et al, 2014). With the minfi package the same pre-processing steps as recommended in Illumina's BeadStudio software were performed.

In addition, the following filtering criteria were applied: Removal of probes targeting the X and Y chromosomes (n = 11,551), removal of probes containing a single nucleotide polymorphism (dbSNP132 Common) within five base pairs of and including the targeted CpG-site (n = 24,536), and probes not mapping uniquely to the human reference genome (hgl9) allowing for one mismatch (n = 9,993). In total, 438,370 probes were kept for analysis.

Training the classifier

To learn a classification of 1899 samples that were assigned to 72 different brain tumour subtypes the Random Forest (RF) algorithm implemented in the R package randomForest (Liaw and Wiener, 2002) was used. The RF algorithm is a so-called ensemble method that combines the predictions of several 'weak' classifiers to achieve improved prediction accuracy. The RF uses binary classification trees (Classification and Regression Trees (CART); Breiman et al., 1983) as 'weak' classifiers. Each of these trees represents a sequence of binary splitting rules that are learned by recursive binary splitting. The CART algorithm starts with all samples assigned to a 'root' node and tries to find the variable, e.g., a measured CpG probe, and a corresponding cut-off that results in the purest split into the different classes. To measure this gain in class 'purity' the Gini index, a classical statistical measure for inequality, is used. To fit a tree the CART algorithm iteratively repeats these steps until no further improvements can be made, i.e., only samples of the same class are assigned to the final 'leaf node, or a prespecified node size is achieved. To predict the class of a new diagnostic case the binary splitting rules are compared with the new data starting in the root node down to one of the leaf nodes. The tree then predicts or votes for the class dominating that leaf node. However, to improve the prediction accuracy of a single tree the RF algorithm combines thousands of trees by bootstrap aggregation (bagging). In brief, each tree is fitted using training data sets that are generated by drawing bootstrap samples, i.e., randomly selecting two-third of the data with replacement. In addition, at each node only a random subset of the available variables are used to find an optimal splitting rule. To predict the class of a diagnostic sample the RF takes the majority vote of all trees in the forest.

To learn the classification the default parameter settings of the randomForest function were used and 10,000 decision trees were fitted. In addition, to take the highly imbalanced class sizes into account a down-sampling strategy was followed, i.e., to fit a decision tree the number of bootstrap samples drawn from each class was equal to the number of samples in the minority class. To further improve prediction performance of the classifier a variable selection was performed, i.e. in a first step the algorithm is used to calculate the variable importance, e.g. the average improvement in Gini purity of a CpG probe when used for a splitting rule. The final classifier was trained using only the 30,000 CpG probes with highest variable importance measure.

An overview of the training of the classifier is provided in Figure 7.

Internal validation

To validate the resulting classifier and estimate its performance in predicting future diagnostic cases a repeated five-fold cross-validation was applied. In example, in each cross-validation run the reference set is randomly split into five parts. Then four-fifth of the data is used to train the RF classifier as described above and one-fifth is used for prediction. Currently the estimated test error of the classifier is around 3.1%.

Example 1: Distinguishing WNT medulloblastoma from Group 3 medulloblastoma

Medulloblastoma is the most common malignant paediatric brain tumour and comprises four distinct molecular variants. These variants are known as WNT, SHH, Group 3, and Group 4. These variants are histologically indistinguishable, but clearly separable by DNA methylation patterns (see Figure 4). WNT tumours show activated Wnt signalling pathway and carry a favourable prognosis. SHH medulloblastoma show Hedgehog signalling pathway activation and are known to have an intermediate to good prognosis. While both WNT and SSH variants are molecularly already well characterised, the genetic programs driving the pathogenesis of Group 3 and Group 4 medulloblastoma remain largely unknown.

Example 2: Change of Diagnosis of an anaplastic astrocytoma WHO III

A 1944 born female brain tumour patient was diagnosed based on histology (see Figure 5A) to suffer from an anaplastic astrocytoma WHO III. Using the inventive classification procedure, a classifier score of 0.335 changed the diagnosis to Glioblastoma WHO IV (see Figure 5B).

Example 3: Change of Diagnosis of Schwannoma

A 1969 born male patient was based on the histology diagnosed with Schwannoma (Figures 6A and 6B). The classification procedure of the present disclosure however was able to diagnose the patient to suffer from Meningioma WHO I (see Figure 6C).

Example 4: DNA methylation-based classification of tumour entities using three gene sites

Atypical teratoid rhabdoid tumour (ATRT) is a rare paediatric brain tumour that can be subdivided into three molecular subgroups: ATRT-TYR, ATRT-SHH and ATRT-MYC (Ho et al. 2020, PMID: 31889194).

The inventors have identified genes that include CpG sites that are most important for the classification of brain tumours and molecular subtypes. The importance of these CpGs for the classification has been measured by applying the permutation-based variable importance measure of the Random Forest (RF) algorithm (Strobl et al. 2007, PMID: 17254353). Among others the three genes PAX6, PTPRN2 and OSTM1 include many important CpGs for the classification. Figure 8 displays the PTPRN2 gene and the CpG sites located on it. Most of the CpGs have a positive variable importance measure, indicating that these CpGs are predictive for the classification of brain tumours.

In the following it is demonstrated how the CpGs located on the three genes PAX6, PTPRN2 and OSTM1 can be used to classify ATRTs into their three molecular subtypes by applying different unsupervised and supervised statistical methods. After pre-processing, the inventors identified 1022 CpGs located on the three genes. Applying unsupervised, hierarchical clustering to the methylation values of the 100 CpGs with highest standard deviation across 75 ATRT samples, an almost perfect separation into the three molecular subtypes of ATRT can be found (Figure 9).

Next principal component analysis (PCA) is applied as an example for a linear dimension reduction method to the methylation values of all 1022 CpGs. Projecting the samples on the first two principal components (PC) that explain most of the variability in the data, a grouping into the three molecular subtypes can be found (Figure 10A). In addition, t-distributed stochastic neighbour embedding (t-SNE) has been applied, as an example for a non-linear dimension reduction method, to the methylation data and the resulting projection also shows a clustering of the three ATRT subtypes (Figure 10B). Other linear and non-linear dimension reduction methods that can be applied to achieve similar results are for example multidimensional scaling (MDS), factor analysis (FA), non-negative matrix factorization (NMF), truncated singular value decomposition (SVD), stochastic neighbour embedding (SNE), uniform manifold approximation and projection for dimension reduction (UMAP) and linear discriminant analysis (LDA).

To show how supervised statistical methods can be applied to fit a model that predicts ATRT subtypes, a classification and regression tree (CART) has been applied to methylation data (Figure 10C). At each node, the CART algorithm automatically tries out all available 1022 CpGs probes and possible cut-offs and selects the CpG probe and corresponding cut-off that leads to the purest split into the ATRT subtypes. The algorithm stops, as soon as the class purity measured by the Gini coefficient cannot be further improved. Here the CART algorithm found two sequential splitting rules (Figure 10D) that involve only two CpG probes that result in an almost perfect separation of the ATRT subclasses. Random Forests usually combine hundreds or thousands of CART trees by bootstrap aggregation (bagging) to achieve an improved prediction accuracy. Other supervised methods that can be applied to fit models with comparable prediction performance, are for example gradient boosting machines (GBM), support vector machines (SVM), multinomial logistic regression models and (deep) neural nets. Example 5: Gene expression data used for the classification of tumour entities originally identified in DNA-methylation data

By analysing DNA-methylation data and training machine learning models for the classification of brain tumours, 688 genes have been identified that include CpG sites that can be considered most important for the classification of molecular brain tumour types. To show that these brain tumour entities can also be recognized in gene expression data and that this data can be used to train similarly performing machine learning models, 1167 brain tumour samples were analysed for which both DNA-methylation as well as gene expression data is available. This paired gene expression and methylation data set includes samples from 79 of the in total 184 classes that were defined on the DNA-methylation level.

Figure 11 shows the 1167 samples projected onto a t-distributed stochastic neighbour embedding (tSNE) that was applied to the gene expression data of the 688 most important genes identified in the methylation data. The colouring and the labelling of the groups are according to the class, and the samples are classified by the DNA-methylation classifier. The general clustering of the classes is very similar to a tSNE performed on DNA-methylation and even new sub-entities such as the medulloblastoma (MB) group 3 and 4 subtypes I- VIII can be identified. This proves that the gene expression data of the 688 identified genes is highly predictive for the 184 classes.

To show that the gene expression data can also be used to train supervised machine learning models, the gene expression data set was reduced to 1057 samples belonging to 50 classes with a minimal class sample size of 7 samples. The inventors then trained a basic random forest (RF) model and a lasso-penalized multinomial logistic regression model to this data set and validated the performance of both models by 3-fold cross-validation (CV). The CV estimated an accuracy of 0.788 for the RF (Figure 12B) and an accuracy 0.766 for the logistic regression model (Figure 12A), what proves that gene expression can be used to train similar classification models.

Accordingly, it has been shown by the inventors that the biological state used to train the classification algorithm is not limited to methylation, but can also be another biological state such as gene expression. Example 6: Simulation study to investigate brain tumor classifier performance for classifiers trained using CpG-probes located on random subsets of signature genes and random hgl9 genes

To show that subsets of the 688 signature genes are already predictive for defined brain tumor methylation classes, the inventors performed a simulation study. In this study Random Forest classifiers were trained using CpG probes located on different random subsets of the 688 signature genes. The number of genes were varied from 3 to 688 in 20 equal steps and for each number of genes training was repeated at least 3 times. In addition, the inventors also trained classifiers using CpG probes located on genes randomly sampled from all known genes available in the hgl9 genome. For each trained classifier the performance was measured by the overall accuracy and the number of classes for which the class wise accuracy was greater 0.8.

Figure 13 shows the results of this simulation study. For subsets of three genes the difference between genes selected from the signature gene list in Table 1 compared to randomly selected genes is most distinct, i.e. the overall accuracy for the signature genes is around 0.8 while for the random gene classifiers it is always below 0.5. When increasing the number of genes, the overall accuracy for both the signature gene classifiers as well as the random gene classifiers increases to levels around 0.90 accuracy and above. The signature gene classifiers perform always better as the classifiers trained on random genes. When considering the number of classes for which a class accuracy of greater 0.8 was achieved, the simulation shows, that the genes in Table 1 are important to reliably predict more specific classes.

Example 7: Classifiers for other pan -cancer tumors

To show that the signature gene list can also be used to train well performing classification models to predict other cancer types, the inventors trained a RF classifier on a large cohort of publicly available DNA-methylation array samples from the Cancer Genome Atlas Project (TCGA).

Figure 14 shows a tSNE of 9084 sample from 31 different TCGA projects that investigated different cancer types, e.g. LU AD is the abbreviation lung adenocarcinoma, BRCA for breast cancer etc. A complete list of the TCGA projects and their abbreviations can be found under the following link: https://portal. dc.cancer. ov/projects. The inventors defined for each pro- ject a tumor and control tissue class where possible, resulting in total 53 classes. Training a RF classifier using all CpGs located on genes listed on the signature list of Table 1 on this data set, the resulting classifier achieves an overall accuracy of 0.9226, as measured by a 3- fold statistical cross-validation (Figure 15: the confusion matrix on the left shows the result of the 3-fold cross-validation; the right plot shows the tSNE dimension reduction highlighting the samples that were falsely predicted in the cross-validation. Errors typically occur between related entities, such as Lung Squamous Cell Carcinoma (LUSC) and Lung Adenocarcinoma (LU AD)).

Applying other statistical or machine learning algorithms, that are suitable for multiclass classification tasks, prediction models with a comparable accuracy can be fitted, as it is shown in Figure 16. Figure 16 shows confusion matrices for four different statistical or machine learning models trained on the TCGA cohort shown in Figure 14. The regularized logistic regression model showed the best overall accuracy of 0.9343, followed by the linear-kernel support vector machine (SVM) with accuracy 0.9299, the extreme gradient boosted trees (XGBoost) classifier with accuracy 0.9239 and a radial basis function kernel SVM with an accuracy of 0.9101. More careful hyper-parameter tuning might improve the performance of all presented prediction models.

References:

R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

MJ Aryee, AE Jaffe, H Corrada-Bravo, C Ladd- Acosta, AP Feinberg, KD Hansen, RA Irizarry. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA Methylation microarrays. Bioinformatics 2014, In press. doi: 10.1093/bioinformatics/btu049.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Bioconductor: Open software development for computational biology and bioinformatics R. Gentleman, V. J. Carey, D. M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, and others 2004, Genome Biology, Vol. 5, R80.

Tables: Table 1: List of gene sites according to the disclosure including their genetic locus and Sequence ID in the sequence listing. The sequence listing associated with this application is filed in electronic format and hereby incorporated by reference into the specification in its entirety.

Table 2: List of cancer types according to the disclosure

Column 1 lists the abbreviations of the cancer types used herein. The WHO 2020 entity or cancer type names are shown in Column 2. Column 3 provides a descriptor for the molecular class and Column 4 lists the PubMed Number (PMID). Where no PMID number appears in

Column 4 the method according to the disclosure uncovered cancer subspecies that where not known or published before and thus have no PMID.

Tables 3 to 172: Classification of cancer types listed in Table 2 according to the disclosure.

The classification data for each cancer type as listed in Table 2 is shown in an individual table. Each table comprises the following columns:

Column 1 shows the selected gene sites for the classification of the cancer type. Column 2 shows the overall statistical importance (imp_sum) of a specific gene site for the classification of the cancer type. The overall importance of the specific gene site (imp_sum) is calculated by multiplying the number of single measurement points (n_probes) of Column 4 with the mean variable importance (imp_mean) of Column 3. Higher values represent more important gene sites. Column 3 shows the mean variable importance (imp_mean) of all of the single measurement points (n_probes) of the specific gene sites according to the statistical model used (e.g. based on Random Forest)

Column 4 shows the number of single measurement points (n_probes; CpG site methylation probes that fall within the gene site).

TABLE 3: Cancer Type A_IDH SDK1 5.072705 0.253635 20

Gene site imp sum imp mean n ABR 4.501446 0.225072 20 PTPRN2 18.78638 0.229102 82 MAD1L1 11.21992 0.590522 19 PRDM16 15.88426 0.223722 71 SMG1P2 5.771893 0.303784 19 HDAC4 11.38158 0.30761 37 BOLA2 5.771893 0.303784 19 PAX6 7.719922 0.220569 35 LOC613038 5.771893 0.303784 19 RBFOX3 5.391468 0.154042 35 CASZ1 4.031351 0.212176 19 DIP2C 11.84772 0.370241 32 FOXK1 6.749132 0.374952 18 SOX2-OT 9.378707 0.323404 29 ANKRD11 4.824927 0.268051 18 GALNT9 4.056375 0.150236 27 TBC1D16 4.176223 0.232012 18 ADARB2 6.339109 0.243812 26 SEPTIN9 3.781195 0.210066 18 SHANK2 4.920743 0.189259 26 MCF2L 3.725642 0.20698 18 AGAP1 7.296626 0.291865 25 OPCML 7.22948 0.425264 17 CAMTAI 5.092806 0.203712 25 FOXP1 7.461073 0.466317 16 PDGFRA 4.139033 0.165561 25 NAV2 4.408791 0.275549 16 SATB2 5.319752 0.221656 24 GLI2 8.586287 0.572419 15 MEIS1 4.304819 0.179367 24 BAIAP2 4.850054 0.323337 15 RPTOR 11.20222 0.487053 23 KNDC1 4.040584 0.269372 15 NCOR2 4.696695 0.204204 23 NFATC1 3.893129 0.259542 15 INPP5A 3.980493 0.173065 23 RPS6KA2 5.709661 0.407833 14 RIMBP2 3.715073 0.161525 23 IQSEC1 4.288682 0.306334 14 SKI 9.355866 0.445517 21 ARHGEF10 4.250505 0.303607 14

FRMD4A 6.390597 0.31953 20 PRKAG2 4.116933 0.294067 14 CUX1 3.667762 0.261983 14 RBMS3 4.328619 1.082155 4

GNG7 3.48551 0.248965 14 DTNA 3.8923 0.973075 4

MSI2 6.236622 0.47974 13 VOPP1 3.405106 0.851277 4

MYT1L 4.125383 0.317337 13 SRRM3 3.823662 1.274554 3

CMIP 4.831247 0.402604 12 DAGEB 3.455348 1.151783 3

ADGRD1 4.598185 0.383182 12 ANKLE2 4.083121 2.04156 2

ZC3H3 4.555928 0.379661 12 SLC25A10 3.753383 1.876692 2

MIRLET7BHG 4.206607 0.350551 12 SOXIO 3.463676 1.731838 2

RASA3 3.881123 0.323427 12

MEGF6 3.49592 0.291327 12 Cancer Type

Table 4

FGFR2 0.3 A_IDH_HG

3.946181 58744 11 Gene s imp sum imp mean n

SPON2 0.343842 1 ite

3.782265 1 PTPRN2 13.16665 0.160569 82

ZC3H12D 3.768599 0.3426 11 PRDM16 11.2564 0.158541 71

VGEE4 3.446999 0.313364 11 PCDHGA1 6.017158 0.101986 59

ACOT7 4.628745 0.462874 10 PCDHGA2 5.700772 0.100014 57

SH3RF3 3.971742 0.397174 10 PCDHGA3 5.384386 0.099711 54

RGS12 3.917101 0.39171 10 PCDHGB1 5.384386 0.101592 53

AKAP13 3.404835 0.340483 10 PCDHGA4 5.384386 0.105576 51

SND1 6.763759 0.751529 9 PCDHGB2 5.068 0.103429 49

ATP11A 5.979014 0.664335 9 PCDHGA5 5.068 0.10783 47

ADAMTS2 5.342213 0.593579 9 PCDHGB3 5.068 0.11786 43

TSPAN9 4.494867 0.49943 9 PCDHGA6 5.068 0.1267 40

AXIN2 4.478168 0.497574 9 HDAC4 12.55202 0.339244 37

TRAPPCI 2 4.45643 0.495159 9 PCDHGA7 4.751614 0.128422 37

SEC22A18 4.308821 0.478758 9 PAX6 9.136798 0.261051 35

NEAT1 3.415812 0.379535 9 RBFOX3 9.124187 0.260691 35

ASAP1 3.398391 0.377599 9 PCDHGB4 4.751614 0.13576 35

MSRA 4.796431 0.599554 8 PCDHGA8 4.751614 0.13576 35

DNMT3A 4.299295 0.537412 8 DIP2C 9.649572 0.301549 32

AFF3 4.03016 0.50377 8 PCDHGB5 4.435228 0.138601 32

RORA 3.933212 0.491652 8 PCDHGA9 4.435228 0.143072 31

DEEU1 3.641639 0.455205 8 SOX2-OT 10.27019 0.354145 29

DUSP6 5.017101 0.716729 7 PCDHGA10 3.846128 0.137362 28

VPS 13D 4.243833 0.606262 7 GAENT9 4.09556 0.151687 27

NAVI 4.237089 0.605298 7 ADARB2 5.791898 0.222765 26

EINC00461 4.202952 0.600422 7 AGAP1 8.559905 0.342396 25

C19orf25 3.637842 0.519692 7 PDGFRA 6.841003 0.27364 25

FBXE18 4.410866 0.735144 6 CAMTAI 5.65441 0.226176 25

CRADD 4.042402 0.673734 6 MEIS1 11.15091 0.464621 24

STK10 3.58235 0.597058 6 SATB2 8.839103 0.368296 24

ERRFIP1 3.445461 0.574243 6 PCDHGB7 3.846128 0.160255 24

RUNDC3A 4.649823 0.929965 5

RPTOR 7.902877 0.343603 23

ARHGEF7 4.081638 0.816328 5 INPP5A 5.966938 0.259432 23

TSN AX-DISCI 4.017901 0.80358 5 RIMBP2 5.064586 0.220199 23

MRC2 3.944978 0.788996 5 HOXB3 3.589754 0.156076 23

BCAR1 3.588348 0.71767 5 PRKCZ 5.390894 0.245041 22

TK1 3.547527 0.709505 5 SKI 6.459381 0.30759 21

STAP2 4.426476 1.106619 4 ZIC4 4.94215 0.23534 21 NR2E1 3.648623 0.456078 8

SIM2 3.756501 0.178881 21 NAVI 4.624354 0.660622 7

FRMD4A 3.866106 0.193305 20 VPS13D 3.796267 0.542324 7

MAD1L1 10.17086 0.535308 19 C19orf25 3.791917 0.541702 7

ZNF423 5.772862 0.303835 19 LINC01140 3.549345 0.507049 7

SMG1P2 5.633616 0.296506 19 FBXL18 4.832711 0.805452 6

BOLA2 5.633616 0.296506 19 SRGAP3 4.349279 0.72488 6

LOC613038 5.633616 0.296506 19 CRACR2A 3.642366 0.607061 6

CASZ1 4.639517 0.244185 19 RUNDC3A 5.364042 1.072808 5

FOXK1 5.824185 0.323566 18 MRC2 4.240738 0.848148 5

ANKRD11 5.042924 0.280162 18 TSNAX-DISC1 4.221202 0.84424 5

SEPTIN9 4.66177 0.258987 18 ARHGEF7 4.089307 0.817861 5

TBC1D16 3.842806 0.213489 18 STAP2 7.704487 1.926122 4

RBFOX1 3.695191 0.205288 18 RBMS3 4.25923 1.064808 4

OPCML 7.050041 0.414708 17 VOPP1 3.764 0.941 4

PAX6-AS1 4.863903 0.286112 17 SRRM3 5.500931 1.833644 3

RCN1 4.863903 0.286112 17

TBX15 3.726216 0.219189 17 TABLE 5: Cancer Type ANTCON

NAV2 4.581486 0.286343 16 Gene site imp sum imp mean n

FOXP1 4.081864 0.255117 16 PTPRN2 7.483021 0.091256 82

GLI2 10.28032 0.685355 15 PRDM16 4.367174 0.061509 71

RPS6KA2 5.678692 0.405621 14 PCDHGA1 2.965166 0.050257 59

CUX1 4.301523 0.307252 14 PCDHGA2 2.965166 0.05202 57

IQSEC1 3.938498 0.281321 14 PCDHGA3 2.965166 0.05491 54

MSI2 5.975883 0.459683 13 PCDHGB1 2.965166 0.055947 53

MYT1L 5.311196 0.408554 13 PCDHGA4 2.965166 0.058141 51

SPTBN4 4.376569 0.336659 13 PCDHGB2 2.965166 0.060514 49

CMIP 4.991631 0.415969 12 PCDHGA5 2.531088 0.053853 47

ZC3H3 4.560729 0.380061 12 PCDHGB3 2.531088 0.058863 43

MIRLET7BHG 4.517836 0.376486 12 PCDHGA6 2.214702 0.055368 40

GLUD1P2 4.213095 0.383009 11 HDAC4 5.100359 0.137848 37

VGLL4 3.803764 0.345797 11 PCDHGA7 2.214702 0.059857 37

RAD51B 3.543642 0.322149 11 PAX6 4.939121 0.141118 35

ACOT7 5.348642 0.534864 10 PCDHGB4 2.214702 0.063277 35 NR2F1-AS1 4.332052 0.433205 10 PCDHGA8 2.214702 0.063277 35 ATP11A 6.242261 0.693585 9 PCDHGB5 2.214702 0.069209 32 SND1 5.421156 0.602351 9 PCDHGA9 2.214702 0.071442 31

TRAPPCI 2 4.750868 0.527874 9 SOX2-OT 5.824753 0.200854 29

ASAP1 4.177354 0.46415 9 SHANK2 2.07689 0.07988 26

ADAMTS2 3.748026 0.416447 9 CAMTAI 3.156495 0.12626 25

RUNX1 3.706722 0.411858 9 AGAP1 2.633589 0.105344 25

APBA2 3.609137 0.401015 9 PDGFRA 2.134721 0.085389 25

ADGRB1 3.604336 0.400482 9 SATB2 4.601253 0.191719 24

TXNRD1 3.556455 0.395162 9 RPTOR 4.447377 0.193364 23

DNMT3A 5.65658 0.707073 8 NXN 2.150077 0.093482 23 LINC00311 4.894521 0.611815 8 PRKCZ 2.51794 0.114452 22 MSRA 4.026572 0.503321 8 SKI 2.796501 0.133167 21 PPP2R2B 3.77597 0.471996 8 ZNF423 4.010188 0.211063 19 MAD1L1 3.859638 0.203139 19 VPS 13D 2.207261 0.315323 7

SMG1P2 3.770753 0.198461 19 RBMS1 1.957648 0.279664 7

BOLA2 3.770753 0.198461 19 EPHA10 1.996731 0.332788 6

LOC613038 3.770753 0.198461 19 MYO 16 1.956058 0.32601 6

CASZ1 1.910046 0.100529 19 SLC22A18AS 1.912184 0.318697 6

ANKRD11 1.917186 0.10651 18 RUNDC3A 3.204346 0.640869 5

OPCML 3.205323 0.188548 17 SLC8A2 2.163285 0.432657 5

TBX15 1.941086 0.114182 17 ARHGEF7 2.052253 0.410451 5

FOXP1 3.221398 0.201337 16 CNMD 1.973732 0.394746 5

NAV2 2.538252 0.158641 16 THRB 1.940011 0.388002 5

GLI2 6.990535 0.466036 15 ONECUT2 2.858992 0.714748 4

NFATC1 2.039808 0.135987 15 STAP2 2.282702 0.570675 4

TBX5 2.788694 0.199192 14 RBMS3 2.014411 0.503603 4

CUX1 2.302986 0.164499 14 LINC00856 1.991078 0.49777 4

ARHGEF10 2.283155 0.163083 14 SRRM3 3.72052 1.240173 3

IQSEC1 2.078601 0.148472 14 GRIN2B 3.033253 1.011084 3

RPS6KA2 1.916508 0.136893 14 DICER1 2.143218 0.714406 3

MSI2 3.663853 0.281835 13 SOXIO 3.646176 1.823088 2

MYT1L 3.019476 0.232267 13 SLC25A10 2.213726 1.106863 2

CMIP 2.634173 0.219514 12 KCNB1 2.174145 1.087073 2

MIRLET7BHG 2.582972 0.215248 12 CFLAR 2.014526 1.007263 2

ZC3H12D 2.029367 0.184488 11 GRIN1 1.944439 0.972219 2

VGLL4 2.027528 0.184321 11 MAPK8IP1 I.980944 1.980944 1

RAD51B 1.977181 0.179744 11

LBX1-AS1 3.913136 0.391314 10 TA RT F f.- Cancer Type

SPPL2B ATRT_MYC

3.257854 0.325785 10

Gene si imp sum imp mean n

GRID1 te

2.263188 0.226319 10

PTPRN2 17.73723 0.216308 82

TSPAN4 2.108335 0.210833 10 PRDM16 13.36136 0.188188 71

SKOR1 1.946083 0.194608 10 PCDHGA1 I I.41704 0.193509 59

RGS12 1.934755 0.193475 10 PCDHGA2 10.35292 0.18163 57

ATP11A 3.784347 0.420483 9 PCDHGA3 9.908546 0.183492 54

ADGRB1 3.322193 0.369133 9 PCDHGB1 9.908546 0.186954 53

RUNX1 3.083942 0.34266 9 PCDHGA4 9.908546 0.194285 51

SND1 2.935944 0.326216 9 PCDHGB2 9.168339 0.187109 49

ZNF833P 2.584271 0.287141 9 PCDHGA5 9.168339 0.195071 47

AXIN2 2.468323 0.274258 9 PCDHGB3 7.663836 0.178229 43

ADAMTS2 2.162573 0.240286 9 PCDHGA6 7.34745 0.183686 40

ASAP1 2.085051 0.231672 9

HDAC4 20.61752 0.55723 37

NOTCH 1 2.064723 0.229414 9 PCDHGA7 7.031064 0.190029 37

NEAT1 1.974612 0.219401 9 PCDHGB4 7.031064 0.200888 35

VRK2 2.995784 0.374473 8 PCDHGA8 7.031064 0.200888 35

LINC00311 2.230276 0.278785 8 PAX6 5.397601 0.154217 35

NXPH1 2.151553 0.268944 8 DIP2C 10.77775 0.336805 32

MBP 2.102791 0.262849 8 PCDHGB5 6.27593 0.196123 32

NR2E1 1.898316 0.237289 8 PCDHGA9 6.27593 0.202449 31

DUSP6 2.822265 0.403181 7 SOX2-OT 6.391698 0.220403 29

NAVI 2.582267 0.368895 7 PCDHGB6 5.959544 0.205502 29

TOX2 2.386634 0.340948 7 PCDHGA10 5.959544 0.212841 28 GNA12 5.032973 0.419414 12

SHANK2 4.056797 0.156031 26 TNS3 4.957592 0.413133 12

AGAP1 11.14937 0.445975 25 FBRSL1 4.5699 0.380825 12

CAMTAI 5.943709 0.237748 25 TBX4 4.111141 0.342595 12

PDGFRA 5.604404 0.224176 25 CTNNA2 4.073641 0.33947 12

PCDHGB7 5.535067 0.230628 24 ADGRD1 4.025228 0.335436 12

RPTOR 11.41745 0.496411 23 ZC3H12D 4.849544 0.440868 11

NCOR2 8.405351 0.36545 23 CTBP2 4.362049 0.39655 11

NXN 7.435412 0.323279 23 ACOT7 4.431353 0.443135 10

PCDHGA11 5.535067 0.240655 23 NBEA 3.965625 0.396562 10

PRKCZ 5.398038 0.245365 22 SND1 8.53951 0.948834 9

SKI 11.2062 0.533629 21 ADAMTS2 6.888579 0.765398 9

HOXA-AS3 4.54373 0.216368 21 ATP11A 6.762114 0.751346 9

SDK1 5.016131 0.250807 20 KCNH2 5.308144 0.589794 9

FRMD4A 4.007902 0.200395 20 TRAPPCI 2 4.83608 0.537342 9

ABR 3.959355 0.197968 20 CACNA2D4 4.766338 0.529593 9

MAD1L1 12.71891 0.669416 19 MGMT 4.576613 0.508513 9

ZNF423 6.244655 0.328666 19 ASAP1 4.566396 0.507377 9

SMG1P2 5.741184 0.302168 19 ZNF833P 4.242022 0.471336 9

BOLA2 5.741184 0.302168 19 TSPAN9 4.038929 0.44877 9

LOC613038 5.741184 0.302168 19 VRK2 4.017195 0.502149 8

KCNQ1 5.021333 0.264281 19 SYNJ2 3.989878 0.498735 8

CASZ1 5.018076 0.264109 19 ITPKB 5.378408 0.768344 7

CFAP46 4.552203 0.23959 19 NAVI 5.039236 0.719891 7

FOXK1 10.48601 0.582556 18 RXRA 4.298066 0.614009 7

TBC1D16 7.537054 0.418725 18 CRADD 4.812334 0.802056 6

ANKRD11 6.677221 0.370957 18 FBXL18 4.670889 0.778482 6

RBFOX1 4.311269 0.239515 18 TSNAX-DISC1 5.461204 1.092241 5

SEPTIN9 4.091576 0.22731 18 ARHGEF7 5.319103 1.063821 5

OPCML 4.016549 0.236268 17 RUNDC3A 4.452929 0.890586 5

FOXP1 4.010279 0.250642 16 NHSL1 4.604302 1.151075 4

GLI2 6.717306 0.44782 15 RALGAPA2 4.665413 2.332707 2

BAIAP2 6.057075 0.403805 15

SLX1B-

TABLE 7: Cancer Type

SULT1A4 5.70781 0.380521 15 ATRT_SHH

SLX1A 5.70781 0.380521 15 Gene site imp sum imp mean n

LOC606724 5.70781 0.380521 15 PTPRN2 24.89162 0.303556 82

ZBTB20 4.322333 0.288156 15 PRDM16 15.79215 0.222425 71

MIR548F5 5.757953 0.411282 14 PCDHGA1 8.715701 0.147724 59

IQSEC1 5.167301 0.369093 14 PCDHGA2 8.202776 0.143908 57

C7orf50 5.156157 0.368297 14 PCDHGA3 8.202776 0.151903 54

RPS6KA2 4.986439 0.356174 14 PCDHGB1 8.202776 0.154769 53

ARHGEF10 4.555787 0.325413 14 PCDHGA4 7.88639 0.154635 51

PRKAG2 4.408054 0.314861 14 PCDHGB2 7.298762 0.148954 49

MSI2 7.594149 0.584165 13 PCDHGA5 7.298762 0.155293 47

MYT1L 4.818631 0.370664 13 PCDHGB3 6.282316 0.1461 43

CMIP 7.520737 0.626728 12 PCDHGA6 5.834838 0.145871 40

ZC3H3 5.633656 0.469471 12 HDAC4 18.17436 0.491199 37 PCDHGA7 5.202066 0.140596 37 PRKAG2 5.378981 0.384213 14 PAX6 5.810192 0.166005 35 IQSEC1 4.92666 0.351904 14 RBFOX3 4.941735 0.141192 35 MSI2 8.50001 0.653847 13 PCDHGB4 4.88568 0.139591 35 GSE1 5.467176 0.420552 13 PCDHGA8 4.88568 0.139591 35 MYT1L 5.392954 0.414843 13 DIP2C 10.64599 0.332687 32 CMIP 5.995666 0.499639 12 GALNT9 7.350354 0.272235 27 ADGRD1 5.49992 0.458327 12 SHANK2 5.282632 0.203178 26 FBRSL1 5.377398 0.448117 12 AGAP1 11.67873 0.467149 25 GNA12 4.901687 0.408474 12 CAMTAI 10.30116 0.412046 25 ZC3H3 4.777128 0.398094 12 PDGFRA 5.066539 0.202662 25 ZC3H12D 5.069794 0.46089 11 RPTOR 13.54718 0.589008 23 ANAPC16 4.337299 0.3943 11 INPP5A 8.11614 0.352876 23 CTBP2 4.211653 0.382878 11 NXN 8.036403 0.349409 23 AKAP13 5.934512 0.593451 10 NCOR2 7.002394 0.304452 23 TSPAN4 5.465636 0.546564 10 RIMBP2 4.968555 0.216024 23 ACOT7 4.63607 0.463607 10 PRKCZ 6.218634 0.282665 22 RGS12 4.236383 0.423638 10 SKI 9.977176 0.475104 21 GAS7 4.190349 0.419035 10 HOXA-AS3 5.84487 0.278327 21 ATP11A 8.092174 0.89913 9 ABR 5.093333 0.254667 20 SND1 7.130321 0.792258 9 SDK1 4.517652 0.225883 20 ADAMTS2 6.985639 0.776182 9 MAD1L1 12.65219 0.665905 19 TSPAN9 5.699636 0.633293 9 SMG1P2 7.06765 0.371982 19 KCNH2 5.598484 0.622054 9 BOLA2 7.06765 0.371982 19 TRAPPCI 2 5.177346 0.575261 9 LOC613038 7.06765 0.371982 19 MGMT 5.073455 0.563717 9 ZNF423 5.716777 0.300883 19 ASAP1 4.968154 0.552017 9 CASZ1 5.598989 0.294684 19 DNMT3A 4.980074 0.622509 8 KCNQ1 4.405857 0.231887 19 DLEU1 4.929789 0.616224 8 FOXK1 9.609548 0.533864 18 SYNJ2 4.464514 0.558064 8 TBC1D16 7.431807 0.412878 18 VPS 13D 5.363926 0.766275 7 MCF2L 6.035987 0.335333 18 ITPKB 5.030327 0.718618 7 ANKRD11 5.04371 0.280206 18 C19orf25 4.429583 0.632798 7 SEPTIN9 4.418339 0.245463 18 NAVI 4.398237 0.62832 7 OPCML 5.149965 0.302939 17 RXRA 4.217134 0.602448 7 EBF3 5.495662 0.343479 16 CRADD 4.749641 0.791607 6 NAV2 4.483761 0.280235 16 FBXL18 4.234717 0.705786 6 FOXP1 4.190478 0.261905 16 ARHGEF7 5.396565 1.079313 5 GLI2 7.394108 0.492941 15 TSN AX-DISCI 5.184178 1.036836 5 BAIAP2 5.826734 0.388449 15 RUNDC3A 4.527973 0.905595 5 SLX1B- BCAR1 4.171778 0.834356 5 SULT1A4 4.969677 0.331312 15 NHSL1 5.159373 1.289843 4 SLX1A 4.969677 0.331312 15 LOC606724 4.969677 0.331312 15

TA RT F S- Cancer Type KIRREL3 4.942031 0.329469 15 ATRT TYR NFATC1 4.188296 0.27922 15 Gene site imp sum imp mean n RPS6KA2 8.057203 0.575514 14 PTPRN2 17.17779 0.209485 82 CUX1 5.824604 0.416043 14 PRDM16 13.19798 0.185887 71 C7orf50 5.488536 0.392038 14 PCDHGA1 7.447791 0.126234 59 PCDHGA2 7.050652 0.123696 57 RCN1 4.816456 0.283321 17 PCDHGA3 6.919764 0.128144 54 FOXP1 7.32639 0.457899 16 PCDHGB1 6.919764 0.130562 53 GLI2 7.809527 0.520635 15 PCDHGA4 6.603378 0.129478 51 KIRREL3 7.209825 0.480655 15 PCDHGB2 6.603378 0.134763 49 BAIAP2 6.29041 0.419361 15 PCDHGA5 6.286992 0.133766 47 ZBTB20 5.342014 0.356134 15 PCDHGB3 5.970606 0.138851 43 SLX1B- SULT1A4 5.163699 0.344247 15 PCDHGA6 5.724475 0.143112 40 SLX1A 5.163699 0.344247 15 HDAC4 20.70341 0.559552 37 LOC606724 5.163699 0.344247 15 PCDHGA7 5.091703 0.137614 37 RPS6KA2 6.698645 0.478475 14 RBFOX3 6.927478 0.197928 35 IQSEC1 6.069825 0.433559 14 PAX6 6.599989 0.188571 35 PRKAG2 5.738334 0.409881 14 PCDHGB4 5.091703 0.145477 35 CUX1 5.302633 0.378759 14 PCDHGA8 5.091703 0.145477 35 C7orf50 4.746734 0.339052 14 DIP2C 11.60772 0.362741 32 MIR548F5 4.480361 0.320026 14 PCDHGB5 4.775317 0.149229 32 MSI2 6.390182 0.491552 13 PCDHGA9 4.775317 0.154042 31 MYT1L 5.351189 0.41163 13 SOX2-OT 8.196193 0.282627 29 GSE1 4.631692 0.356284 13 PCDHGB6 4.458931 0.153756 29 CMIP 7.168856 0.597405 12 PCDHGA10 4.458931 0.159248 28 FBRSL1 6.380225 0.531685 12 GALNT9 4.845115 0.179449 27 ZC3H3 5.577344 0.464779 12 SHANK2 7.031974 0.270461 26 MAML3 5.348197 0.445683 12 ADARB2 4.425699 0.170219 26 GNA12 5.327119 0.443927 12 AGAP1 13.75814 0.550325 25 ADGRD1 5.196335 0.433028 12 CAMTAI 8.294735 0.331789 25 TNS3 4.431316 0.369276 12 MEIS1 6.612173 0.275507 24 RAD51B 4.560009 0.414546 11 RPTOR 13.07668 0.568551 23 TSPAN4 6.463955 0.646395 10 NXN 10.20379 0.443643 23 AKAP13 5.713526 0.571353 10 INPP5A 6.643652 0.288854 23 ACOT7 5.249279 0.524928 10 NCOR2 6.499293 0.282578 23 SND1 7.870976 0.874553 9 RIMBP2 4.784154 0.208007 23 ATP11A 7.260096 0.806677 9 PRKCZ 8.619356 0.391789 22 ADAMTS2 6.931892 0.77021 9 SKI 11.00712 0.524148 21 TSPAN9 4.861158 0.540129 9 FRMD4A 7.127492 0.356375 20 KCNH2 4.764548 0.529394 9 ABR 5.1764 0.25882 20 CACNA2D4 4.710694 0.52341 9 SDK1 4.96091 0.248046 20 DNMT3A 4.897363 0.61217 8 MAD1L1 12.46744 0.656181 19 DLEU1 4.821535 0.602692 8 SMG1P2 6.447881 0.339362 19 SYNJ2 4.589362 0.57367 8 BOLA2 6.447881 0.339362 19 VPS 13D 5.492812 0.784687 7 LOC613038 6.447881 0.339362 19 NAVI 5.347137 0.763877 7 KCNQ1 5.898287 0.310436 19 RXRA 4.834905 0.690701 7 CASZ1 5.485553 0.288713 19 CXXC5 4.78724 0.683891 7 ZNF423 5.462272 0.287488 19 FBXL18 4.838799 0.806467 6 CFAP46 5.159089 0.271531 19 CRADD 4.809103 0.801517 6 FOXK1 10.70561 0.594756 18

TSN AX-DISCI 5.422562 1.084512 5 TBC1D16 6.899829 0.383324 18 ARHGEF7 4.794453 0.958891 5 ANKRD11 5.454725 0.30304 18 RUNDC3A 4.504949 0.90099 5 PAX6-AS1 4.816456 0.283321 17 NHSL1 5.116568 1.279142 4 ANKRD11 3.55617 0.197565 18 RALGAPA2 4.45333 2.226665 2 MCF2L 3.27087 0.181715 18

OPCML 3.559998 0.209412 17

TABLE 9: Cancer Type CHGL TBX15 3.539969 0.208233 17 Gene site imp sum imp mean n FOXP1 5.668133 0.354258 16 PTPRN2 14.79861 0.180471 82 NAV2 4.20854 0.263034 16 PRDM16 12.8778 0.181377 71 GLI2 6.894022 0.459601 15 PCDHGA1 5.515199 0.093478 59 NHX 4.539748 0.30265 15 PCDHGA2 5.198813 0.091207 57 RPS6KA2 5.924986 0.423213 14 PCDHGA3 5.198813 0.096274 54 PRKAG2 5.161181 0.368656 14 PCDHGB1 5.198813 0.098091 53 C7orf50 4.374604 0.312472 14 PCDHGA4 4.767947 0.093489 51 CUX1 4.301476 0.307248 14 PCDHGB2 4.451561 0.090848 49 IQSEC1 4.248587 0.30347 14 PCDHGA5 4.135175 0.087982 47 MSI2 5.792215 0.445555 13 PCDHGB3 3.502403 0.081451 43 GSE1 4.661854 0.358604 13 PCDHGA6 3.502403 0.08756 40 MYT1L 3.970198 0.3054 13 HDAC4 12.87305 0.34792 37 CMIP 4.801019 0.400085 12 PCDHGA7 3.502403 0.09466 37 MIRLET7BHG 4.003606 0.333634 12 PAX6 7.160987 0.2046 35 FBRSL1 3.821366 0.318447 12 RBFOX3 3.818802 0.109109 35 ZC3H3 3.753333 0.312778 12 PCDHGB4 3.502403 0.100069 35 RASA3 3.657104 0.304759 12 PCDHGA8 3.502403 0.100069 35 ZC3H12D 3.612422 0.328402 11 DIP2C 8.043739 0.251367 32 CTBP2 3.525179 0.320471 11 PCDHGB5 3.502403 0.10945 32 CACNA1C 3.372955 0.306632 11

PCDHGA9 3.502403 0.112981 31 AKAP13 5.056953 0.505695 10 SOX2-OT 4.335995 0.149517 29 CHST11 3.124458 0.312446 10 SHANK2 5.495176 0.211353 26 RGS12 3.122263 0.312226 10 ADARB2 4.218601 0.162254 26 TSPAN4 3.114551 0.311455 10 AGAP1 8.389655 0.335586 25 TRAPPCI 2 4.011072 0.445675 9 CAMTAI 7.901409 0.316056 25 ATP11A 3.780115 0.420013 9 PDGFRA 4.726646 0.189066 25 SND1 3.550011 0.394446 9 SATB2 5.128386 0.213683 24 RUNX1 3.508329 0.389814 9 RPTOR 10.87751 0.472935 23 CACNA2D4 3.410833 0.378981 9 NCOR2 4.219558 0.183459 23 MGMT 3.143697 0.3493 9 INPP5A 4.041 0.175696 23 ADAMTS2 3.099666 0.344407 9 RIMBP2 3.839015 0.166914 23 NOTCH 1 3.071767 0.341307 9 PRKCZ 4.948854 0.224948 22 DNMT3A 4.219117 0.52739 8 SKI 8.260395 0.393352 21 DLEU1 4.091619 0.511452 8 ZIC4 3.647669 0.173699 21 ESRRG 3.813668 0.476709 8 SDK1 6.056386 0.302819 20 MCC 3.480227 0.435028 8 ABR 5.322552 0.266128 20 MSRA 3.13775 0.392219 8 FRMD4A 4.655164 0.232758 20 AFF3 3.097254 0.387157 8 MAD1L1 9.009302 0.474174 19 LINC00311 3.083062 0.385383 8 ZNF423 7.063639 0.37177 19 NAVI 4.437205 0.633886 7 CASZ1 4.550555 0.239503 19 LHPP 3.999415 0.571345 7 SEPTIN9 5.657282 0.314293 18 C19orf25 3.883883 0.55484 7

TBC1D16 5.558976 0.308832 18 MIR548H4 3.465819 0.495117 7 FOXK1 4.715776 0.261988 18 FOXP4 3.315285 0.473612 7 LINC01140 3.25579 0.465113 7 SKI 9.865213 0.469772 21 RXRA 3.076949 0.439564 7 FRMD4A 6.525814 0.326291 20 SLC22A18AS 4.438269 0.739711 6 SDK1 5.35532 0.267766 20 FBXL18 3.916143 0.65269 6 MAD1L1 13.4089 0.705732 19 RUNDC3A 4.641016 0.928203 5 CASZ1 6.812935 0.358576 19 TSN AX-DISCI 3.590508 0.718102 5 ZNF423 6.718331 0.353596 19 STAP2 3.380822 0.845206 4 SMG1P2 5.821967 0.306419 19 IGDCC4 3.084475 0.771119 4 BOLA2 5.821967 0.306419 19 DAGLB 3.187697 1.062566 3 LOC613038 5.821967 0.306419 19

FOXK1 8.589269 0.477182 18

TABLE 10: Cancer Type CHORDM TBC1D16 7.431356 0.412853 18 Gene site imp sum imp mean n ANKRD11 5.957897 0.330994 18

PTPRN2 16.55238 0.201858 82 SEPTIN9 5.504379 0.305799 18 PRDM16 14.25707 0.200804 71 OPCML 4.593892 0.270229 17 PCDHGA1 7.664046 0.129899 59 FOXP1 6.10553 0.381596 16 PCDHGA2 7.34766 0.128906 57 SORBS2 6.052013 0.378251 16 PCDHGA3 7.031274 0.130209 54 EBF3 5.932069 0.370754 16 PCDHGB1 7.031274 0.132666 53 NAV2 5.279409 0.329963 16 PCDHGA4 7.031274 0.137868 51 ZBTB20 6.598775 0.439918 15 PCDHGB2 7.031274 0.143495 49 GLI2 5.800381 0.386692 15 PCDHGA5 6.714888 0.14287 47 NHX 5.646003 0.3764 15 PCDHGB3 6.082116 0.141445 43 SLX1B-

SULT1A4 5.321711 0.354781 15 PCDHGA6 6.398502 0.159963 40

SLX1A 5.321711 0.354781 15 HDAC4 21.54355 0.582258 37

LOC606724 5.321711 0.354781 15 PCDHGA7 7.031274 0.190034 37

BAIAP2 4.860121 0.324008 15 PAX6 10.37192 0.296341 35

KNDC1 4.851891 0.323459 15 RBFOX3 8.759702 0.250277 35

CUX1 7.205997 0.514714 14 PCDHGB4 7.031274 0.200894 35

RPS6KA2 6.180081 0.441434 14 PCDHGA8 7.031274 0.200894 35

IQSEC1 5.85971 0.418551 14 DIP2C 12.96907 0.405283 32

C7orf50 5.675578 0.405398 14 PCDHGB5 6.714888 0.20984 32

PRKAG2 5.473467 0.390962 14 PCDHGA9 6.714888 0.216609 31

ARHGEF10 4.720729 0.337195 14 PCDHGB6 6.398502 0.220638 29

MSI2 7.483359 0.575643 13 SOX2-OT 6.1506 0.21209 29

MYT1L 5.628405 0.432954 13 PCDHGA10 5.967975 0.213142 28

GSE1 5.108727 0.392979 13 GALNT9 6.874988 0.254629 27

RFX4 5.022644 0.386357 13 SHANK2 6.202549 0.23856 26

CMIP 6.303599 0.5253 12 AGAP1 12.407 0.49628 25

FBRSL1 5.567028 0.463919 12

CAMTAI 5.741739 0.22967 25

RASA3 5.536303 0.461359 12 SATB2 5.401018 0.225042 24

ZC3H3 5.072695 0.422725 12 PCDHGB7 5.335203 0.2223 24

MIRLET7BHG 4.778244 0.398187 12 RPTOR 11.37436 0.494538 23

TNS3 4.728176 0.394015 12 NCOR2 10.30472 0.448031 23

ZC3H12D 5.34907 0.486279 11 INPP5A 6.682606 0.290548 23

RAD51B 5.248837 0.477167 11 NXN 5.454332 0.237145 23

CTBP2 4.770185 0.433653 11 PCDHGA11 5.335203 0.231965 23

ACOT7 6.555788 0.655579 10 RIMBP2 5.27324 0.229271 23

TSPAN4 5.56361 0.556361 10 PRKCZ 6.818188 0.309918 22 KLHL29 4.70729 0.470729 10 SKI 11.48203 0.546763 21 ATP11A 8.056261 0.89514 9 ZIC4 3.924716 0.186891 21 SND1 6.661356 0.740151 9 SIM2 3.534931 0.16833 21 ADAMTS2 6.218541 0.690949 9 ABR 7.657151 0.382858 20 CACNA2D4 5.379067 0.597674 9 FRMD4A 6.170234 0.308512 20 TSPAN9 4.561383 0.50682 9 SDK1 4.298032 0.214902 20 MSRA 4.804979 0.600622 8 MAD1L1 10.78128 0.567436 19 SMAD3 4.791134 0.598892 8 ZNF423 7.597013 0.399843 19 DNMT3A 4.742032 0.592754 8 SMG1P2 6.87194 0.361681 19 SYNJ2 4.676059 0.584507 8 BOLA2 6.87194 0.361681 19 C19orf25 5.429518 0.775645 7 LOC613038 6.87194 0.361681 19 GAK 5.087383 0.726769 7 CASZ1 5.104204 0.268642 19 VPS 13D 4.969722 0.70996 7 TBC1D16 6.092089 0.338449 18 FBXL18 5.310933 0.885156 6 FOXK1 6.044418 0.335801 18

TSN AX-DISCI 6.356626 1.271325 5 SEPTIN9 4.550265 0.252792 18 RUNDC3A 5.285637 1.057127 5 OPCML 7.381886 0.434229 17 ARHGEF7 5.257238 1.051448 5 TBX15 3.551385 0.208905 17

FOXP1 4.744313 0.29652 16

Cancer Type NAV2 4.17554 0.260971 16

I .ABl.E 11: CN GLI2 9.66338 0.644225 15

Gene site imp sum imp mean n SLX1B- SULT1A4 4.42696 0.295131 15 PTPRN2 17.41315 0.212355 82 SLX1A 4.42696 0.295131 15 PRDM16 18.11757 0.255177 71 LOC606724 4.42696 0.295131 15 PCDHGA1 4.439372 0.075244 59 BAIAP2 4.293779 0.286252 15 PCDHGA2 4.439372 0.077884 57 ZBTB20 3.970811 0.264721 15 PCDHGA3 4.755758 0.08807 54 NHX 3.576827 0.238455 15 PCDHGB1 4.755758 0.089731 53 PRKAG2 5.562683 0.397335 14 PCDHGA4 4.755758 0.09325 51 RPS6KA2 4.538682 0.324192 14 PCDHGB2 4.439372 0.090599 49 MOB2 3.632116 0.259437 14 PCDHGA5 4.122986 0.087723 47 IQSEC1 3.623361 0.258811 14 PCDHGB3 3.501677 0.081434 43 MSI2 7.276147 0.559704 13 HDAC4 9.842804 0.266022 37 GSE1 4.103655 0.315666 13 PAX6 8.660398 0.24744 35 MYT1L 3.811088 0.293161 13 RBFOX3 8.540415 0.244012 35 CLYBL 3.726201 0.286631 13 DIP2C 8.478792 0.264962 32 MAML3 5.722683 0.47689 12 SOX2-OT 9.240267 0.31863 29

MIRLET7BHG 4.931143 0.410929 12 GALNT9 4.333948 0.160517 27

ZC3H3 4.921139 0.410095 12

ADARB2 5.603375 0.215514 26

CMIP 4.50246 0.375205 12 SHANK2 4.911326 0.188897 26

TNS3 4.033096 0.336091 12 AGAP1 8.213638 0.328546 25

MEGF6 3.679307 0.306609 12 CAMTAI 6.099428 0.243977 25

ZC3H12D 5.752693 0.522972 11 SATB2 5.464538 0.227689 24

VGLL4 4.195828 0.381439 11 RPTOR 10.66881 0.463861 23

SPON2 4.119606 0.37451 11 HOXB3 5.490551 0.23872 23

GLUD1P2 3.612424 0.328402 11 NCOR2 4.956795 0.215513 23

ACOT7 4.552161 0.455216 10 INPP5A 3.816948 0.165954 23

ATP11A 5.824264 0.64714 9 PRKCZ 6.234103 0.283368 22

TRAPPCI 2 4.941906 0.549101 9 SND1 4.597402 0.510823 9 RIMBP2 4.348611 0.18907 23

KCNH2 4.245815 0.471757 9 HOXB3 4.035728 0.175466 23

CACNA2D4 4.064338 0.451593 9 SKI 9.461609 0.450553 21

AXIN2 3.940874 0.437875 9 HOXA-AS3 3.253921 0.154949 21

ADAMTS2 3.932094 0.436899 9 SIM2 3.19159 0.15198 21

TSPAN9 3.817944 0.424216 9 FRMD4A 4.992572 0.249629 20

GPC6 3.730134 0.414459 9 ABR 4.973264 0.248663 20

LHX4 4.812097 0.601512 8 SDK1 4.302409 0.21512 20

LINC00311 3.881015 0.485127 8 MAD1L1 11.44015 0.602113 19

MSRA 3.742145 0.467768 8 ZNF423 7.98033 0.420017 19

AFF3 3.68184 0.46023 8 CASZ1 6.244159 0.32864 19

RORA 3.582455 0.447807 8 SMG1P2 6.101449 0.321129 19

RXRA 4.956798 0.708114 7 BOLA2 6.101449 0.321129 19

DUSP6 4.574231 0.653462 7 LOC613038 6.101449 0.321129 19

NAVI 4.421833 0.63169 7 KCNQ1 4.178427 0.219917 19

VPS 13D 3.505702 0.500815 7 FOXK1 6.189604 0.343867 18

FMNL2 4.738833 0.789805 6 ANKRD11 4.543665 0.252426 18

FBXL18 4.621327 0.770221 6 MCF2L 3.783785 0.21021 18

ARHGEF7 4.827002 0.9654 5 SEPTIN9 3.743418 0.207968 18

TSN AX-DISCI 4.246502 0.8493 5 OPCML 6.010054 0.353533 17

TOLLIP 3.980093 0.796019 5 FOXP1 5.119723 0.319983 16

AP2A2 3.538338 0.707668 5 GLI2 8.505799 0.567053 15

RBMS3 5.039035 1.259759 4 DLX6-AS1 8.040784 0.536052 15

DINA 3.667401 0.91685 4 BAIAP2 4.472712 0.298181 15

SLC25A22 3.735059 1.24502 3 COL23A1 3.569903 0.237994 15

SLC25A10 4.452157 2.226079 2 SLX1B-

ANKLE2 4.048098 2.024049 2 SULT1A4 3.137898 0.209193 15

SLX1A 3.137898 0.209193 15 2: Cancer T LOC606724 3.137898 0.209193 15

TABLE 1 ype

CNS_NB_FOXR2 RPS6KA2 6.920327 0.494309 14

Gene site imp sum imp mean n CUX1 5.050344 0.360739 14 PTPRN2 22.2855 0.271774 82 IQSEC1 4.966123 0.354723 14 PRDM16 8.402066 0.118339 71 PRKAG2 4.891786 0.349413 14 HDAC4 10.81953 0.29242 37 GNG7 3.327657 0.23769 14 RBFOX3 8.390319 0.239723 35 MSI2 6.349105 0.488393 13 PAX6 3.863507 0.110386 35 MYT1L 5.132238 0.394788 13 DIP2C 9.245805 0.288931 32 MIRLET7BHG 4.702356 0.391863 12 SOX2-OT 8.203317 0.282873 29 ADGRD1 4.548518 0.379043 12 GALNT9 3.830935 0.141886 27 CMIP 4.280517 0.35671 12 SHANK2 5.300173 0.203853 26 ZC3H3 3.399395 0.283283 12 ADARB2 5.192971 0.19973 26 VGLL4 4.005113 0.364101 11 AGAP1 8.539411 0.341576 25 GLUD1P2 3.982516 0.362047 11 CAMTAI 8.451029 0.338041 25 RAD51B 3.957538 0.359776 11 PDGFRA 7.359602 0.294384 25 CTBP2 3.237034 0.294276 11 SATB2 4.178537 0.174106 24 SH3RF3 5.121554 0.512155 10

RPTOR 8.841747 0.384424 23 ACOT7 4.581365 0.458136 10 INPP5A 5.291318 0.230057 23 ETS1 3.628299 0.36283 10 NCOR2 4.690211 0.203922 23 NR2F1-AS1 3.550673 0.355067 10 ATP11A 6.369748 0.70775 9 ADARB2 2.571709 0.098912 26

SND1 6.008691 0.667632 9 SHANK2 2.41474 0.092875 26

TRAPPCI 2 5.27194 0.585771 9 AGAP1 5.858976 0.234359 25 TSPAN9 5.194926 0.577214 9 CAMTAI 3.003147 0.120126 25 ADAMTS2 4.498595 0.499844 9 PDGFRA 2.160727 0.086429 25

AXIN2 4.294901 0.477211 9 NCOR2 3.343412 0.145366 23

CACNA2D4 3.895577 0.432842 9 RPTOR 3.27489 0.142387 23

ASAP1 3.653458 0.40594 9 RIMBP2 2.615112 0.113701 23

APBA2 3.36757 0.374174 9 HOXB3 1.836799 0.079861 23 LINC00311 4.87739 0.609674 8 INPP5A 1.791248 0.07788 23 DNMT3A 4.17539 0.521924 8 PRKCZ 4.651515 0.211433 22 DLX5 3.590566 0.448821 8 SKI 4.121479 0.196261 21

MSRA 3.448761 0.431095 8 SDK1 4.28274 0.214137 20 ASPSCR1 3.408459 0.426057 8 ABR 2.438093 0.121905 20 NAVI 5.037875 0.719696 7 FRMD4A 2.286896 0.114345 20 DUSP6 4.399055 0.628436 7 MAD1L1 10.02104 0.527423 19 VPS 13D 3.75286 0.536123 7 ZNF423 3.349011 0.176264 19

LINC00461 3.67187 0.524553 7 CFAP46 2.950856 0.155308 19 FBXL18 4.805531 0.800922 6 SMG1P2 2.053511 0.10808 19 FAM181A 3.87676 0.646127 6 BOLA2 2.053511 0.10808 19 RUNDC3A 5.224921 1.044984 5 LOC613038 2.053511 0.10808 19

ARHGEF7 4.53184 0.906368 5 KCNQ1 1.872165 0.098535 19

PRR5L 4.04784 0.809568 5 FOXK1 3.115821 0.173101 18

TSN AX-DISCI 4.001967 0.800393 5 ANKRD11 2.45527 0.136404 18

ASAP2 3.215839 0.643168 5 SEPTIN9 1.799112 0.099951 18 DNAAF5 3.191462 0.638292 5 OPCML 3.581783 0.210693 17 RBMS3 4.467137 1.116784 4 FOXP1 3.24319 0.202699 16 STAP2 4.100788 1.025197 4 GLI2 6.248451 0.416563 15

VOPP1 3.416731 0.854183 4 KNDC1 5.155305 0.343687 15 DAGLB 3.791019 1.263673 3 BAIAP2 3.131474 0.208765 15 GRIN2B 3.759161 1.253054 3 KIRREL3 2.629434 0.175296 15 SOX10 4.869568 2.434784 2 LRMDA 1.991942 0.132796 15

KIF21B 3.835147 1.917573 2 ZBTB20 1.813691 0.120913 15 SLC25A10 3.765673 1.882836 2 RPS6KA2 6.042836 0.431631 14 ANKLE2 3.723663 1.861831 2 IQSEC1 2.371576 0.169398 14 CHTF18 3.366331 1.683166 2 CUX1 2.200599 0.157186 14

MSI2 4.261561 0.327812 13

TABLE 13: Cancer Type GSE1 2.42283 0.186372 13

CNS_SARC_DICER RFX4 2.042377 0.157106 13

Gene site imp sum imp mean n

CLYBL 1.986742 0.152826 13

PTPRN2 11.53676 0.140692 82

MYT1L 1.894854 0.145758 13

PRDM16 6.861192 0.096637 71

FBRSL1 2.967181 0.247265 12

HDAC4 8.635581 0.233394 37

MEGF6 2.13011 0.177509 12 RBFOX3 5.971672 0.170619 35 ADGRD1 2.11279 0.176066 12 PAX6 3.621629 0.103475 35

ZC3H3 2.061065 0.171755 12 DIP2C 3.328282 0.104009 32

MAML3 1.949386 0.162449 12

SOX2-OT 2.161325 0.074528 29

RASA3 1.929851 0.160821 12 GALNT9 6.89057 0.255206 27

COLA Al 2.574608 0.234055 11 ZC3H12D 1.892763 0.172069 11 PRDM16 17.72925 0.249708 71 ESRI 1.792318 0.162938 11 PCDHGA1 4.050169 0.068647 59 AKAP13 2.77468 0.277468 10 PCDHGA2 4.050169 0.071056 57 SH3RF3 2.737184 0.273718 10 PCDHGA3 3.749203 0.06943 54 TSPAN4 2.500322 0.250032 10 PCDHGB1 3.432817 0.06477 53 KLHL29 2.492991 0.249299 10 PCDHGA4 3.432817 0.06731 51 IGF1R 2.051063 0.205106 10 HDAC4 17.89076 0.483534 37 ACOT7 1.938008 0.193801 10 PAX6 7.824002 0.223543 35 SND1 2.848127 0.316459 9 RBFOX3 4.83382 0.138109 35 CACNA2D4 2.749816 0.305535 9 DIP2C 6.771467 0.211608 32 MGMT 2.501468 0.277941 9 SOX2-OT 8.34411 0.287728 29 KCNMA1 1.810327 0.201147 9 GALNT9 6.145555 0.227613 27 DLEU1 2.865375 0.358172 8 SHANK2 5.143475 0.197826 26 CRISPLD2 2.669829 0.333729 8 AGAP1 11.69691 0.467877 25 SYNJ2 2.371457 0.296432 8 PDGFRA 7.713638 0.308546 25 MACROD1 2.280948 0.285119 8 CAMTAI 7.140097 0.285604 25 TRAPPC9 2.117258 0.264657 8 SATB2 6.589201 0.27455 24 VRK2 2.064327 0.258041 8 RPTOR 12.02057 0.522634 23 AFF3 2.032248 0.254031 8 NXN 9.125426 0.396758 23 WWP2 2.029504 0.253688 8 INPP5A 7.926578 0.344634 23 CDH4 1.948911 0.243614 8 NCOR2 7.898032 0.343393 23 LINC00311 1.834155 0.229269 8 RIMBP2 5.93563 0.258071 23 CACHD1 1.801775 0.225222 8 PRKCZ 6.332177 0.287826 22 C19orf25 3.067579 0.438226 7 SKI 9.294558 0.442598 21 GAK 2.484219 0.354888 7 ZIC4 5.636609 0.26841 21 LINC01749 2.383224 0.340461 7 SDK1 6.150119 0.307506 20 FOXP4 1.929913 0.275702 7 FRMD4A 4.477427 0.223871 20 TRIM2 1.835568 0.262224 7 MAD1L1 11.13833 0.586228 19 FBXL18 3.350944 0.558491 6 ZNF423 6.304675 0.331825 19 ANKS1A 2.362196 0.393699 6 SMG1P2 4.879903 0.256837 19 STRA6 2.263036 0.377173 6 BOLA2 4.879903 0.256837 19 CRADD 2.148496 0.358083 6 LOC613038 4.879903 0.256837 19 RUNDC3A 4.048412 0.809682 5 CASZ1 4.189655 0.220508 19 BCAR1 2.566179 0.513236 5 KCNQ1 3.413829 0.179675 19

TSN AX-DISCI 2.53612 0.507224 5 FOXK1 6.901213 0.383401 18 VAV2 2.223338 0.444668 5 SEPTIN9 5.266403 0.292578 18

TK1 2.093538 0.418708 5 ANKRD11 3.946175 0.219232 18 ARHGEF7 2.014566 0.402913 5 PAX6-AS1 4.201016 0.247119 17 OLFM1 2.567071 0.641768 4 RCN1 4.201016 0.247119 17 DICER1 2.027828 0.675943 3 OPCML 3.583486 0.210793 17 KLHL26 1.803271 0.60109 3 HBG2 3.561709 0.209512 17 TBC1D7 1.799689 0.599896 3 EBF3 4.986091 0.311631 16 DISCI 2.624109 1.312055 2 SORBS2 4.218948 0.263684 16 RNF216 1.834681 0.917341 2 NAV2 4.216962 0.26356 16 FOXP1 3.885851 0.242866 16

TABLE 14: Cancer Type CPC_A SLX1B- SULT1A4 4.717464 0.314498 15 Gene site imp sum imp mean n SLX1A 4.717464 0.314498 15 PTPRN2 16.00842 0.195225 82 LOC606724 4.717464 0.314498 15 ARHGEF7 4.898215 0.979643 5 KIRREL3 4.545293 0.30302 15 RUNDC3A 4.211512 0.842302 5 LRMDA 4.192857 0.279524 15 NDRG4 3.330074 0.666015 5 GLI2 4.087578 0.272505 15 CHTF18 4.714201 2.3571 2 BAIAP2 3.473124 0.231542 15 IQSEC1 6.528853 0.466347 14 TABLE 15: Cancer Type CPC_B MIR548F5 6.386218 0.456158 14 Gene site imp sum imp mean n RPS6KA2 5.41149 0.386535 14 PTPRN2 3.995573 0.048726 82 CUX1 5.263852 0.375989 14 PCDHGA1 4.32325 0.073275 59 C7orf50 4.43927 0.317091 14 PCDHGA2 4.32325 0.075846 57 ARHGEF10 4.314892 0.308207 14 PCDHGA3 4.006864 0.074201 54 PRKAG2 3.594082 0.25672 14 PCDHGB1 4.006864 0.075601 53 MSI2 5.326164 0.409705 13 PCDHGA4 4.006864 0.078566 51 RFX4 3.666617 0.282047 13 PCDHGB2 3.374092 0.068859 49 GSE1 3.615205 0.278093 13 PCDHGA5 3.374092 0.071789 47 GNA12 4.872908 0.406076 12 PCDHGB3 2.531088 0.058863 43 CMIP 4.719319 0.393277 12 PCDHGA6 2.531088 0.063277 40 ZC3H3 4.661378 0.388448 12 HDAC4 3.015967 0.081513 37 MAML3 4.122071 0.343506 12 PCDHGA7 2.531088 0.068408 37 TNS3 3.910643 0.325887 12 PCDHGB4 2.531088 0.072317 35 FBRSL1 3.519862 0.293322 12 PCDHGA8 2.531088 0.072317 35 CTBP2 4.266982 0.387907 11 RBFOX3 1.87321 0.05352 35 RAD51B 3.992868 0.362988 11 DIP2C 2.239472 0.069984 32 ANAPC16 3.804301 0.345846 11 PCDHGB5 1.898316 0.059322 32 VGLL4 3.651058 0.331914 11 PCDHGA9 1.898316 0.061236 31 NR5A2 4.686618 0.468662 10 PCDHGB6 1.58193 0.054549 29 AKAP13 4.170628 0.417063 10 PCDHGA10 1.58193 0.056498 28 NBEA 3.853657 0.385366 10 AGAP1 2.988213 0.119529 25 TSPAN4 3.472743 0.347274 10 CAMTAI 2.533234 0.101329 25 EBF1 3.38921 0.338921 10 RPTOR 3.283683 0.142769 23 ANKS1B 3.359524 0.335952 10 NXN 1.518958 0.066042 23 SND1 6.548215 0.727579 9 HOXB3 1.488788 0.06473 23 ADAMTS2 4.747935 0.527548 9 SIM2 1.812383 0.086304 21 TSPAN9 4.682856 0.520317 9 SKI 1.525215 0.072629 21 ATP11A 4.673193 0.519244 9 SDK1 1.998018 0.099901 20 TRAPPCI 2 3.411332 0.379037 9 MAD1L1 4.696756 0.247198 19 VRK2 6.497503 0.812188 8 SMG1P2 2.474409 0.130232 19 LINC00311 4.600767 0.575096 8 BOLA2 2.474409 0.130232 19 MSRA 4.151404 0.518926 8 LOC613038 2.474409 0.130232 19 SYNJ2 4.120538 0.515067 8 FOXK1 2.382865 0.132381 18 DLEU1 4.017219 0.502152 8 TBX15 2.255946 0.132703 17 MCIDAS 3.434927 0.429366 8 FOXP1 2.77394 0.173371 16 MIR548H4 4.040935 0.577276 7 SORBS2 1.712981 0.107061 16 NAVI 3.918137 0.559734 7 GLI2 1.673968 0.111598 15 RXRA 3.704701 0.529243 7 CUX1 3.02004 0.215717 14 GAK 3.380035 0.482862 7 C7orf50 1.96638 0.140456 14 CRADD 4.051051 0.675175 6 GSE1 2.337064 0.179774 13 ARHGAP18 3.476569 0.579428 6 MYT1L 2.080863 0.160066 13 MAML3 1.713439 0.142787 12 CAPG 1.412244 0.353061 4 ADGRD1 1.684387 0.140366 12 KCNIP1 1.712071 0.57069 3 CCDC140 2.964768 0.269524 11 DICER1 1.670578 0.556859 3 FGFR2 2.171443 0.197404 11 HOTTIP 1.55145 0.51715 3 RAD51B 1.737753 0.157978 11 SLC6A9 1.45193 0.483977 3 LBX1-AS1 2.690424 0.269042 10 BFSP2 1.441111 0.48037 3 TFAP2B 2.118207 0.211821 10 CHTF18 3.676269 1.838134 2 AKAP13 1.830847 0.183085 10 TRIM65 2.778116 1.389058 2 ACOT7 1.631082 0.163108 10 TSPAN14 1.626202 0.813101 2 WT1 1.593231 0.159323 10 SLC25A10 1.562957 0.781479 2 BCL11B 1.588108 0.158811 10 C6orf223 1.553416 1.553416 1 TSPAN4 1.538438 0.153844 10 SND1 3.245597 0.360622 9 TABLE 16: Cancer Type CPH_ADM ZNF833P 3.156911 0.350768 9 Gene site imp sum imp mean n PAX3 2.274132 0.252681 9 PTPRN2 12.29209 0.149904 82 ATP11A 2.166285 0.240698 9 PRDM16 11.69095 0.164661 71 KCNH2 2.02368 0.224853 9 PCDHGA1 4.493803 0.076166 59 CACNA2D4 1.837571 0.204175 9 PCDHGA2 4.177417 0.073288 57 TRAPPCI 2 1.681783 0.186865 9 PCDHGA3 3.544645 0.065642 54 TSPAN9 1.574946 0.174994 9 PCDHGB1 3.544645 0.06688 53 MSRA 2.697729 0.337216 8 PCDHGA4 3.544645 0.069503 51 MACROD1 2.641009 0.330126 8 HDAC4 14.58025 0.394061 37 VRK2 2.342444 0.292806 8 RBFOX3 7.388665 0.211105 35 DNMT3A 2.147233 0.268404 8 PAX6 4.641956 0.132627 35 SYNJ2 1.672535 0.209067 8 DIP2C 8.478668 0.264958 32 PPP2R2B 1.66247 0.207809 8 SOX2-OT 4.977386 0.171634 29 RORA 1.527647 0.190956 8 SHANK2 6.65119 0.255815 26 SHROOM3 1.407036 0.17588 8 AGAP1 8.820289 0.352812 25 LINC00461 2.12156 0.30308 7 CAMTAI 6.518146 0.260726 25 HOTAIR 1.935889 0.276556 7 PDGFRA 4.49589 0.179836 25 ITPK1 1.647546 0.235364 7 RPTOR 10.7314 0.466583 23 MIR548H4 1.469684 0.209955 7 NCOR2 6.327405 0.275105 23 RXRA 1.445779 0.20654 7 NXN 5.500813 0.239166 23 PAX1 3.520315 0.586719 6 RIMBP2 4.098597 0.1782 23 COLECI 1 2.263679 0.37728 6 INPP5A 3.38079 0.146991 23 SLC22A18AS 2.124858 0.354143 6 PRKCZ 5.020701 0.228214 22 FBXL18 1.670225 0.278371 6 SKI 8.543431 0.40683 21 RUNDC3A 2.723196 0.544639 5 ABR 4.556746 0.227837 20 TSN AX-DISCI 2.175881 0.435176 5 FRMD4A 4.506761 0.225338 20 CASP8 1.593029 0.318606 5 SDK1 3.860832 0.193042 20 MLC1 2.23873 0.559683 4 MAD1L1 14.50828 0.763594 19 GSG1 1.790305 0.447576 4 SMG1P2 5.448495 0.286763 19 IGSF21 1.765871 0.441468 4 BOLA2 5.448495 0.286763 19 GRHL2 1.718594 0.429648 4 LOC613038 5.448495 0.286763 19 DTNA 1.632281 0.40807 4 CASZ1 5.147721 0.270933 19 FLJ 12825 1.600283 0.400071 4 ZNF423 4.71975 0.248408 19 TUBA1C 1.482734 0.370684 4 KCNQ1 3.549108 0.186795 19 VOPP1 1.451522 0.36288 4 FOXK1 5.453911 0.302995 18 SEPTIN9 4.703173 0.261287 18 C19orf25 4.178021 0.59686 7 TBC1D16 3.984165 0.221342 18 GAK 3.726453 0.53235 7 ANKRD11 3.756247 0.20868 18 VPS 13D 3.699749 0.528536 7 OPCML 4.20818 0.24754 17 CRADD 3.663532 0.610589 6 HBG2 3.602584 0.211917 17 FBXE18 3.652621 0.60877 6 FOXP1 6.343187 0.396449 16 MYO 16 3.51998 0.586663 6 NAV2 3.50163 0.218852 16 SEC22A18AS 3.508111 0.584685 6 GLI2 6.478656 0.43191 15 KDM4B 3.339818 0.556636 6 KIRREL3 4.649562 0.309971 15 RERE 3.31663 0.552772 6 NHX 3.772506 0.2515 15 TSNAX-DISC1 4.961219 0.992244 5 BAIAP2 3.692668 0.246178 15 ARHGEF7 4.614511 0.922902 5 ZBTB20 3.549662 0.236644 15 RUNDC3A 4.443008 0.888602 5 RPS6KA2 7.461138 0.532938 14 NHSL1 3.883288 0.970822 4 IQSEC1 5.061474 0.361534 14 GSG1 3.562403 0.890601 4 CUX1 4.846724 0.346195 14 DAGLB 3.468613 1.156204 3 ARHGEF10 4.598722 0.32848 14 SLC25A10 3.744963 1.872481 2 C7orf50 3.766058 0.269004 14 ANKLE2 3.742803 1.871401 2 MOB2 3.730462 0.266462 14 CHTF18 3.59738 1.79869 2 MSI2 5.49049 0.422345 13 GSE1 4.094565 0.314967 13 TABLE 17: Cancer Type CPH_PAP MYT1L 3.791384 0.291645 13 Gene site imp sum imp mean n REX4 3.533487 0.271807 13 PTPRN2 15.76305 0.192232 82 CMIP 5.387646 0.44897 12 PRDM16 13.67823 0.192651 71 ZC3H3 4.529418 0.377451 12 PCDHGA1 5.703731 0.096673 59 FBRSL1 4.508729 0.375727 12 PCDHGA2 6.020117 0.105616 57 GNA12 4.186371 0.348864 12 PCDHGA3 5.703731 0.105625 54 RASA3 3.47519 0.289599 12 PCDHGB1 5.703731 0.107618 53 VGLL4 4.800447 0.436404 11 PCDHGA4 5.387345 0.105634 51 TBCD 3.965128 0.360466 11 PCDHGB2 5.387345 0.109946 49 CTBP2 3.827094 0.347918 11 PCDHGA5 5.387345 0.114624 47 FGFR2 3.47253 0.315685 11 PCDHGB3 5.387345 0.125287 43 RAD51B 3.396735 0.308794 11 PCDHGA6 4.987145 0.124679 40 TSPAN4 3.432232 0.343223 10 HDAC4 20.3413 0.549765 37 KLHL29 3.36824 0.336824 10 PCDHGA7 4.54013 0.122706 37 SND1 5.50511 0.611679 9 PAX6 9.788933 0.279684 35 ATP11A 5.258679 0.584298 9 RBFOX3 5.926882 0.169339 35 TSPAN9 4.32151 0.480168 9 PCDHGB4 4.54013 0.129718 35 CACNA2D4 4.154955 0.461662 9 PCDHGA8 4.54013 0.129718 35 MGMT 3.560993 0.395666 9 DIP2C 10.39729 0.324915 32 AXIN2 3.380254 0.375584 9 PCDHGB5 4.54013 0.141879 32 NOTCH 1 3.374999 0.375 9 PCDHGA9 4.54013 0.146456 31 LINC00311 5.384992 0.673124 8 SOX2-OT 6.276694 0.216438 29 VRK2 4.874748 0.609343 8 PCDHGB6 4.093199 0.141145 29 AFF3 3.800927 0.475116 8 PCDHGA10 4.093199 0.146186 28 DNMT3A 3.791412 0.473927 8 SHANK2 6.600753 0.253875 26 MSRA 3.519684 0.439961 8 ADARB2 4.821925 0.185459 26 NAVI 4.601684 0.657383 7 AGAP1 12.15099 0.48604 25 MIR548H4 4.217779 0.60254 7 CAMTAI 7.096096 0.283844 25 PDGFRA 6.559998 0.2624 25 ZC3H3 5.118353 0.426529 12

RPTOR 13.67435 0.594537 23 RAD51B 5.26413 0.478557 11

NXN 8.651145 0.376137 23 TBCD 4.663455 0.42395 11

NCOR2 8.222368 0.357494 23 CTBP2 4.094029 0.372184 11

RIMBP2 4.835142 0.210224 23 CHST11 4.802943 0.480294 10

PRKCZ 5.785762 0.262989 22 AKAP13 4.651205 0.46512 10

SKI 7.781461 0.370546 21 ACOT7 4.501323 0.450132 10

FRMD4A 5.631758 0.281588 20 TSPAN4 4.229275 0.422928 10

SDK1 5.359402 0.26797 20 SND1 7.744689 0.860521 9

ABR 4.898258 0.244913 20 ATP11A 6.174457 0.686051 9

MAD1L1 12.41947 0.653656 19 TRAPPCI 2 5.034399 0.559378 9

ZNF423 5.52617 0.290851 19 ADAMTS2 4.879392 0.542155 9

SMG1P2 5.363616 0.282296 19 TSPAN9 4.564063 0.507118 9

BOLA2 5.363616 0.282296 19 LINC00311 5.309574 0.663697 8

LOC613038 5.363616 0.282296 19 MSRA 4.863272 0.607909 8

CASZ1 5.225511 0.275027 19 VRK2 4.291413 0.536427 8

KCNQ1 4.215948 0.221892 19 C19orf25 5.576676 0.796668 7

FOXK1 7.879019 0.437723 18 NAVI 4.89231 0.698901 7

TBC1D16 6.176091 0.343116 18 MIR548H4 4.148861 0.592694 7

MCF2L 5.990435 0.332802 18 STK10 4.361242 0.726874 6

PAX6-AS1 4.363491 0.256676 17 SLC22A18AS 4.298423 0.716404 6

RCN1 4.363491 0.256676 17 CRADD 4.076431 0.679405 6

OPCML 4.322223 0.254248 17 TSNAX-DISC1 5.131162 1.026232 5

FOXP1 7.606903 0.475431 16 KLHL25 4.900367 0.980073 5

NAV2 5.920485 0.37003 16 RUNDC3A 4.785457 0.957091 5

EBF3 4.823856 0.301491 16 NHSL1 4.953431 1.238358 4

SORBS2 4.427937 0.276746 16

GLI2 6.928675 0.461912 15 TABLE 18: Cancer Type CPP_AD

KIRREL3 5.898037 0.393202 15 Gene site imp sum imp mean n

ZBTB20 5.401269 0.360085 15 PTPRN2 11.31281 0.137961 82

SLX1B- PRDM16 13.74184 0.193547 71

SULT1A4 4.904351 0.326957 15

PCDHGA1 3.610679 0.061198 59

SLX1A 4.904351 0.326957 15

PCDHGA2 3.294293 0.057795 57

LOC606724 4.904351 0.326957 15

PCDHGA3 2.977907 0.055146 54

BAIAP2 4.79365 0.319577 15

PCDHGB1 2.977907 0.056187 53

NHX 4.373374 0.291558 15

PCDHGA4 2.977907 0.05839 51

RPS6KA2 6.634642 0.473903 14

PCDHGB2 2.977907 0.060774 49

C7orf50 6.268865 0.447776 14

PCDHGA5 2.977907 0.06336 47

CUX1 6.209467 0.443533 14

PCDHGB3 3.294293 0.076611 43

IQSEC1 5.122236 0.365874 14

PCDHGA6 2.977907 0.074448 40

PRKAG2 4.918648 0.351332 14

HDAC4 12.8492 0.347276 37

MSI2 7.371489 0.567038 13

PCDHGA7 2.977907 0.080484 37

MYT1L 4.530897 0.348531 13

RBFOX3 5.853322 0.167238 35

GSE1 4.466342 0.343565 13

DIP2C 9.700563 0.303143 32

RFX4 4.465521 0.343502 13

SOX2-OT 3.208169 0.110627 29

CMIP 7.333738 0.611145 12

GALNT9 3.005051 0.111298 27

FBRSL1 5.930377 0.494198 12

SHANK2 5.434307 0.209012 26

GNA12 5.617094 0.468091 12

AGAP1 6.813914 0.272557 25 CAMTAI 3.302831 0.132113 25 VGLL4 3.177852 0.288896 11 MEIS1 5.0502 0.210425 24 FGFR2 3.173879 0.288534 11 RPTOR 11.01185 0.478776 23 TSPAN4 3.933783 0.393378 10 NXN 6.149227 0.267358 23 AKAP13 3.582406 0.358241 10 NCOR2 5.110155 0.222181 23 KLHL29 3.303085 0.330308 10 PRKCZ 5.866388 0.266654 22 AUTS2 2.990605 0.29906 10 SKI 10.44832 0.497539 21 SND1 6.386064 0.709563 9 ZIC4 3.92162 0.186744 21 ATP11A 4.668347 0.518705 9 FRMD4A 4.297019 0.214851 20 ADAMTS2 4.469375 0.496597 9 ABR 3.536087 0.176804 20 TSPAN9 4.339742 0.482194 9 MAD1L1 8.027724 0.422512 19 TRAPPCI 2 3.951345 0.439038 9 CASZ1 4.119798 0.216831 19 CACNA2D4 3.72499 0.413888 9 ZNF423 4.010193 0.211063 19 GPC6 3.711232 0.412359 9 SMG1P2 2.998275 0.157804 19 KCNH2 3.473724 0.385969 9 BOLA2 2.998275 0.157804 19 SSBP3 3.197893 0.355321 9 LOC613038 2.998275 0.157804 19 RUNX1 3.146465 0.349607 9 FOXK1 4.862215 0.270123 18 VRK2 4.205316 0.525664 8 TBC1D16 4.487671 0.249315 18 DLEU1 3.95583 0.494479 8 SEPTIN9 4.384937 0.243608 18 PPP2R2B 3.906005 0.488251 8 ANKRD11 3.167504 0.175972 18 MSRA 3.845906 0.480738 8

OPCML 5.13777 0.302222 17 NAVI 3.707811 0.529687 7 FOXP1 5.439543 0.339971 16 PITPNC1 3.328507 0.475501 7 EBF3 4.678157 0.292385 16 CXXC5 3.101939 0.443134 7 NAV2 4.315649 0.269728 16 LINC01140 3.012474 0.430353 7 SORBS2 3.006473 0.187905 16 SLC22A18AS 3.909045 0.651507 6 NHX 4.173358 0.278224 15 CRADD 3.165654 0.527609 6 GLI2 3.837164 0.255811 15 RUNDC3A 4.433736 0.886747 5 BAIAP2 3.482668 0.232178 15 TSNAX-DISC1 3.732736 0.746547 5 KIRREL3 3.370273 0.224685 15 ARHGEF7 3.234689 0.646938 5 NFATC1 3.068066 0.204538 15 EXT1 3.532838 0.88321 4 RPS6KA2 5.846022 0.417573 14 CRB2 3.04556 0.76139 4 CUX1 4.552806 0.3252 14 KCNIP1 2.970068 0.990023 3 PRKAG2 4.5192 0.3228 14 TRIM65 3.537311 1.768656 2 MIR548F5 3.732932 0.266638 14 TBX5 3.406149 0.243296 14 TABLE 19: Cancer Type CPPJNF C7orf50 3.159648 0.225689 14 Gene site imp sum imp mean n GNG7 3.099068 0.221362 14 PTPRN2 15.19001 0.185244 82 MYT1L 3.688104 0.2837 13 PRDM16 14.43299 0.203282 71 GSE1 3.54579 0.272753 13 PCDHGA1 3.724804 0.063132 59 MSI2 3.162515 0.24327 13 PCDHGA2 3.408418 0.059797 57 CMIP 3.815283 0.31794 12 PCDHGA3 3.092032 0.05726 54 GNA12 3.335193 0.277933 12 PCDHGB1 3.092032 0.05834 53 TNS3 3.209968 0.267497 12 PCDHGB2 3.092032 0.063103 49

ADGRD1 3.129744 0.260812 12 HDAC4 10.31392 0.278755 37 MIRLET7BHG 3.055175 0.254598 12 RBFOX3 6.644166 0.189833 35 ZC3H12D 3.93079 0.357345 11 PAX6 3.977644 0.113647 35 RAD51B 3.279301 0.298118 11 DIP2C 5.98807 0.187127 32 SPON2 3.277534 0.297958 11 SOX2-OT 4.371246 0.150733 29 GALNT9 4.601184 0.170414 27 MEGF6 3.341803 0.278484 12

SHANK2 4.820486 0.185403 26 TNS3 3.257131 0.271428 12

AGAP1 7.010968 0.280439 25 MAML3 2.939116 0.244926 12

CAMTAI 5.642138 0.225686 25 RAD51B 4.043203 0.367564 11

RPTOR 11.11451 0.48324 23 CTBP2 3.630376 0.330034 11

NXN 6.525128 0.283701 23 VGLL4 3.285303 0.298664 11

RIMBP2 4.030315 0.175231 23 TBCD 3.162087 0.287462 11

NCOR2 3.582323 0.155753 23 SPON2 3.057463 0.277951 11

PRKCZ 6.332197 0.287827 22 TSPAN4 4.814937 0.481494 10

SKI 7.47976 0.356179 21 AKAP13 3.798065 0.379806 10

ZIC4 4.477112 0.213196 21 ACOT7 3.29048 0.329048 10

SDK1 4.570761 0.228538 20 KLHL29 3.284723 0.328472 10

FRMD4A 3.647532 0.182377 20 AUTS2 2.927169 0.292717 10

ABR 3.602999 0.18015 20 SND1 6.036166 0.670685 9

MAD1L1 7.775015 0.409211 19 ATP11A 4.604429 0.511603 9

ZNF423 5.623971 0.295998 19 TSPAN9 3.7764 0.4196 9

SMG1P2 4.520349 0.237913 19 ADAMTS2 3.507534 0.389726 9

BOLA2 4.520349 0.237913 19 KCNH2 3.472232 0.385804 9

LOC613038 4.520349 0.237913 19 AXIN2 3.263217 0.36258 9

CASZ1 4.385646 0.230823 19 CACNA2D4 3.066299 0.3407 9

KCNQ1 3.410898 0.179521 19 NOTCH1 2.973379 0.330375 9

FOXK1 4.972638 0.276258 18 PPP2R2B 4.877357 0.60967 8 SEPTIN9 4.438428 0.246579 18 VRK2 4.873352 0.609169 8 OPCML 3.528898 0.207582 17 LINC00311 3.42386 0.427983 8

TBX15 3.24008 0.190593 17 GAK 3.4657 0.4951 7

NAV2 6.568887 0.410555 16 MIR548H4 3.216472 0.459496 7

FOXP1 5.218166 0.326135 16 RXRA 3.162505 0.451786 7

EBF3 3.681068 0.230067 16 NAVI 3.044468 0.434924 7

GLI2 5.955313 0.397021 15 PACRG 3.023698 0.431957 7

KIRREL3 3.935595 0.262373 15 SLC22A18AS 3.344646 0.557441 6

ZBTB20 3.197168 0.213145 15 COLECI 1 2.917414 0.486236 6

BAIAP2 3.103791 0.206919 15 RUNDC3A 4.628337 0.925667 5

RPS6KA2 5.853346 0.418096 14 TSNAX-DISC1 3.530465 0.706093 5

CUX1 5.323808 0.380272 14 PRR5L 3.35567 0.671134 5

IQSEC1 3.75573 0.268266 14 ARHGEF7 3.14822 0.629644 5

PRKAG2 3.191906 0.227993 14 EXT1 3.444927 0.861232 4

C7orf50 3.078872 0.219919 14 DAGLB 3.089325 1.029775 3

CACNA1H 2.971891 0.212278 14 TRIM65 3.467956 1.733978 2

MSI2 5.777816 0.444447 13 SLC25A10 2.9975 1.49875 2

MYT1L 3.567409 0.274416 13 ANKLE2 2.970224 1.485112 2

RFX4 3.279066 0.252236

SPTBN4 3.22895 0.248381 13 TABLE 20: Cancer Type CRINET

GSE1 3.121687 0.24013 13 Gene site imp sum imp mean n

KIF26B 2.894191 0.22263 13 PTPRN2 10.96688 0.133742 82

ADGRD1 4.78956 0.39913 12 PRDM16 3.231634 0.045516 71

ZC3H3 4.638332 0.386528 12 HDAC4 9.014051 0.243623 37

CMIP 3.69183 0.307652 12 RBFOX3 2.723093 0.077803 35

MIRLET7BHG 3.548295 0.295691 12 DIP2C 5.186759 0.162086 32 SOX2-OT 2.214702 0.076369 29 TSPAN4 2.775921 0.277592 10

SHANK2 3.170026 0.121924 26 ACOT7 2.749893 0.274989 10

AGAP1 6.532723 0.261309 25 SH3RF3 2.591023 0.259102 10

PDGFRA 2.020678 0.080827 25 RGS12 2.235544 0.223554 10

CAMTAI 1.94058 0.077623 25 ASIC2 1.994926 0.199493 10

MEIS1 2.337064 0.097378 24 ADAMTS2 3.822137 0.424682 9

RPTOR 5.3942 0.23453 23 SND1 3.643114 0.40479 9

NXN 3.022357 0.131407 23 KCNH2 3.47615 0.386239 9

PRKCZ 2.86389 0.130177 22 ATP11A 3.127011 0.347446 9

SKI 5.215756 0.248369 21 RUNX1 2.356786 0.261865 9

FRMD4A 3.17079 0.158539 20 TRAPPCI 2 2.069967 0.229996 9

ABR 2.662139 0.133107 20 CACNA2D4 2.067426 0.229714 9

MAD1L1 4.844803 0.25499 19 ASAP1 1.936216 0.215135 9

KCNQ1 2.893974 0.152314 19 DLEU1 3.183968 0.397996 8

SMG1P2 2.681757 0.141145 19 SYNJ2 2.551492 0.318936 8

BOLA2 2.681757 0.141145 19 LINC00311 2.02507 0.253134 8

LOC613038 2.681757 0.141145 19 MIR548H4 2.87209 0.410299 7

ZNF423 2.506076 0.131899 19 NAVI 2.631564 0.375938 7

CASZ1 2.414157 0.127061 19 VPS 13D 2.378957 0.339851 7

RBFOX1 4.229047 0.234947 18 TRIM2 2.313096 0.330442 7

FOXK1 3.165101 0.175839 18 RXRA 2.205592 0.315085 7

MCF2L 2.018677 0.112149 18 CXXC5 2.127757 0.303965 7

SEPTIN9 1.986037 0.110335 18 FBXL18 3.631538 0.605256 6

OPCML 2.287588 0.134564 17 CRADD 2.401529 0.400255 6

FOXP1 2.606269 0.162892 16 ANKS1A 2.142732 0.357122 6

NAV2 2.149384 0.134336 16 FMNL2 1.920409 0.320068 6

GLI2 4.099938 0.273329 15 PRKCH 1.886246 0.314374 6

KIRREL3 3.891284 0.259419 15 RUNDC3A 3.235876 0.647175 5

ZBTB20 3.184826 0.212322 15 ARHGEF7 3.176019 0.635204 5

SLX1B- ATXN7L1 2.714337 0.542867 5

SULT1A4 2.560456 0.170697 15 TSN AX-DISCI 2.582524 0.516505 5

SLX1A 2.560456 0.170697 15 BACH2 2.486198 0.49724 5

LOC606724 2.560456 0.170697 15 ATP2B4 2.417072 0.483414 5

BAIAP2 2.554968 0.170331 15 DNM3 2.151421 0.430284 5

LRMDA 2.029367 0.135291 15 RAPGEF4 2.05881 0.411762 5

RPS6KA2 4.199917 0.299994 14 TMEM132C 1.994235 0.398847 5

C7orf50 2.496931 0.178352 14 PRR5L 1.891281 0.378256 5

CUX1 2.305252 0.164661 14 NHSL1 3.397888 0.849472 4

IQSEC1 2.012079 0.14372 14 IGSF21 2.815659 0.703915 4

MYT1L 2.987968 0.229844 13 RBMS3 2.310324 0.577581 4

MSI2 2.073547 0.159504 13 DTNA 2.266953 0.566738 4

CMIP 4.596826 0.383069 12 SLC6A9 2.544667 0.848222 3

ADGRD1 2.993272 0.249439 12 SPATAI 3 2.438531 0.812844 3

ZC3H3 2.864994 0.23875 12 DICER1 2.094806 0.698269 3

FBRSL1 2.627979 0.218998 12

RALGAPA2 3.044181 1.52209 2

RAD51B 2.654735 0.24134 11 CACNA1D 2.116989 1.058494 2

CTBP2 2.1824 0.1984 11 SLC25A10 2.067739 1.033869 2

AKAP13 2.799577 0.279958 10 RUBCN 1.946876 1.946876 1 MYT1L 3.091459 0.237805 13

CMIP 5.628687 0.469057 12

TABLE 21: Cancer Type DGONC

MEGF6 4.733266 0.394439 12 Gene site imp sum imp mean n

ZC3H3 4.539162 0.378264 12 PTPRN2 13.13503 0.160183 82 MIRLET7BHG 4.231054 0.352588 12 PRDM16 10.3477 0.145742 71 FBRSL1 4.164398 0.347033 12 HDAC4 11.05626 0.298818 37 CTNNA2 3.999547 0.333296 12 RBFOX3 8.461747 0.241764 35 TNS3 3.194033 0.266169 12 PAX6 6.129446 0.175127 35 ADGRD1 3.077153 0.256429 12 DIP2C 6.563504 0.205109 32 RAD51B 4.43364 0.403058 11 SOX2-OT 6.35483 0.219132 29 VGLL4 3.615133 0.328648 11 GALNT9 2.974276 0.110158 27 CTBP2 3.204323 0.291302 11 SHANK2 4.935714 0.189835 26 FGFR2 2.960961 0.269178 11 CAMTAI 6.542261 0.26169 25 ACOT7 4.589562 0.458956 10 AGAP1 6.192129 0.247685 25 ATP11A 6.140285 0.682254 9 PDGFRA 5.486155 0.219446 25 SND1 5.557866 0.617541 9 MEIS1 3.384971 0.14104 24

ASAP1 4.317153 0.479684 9 RPTOR 6.534565 0.284112 23

AXIN2 4.179311 0.464368 9 HOXB3 3.613134 0.157093 23

ADGRB1 3.586638 0.398515 9 RIMBP2 3.415794 0.148513 23

ADAMTS2 3.585293 0.398366 9 NXN 2.923121 0.127092 23 TSPAN9 3.570083 0.396676 9 PRKCZ 4.393011 0.199682 22 TRAPPCI 2 3.478144 0.38646 9 SKI 9.18904 0.437573 21 PACS2 3.445642 0.382849 9 FRMD4A 5.480103 0.274005 20 RUNX1 3.322449 0.369161 9 ABR 3.264658 0.163233 20 CACNA2D4 3.235785 0.359532 9 MAD1L1 10.47917 0.551535 19

LINC00311 4.871942 0.608993 8 SMG1P2 7.187217 0.378275 19

GRIK2 3.741669 0.467709 8 BOLA2 7.187217 0.378275 19

MSRA 3.510317 0.43879 8 LOC613038 7.187217 0.378275 19

RORA 3.03757 0.379696 8 ZNF423 7.024963 0.369735 19

DLEU1 3.024723 0.37809 8 CASZ1 4.495115 0.236585 19

NAVI 4.171854 0.595979 7 ANKRD11 4.490421 0.249468 18 LINC00461 3.869468 0.552781 7 SEPTIN9 4.125948 0.229219 18 DUSP6 3.7407 0.534386 7 FOXK1 4.076412 0.226467 18 LINC01140 3.17112 0.453017 7 RBFOX1 3.121084 0.173394 18

CXXC5 3.156982 0.450997 7 OPCML 5.364502 0.315559 17 FBXL18 4.736061 0.789343 6 FOXP1 5.925927 0.37037 16 KDM4B 3.908086 0.651348 6 NAV2 3.796292 0.237268 16 MYO 16 3.643539 0.607256 6 GLI2 9.47517 0.631678 15

CRADD 3.620798 0.603466 6 BAIAP2 3.763679 0.250912 15 FAM181A 3.100463 0.516744 6 ZBTB20 3.758867 0.250591 15 COQ8A 2.955684 0.492614 6 LRMDA 3.368728 0.224582 15 FMNL2 2.906814 0.484469 6 RPS6KA2 6.19269 0.442335 14 RUNDC3A 4.876724 0.975345 5 PRKAG2 3.71711 0.265508 14 TSNAX-DISC1 3.841281 0.768256 5 C7orf50 3.299165 0.235655 14 ARHGEF7 3.637038 0.727408 5 IQSEC1 3.229465 0.230676 14 SLC8A2 3.142738 0.628548 5 ARHGEF10 2.978949 0.212782 14 TEAD1 3.001606 0.600321 5 MSI2 4.797151 0.369012 13 RBMS3 4.49997 1.124992 4 STAP2 3.308779 0.827195 4 SLX1B- SULT1A4 2.486581 0.165772 15

GRIN2B 3.874143 1.291381 3 SLX1A 2.486581 0.165772 15

SRRM3 3.759727 1.253242 3 LOC606724 2.486581 0.165772 15

TTC12 3.117951 1.039317 3 CUX1 3.773402 0.269529 14

DAGLB 2.897008 0.965669 3 IQSEC1 3.532998 0.252357 14

SOXIO 4.815607 2.407804 2 RPS6KA2 3.149474 0.224962 14

SLC25A10 3.451075 1.725538 2

ANKLE2 3.363035 1.681517 2 ARHGEF10 2.925318 0.208951 14

PPP2R2A 2.730323 0.195023 14

TABLE 22: Cancer Type DLBCL TBX5 2.275259 0.162518 14 SYCP2L 2.187062 0.156219 14 Gene site imp sum imp mean n RFX4 3.210626 0.246971 13 PTPRN2 7.35736 0.089724 82 MSI2 2.979907 0.229224 13 PRDM16 5.048598 0.071107 71 HOXA10- PCDHGA1 2.529679 0.042876 59 HOXA9 2.337064 0.179774 13 PCDHGA2 2.529679 0.04438 57 CMIP 4.129253 0.344104 12 PCDHGA3 2.529679 0.046846 54 FBRSL1 3.906961 0.32558 12 PCDHGB1 2.529679 0.04773 53 CTNNA2 3.079083 0.25659 12 PCDHGA4 2.213293 0.043398 51 ISLR2 2.956629 0.246386 12 HDAC4 10.21894 0.276188 37 ZC3H3 2.838728 0.236561 12 PAX6 6.266407 0.17904 35 GNA12 2.558761 0.21323 12 RBFOX3 3.511339 0.100324 35 ADGRD1 2.332938 0.194411 12 DIP2C 3.718857 0.116214 32 MAML3 2.121208 0.176767 12 SOX2-OT 4.25213 0.146625 29 ZC3H12D 3.371605 0.30651 11 SHANK2 2.849971 0.109614 26 RAD51B 3.168855 0.288078 11 AGAP1 4.49937 0.179975 25 GLUD1P2 2.92937 0.266306 11 PDGFRA 2.749791 0.109992 25 VGLL4 2.276061 0.206915 11 SATB2 3.286222 0.136926 24 ACOT7 4.342496 0.43425 10 MEIS1 3.073452 0.128061 24 SKOR1 2.667111 0.266711 10 INPP5A 2.628684 0.114291 23 AKAP13 2.526244 0.252624 10 NCOR2 2.577036 0.112045 23 JUP 2.2755 0.22755 10 SKI 5.349457 0.254736 21 NR2F1-AS1 2.098752 0.209875 10 HOXA-AS3 3.448386 0.164209 21 ATP11A 4.725982 0.525109 9 SIM2 2.978525 0.141835 21 SND1 3.979144 0.442127 9 SDK1 2.685795 0.13429 20 ADAMTS2 3.896946 0.432994 9 ABR 2.520912 0.126046 20 MGMT 2.34924 0.261027 9 MAD1L1 7.437988 0.391473 19 RUNX1 2.314993 0.257221 9 ZNF423 3.677587 0.193557 19 VAX1 2.125157 0.236129 9 CASZ1 3.599919 0.189469 19 LHX4 3.689458 0.461182 8 SMG1P2 2.65989 0.139994 19 MSRA 3.232702 0.404088 8 BOLA2 2.65989 0.139994 19 LMX1B 2.456416 0.307052 8 LOC613038 2.65989 0.139994 19 TRAPPC9 2.186304 0.273288 8 SEPTIN9 3.787837 0.210435 18 CXXC5 3.199163 0.457023 7 FOXK1 3.702087 0.205671 18 WWOX 2.715789 0.38797 7 TBC1D16 2.563787 0.142433 18 ITPK1 2.388983 0.341283 7 HOXA3 2.37731 0.132073 18 IQCE 2.341671 0.334524 7 TBX15 2.44435 0.143785 17 LINC01140 2.282949 0.326136 7 EBF3 4.650985 0.290687 16 LDLRAD4 2.247761 0.321109 7 FOXP1 2.433564 0.152098 16 VPS 13D 2.207715 0.315388 7 SKI 8.889427 0.423306 21 CLDN10 2.124797 0.303542 7 FRMD4A 5.673992 0.2837 20 FBXL18 3.068542 0.511424 6 ABR 4.382257 0.219113 20 FMNL2 2.730825 0.455137 6 MAD1L1 8.172234 0.430118 19 LRRFIP1 2.390845 0.398474 6 ZNF423 7.770215 0.408959 19 MIR548G 2.275784 0.379297 6 SMG1P2 4.301612 0.226401 19 LYPD1 2.127456 0.354576 6 BOLA2 4.301612 0.226401 19 ARHGEF7 3.512343 0.702469 5 LOC613038 4.301612 0.226401 19 CCR6 2.624993 0.524999 5 FOXK1 5.49026 0.305014 18 AP2A2 2.257238 0.451448 5 ANKRD11 4.019946 0.22333 18 NHSL1 2.697164 0.674291 4 MCF2L 3.058393 0.169911 18 SPTBN1 2.676614 0.669154 4 OPCML 6.360814 0.374166 17 DTNA 2.447217 0.611804 4 NAV2 3.273524 0.204595 16 TBC1D7 2.617603 0.872534 3 GLI2 9.264765 0.617651 15 DICER1 2.396974 0.798991 3 BAIAP2 4.361371 0.290758 15 CDC42BPB 2.22176 0.740587 3 EMX2OS 2.952614 0.196841 15 DAGLB 2.118044 0.706015 3 CACNA1H 3.460287 0.247163 14

C7orf50 3.304953 0.236068 14

TABLE 23: Cancer Type DLGNT_1 RPS6KA2 3.05343 0.218102 14 Gene site imp sum imp mean n CUX1 2.937762 0.20984 14 PTPRN2 17.73156 0.216239 82 MSI2 4.465639 0.343511 13 PRDM16 8.961345 0.126216 71 GSE1 3.69265 0.28405 13 PCDHGA1 3.540457 0.060008 59 KIF26B 3.240362 0.249259 13 PCDHGA2 3.540457 0.062113 57 MYT1L 2.91326 0.224097 13 PCDHGA3 3.115981 0.057703 54 CMIP 5.886653 0.490554 12 PCDHGB1 3.115981 0.058792 53 ZC3H3 3.878974 0.323248 12 PCDHGA4 3.432367 0.067301 51 MIRLET7BHG 3.866699 0.322225 12 PCDHGB2 3.432367 0.070048 49 ADGRD1 3.850417 0.320868 12 PCDHGA5 3.432367 0.073029 47 MAML3 3.735777 0.311315 12 HDAC4 10.20179 0.275724 37 FGFR2 4.667649 0.424332 11 PAX6 7.077885 0.202225 35 RAD51B 4.545562 0.413233 11 RBFOX3 4.135104 0.118146 35 GLUD1P2 3.061923 0.278357 11 DIP2C 7.384183 0.230756 32 ZC3H12D 3.043476 0.27668 11 SOX2-OT 6.020509 0.207604 29 AKAP13 4.186819 0.418682 10 GALNT9 3.126337 0.11579 27 KLHL29 4.089224 0.408922 10 ADARB2 4.454864 0.171341 26 TSPAN4 3.372985 0.337299 10 SHANK2 3.51474 0.135182 26 GRID1 3.178463 0.317846 10 AGAP1 8.824816 0.352993 25 SND1 5.409044 0.601005 9 PDGFRA 4.565095 0.182604 25 ATP11A 4.057139 0.450793 9 CAMTAI 3.907532 0.156301 25 TRAPPCI 2 3.789579 0.421064 9

MEIS1 6.510303 0.271263 24 ASAP1 3.622777 0.402531 9 SATB2 4.395429 0.183143 24 TSPAN9 3.493469 0.388163 9 HOXB3 10.90119 0.473965 23 AXIN2 3.097557 0.344173 9 RPTOR 7.373151 0.320572 23 PACS2 2.99983 0.333314 9 NCOR2 5.682337 0.247058 23 ADGRB1 2.994008 0.332668 9 INPP5A 4.658587 0.202547 23 SLC22A18 2.950168 0.327796 9 NXN 4.158511 0.180805 23 SSBP3 2.93931 0.32659 9 RIMBP2 3.499651 0.152159 23 NOTCH 1 2.92436 0.324929 9 ADAMTS2 2.919976 0.324442 9 AGAP1 6.999717 0.279989 25

LINC00311 4.667601 0.58345 8 PDGFRA 4.465416 0.178617 25

GRIK2 3.593081 0.449135 8 SATB2 5.084407 0.21185 24

MSRA 3.52281 0.440351 8 MEIS1 3.433495 0.143062 24

DPP6 2.871326 0.358916 8 NXN 4.316404 0.18767 23

DUSP6 5.050046 0.721435 7 RPTOR 3.892675 0.169247 23

LINC00461 4.063855 0.580551 7 PRKCZ 3.490803 0.158673 22

NAVI 3.68482 0.526403 7 SKI 5.236229 0.249344 21

HOXB-AS3 3.328612 0.475516 7 FRMD4A 4.39425 0.219713 20

FHIT 3.055776 0.436539 7 ABR 2.5481 0.127405 20

C19orf25 2.863584 0.409083 7 MAD1L1 6.792536 0.357502 19

FAM181A 3.778152 0.629692 6 CASZ1 3.666051 0.19295 19

COQ8A 3.077893 0.512982 6 SMG1P2 3.356343 0.17665 19

CRADD 2.969012 0.494835 6 BOLA2 3.356343 0.17665 19 RUNDC3A 4.806159 0.961232 5 LOC613038 3.356343 0.17665 19 ARHGEF7 3.251554 0.650311 5 ZNF423 2.657667 0.139877 19

PRR5L 3.176393 0.635279 5 ANKRD11 4.535455 0.25197 18

TSN AX-DISCI 2.850348 0.57007 5 MCF2L 4.054608 0.225256 18

RBMS3 3.900037 0.975009 4 FOXK1 3.405545 0.189197 18

CRB2 3.186812 0.796703 4 OPCML 5.077194 0.298658 17

LINC00856 3.068772 0.767193 4 FOXP1 4.235477 0.264717 16

GRIN2B 3.258367 1.086122 3 SORBS2 2.589215 0.161826 16

LOXL3 2.79616 0.932053 3 GLI2 4.691495 0.312766 15

SOXIO 4.490155 2.245078 2 RPS6KA2 3.790283 0.270734 14

CUX1 2.480754 0.177197 14

TABLE 24: Cancer Type DLGNT_2 IQSEC1 2.470793 0.176485 14

Gene site imp sum imp mean MSI2 3.922805 0.301754 13

PTPRN2 7.060492 0.086104

MYT1L 3.569878 0.274606 13

PRDM16 6.311401 0.088893

MIR9-3HG 2.559278 0.196868 13

PCDHGA1 4.401863 0.074608

KIF26B 2.340501 0.180039 13

PCDHGA2 4.401863 0.077226

GSE1 2.253312 0.173332 13

PCDHGA3 5.097912 0.094406

ADGRD1 3.133694 0.261141 12

PCDHGB1 5.097912 0.096187

FBRSL1 2.796324 0.233027 12

PCDHGA4 5.414298 0.106163

CMIP 2.782287 0.231857 12

PCDHGB2 5.414298 0.110496

TNS3 2.531411 0.210951 12

PCDHGA5 5.414298 0.115198

MIRLET7BHG 2.453634 0.20447 12

PCDHGB3 4.336825 0.100856

RAD51B 2.998745 0.272613 11

PCDHGA6 3.279577 0.081989

SLC9A3 2.70451 0.245865 11

HDAC4 5.169197 0.139708

SPON2 2.249612 0.20451 11

PCDHGA7 2.431053 0.065704

LBX1-AS1 3.804831 0.380483 10

PAX6 6.464383 0.184697

GRID1 3.744038 0.374404 10

PCDHGB4 2.431053 0.069459

AKAP13 3.081928 0.308193 10

PCDHGA8 2.431053 0.069459

ACOT7 2.348144 0.234814 10

RBFOX3 2.376116 0.067889

SH3RF3 2.321484 0.232148 10

DIP2C 5.085695 0.158928 NR2F1-AS1 2.227467 0.222747 10

SOX2-OT 3.01975 0.104129 SND1 4.69145 0.521272 9

GALNT9 3.107066 0.115077 ATP11A 4.206191 0.467355 9

SHANK2 3.110309 0.119627 NOTCH 1 3.06654 0.340727 9 ASAP1 2.962156 0.329128 9 RBFOX3 4.758746 0.135964 35 TSPAN9 2.632807 0.292534 9 PCDHGB4 4.085106 0.116717 35 ADAMTS2 2.358105 0.262012 9 PCDHGA8 4.085106 0.116717 35 KCNMA1 2.311715 0.256857 9 DIP2C 4.051875 0.126621 32 CACNA2D4 2.278375 0.253153 9 PCDHGB5 3.76872 0.117772 32 MSRA 3.321819 0.415227 8 PCDHGA9 3.452334 0.111366 31 ESRRG 2.820181 0.352523 8 SOX2-OT 6.608075 0.227865 29 HMGA2 2.564935 0.320617 8 GALNT9 3.416686 0.126544 27 LINC00311 2.477684 0.309711 8 ADARB2 6.029704 0.231912 26 DUSP6 3.322224 0.474603 7 SHANK2 5.422076 0.208541 26 NAVI 3.275629 0.467947 7 AGAP1 6.047859 0.241914 25 CDYL 2.77406 0.396294 7 CAMTAI 5.252322 0.210093 25 TACC2 2.509948 0.358564 7 PDGFRA 4.033234 0.161329 25 VPS 13D 2.346989 0.335284 7 SATB2 8.554768 0.356449 24 TOX2 2.226045 0.318006 7 MEIS1 3.819655 0.159152 24 FAM181A 2.772031 0.462005 6 RPTOR 8.828935 0.383867 23 SLC22A18AS 2.612686 0.435448 6 INPP5A 5.385072 0.234134 23 FMNL2 2.316991 0.386165 6 NCOR2 5.028779 0.218643 23 WFIKKN2 2.263369 0.377228 6 NXN 3.427883 0.149038 23 RUNDC3A 3.855633 0.771127 5 SKI 5.322244 0.25344 21 TSN AX-DISCI 3.171449 0.63429 5 FRMD4A 3.658609 0.18293 20 ARHGEF7 2.9695 0.5939 5 ABR 3.419208 0.17096 20 STARD13 2.39862 0.479724 5 MAD1L1 8.348755 0.439408 19 RBMS3 2.645573 0.661393 4 CASZ1 5.312578 0.279609 19 LINC00856 2.427806 0.606951 4 ZNF423 4.896747 0.257724 19 VOPP1 2.323359 0.58084 4 SMG1P2 4.675455 0.246077 19 GRIN2B 3.269344 1.089781 3 BOLA2 4.675455 0.246077 19 DICER1 2.681425 0.893808 3 LOC613038 4.675455 0.246077 19 TTC12 2.289017 0.763006 3 KCNQ1 3.153024 0.165949 19 SOX10 4.151197 2.075598 2 FOXK1 7.009082 0.389393 18 SLC25A10 2.504357 1.252178 2 SEPTIN9 4.708469 0.261582 18 OPCML 4.150794 0.244164 17

TABLE 25: Cancer Type DMG_EGFR PAX6-AS1 3.578693 0.210511 17 Gene site imp sum imp mean n RCN1 3.578693 0.210511 17 PTPRN2 15.85834 0.193394 82 FOXP1 4.90985 0.306866 16 PRDM16 12.21921 0.172102 71 GLI2 9.248652 0.616577 15 PCDHGA1 6.245608 0.105858 59 ZBTB20 3.101754 0.206784 15 PCDHGA2 6.561994 0.115123 57 CUX1 3.980969 0.284355 14 PCDHGA3 5.929222 0.1098 54 RPS6KA2 3.593044 0.256646 14 PCDHGB1 5.929222 0.111872 53 SYCP2L 3.104278 0.221734 14 PCDHGA4 5.612836 0.110056 51 MSI2 4.747729 0.36521 13 PCDHGB2 5.612836 0.114548 49 RFX4 4.046308 0.311254 13 PCDHGA5 5.29645 0.11269 47 MYT1L 3.388034 0.260618 13 PCDHGB3 4.663678 0.108458 43 GSE1 3.351157 0.257781 13 PCDHGA6 4.085106 0.102128 40 CLYBL 3.188773 0.24529 13 HDAC4 9.855461 0.266364 37 ISLR2 4.716973 0.393081 12 PCDHGA7 4.085106 0.110408 37 TNS3 4.219632 0.351636 12 PAX6 8.05116 0.230033 35 CMIP 3.56734 0.297278 12 ZC3H3 3.444859 0.287072 12 PCDHGB2 4.143029 0.084552 49

ADGRD1 3.195367 0.266281 12 PCDHGA5 4.065802 0.086506 47

ZC3H12D 4.377418 0.397947 11 HDAC4 10.96991 0.296484 37

VGLL4 3.693413 0.335765 11 PAX6 10.49824 0.29995 35

ACOT7 4.221232 0.422123 10 RBFOX3 9.787693 0.279648 35 NR2F1-AS1 3.314474 0.331447 10 PCDHGB4 4.075303 0.116437 35 GAS7 3.254024 0.325402 10 PCDHGA8 4.075303 0.116437 35

IGF1R 3.193725 0.319373 10 DIP2C 8.807282 0.275228 32

SH3RF3 3.144937 0.314494 10 SOX2-OT 9.756366 0.336426 29

OTX1 3.111864 0.311186 10 GALNT9 5.452091 0.201929 27

NTM 3.077721 0.307772 10 SHANK2 7.383968 0.283999 26

ATP11A 5.154603 0.572734 9 ADARB2 6.762464 0.260095 26

SND1 4.57076 0.507862 9 AGAP1 8.206947 0.328278 25

TSPAN9 4.039484 0.448832 9 PDGFRA 8.082227 0.323289 25

GPC6 3.707782 0.411976 9 CAMTAI 6.751014 0.270041 25

ADAMTS2 3.279404 0.364378 9 SATB2 8.80167 0.366736 24

APBA2 3.119427 0.346603 9 MEIS1 6.106283 0.254428 24

ASAP1 3.082338 0.342482 9 RPTOR 10.21494 0.444128 23

ESRRG 4.257093 0.532137 8 INPP5A 5.743264 0.249707 23

DLEU1 3.977631 0.497204 8 RIMBP2 4.992673 0.217073 23

LINC00311 3.497257 0.437157 8 PRKCZ 5.61555 0.255252 22

SHROOM3 3.441927 0.430241 8 SKI 8.819106 0.419957 21

CACHD1 3.393871 0.424234 8 SIM2 5.705069 0.27167 21

NR2E1 3.173751 0.396719 8 FRMD4A 6.527279 0.326364 20

LRRC61 3.162027 0.395253 8 ABR 5.269892 0.263495 20

MBP 3.081164 0.385145 8 SDK1 3.978181 0.198909 20

RBM20 5.516208 0.78803 7 MAD1L1 11.64079 0.612673 19

DUSP6 4.582044 0.654578 7 ZNF423 8.071874 0.424835 19

CDYL 4.232254 0.604608 7 SMG1P2 6.846055 0.360319 19 SATB2-AS1 4.553871 0.758978 6 BOLA2 6.846055 0.360319 19

LYPD1 3.684283 0.614047 6 LOC613038 6.846055 0.360319 19

FAM181A 3.570674 0.595112 6 CASZ1 6.291507 0.331132 19

FBXL18 3.354561 0.559094 6 KCNQ1 4.355987 0.229262 19

ARHGEF7 3.328795 0.665759 5 MCF2L 5.979139 0.332174 18 TSN AX-DISCI 3.270572 0.654114 5 FOXK1 5.014289 0.278572 18 SOX10 3.962391 1.981196 2 SEPTIN9 4.42487 0.245826 18

SLC25A10 3.492986 1.746493 2 OPCML 6.324302 0.372018 17

PITX3 3.419494 1.709747 2 FOXP1 6.538128 0.408633 16

SORBS2 4.894695 0.305918 16

TABLE 26: Cancer Type DMG_K27 NAV2 4.071884 0.254493 16

Gene site imp sum imp mean n GLI2 9.212083 0.614139 15

PTPRN2 27.0199 0.329511 82 BAIAP2 6.044562 0.402971 15

PRDM16 16.37316 0.230608 71 ZBTB20 5.38983 0.359322 15

PCDHGA1 5.539625 0.093892 59 LRMDA 4.689959 0.312664 15

PCDHGA2 5.223239 0.091636 ₅₇ SLX1B-

SULT1A4 4.034964 0.268998 15

PCDHGA3 4.143029 0.076723 54

SLX1A 4.034964 0.268998 15

PCDHGB1 4.143029 0.07817

⁵³ LOC606724 4.034964 0.268998 15

PCDHGA4 4.143029 0.081236 51 RPS6KA2 7.032418 0.502316 14 TABLE 27: Cancer Type DMT_SMARCB1

PRKAG2 5.203722 0.371694 14

Gene site imp sum imp mean n

CACNA1H 4.995215 0.356801 14

PTPRN2 5.17006 0.06305 82

CUX1 4.433446 0.316675 14

PRDM16 6.362014 0.089606 71

ARHGEF10 4.264465 0.304605 14

PCDHGA1 5.844879 0.099066 59

MSI2 6.463358 0.497181 13

PCDHGA2 5.528493 0.096991 57

MYT1L 5.756885 0.442837 13

PCDHGA3 5.528493 0.102379 54

GSE1 4.627927 0.355994 13

PCDHGB1 5.528493 0.104311 53

MIRLET7BHG 4.998906 0.416576 12

PCDHGA4 5.528493 0.108402 51

CMIP 4.933786 0.411149 12

PCDHGB2 5.844879 0.119283 49

ZC3H3 4.313245 0.359437 12

PCDHGA5 5.528493 0.117628 47

FBRSL1 4.041739 0.336812 12

PCDHGB3 5.528493 0.12857 43

ZC3H12D 6.425285 0.584117 11

PCDHGA6 4.773358 0.119334 40

VGLL4 5.302111 0.48201 11 HDAC4 12.66161 0.342206 37

GLUD1P2 5.213483 0.473953 11 PCDHGA7 5.089744 0.137561 37

RAD51B 4.828441 0.438949 11 PCDHGB4 5.089744 0.145421 35

LBX1-AS1 6.635166 0.663517 10

PCDHGA8 5.089744 0.145421 35

TFAP2A 6.403444 0.640344 10

PAX6 4.859457 0.138842 35

NTM 4.444618 0.444462 10

DIP2C 7.547304 0.235853 32

ACOT7 4.423884 0.442388 10

PCDHGB5 4.327758 0.135242 32

ATP11A 6.416854 0.712984 9

PCDHGA9 4.327758 0.139605 31

SND1 5.460144 0.606683 9

PCDHGB6 4.011372 0.138323 29

ADGRB1 5.015126 0.557236 9 SOX2-OT 3.122386 0.107668 29

TSPAN9 4.762758 0.529195 9 PCDHGA10 4.011372 0.143263 28

TRAPPCI 2 4.673668 0.519296 9 SHANK2 2.884832 0.110955 26

ASAP1 4.649147 0.516572 9

AGAP1 9.038532 0.361541 25

ADAMTS2 4.59228 0.510253 9

CAMTAI 4.948669 0.197947 25

AXIN2 4.006386 0.445154 9

PDGFRA 2.775512 0.11102 25

LINC00311 4.851372 0.606421 8

PCDHGB7 3.694986 0.153958 24

GRIK2 4.699392 0.587424 8

RPTOR 7.462939 0.324476 23

MSRA 4.565799 0.570725 8

NCOR2 4.665983 0.202869 23

ESRRG 4.179633 0.522454 8

INPP5A 4.39195 0.190954 23

NXPH1 3.939175 0.492397 8

PCDHGA11 3.694986 0.160652 23

RBM20 4.642311 0.663187 7

NXN 2.795014 0.121522 23

LINC00461 4.476811 0.639544 7

SKI 7.436983 0.354142 21

SOX6 4.441231 0.634462 7

FRMD4A 3.768225 0.188411 20

DUSP6 4.180105 0.597158 7

SDK1 2.96255 0.148127 20

FBXL18 4.11915 0.686525 6

ABR 2.65345 0.132673 20

RUNDC3A 5.093146 1.018629 5

MAD1L1 6.562907 0.345416 19

TSN AX-DISCI 4.796075 0.959215 5

ZNF423 4.355779 0.229252 19

HHEX 4.600647 0.920129 5

CASZ1 3.440739 0.181092 19

LOC100132215 4.562513 0.912503 5

KCNQ1 3.203313 0.168595 19

STAP2 4.697057 1.174264 4 SMG1P2 2.831309 0.149016 19

RBMS3 4.522479 1.13062 4

BOLA2 2.831309 0.149016 19

GRIN2B 4.179322 1.393107 3

LOC613038 2.831309 0.149016 19

SOXIO 5.183544 2.591772 2

FOXK1 7.395893 0.410883 18

TBC1D16 3.543282 0.196849 18 SEPTIN9 2.916372 0.162021 18 ATXN7L1 3.259591 0.651918 5

ANKRD11 2.809903 0.156106 18 BCAR1 2.875345 0.575069 5

EBF3 3.501225 0.218827 16 NPHP4 2.611543 0.522309 5

BAIAP2 3.977481 0.265165 15 NHSL1 3.377846 0.844462 4

GLI2 3.757937 0.250529 15 ABAT 3.052387 0.763097 4

KIRREL3 2.516101 0.16774 15 SPATA13 2.604406 0.868135 3

RPS6KA2 4.560523 0.325752 14 RALGAPA2 4.200075 2.100037 2

CUX1 3.824346 0.273168 14

IQSEC1 3.614146 0.258153 14 TABLE 28: Cancer Type DNET

ARHGEF10 3.410766 0.243626 14 Gene site imp sum imp mean n

PCDHGA12 3.3786 0.241329 14 PTPRN2 26.04532 0.317626 82

PRKAG2 2.733286 0.195235 14 PRDM16 17.79369 0.250615 71

MSI2 2.809603 0.216123 13 PCDHGA1 6.699804 0.113556 59

CMIP 4.493421 0.374452 12 PCDHGA2 6.383418 0.11199 57

ZC3H3 3.334721 0.277893 12 PCDHGA3 7.332576 0.135788 54

FBRSL1 3.011552 0.250963 12 PCDHGB1 7.332576 0.13835 53

GNA12 2.547348 0.212279 12 PCDHGA4 7.648962 0.14998 51

RAD51B 3.828061 0.348006 11 PCDHGB2 7.648962 0.156101 49

FGFR2 3.088996 0.280818 11 PCDHGA5 7.01619 0.149281 47

PCDHGC3 2.604336 0.236758 11 PCDHGB3 6.699804 0.155809 43

TSPAN4 3.397272 0.339727 10 PCDHGA6 6.075185 0.15188 40

CHST11 3.113301 0.31133 10 HDAC4 14.4597 0.390803 37

FMN1 2.877649 0.287765 10 PCDHGA7 5.710249 0.154331 37

MAML2 2.793081 0.279308 10 PAX6 10.60999 0.303143 35

AKAP13 2.737725 0.273772 10 RBFOX3 10.5905 0.302586 35

ATP11A 5.858672 0.650964 9 PCDHGB4 5.435549 0.155301 35

SND1 5.645283 0.627254 9 PCDHGA8 5.435549 0.155301 35

TRAPPCI 2 3.39871 0.377634 9 DIP2C 12.4335 0.388547 32

MGMT 3.231188 0.359021 9 SOX2-OT 13.19713 0.455073 29

KCNH2 2.502517 0.278057 9 SHANK2 6.302076 0.242388 26

DNMT3A 3.015792 0.376974 8 AGAP1 11.54586 0.461835 25

VEPH1 2.685126 0.335641 8 CAMTAI 9.288115 0.371525 25

SMAD3 2.591666 0.323958 8 PDGFRA 8.112691 0.324508 25

RORA 2.493642 0.311705 8 MEIS1 8.001583 0.333399 24

GAK 3.52123 0.503033 7 SATB2 6.968141 0.290339 24

C19orf25 3.213252 0.459036 7 RPTOR 12.74644 0.554193 23

ITPKB 3.073253 0.439036 7 NCOR2 8.19049 0.356108 23

NAVI 2.840115 0.405731 7 INPP5A 6.71395 0.291911 23

VPS 13D 2.710978 0.387283 7 HOXB3 5.973479 0.259716 23

GLT8D2 3.456794 0.576132 6 NXN 5.910295 0.256969 23

FBXL18 3.165773 0.527629 6 PRKCZ 8.038706 0.365396 22

CRADD 2.900562 0.483427 6 SKI 13.61224 0.648202 21

ANKS1A 2.847804 0.474634 6 SIM2 7.657535 0.364645 21

SH3BP4 2.516224 0.419371 6 ZIC4 5.477019 0.26081 21

COQ8A 2.506326 0.417721 6 FRMD4A 9.657993 0.4829 20

RUNDC3A 4.169424 0.833885 5 ABR 7.607979 0.380399 20

ARHGEF7 3.675151 0.73503 5 SDK1 6.329743 0.316487 20 TSN AX-DISCI 3.532458 0.706492 5 MAD1L1 13.60926 0.716277 19 ZNF423 11.35844 0.597813 19 ASAP1 5.147062 0.571896 9 SMG1P2 9.201404 0.484284 19 RUNX1 5.012481 0.556942 9 BOLA2 9.201404 0.484284 19 ADAMTS2 5.011711 0.556857 9 LOC613038 9.201404 0.484284 19 LINC00311 5.629979 0.703747 8 FOXK1 8.735914 0.485329 18 MSRA 5.060013 0.632502 8 SEPTIN9 6.772426 0.376246 18 DLEU1 5.004066 0.625508 8 MCF2L 6.663531 0.370196 18 BAHCC1 4.914394 0.614299 8 TBC1D16 5.385253 0.299181 18 DUSP6 7.849487 1.121355 7 OPCML 8.057438 0.473967 17 LINC00461 6.097674 0.871096 7 TBX15 5.724624 0.336743 17 FBXL18 5.180878 0.86348 6 PAX6-AS1 5.605127 0.329713 17 RUNDC3A 5.74448 1.148896 5

RCN1 5.605127 0.329713 17 TSNAX-DISC1 5.218257 1.043651 5 FOXP1 6.955576 0.434724 16 RBMS3 5.248101 1.312025 4 SORBS2 5.457054 0.341066 16 SOXIO 5.594431 2.797216 2 NAV2 5.057675 0.316105 16 GLI2 12.35834 0.82389 15 TABLE 29: Cancer Type EFT_CIC ZBTB20 6.868129 0.457875 15 Gene site imp sum imp mean n LRMDA 5.440614 0.362708 15 PTPRN2 15.69893 0.19145 82

KIRREL3 5.046605 0.33644 15 PRDM16 10.37322 0.146102 71 EMX2OS 4.94183 0.329455 15 PCDHGA1 3.849692 0.065249 59 IQSEC1 8.156287 0.582592 14 PCDHGA2 3.849692 0.067538 57 RPS6KA2 6.561333 0.468667 14 PCDHGA3 4.296947 0.079573 54 CUX1 6.051219 0.43223 14 PCDHGB1 4.296947 0.081074 53 PRKAG2 4.96462 0.354616 14 PCDHGA4 4.296947 0.084254 51 MSI2 8.776704 0.675131 13 PCDHGB2 4.296947 0.087693 49 RFX4 6.597553 0.507504 13 PCDHGA5 4.296947 0.091424 47 MYT1L 5.581442 0.429342 13 PCDHGB3 3.542328 0.08238 43 CMIP 7.341354 0.61178 12 HDAC4 20.06051 0.542176 37 ADGRD1 6.359399 0.52995 12 RBFOX3 7.234492 0.2067 35

ZC3H3 6.332313 0.527693 12 DIP2C 9.369931 0.29281 32 MIRLET7BHG 5.644003 0.470334 12 GALNT9 3.307723 0.122508 27 CTNNA2 5.265326 0.438777 12 AGAP1 11.98824 0.47953 25 RAD51B 6.834098 0.621282 11 CAMTAI 6.759721 0.270389 25 VGLL4 5.941739 0.540158 11 PDGFRA 4.565291 0.182612 25 FGFR2 5.805118 0.527738 11 MEIS1 3.306195 0.137758 24 CCDC140 5.399858 0.490896 11 RPTOR 14.38339 0.625365 23

LBX1-AS1 6.466089 0.646609 10 NCOR2 8.787906 0.382083 23 ACOT7 5.880168 0.588017 10 RIMBP2 5.939037 0.258219 23 SH3RF3 5.368831 0.536883 10 INPP5A 5.394275 0.234534 23 AKAP13 5.073876 0.507388 10 NXN 3.774468 0.164107 23 CHST11 4.895963 0.489596 10 PRKCZ 4.532974 0.206044 22 ATP11A 6.921049 0.769005 9 SKI 9.570696 0.455747 21 SND1 6.468896 0.718766 9 ZIC4 4.057271 0.193203 21

KCNMA1 5.922196 0.658022 9 FRMD4A 6.351301 0.317565 20 NOTCH 1 5.900979 0.655664 9 SDK1 3.556188 0.177809 20 ADGRB1 5.876715 0.652968 9 ABR 3.35357 0.167679 20 TSPAN9 5.852712 0.650301 9 MAD1L1 11.30899 0.59521 19 TRAPPCI 2 5.310382 0.590042 9 CASZ1 4.205329 0.221333 19 KCNQ1 4.153197 0.218589 19 MACROD1 3.917303 0.489663 8 SMG1P2 3.946729 0.207723 19 VRK2 3.883965 0.485496 8 BOLA2 3.946729 0.207723 19 SMAD3 3.497736 0.437217 8 LOC613038 3.946729 0.207723 19 DNMT3A 3.26916 0.408645 8 FOXK1 9.052327 0.502907 18 NAVI 5.350086 0.764298 7 TBC1D16 7.005745 0.389208 18 C19orf25 4.618985 0.659855 7 ANKRD11 4.84078 0.268932 18 GAK 4.613203 0.659029 7 SEPTIN9 4.804021 0.26689 18 VPS13D 4.405545 0.629364 7 OPCML 4.784635 0.281449 17 CXXC5 4.085912 0.583702 7 HBG2 3.396259 0.19978 17 RXRA 3.73027 0.532896 7 FOXP1 5.207881 0.325493 16 FBXL18 4.445815 0.740969 6 EBF3 3.74803 0.234252 16 RADIL 3.640124 0.606687 6 NAV2 3.705764 0.23161 16 SLC22A18AS 3.310195 0.551699 6 GLI2 5.479074 0.365272 15 RUNDC3A 4.686968 0.937394 5 ZBTB20 5.327162 0.355144 15 IDE 4.480619 0.896124 5 NHX 3.577441 0.238496 15 ARHGEF7 4.161226 0.832245 5 RPS6KA2 8.859121 0.632794 14 TSNAX-DISC1 3.98883 0.797766 5 CUX1 5.914514 0.422465 14 TEAD1 3.752391 0.750478 5 IQSEC1 5.317055 0.37979 14 BACH2 3.39684 0.679368 5 PRKAG2 3.27492 0.233923 14 BCAR1 3.264798 0.65296 5 MYT1L 4.496364 0.345874 13 LPCAT1 3.317234 0.829308 4 MSI2 4.48584 0.345065 13 GSE1 4.053829 0.311833 13 TABLE 30: Cancer Type EMB_ND_A GNA12 8.060213 0.671684 12 Gene site imp sum imp mean n ZC3H3 4.269692 0.355808 12 PTPRN2 6.81907 0.083159 82 CMIP 3.859055 0.321588 12 PRDM16 6.657308 0.093765 71 FBRSL1 3.514157 0.292846 12 HDAC4 2.10005 0.056758 37 RASA3 3.397433 0.283119 12 RBFOX3 2.716649 0.077619 35 ISLR2 3.288839 0.27407 12 DIP2C 2.712477 0.084765 32 VGLL4 4.146115 0.37692 11 SOX2-OT 1.898316 0.065459 29 ZC3H12D 4.014933 0.364994 11 GALNT9 2.996064 0.110965 27 CTBP2 3.450046 0.313641 11 SHANK2 1.976531 0.07602 26 TBCD 3.342356 0.303851 11 ADARB2 1.947008 0.074885 26 TSPAN4 5.379919 0.537992 10 CAMTAI 7.662871 0.306515 25 AKAP13 4.374475 0.437448 10 AGAP1 3.756501 0.15026 25 ACOT7 4.174203 0.41742 10 PDGFRA 1.823498 0.07294 25 SH3RF3 3.868139 0.386814 10 SATB2 1.712981 0.071374 24 CHST11 3.349543 0.334954 10 NCOR2 3.21767 0.139899 23 FMN1 3.297768 0.329777 10 INPP5A 2.483872 0.107994 23 ETS1 3.294779 0.329478 10 RIMBP2 1.898316 0.082535 23 SND1 7.89485 0.877206 9 RPTOR 1.837491 0.079891 23 ATP11A 7.543381 0.838153 9 PRKCZ 3.665876 0.166631 22 TSPAN9 4.661157 0.517906 9 FRMD4A 2.923035 0.146152 20 ADAMTS2 4.265749 0.473972 9 SDK1 2.866507 0.143325 20 PACS2 4.158122 0.462014 9 ABR 2.14641 0.107321 20 AXIN2 3.966172 0.440686 9 MAD1L1 10.86609 0.571899 19 CACNA2D4 3.653871 0.405986 9 CASZ1 3.960455 0.208445 19 DLEU1 4.601812 0.575227 8 SMG1P2 2.340821 0.123201 19 BOLA2 2.340821 0.123201 19 NAVI 1.662738 0.237534 7 LOC613038 2.340821 0.123201 19 IQCE 1.633709 0.233387 7 KCNQ1 1.712981 0.090157 19 LHX2 1.622807 0.23183 7 CFAP46 1.704292 0.0897 19 KDM4B 2.039913 0.339986 6 MCF2L 2.826414 0.157023 18 TSNAX-DISC1 3.035165 0.607033 5 ANKRD11 2.77096 0.153942 18 SNX29 2.269418 0.453884 5 RBFOX1 2.604038 0.144669 18 ARHGEF7 2.229606 0.445921 5 FOXK1 2.189679 0.121649 18 CHN2 1.950905 0.390181 5 TBC1D16 1.699622 0.094423 18 TK1 1.933623 0.386725 5 BAIAP2 2.568393 0.171226 15 PRR5L 1.854182 0.370836 5 NHX 2.442774 0.162852 15 CCDC88C 1.76954 0.353908 5 KIRREL3 1.630378 0.108692 15 SDK2 1.745977 0.349195 5 ARHGEF10 2.854761 0.203912 14 GSG1 3.227564 0.806891 4 IQSEC1 2.333749 0.166696 14 TUB Al C 3.217455 0.804364 4 PRKAG2 1.783804 0.127415 14 CPE 1.630466 0.407616 4 MSI2 5.218074 0.40139 13 PARD3B 1.625209 0.406302 4 CLYBL 1.712981 0.131768 13 DICER1 2.028776 0.676259 3 FBRSL1 3.203264 0.266939 12 RASGRP3 1.741203 0.580401 3 ZC3H3 2.935532 0.244628 12 ANKLE2 3.330966 1.665483 2 CMIP 2.058885 0.171574 12 KIF21B 2.313804 1.156902 2 MEGF6 1.704495 0.142041 12 CHTF18 2.289967 1.144983 2 RAD51B 2.142947 0.194813 11 DISCI 1.885237 0.942618 2 ZC3H12D 1.801775 0.163798 11 SLC7A5 1.810281 0.90514 2 AKAP13 2.707485 0.270749 10 SLC25A10 1.742511 0.871255 2 AUTS2 2.447964 0.244796 10 ERI3 1.687948 0.843974 2 SPPL2B 1.898316 0.189832 10 DNAJC27 1.831026 1.831026 1 LMF1 1.699622 0.169962 10 ARL6IP6 1.658743 1.658743 1 ADAMTS2 4.204788 0.467199 9 GTF2E2 1.613845 1.613845 1 SSBP3 2.535213 0.28169 9 TRAPPCI 2 2.528214 0.280913

TABLE 31: Cancer Type ENB

ATP11A 2.339968 0.259996

09533 0.256615 Gene sit imp sum imp mean n GPC6 2.3

e PTPRN2 15.29415 0.186514 82 TSPAN9 2.27613 0.252903

PRDM16 15.30242 0.215527 71 CPNE4 1.626078 0.180675

HDAC4 PPP2R2B 17.79599 0.480973 37

2.901208 0.362651

RBFOX3 10.28258 0.293788 35 VRK2 2.560103 0.320013

MSRA 2.175092 0.271887

PAX6 4.405568 0.125873 35 DIP 8.96023 0.280007 32 MACROD1 2C

2.015901 0.251988

POU6F2 GALNT9 4.383891 0.162366 27

1.898316 0.237289

SHANK2 7.017624 0.269909 26 DNMT3A 1.853521 0.23169

AGAP1 10.51418 0.420567 25 LINC00311 1.747994 0.218499

CAMTAI 7.082051 0.283282 25 ESRRG 1.676804 0.2096

SATB2 5.011261 0.208803 24 PRKCA 2.295029 0.327861 ME 4.017727 0.167405 24 G 2.27 0.325681

IS1 PACR 9764 RPTOR 8.723779 0.379295 23 RXRA 2.098188 0.299741 NXN 7.212264 0.313577 23 TBR1 2.028354 0.289765 INPP5A

1 6.465217 0.281096 23 PITPNC1 .86804 0.266863 NCOR2 MIR548H4 5.465209 0.237618 23

1.663976 0.237711 RIMBP2 4.603744 0.200163 23 ZC3H3 5.709299 0.475775 12 PRKCZ 5.805332 0.263879 22 GNA12 4.857636 0.404803 12 SKI 8.012568 0.381551 21 TNS3 4.451887 0.370991 12 ZIC4 4.212597 0.2006 21 FBRSL1 4.206857 0.350571 12 HOXA-AS3 3.507593 0.167028 21 ADGRD1 4.040292 0.336691 12 ABR 3.548115 0.177406 20 MEGF6 3.908699 0.325725 12 FRMD4A 3.209784 0.160489 20 CTBP2 4.783155 0.434832 11 MAD1L1 7.124601 0.374979 19 FGFR2 3.28003 0.298185 11 SMG1P2 6.130362 0.322651 19 TSPAN4 4.591197 0.45912 10 BOLA2 6.130362 0.322651 19 AKAP13 4.440316 0.444032 10 LOC613038 6.130362 0.322651 19 ACOT7 3.995017 0.399502 10 CASZ1 5.65431 0.297595 19 BCL11B 3.458283 0.345828 10 KCNQ1 5.43857 0.286241 19 CHST11 3.347793 0.334779 10 ZNF423 4.858426 0.255707 19 IGF1R 3.325429 0.332543 10 FOXK1 6.753271 0.375182 18 AUTS2 3.281351 0.328135 10 MCF2L 5.352531 0.297363 18 SND1 7.441603 0.826845 9 ANKRD11 5.20414 0.289119 18 ATP11A 6.169478 0.685498 9 HOXA3 4.506994 0.250389 18 ADAMTS2 4.343738 0.482638 9 TBC1D16 4.213596 0.234089 18 VRK2 4.873592 0.609199 8 SEPTIN9 3.215852 0.178658 18 TRAPPC9 4.102197 0.512775 8 RBFOX1 3.208115 0.178229 18 LINC00311 3.840759 0.480095 8 OPCML 4.373813 0.257283 17 DLEU1 3.8326 0.479075 8 PAX6-AS1 3.665276 0.215604 17 PPP2R2B 3.383298 0.422912 8 RCN1 3.665276 0.215604 17 RORA 3.341857 0.417732 8 SORBS2 3.618661 0.226166 16 DNMT3A 3.198196 0.399774 8 FOXP1 3.471364 0.21696 16 MIR548H4 3.907537 0.55822 7 NAV2 3.225146 0.201572 16 NAVI 3.394831 0.484976 7 GLI2 5.610039 0.374003 15 C19orf25 3.289949 0.469993 7 ZBTB20 4.909148 0.327277 15 TSNAX-DISC1 4.775104 0.955021 5 SLX1B- ARHGEF7 3.753903 0.750781 5 SULT1A4 4.84874 0.323249 15 RUNDC3A 3.589411 0.717882 5 SLX1A 4.84874 0.323249 15 PRR5L 3.44163 0.688326 5 LOC606724 4.84874 0.323249 15 AP2A2 3.376591 0.675318 5 BAIAP2 4.556419 0.303761 15 LIPE-AS1 3.656927 0.914232 4 DLX6-AS1 4.249325 0.283288 15 DAGLB 3.708907 1.236302 3 KIRREL3 3.858567 0.257238 15 DICER1 3.585845 1.195282 3 LRMDA 3.23644 0.215763 15 TRIO 3.300107 1.100036 3 RPS6KA2 5.007312 0.357665 14 MOB2 4.504779 0.32177 14

TABLE 32: Cancer Type EPN_MPE IQSEC1 4.409895 0.314993 14

Gene site imp sum imp mean n CUX1 4.066748 0.290482 14

PTPRN2 14.32221 0.174661 82 CACNA1H 4.052721 0.28948 14

PRDM16 16.92066 0.238319 71 MIR548F5 3.380989 0.241499 14

PCDHGA1 6.198403 0.105058 59 GNG7 3.198749 0.228482 14

PCDHGA2 5.882017 0.103193 57 GSE1 5.068009 0.389847 13

PCDHGA3 5.565631 0.103067 54 MSI2 4.956654 0.381281 13

PCDHGB1 5.565631 0.105012 53 RFX4 3.670637 0.282357 13

PCDHGA4 5.565631 0.10913 51 CMIP 7.143086 0.595257 12

PCDHGB2 5.249245 0.107127 49 PCDHGA5 5.565631 0.118418 47 RFX4 5.185562 0.398889 13 PCDHGB3 5.249245 0.122075 43 MYT1L 4.949706 0.380747 13 PCDHGA6 4.932859 0.123321 40 KIF26B 4.137998 0.318308 13 HDAC4 10.4859 0.283403 37 CLYBL 3.641455 0.280112 13 PCDHGA7 4.886933 0.132079 37 MIRLET7BHG 6.049958 0.504163 12 RBFOX3 7.484427 0.213841 35 ADGRD1 4.478436 0.373203 12 PCDHGB4 4.886933 0.139627 35 ZC3H3 3.849677 0.320806 12 PCDHGA8 4.886933 0.139627 35 TNS3 3.798714 0.31656 12 DIP2C 11.24452 0.351391 32 CMIP 3.686039 0.30717 12 PCDHGB5 4.570547 0.14283 32 MEGF6 3.541811 0.295151 12 PCDHGA9 4.254161 0.137231 31 ZC3H12D 5.475938 0.497813 11 SOX2-OT 4.810056 0.165864 29 VGLL4 4.601871 0.418352 11 PCDHGB6 3.86752 0.133363 29 CTBP2 4.487393 0.407945 11 SHANK2 4.851686 0.186603 26 RAD51B 3.608856 0.328078 11 ADARB2 4.570843 0.175802 26 ACOT7 4.626879 0.462688 10 AGAP1 7.877983 0.315119 25 AKAP13 4.587557 0.458756 10 CAMTAI 5.644876 0.225795 25 KLHL29 4.04698 0.404698 10 SATB2 3.821489 0.159229 24 FMN1 3.83224 0.383224 10 RPTOR 10.40549 0.452412 23 TSPAN4 3.7268 0.37268 10 HOXB3 6.248789 0.271686 23 SND1 5.70032 0.633369 9 NCOR2 5.752322 0.250101 23 ATP11A 5.41207 0.601341 9 INPP5A 3.534761 0.153685 23 ADAMTS2 5.056659 0.561851 9 SKI 8.303878 0.395423 21 ASAP1 4.247257 0.471917 9 SIM2 3.533503 0.168262 21 AXIN2 4.138498 0.459833 9 FRMD4A 4.183428 0.209171 20 TSPAN9 4.122258 0.458029 9 ABR 3.920332 0.196017 20 TRAPPCI 2 3.624908 0.402768 9 SDK1 3.803673 0.190184 20 RUNX1 3.610029 0.401114 9 MAD1L1 11.24071 0.591616 19 LINC00311 4.495447 0.561931 8 ZNF423 8.169802 0.42999 19 LHX4 4.416192 0.552024 8 CASZ1 5.379156 0.283113 19 DLEU1 3.893139 0.486642 8 CFAP46 4.035361 0.212387 19 MACROD1 3.759084 0.469885 8 FOXK1 4.71225 0.261792 18 MCC 3.698126 0.462266 8 TBC1D16 4.476315 0.248684 18 WWP2 3.540691 0.442586 8 RBFOX1 3.56034 0.197797 18 SYNJ2 3.510006 0.438751 8 OPCML 7.709194 0.453482 17 NAVI 5.25118 0.750169 7 FOXP1 5.359968 0.334998 16 RXRA 4.033838 0.576263 7 NAV2 4.168476 0.26053 16 FBXL18 3.917171 0.652862 6 GLI2 6.447499 0.429833 15 LRRFIP1 3.740917 0.623486 6 ZBTB20 4.319508 0.287967 15 SLC22A18AS 3.641649 0.606941 6 LRMDA 3.985014 0.265668 15 RUNDC3A 4.523264 0.904653 5 NHX 3.73468 0.248979 15 TSNAX-DISC1 4.43698 0.887396 5 CUX1 6.207969 0.443426 14 PRR5L 3.555141 0.711028 5 RPS6KA2 5.815656 0.415404 14 SLC25A10 4.694753 2.347376 2 PRKAG2 4.811351 0.343668 14 ANKLE2 3.867527 1.933764 2 ARHGEF10 3.644336 0.26031 14 C7orf50 3.528157 0.252011 14 TABLE 33: Cancer Type EPN_PF_SE HOXC4 6.713735 0.516441 13 Gene site imp sum imp mean n MSI2 6.683626 0.514125 13 PTPRN2 16.74131 0.204162 S PRDM16 18.68393 0.263154 71 SORBS2 5.478021 0.342376 16 PCDHGA1 5.212948 0.088355 59 NAV2 4.87996 0.304997 16 PCDHGA2 5.212948 0.091455 57 FOXP1 4.849099 0.303069 16 PCDHGA3 5.212948 0.096536 54 GLI2 9.889871 0.659325 15 PCDHGB1 5.212948 0.098358 53 BAIAP2 5.307801 0.353853 15 PCDHGA4 5.212948 0.102215 51 NHX 4.855866 0.323724 15 PCDHGB2 5.212948 0.106387 49 KIRREL3 4.673915 0.311594 15 PCDHGA5 5.212948 0.110914 47 ZBTB20 4.470387 0.298026 15 PCDHGB3 4.580176 0.106516 43 RPS6KA2 7.145132 0.510367 14 HDAC4 13.05699 0.352892 37 CUX1 6.958483 0.497035 14 PAX6 14.08594 0.402455 35 PRKAG2 6.515057 0.465361 14 RBFOX3 9.531626 0.272332 35 C7orf50 5.629425 0.402102 14 DIP2C 11.05853 0.345579 32 IQSEC1 4.598494 0.328464 14 SOX2-OT 10.77132 0.371425 29 MSI2 7.252988 0.557922 13 GALNT9 6.548071 0.242521 27 CLYBL 6.49018 0.499245 13 SHANK2 7.222144 0.277775 26 GSE1 5.760461 0.443112 13 ADARB2 7.06597 0.271768 26 KIF26B 4.998393 0.384492 13 AGAP1 9.531944 0.381278 25 RFX4 4.463465 0.343343 13 CAMTAI 6.908462 0.276338 25 MYT1L 4.411582 0.339352 13 SATB2 4.923948 0.205165 24 ZC3H3 6.609641 0.550803 12 NCOR2 9.16504 0.39848 23 MIRLET7BHG 5.018458 0.418205 12 RPTOR 9.035759 0.392859 23 CMIP 4.987616 0.415635 12 INPP5A 6.803703 0.295813 23 RASA3 4.825289 0.402107 12 RIMBP2 6.063599 0.263635 23 TNS3 4.58364 0.38197 12 HOXB3 6.055053 0.263263 23 FBRSL1 4.523777 0.376981 12 NXN 5.033246 0.218837 23 ZC3H12D 7.527348 0.684304 11 PRKCZ 7.013904 0.318814 22 RAD51B 4.527513 0.411592 11 SKI 12.7486 0.607076 21 VGLL4 4.423538 0.40214 11 ZIC4 6.09695 0.290331 21 ACOT7 5.239658 0.523966 10 SDK1 6.579672 0.328984 20 SND1 6.341657 0.704629 9 ABR 5.743593 0.28718 20 RUNX1 5.143644 0.571516 9 FRMD4A 5.402081 0.270104 20 ATP11A 4.991781 0.554642 9 MAD1L1 11.98565 0.630824 19 ADAMTS2 4.778374 0.53093 9 ZNF423 8.825708 0.464511 19 SPECC1 4.748781 0.527642 9 CASZ1 6.786586 0.357189 19 SLC22A18 4.568763 0.50764 9 SMG1P2 6.215715 0.327143 19 TSPAN9 4.535394 0.503933 9 BOLA2 6.215715 0.327143 19 CACNA2D4 4.441201 0.493467 9 LOC613038 6.215715 0.327143 19 GPC6 4.370579 0.48562 9 KCNQ1 5.03099 0.264789 19 MSRA 5.033826 0.629228 8 SEPTIN9 7.707187 0.428177 18 PRDM6 4.968878 0.62111 8 FOXK1 6.449852 0.358325 18 LHX4 4.742436 0.592804 8 TBC1D16 6.066392 0.337022 18 DLEU1 4.542607 0.567826 8 MCF2L 5.143681 0.28576 18 LINC00311 4.450046 0.556256 8 ANKRD11 4.599412 0.255523 18 RXRA 4.631648 0.661664 7 OPCML 7.137084 0.419828 17 FBXL18 4.514288 0.752381 6 SIM1 4.796091 0.282123 17 PRR5L 5.238089 1.047618 5 PAX6-AS1 4.465569 0.262681 17 TSNAX-DISC1 4.697662 0.939532 5 RCN1 4.465569 0.262681 17 ARHGEF7 4.366168 0.873234 5 RBMS3 5.452308 1.363077 4 SIM1 4.539674 0.26704 17

VOPP1 4.361184 1.090296 4 FOXP1 6.034973 0.377186 16

SLC25A10 4.633583 2.316791 2 EBF3 5.740264 0.358766 16 NAV2 5.648016 0.353001 16

TABLE 34: Cancer Type GLI2 8.575904 0.571727 15

EPN_PFA_la LRMDA 5.238657 0.349244 15

Gene site imp sum imp mean n KIRREL3 5.157525 0.343835 15 PTPRN2 15.52606 0.189342 82 SLX1B- PRDM16 23.29599 0.328113 71 SULT1A4 4.672051 0.31147 15 HDAC4 15.59615 0.421518 37 SLX1A 4.672051 0.31147 15 PAX6 14.80671 0.423049 35 LOC606724 4.672051 0.31147 15 RBFOX3 9.838801 0.281109 35 KNDC1 4.538057 0.302537 15 DIP2C 11.85036 0.370324 32 RPS6KA2 7.648904 0.54635 14 SOX2-OT 10.03478 0.346027 29 CUX1 6.492923 0.46378 14 GALNT9 9.558407 0.354015 27 PRKAG2 5.487928 0.391995 14 ADARB2 8.950612 0.344254 26 IQSEC1 5.464688 0.390335 14 SHANK2 8.436808 0.324493 26 MSI2 6.180107 0.475393 13 CAMTAI 8.475074 0.339003 25 MYT1L 5.996413 0.461263 13 AGAP1 7.717198 0.308688 25 KIF26B 5.857538 0.45058 13 SATB2 12.39102 0.516293 24 GSE1 5.745502 0.441962 13 MEIS1 4.193362 0.174723 24 CLYBL 5.378406 0.413724 13 RPTOR 11.60107 0.504395 23 ADGRD1 6.913338 0.576112 12 HOXB3 8.611445 0.374411 23 ZC3H3 5.485077 0.45709 12 INPP5A 8.234079 0.358003 23 TNS3 5.08837 0.424031 12 NCOR2 6.356224 0.276358 23 MAML3 4.984269 0.415356 12 RIMBP2 6.032696 0.262291 23 FBRSL1 4.978429 0.414869 12 PRKCZ 8.289756 0.376807 22 CMIP 4.842426 0.403536 12 SKI 10.89492 0.518806 21 RASA3 4.540034 0.378336 12 ZIC4 5.134383 0.244494 21 ZC3H12D 7.228159 0.657105 11 SIM2 4.512566 0.214884 21 VGLL4 5.184496 0.471318 11 SDK1 9.202593 0.46013 20 FGFR2 4.752801 0.432073 11 FRMD4A 5.773121 0.288656 20 RAD51B 4.447319 0.404302 11 ABR 5.746127 0.287306 20 PITX2 4.928226 0.492823 10 MAD1L1 11.8922 0.625905 19 CBFA2T3 4.784692 0.478469 10 ZNF423 7.763591 0.40861 19 ACOT7 4.758271 0.475827 10 CASZ1 7.479987 0.393684 19 EBF1 4.337505 0.43375 10 SMG1P2 6.572637 0.345928 19 RUNX1 6.471203 0.719023 9 BOLA2 6.572637 0.345928 19 ATP11A 6.196063 0.688451 9 LOC613038 6.572637 0.345928 19 TSPAN9 5.199594 0.577733 9 CFAP46 5.974489 0.314447 19 SND1 4.946798 0.549644 9 FOXK1 6.754044 0.375225 18 ADAMTS2 4.843958 0.538218 9 SEPTIN9 6.590998 0.366167 18 ZNF833P 4.638324 0.515369 9 TBC1D16 4.678671 0.259926 18 CACNA2D4 4.584298 0.509366 9 ANKRD11 4.397949 0.244331 18 GPC6 4.331115 0.481235 9 OPCML 6.527074 0.383946 17 PRDM6 6.419268 0.802409 8 PAX6-AS1 4.713084 0.27724 17 KIF26A 4.365822 0.545728 8

RCN1 4.713084 0.27724 17 MSRA 4.191841 0.52398 8 TBX15 4.574999 0.269118 17 NAVI 5.777994 0.825428 7 LHX2 4.853107 0.693301 7 CASZ1 7.203368 0.379125 19 TBR1 4.581182 0.654455 7 CFAP46 6.658374 0.350441 19 SATB2-AS1 6.181148 1.030191 6 SMG1P2 5.094956 0.268156 19 FBXL18 4.744899 0.790816 6 BOLA2 5.094956 0.268156 19 ROR1 4.257896 0.709649 6 LOC613038 5.094956 0.268156 19

TSN AX-DISCI 5.005668 1.001134 5 KCNQ1 4.523578 0.238083 19 CNPY1 4.858208 0.971642 5 SEPTIN9 8.631588 0.479533 18

LOC100132215 4.781487 0.956297 5 FOXK1 7.603187 0.422399 18 PRR5L 4.594552 0.91891 5 TBC1D16 5.568902 0.309383 18 RUNDC3A 4.378322 0.875664 5 ANKRD11 4.94688 0.274827 18 RBMS3 5.481089 1.370272 4 PAX6-AS1 9.044886 0.532052 17 SLC25A10 4.564369 2.282184 2 RCN1 9.044886 0.532052 17 OPCML 6.926837 0.407461 17

TABLE 35: Cancer Type SIM1 5.133736 0.301984 17

EPN_PFA_lb EBF3 5.860246 0.366265 16

Gene site imp sum imp mean n NAV2 5.259041 0.32869 16 PTPRN2 17.00674 0.207399 82 FOXP1 4.87325 0.304578 16 PRDM16 21.39185 0.301294 71 SORBS2 4.74376 0.296485 16 PCDHGA1 4.956857 0.084015 59 GLI2 8.270372 0.551358 15 PCDHGA2 4.956857 0.086962 57 SLX1B- PCDHGA3 4.640471 0.085935 54 SULT1A4 4.890562 0.326037 15 PCDHGB1 4.640471 0.087556 53 SLX1A 4.890562 0.326037 15 PCDHGA4 4.324085 0.084786 51 LOC606724 4.890562 0.326037 15 HDAC4 14.23228 0.384656 37 KNDC1 4.76532 0.317688 15 PAX6 13.98822 0.399663 35 LRMDA 4.502978 0.300199 15 RBFOX3 10.43696 0.298199 35 KIRREL3 4.162307 0.277487 15 DIP2C 11.68025 0.365008 32 RPS6KA2 7.531504 0.537965 14 SOX2-OT 7.988427 0.275463 29 CUX1 7.522806 0.537343 14 GALNT9 9.278607 0.343652 27 C7orf50 5.249022 0.37493 14 ADARB2 9.419679 0.362295 26 IQSEC1 4.812408 0.343743 14 SHANK2 7.951411 0.305823 26 SYCP2L 4.425098 0.316078 14 AGAP1 11.28464 0.451385 25 PRKAG2 4.204794 0.300342 14 CAMTAI 8.537349 0.341494 25 MSI2 6.955762 0.535059 13 PDGFRA 6.872677 0.274907 25 CLYBL 5.60432 0.431102 13 SATB2 13.52923 0.563718 24 KIF26B 5.154783 0.396522 13 HOXB3 11.84852 0.515153 23 MYT1L 4.795307 0.36887 13 RPTOR 8.046414 0.349844 23 ADGRD1 5.906996 0.49225 12 NCOR2 7.069104 0.307352 23 MAML3 5.269018 0.439085 12 INPP5A 6.100277 0.265229 23 RASA3 5.263297 0.438608 12 PRKCZ 8.355116 0.379778 22 ZC3H12D 7.595957 0.690542 11

HOXA-AS3 10.37138 0.493875 21 FGFR2 6.744716 0.613156 11 SKI 9.906447 0.471736 21 VGLL4 4.938567 0.448961 11 ZIC4 5.046113 0.240291 21 SKOR1 4.541739 0.454174 10 SIM2 4.694236 0.223535 21 ACOT7 4.528174 0.452817 10 SDK1 8.716676 0.435834 20 EBF1 4.477643 0.447764 10 ABR 6.204368 0.310218 20 SND1 6.236707 0.692967 9 MAD1L1 11.81708 0.621951 19 ATP11A 6.137343 0.681927 9 ZNF423 8.031473 0.422709 19 RUNX1 5.965829 0.66287 9 AXIN2 4.774154 0.530462 9 ZIC4 6.534315 0.311158 21 CACNA2D4 4.638878 0.515431 9 HOXA-AS3 6.528584 0.310885 21 ADAMTS2 4.438726 0.493192 9 SIM2 4.095009 0.195 21 TSPAN9 4.426164 0.491796 9 SDK1 8.876261 0.443813 20 PRDM6 6.398119 0.799765 8 ABR 5.934879 0.296744 20 MSRA 5.256958 0.65712 8 FRMD4A 5.02878 0.251439 20 DLEU1 5.203452 0.650431 8 MAD1L1 11.62792 0.611996 19 LINC00311 4.698301 0.587288 8 ZNF423 8.003832 0.421254 19 AFF3 4.215017 0.526877 8 CASZ1 7.87127 0.414277 19 BAHCC1 4.160761 0.520095 8 SMG1P2 6.37962 0.335769 19 RORA 4.078508 0.509814 8 BOLA2 6.37962 0.335769 19 NAVI 6.307546 0.901078 7 LOC613038 6.37962 0.335769 19 HOXB-AS1 5.440172 0.777167 7 CFAP46 6.180987 0.325315 19 SATB2-AS1 5.309555 0.884926 6 KCNQ1 4.248304 0.223595 19 ROR1 4.858985 0.809831 6 FOXK1 7.751933 0.430663 18 FBXL18 4.25365 0.708942 6 SEPTIN9 7.626152 0.423675 18 CNPY1 5.842525 1.168505 5 TBC1D16 4.759312 0.264406 18 LOC100132215 4.750425 0.950085 5 PAX6-AS1 7.680562 0.451798 17 TSN AX-DISCI 4.549935 0.909987 5 RCN1 7.680562 0.451798 17

RBMS3 5.307114 1.326778 4 OPCML 6.246465 0.367439 17 SLC25A10 4.466961 2.233481 2 SIM1 5.766294 0.339194 17 EBF3 5.762216 0.360138 16

TABLE 36: Cancer Type NAV2 5.017001 0.313563 16

EPN_PFA_lc FOXP1 4.656013 0.291001 16

Gene site imp sum imp mean n GLI2 7.572671 0.504845 15 PTPRN2 11.73423 0.1431 82 LRMDA 5.232108 0.348807 15 PRDM16 21.7832 0.306806 71 KNDC1 4.497326 0.299822 15 HDAC4 11.65731 0.315062 37 BAIAP2 4.222307 0.281487 15 PAX6 11.46737 0.327639 35 SLX1B- RBFOX3 8.085018 0.231001 35 SULT1A4 4.148554 0.27657 15 DIP2C 10.63225 0.332258 32 SLX1A 4.148554 0.27657 15 SOX2-OT 9.704987 0.334655 29 LOC606724 4.148554 0.27657 15 GALNT9 7.860133 0.291116 27 RPS6KA2 7.479873 0.534277 14 ADARB2 8.467804 0.325685 26 CUX1 6.16701 0.440501 14 SHANK2 8.274469 0.318249 26 IQSEC1 4.72887 0.337776 14 AGAP1 8.253203 0.330128 25 SYCP2L 4.00661 0.286186 14 CAMTAI 6.683582 0.267343 25 MSI2 6.405085 0.492699 13 PDGFRA 6.467133 0.258685 25 KIF26B 5.629066 0.433005 13 SATB2 13.66347 0.569311 24 MYT1L 4.50348 0.346422 13 MEIS1 4.600093 0.191671 24 GSE1 4.000032 0.307695 13 HOXB3 13.57523 0.590227 23 ADGRD1 6.29846 0.524872 12 RPTOR 10.26378 0.446251 23 RASA3 4.932304 0.411025 12 NXN 6.038699 0.262552 23 CMIP 4.764815 0.397068 12 NCOR2 5.781112 0.251353 23 ZC3H3 4.64397 0.386997 12 RIMBP2 5.121475 0.222673 23 MAML3 4.500624 0.375052 12 INPP5A 4.056281 0.17636 23 TNS3 4.451974 0.370998 12 PRKCZ 6.347057 0.288503 22 FBRSL1 3.987885 0.332324 12 SKI 10.71367 0.510175 21 ZC3H12D 7.076528 0.643321 11 CCDC140 5.695759 0.517796 11 RBFOX3 7.481977 0.213771 35 TBCD 4.563491 0.414863 11 DIP2C 10.57351 0.330422 32 ACOT7 4.484055 0.448406 10 SOX2-OT 8.581972 0.29593 29 TFAP2B 4.443817 0.444382 10 GALNT9 7.811737 0.289324 27 AKAP13 4.031576 0.403158 10 SHANK2 9.067266 0.348741 26

ATP11A 5.629712 0.625524 9 ADARB2 7.012347 0.269706 26

RUNX1 4.768155 0.529795 9 AGAP1 9.67105 0.386842 25

ADAMTS2 4.381118 0.486791 9 PDGFRA 7.736738 0.30947 25

TSPAN9 4.358179 0.484242 9 CAMTAI 6.544932 0.261797 25

AXIN2 4.28654 0.476282 9 SATB2 8.389307 0.349554 24

IGF2BP1 3.951526 0.439058 9 HOXB3 10.88869 0.473421 23 MSRA 5.028607 0.628576 8 RPTOR 10.71092 0.465692 23 DLEU1 4.521561 0.565195 8 NCOR2 6.734153 0.292789 23 PRDM6 4.441132 0.555142 8 RIMBP2 4.764066 0.207133 23

AFF3 4.261966 0.532746 8 INPP5A 4.069466 0.176933 23 HOXB-AS3 5.999753 0.857108 7 PRKCZ 4.627069 0.210321 22 NAVI 5.943536 0.849077 7 SKI 9.279129 0.441863 21

HOXD3 5.14118 0.734454 7 ZIC4 5.871497 0.279595 21 HOXB-AS1 4.638274 0.662611 7 SDK1 9.318171 0.465909 20 LHX2 4.013036 0.573291 7 ABR 5.91374 0.295687 20 SATB2-AS1 5.34783 0.891305 6 FRMD4A 5.159279 0.257964 20

ROR1 4.741215 0.790203 6 MAD1L1 11.62279 0.611726 19

FBXL18 3.922361 0.653727 6 ZNF423 7.81374 0.411249 19

TSN AX-DISCI 4.656461 0.931292 5 SMG1P2 6.691693 0.352194 19

CNPY1 4.581687 0.916337 5 BOLA2 6.691693 0.352194 19

PRR5L 4.463218 0.892644 5 LOC613038 6.691693 0.352194 19

ARHGEF7 4.278868 0.855774 5 CASZ1 5.736559 0.301924 19

RUNDC3A 4.013063 0.802613 5 CFAP46 5.597067 0.294582 19 RBMS3 5.434225 1.358556 4 SEPTIN9 8.140972 0.452276 18 SLC25A10 4.49209 2.246045 2 FOXK1 6.739737 0.37443 18

TBC1D16 5.201468 0.28897 18

TABLE 37: Cancer Type MCF2L 4.169744 0.231652 18

EPN_PFA_ld

PAX6-AS1 7.221093 0.42477 17

Gene site imp sum imp mean n

RCN1 7.221093 0.42477 17

PTPRN2 14.16403 0.172732 82

OPCML 5.635106 0.331477 17 PRDM16 20.32788 0.286308 71

SIM1 4.81768 0.283393 17 PCDHGA1 4.457227 0.075546 59

EBF3 4.791317 0.299457 16 PCDHGA2 4.773613 0.083748 57

NAV2 4.475462 0.279716 16

PCDHGA3 4.773613 0.0884 54

FOXP1 4.401058 0.275066 16

PCDHGB1 4.773613 0.090068 53

SORBS2 4.070503 0.254406 16

PCDHGA4 4.773613 0.0936 51

GLI2 8.844511 0.589634 15

PCDHGB2 4.773613 0.097421 49

LRMDA 5.609227 0.373948 15

PCDHGA5 4.773613 0.101566 47 KNDC1 5.573397 0.37156 15

PCDHGB3 4.773613 0.111014 43 SLX1B- PCDHGA6 4.140841 0.103521 40 SULT1A4 5.061652 0.337443 15 HDAC4 15.46144 0.417877 37 SLX1A 5.061652 0.337443 15 PCDHGA7 4.140841 0.111915 37 LOC606724 5.061652 0.337443 15

PAX6 12.5182 0.357663 35 BAIAP2 4.715108 0.314341 15 CUX1 7.899281 0.564234 14 PCDHGB1 6.249149 0.117908 53 RPS6KA2 5.501559 0.392969 14 PCDHGA4 6.249149 0.122532 51 PRKAG2 5.237114 0.37408 14 PCDHGB2 6.249149 0.127534 49 C7orf50 4.527671 0.323405 14 PCDHGA5 6.249149 0.132961 47 MSI2 5.354662 0.411897 13 PCDHGB3 6.249149 0.145329 43 MIR9-3HG 4.517006 0.347462 13 PCDHGA6 5.616377 0.140409 40 MYT1L 4.500654 0.346204 13 HDAC4 13.72711 0.371003 37 CLYBL 4.143654 0.318743 13 PCDHGA7 5.616377 0.151794 37 ADGRD1 5.40329 0.450274 12 PAX6 11.95651 0.341615 35 CMIP 4.791817 0.399318 12 RBFOX3 6.248808 0.178537 35 FBRSL1 4.247617 0.353968 12 PCDHGB4 4.983605 0.142389 35 FGFR2 7.174125 0.652193 11 PCDHGA8 4.983605 0.142389 35 ZC3H12D 6.5122 0.592018 11 DIP2C 12.07684 0.377401 32 VGLL4 4.940937 0.449176 11 PCDHGB5 4.983605 0.155738 32 PITX2 4.521435 0.452143 10 PCDHGA9 4.983605 0.160761 31 SKOR1 4.258637 0.425864 10 SOX2-OT 8.488804 0.292717 29 RUNX1 6.700412 0.74449 9 PCDHGB6 4.462174 0.153868 29 ATP11A 5.674704 0.630523 9 PCDHGA10 4.462174 0.159363 28 SND1 5.065513 0.562835 9 GALNT9 7.836874 0.290255 27 IGF2BP1 4.389758 0.487751 9 ADARB2 9.084362 0.349399 26 AXIN2 4.329011 0.481001 9 SHANK2 7.663285 0.294742 26 DLEU1 5.261077 0.657635 8 AGAP1 10.63693 0.425477 25 PRDM6 4.880923 0.610115 8 PDGFRA 7.632836 0.305313 25 LINC00311 4.240971 0.530121 8 CAMTAI 7.157302 0.286292 25 AFF3 4.069896 0.508737 8 SATB2 12.85457 0.535607 24 NAVI 5.018063 0.716866 7 MEIS1 6.554545 0.273106 24 HOXB-AS1 4.986665 0.712381 7 PCDHGB7 4.437355 0.18489 24 HOXB-AS3 4.934192 0.704885 7 RPTOR 10.07816 0.438181 23 HOXD3 4.268153 0.609736 7 HOXB3 8.867335 0.385536 23 SATB2-AS1 4.635977 0.772663 6 RIMBP2 6.878422 0.299062 23 ROR1 4.372884 0.728814 6 NCOR2 6.285882 0.273299 23 CNPY1 5.582239 1.116448 5 INPP5A 5.269959 0.229129 23

TSN AX-DISCI 4.78141 0.956282 5 PRKCZ 8.630716 0.392305 22 LOC100132215 4.543738 0.908748 5 SKI 9.400294 0.447633 21 PRR5L 4.167306 0.833461 5 ZIC4 5.787979 0.275618 21 YJEFN3 4.125815 0.825163 5 HOXA-AS3 5.32864 0.253745 21 NDUFA13 4.125815 0.825163 5 SDK1 8.473301 0.423665 20 RBMS3 5.532971 1.383243 4 ABR 5.397539 0.269877 20 SLC25A10 4.494503 2.247252 2 MAD1L1 12.29839 0.647284 19 ZNF423 7.872681 0.414352 19

TABLE 38: Cancer Type SMG1P2 6.494341 0.341807 19

EPN_PFA_le BOLA2 6.494341 0.341807 19

Gene site imp sum imp mean n LOC613038 6.494341 0.341807 19 PTPRN2 18.00181 0.219534 82 CFAP46 5.689287 0.299436 19 PRDM16 21.74627 0.306286 71 CASZ1 4.609441 0.242602 19 PCDHGA1 6.881921 0.116643 59 SEPTIN9 8.178796 0.454378 18 PCDHGA2 6.565535 0.115185 57 FOXK1 7.898339 0.438797 18 PCDHGA3 6.565535 0.121584 54 TBC1D16 5.470113 0.303895 18 OPCML 6.190017 0.364119 17

TBX15 5 .706091 0.335652 17 TABLE 39: Cancer Type

EPN_PFA_lf

SIM1 5 .192404 0.305436 17

Gene site imp sum imp mean n

EBF3 5 .90028 0.368768 16 PTPRN2 13.09278 0.159668 82

NAV2 5 .221878 0.326367 16 PRDM16 20.14761 0.283769 71

FOXP1 5 .181933 0.323871 16 PCDHGA1 4.014954 0.06805 59

GLI2 9 .065835 0.604389 15

SLX1B- HDAC4 15.21987 0.411348 37

SULT1A4 5 .499113 0.366608 15 PAX6 12.20129 0.348608 35

SLX1A 5 .499113 0.366608 15 RBFOX3 9.580381 0.273725 35

LOC606724 5 .499113 0.366608 15 DIP2C 10.53293 0.329154 32

ZBTB20 5 .387733 0.359182 15 SOX2-OT 6.568508 0.2265 29

KIRREL3 4 .942907 0.329527 15 GALNT9 8.901503 0.329685 27

LRMDA 4 .930817 0.328721 15 ADARB2 8.342014 0.320847 26

BAIAP2 4 .662475 0.310832 15 SHANK2 5.925792 0.227915 26

EMX2OS 4 .429826 0.295322 15 AGAP1 8.423821 0.336953 25

RPS6KA2 6 .445326 0.46038 14 CAMTAI 6.335015 0.253401 25

CUX1 6 .293085 0.449506 14 SATB2 7.790586 0.324608 24

PRKAG2 5 .440297 0.388593 14 MEIS1 4.099364 0.170807 24

MSI2 7 .114332 0.547256 13 RPTOR 10.86983 0.472601 23

KIF26B 5 .683044 0.437157 13 NCOR2 7.09586 0.308516 23

CLYBL 5 .449649 0.419204 13 HOXB3 5.862146 0.254876 23

MYT1L 4 .83363 0.371818 13 RIMBP2 5.378929 0.233866 23

ADGRD1 5 .371757 0.447646 12 INPP5A 4.380518 0.190457 23

ZC3H3 4 .983983 0.415332 12 NXN 4.04771 0.175987 23

RASA3 4 .983927 0.415327 12 PRKCZ 6.916562 0.314389 22

FBRSL1 4 .769712 0.397476 12 SKI 9.348199 0.445152 21

CMIP 4 .637998 0.3865 12 ZIC4 6.393505 0.304453 21

ZC3H12D 6 .775582 0.615962 11 SIM2 5.109225 0.243296 21

FGFR2 6 .004602 0.545873 11 SDK1 5.862156 0.293108 20

VGLL4 5 .254372 0.47767 11 FRMD4A 5.766686 0.288334 20

RUNX1 6 .920844 0.768983 9 ABR 4.713662 0.235683 20

SND1 6 .114865 0.679429 9 MAD1L1 11.80233 0.621175 19

ATP11A 5 .509849 0.612205 9 ZNF423 8.685542 0.457134 19

AXIN2 4 .969639 0.552182 9 SMG1P2 6.149274 0.323646 19

ZNF833P 4 .887175 0.543019 9 BOLA2 6.149274 0.323646 19

ADAMTS2 4 .738917 0.526546 9 LOC613038 6.149274 0.323646 19

TSPAN9 4 .542104 0.504678 9 CFAP46 5.760275 0.303172 19

PRDM6 6 .947315 0.868414 8 CASZ1 4.40723 0.231959 19

AFF3 4 .755751 0.594469 8 SEPTIN9 6.327048 0.351503 18

LHX4 4 .538205 0.567276 8 FOXK1 6.211907 0.345106 18

NAVI 5 .378917 0.768417 7 ANKRD11 4.557418 0.25319 18

HOXB-AS1 5 .110234 0.730033 7 RBFOX1 4.215856 0.234214 18

SATB2-AS1 6 .250726 1.041788 6 TBC1D16 4.160834 0.231157 18

TSN AX-DISCI 5 .009414 1.001883 5 OPCML 5.509342 0.324079 17

CNPY1 4 .547343 0.909469 5 SIM1 5.074315 0.298489 17

RBMS3 5 .281652 1.320413 4 PAX6-AS1 4.557005 0.268059 17

SLC25A10 4 .677629 2.338814 2 RCN1 4.557005 0.268059 17 TBX15 4.011994 0.236 17 SATB2-AS1 4.803834 0.800639 6

FOXP1 5.473053 0.342066 16 FBXL18 4.26291 0.710485 6

EBF3 5.279453 0.329966 16 TSNAX-DISC1 5.572732 1.114546 5

NAV2 5.163328 0.322708 16 ARHGEF7 4.651486 0.930297 5

GLI2 8.377429 0.558495 15 PRR5L 4.485171 0.897034 5

KIRREL3 5.753279 0.383552 15 RBMS3 5.367267 1.341817 4

KNDC1 5.392565 0.359504 15 VOPP1 4.143062 1.035765 4

BAIAP2 4.27612 0.285075 15 SLC25A10 4.685335 2.342668 2

SLX1B- ANKLE2 4.116607 2.058304 2

SULT1A4 4.264888 0.284326 15

SLX1A 4.264888 0.284326 15

TABLE 40: Cancer Type

LOC606724 4.264888 0.284326 15 EPN_PFA_2a

RPS6KA2 5.911852 0.422275 14 Gene site imp sum imp mean n

CUX1 5.504203 0.393157 14 PTPRN2 15.91598 0.194097 82

MSI2 7.326236 0.563557 13 PRDM16 23.91302 0.336803 71

MYT1L 5.63385 0.433373 13 PCDHGA3 4.395619 0.0814 54

KIF26B 5.058706 0.389131 13 PCDHGB1 4.395619 0.082936 53

GSE1 4.889862 0.376143 13 PCDHGA4 4.395619 0.086189 51

CLYBL 4.867994 0.374461 13 PCDHGB2 4.395619 0.089707 49

ZC3H3 5.537883 0.46149 12 PCDHGA5 4.395619 0.093524 47

ADGRD1 5.301199 0.441767 12 HDAC4 14.58312 0.394138 37

TNS3 5.192588 0.432716 12 PAX6 12.40615 0.354462 35

CMIP 4.83491 0.402909 12 RBFOX3 9.246467 0.264185 35

MIRLET7BHG 4.293505 0.357792 12 DIP2C 11.2906 0.352831 32

MAML3 4.005847 0.333821 12 SOX2-OT 8.738627 0.301332 29

ZC3H12D 7.258058 0.659823 11 GALNT9 6.750716 0.250027 27

TBCD 4.863163 0.442106 11 ADARB2 8.387141 0.322582 26

GLUD1P2 4.329645 0.393604 11 SHANK2 7.117336 0.273744 26

ACOT7 4.973412 0.497341 10 AGAP1 9.061107 0.362444 25

PITX2 4.414111 0.441411 10 PDGFRA 6.28606 0.251442 25

ADGRA1 4.322472 0.432247 10 CAMTAI 6.048119 0.241925 25

SND1 5.8504 0.650044 9 SATB2 11.72 0.488333 24

ATP11A 5.752276 0.639142 9 MEIS1 5.705858 0.237744 24

ADAMTS2 4.993887 0.554876 9 RPTOR 10.21814 0.444267 23

CACNA2D4 4.607028 0.511892 9 NCOR2 7.929984 0.344782 23

ZNF833P 4.412196 0.490244 9 HOXB3 6.116576 0.265938 23

AXIN2 4.31999 0.479999 9 RIMBP2 5.100058 0.221742 23

RUNX1 4.309572 0.478841 9 INPP5A 4.733123 0.205788 23

SLC22A18 4.285245 0.476138 9 PRKCZ 8.624156 0.392007 22

MSRA 4.847284 0.60591 8 SKI 9.70777 0.462275 21

PRDM6 4.456104 0.557013 8 ZIC4 6.670683 0.317652 21

LINC00311 4.311468 0.538934 8 HOXA-AS3 6.511321 0.310063 21

DLEU1 4.263023 0.532878 8 ABR 7.33488 0.366744 20

AFF3 4.025175 0.503147 8 SDK1 6.881182 0.344059 20

NAVI 5.601169 0.800167 7 FRMD4A 5.001019 0.250051 20

DUSP6 4.205483 0.600783 7 MAD1L1 10.76549 0.566605 19

TBR1 4.175176 0.596454 7 ZNF423 8.242158 0.433798 19

LHX2 4.10337 0.586196 7 CASZ1 6.766843 0.35615 19 CFAP46 5.636312 0.296648 19 PITX2 5.128856 0.512886 10 SMG1P2 5.570027 0.293159 19 ACOT7 4.918146 0.491815 10 BOLA2 5.570027 0.293159 19 SPPL2B 4.624696 0.46247 10 LOC613038 5.570027 0.293159 19 SND1 6.477967 0.719774 9 SEPTIN9 8.545258 0.474737 18 ATP11A 6.136883 0.681876 9 FOXK1 7.476101 0.415339 18 ADAMTS2 4.85214 0.539127 9 TBC1D16 5.022636 0.279035 18 AXIN2 4.512541 0.501393 9 PAX6-AS1 7.162201 0.421306 17 MSRA 5.754357 0.719295 8 RCN1 7.162201 0.421306 17 LINC00311 4.716263 0.589533 8 OPCML 7.013243 0.412544 17 SOX6 5.215185 0.745026 7 SIM1 5.849929 0.344113 17 ROR1 4.798833 0.799805 6 TBX15 5.613326 0.330196 17 SATB2-AS1 4.516259 0.75271 6 EBF3 6.450012 0.403126 16 CNPY1 5.6611 1.13222 5 NAV2 5.462722 0.34142 16 YJEFN3 5.459693 1.091939 5 FOXP1 4.794453 0.299653 16 NDUFA13 5.459693 1.091939 5 GLI2 8.084175 0.538945 15 TSNAX-DISC1 5.227233 1.045447 5 EMX2OS 5.703506 0.380234 15 RBMS3 4.524161 1.13104 4 KNDC1 5.68967 0.379311 15 SLC25A10 4.544005 2.272002 2 KIRREL3 5.202305 0.34682 15

DLX6-AS1 5.125709 0.341714 15 Cancer Type

SLX1B- l iii.r. 41: EPN_PFA_2b

SULT1A4 5.009364 0.333958 15

Gene site imp sum imp mean n

SLX1A 5.009364 0.333958 15 PTPRN2 16.4127 0.200155 82

LOC606724 5.009364 0.333958 15 PRDM16 22.36941 0.315062 71

LRMDA 4.712534 0.314169 15 PCDHGA1 6.212905 0.105303 59

NFATC1 4.691042 0.312736 15 PCDHGA2 6.212905 0.108998 57

NHX 4.567902 0.304527 15 PCDHGA3 6.212905 0.115054 54

COL23A1 4.555395 0.303693 15 PCDHGB1 5.896519 0.111255 53

BAIAP2 4.490191 0.299346 15 PCDHGA4 5.896519 0.115618 51

RPS6KA2 7.827096 0.559078 14 PCDHGB2 5.472043 0.111674 49

C7orf50 5.63246 0.402319 14 PCDHGA5 5.155657 0.109695 47

PRKAG2 5.5359 0.395421 14 PCDHGB3 4.839271 0.112541 43

CUX1 4.998438 0.357031 14 HDAC4 14.80896 0.400242 37

MSI2 6.849053 0.52685 13 PAX6 13.46938 0.384839 35

CLYBL 6.024401 0.463415 13 RBFOX3 9.79059 0.279731 35

KIF26B 4.922835 0.37868 13 DIP2C 10.95171 0.342241 32

MYT1L 4.757005 0.365923 13 SOX2-OT 6.22414 0.214626 29

MIR9-3HG 4.576315 0.352024 13

GALNT9 6.534668 0.242025 27

TBX4 5.54634 0.462195 12 ADARB2 9.127855 0.351071 26

ZC3H3 5.30363 0.441969 12 SHANK2 7.648487 0.294173 26

MIRLET7BHG 4.931953 0.410996 12 AGAP1 10.27307 0.410923 25

FBRSL1 4.927912 0.410659 12 CAMTAI 5.472853 0.218914 25

CMIP 4.902181 0.408515 12 PDGFRA 5.468118 0.218725 25

TNS3 4.855115 0.404593 12 SATB2 11.96168 0.498403 24

RASA3 4.712357 0.392696 12 RPTOR 10.96186 0.476603 23

ADGRD1 4.583055 0.381921 12 NCOR2 7.371677 0.320508 23

ZC3H12D 7.345878 0.667807 11 NXN 5.650817 0.245688 23

CCDC140 4.570185 0.415471 11 RIMBP2 5.223607 0.227113 23 OTX1 5.952922 0.595292 10

INPP5A 5.095016 0.221522 23 SPPL2B 5.368011 0.536801 10

PRKCZ 7.415118 0.337051 22 IGF1R 4.426751 0.442675 10

SKI 9.66072 0.460034 21 SND1 5.88217 0.653574 9

HOXA-AS3 5.713977 0.272094 21 ATP11A 5.318731 0.59097 9

ZIC4 5.294912 0.252139 21 ADAMTS2 4.829158 0.536573 9

SDK1 7.395307 0.369765 20 RUNX1 4.626345 0.514038 9

ABR 6.916678 0.345834 20 IGF2BP1 4.45 0.494444 9

FRMD4A 5.148597 0.25743 20 PRDM6 5.608682 0.701085 8

MAD1L1 11.11146 0.584814 19 DLEU1 5.328889 0.666111 8

ZNF423 8.81835 0.464124 19 KIF26A 4.561173 0.570147 8

CFAP46 6.346435 0.334023 19 LHX4 4.421062 0.552633 8

SMG1P2 6.214699 0.327089 19 LINC00311 4.418239 0.55228 8

BOLA2 6.214699 0.327089 19 MSRA 4.328181 0.541023 8

LOC613038 6.214699 0.327089 19 DUSP6 5.270804 0.752972 7

CASZ1 5.447154 0.286692 19 NAVI 4.987175 0.712454 7

KCNQ1 5.41355 0.284924 19 SOX6 4.593669 0.656238 7

SEPTIN9 7.560517 0.420029 18 SATB2-AS1 5.56202 0.927003 6

FOXK1 7.422858 0.412381 18 ROR1 4.56876 0.76146 6

TBC1D16 5.337114 0.296506 18 YJEFN3 6.354918 1.270984 5

PAX6-AS1 7.879274 0.463487 17 NDUFA13 6.354918 1.270984 5

RCN1 7.879274 0.463487 17 CNPY1 4.812447 0.962489 5

SIM1 6.533671 0.384334 17 LOC100132215 4.778136 0.955627 5

OPCML 6.023777 0.35434 17 TSN AX-DISCI 4.722043 0.944409 5

FOXP1 4.801912 0.300119 16 ARHGEF7 4.42906 0.885812 5

NAV2 4.439677 0.27748 16 PRR5L 4.349745 0.869949 5

GLI2 8.675359 0.578357 15 RBMS3 5.220141 1.305035 4

KIRREL3 5.848906 0.389927 15 SLC25A10 4.703802 2.351901 2

KNDC1 5.36737 0.357825 15

BAIAP2 4.920026 0.328002 15 Cancer Type

SLX1B- EPN_PFA_2c SULT1A4 4.849583 0.323306 15 Gene site imp sum imp mean n SLX1A 4.849583 0.323306 15 PTPRN2 11.6565 0.142152 82

LOC606724 4.849583 0.323306 15 PRDM16 23.70492 0.333872 71

LRMDA 4.515397 0.301026 15 PCDHGA1 4.37733 0.074192 59

RPS6KA2 7.297086 0.52122 14 PCDHGA2 4.37733 0.076795 57

PRKAG2 5.45784 0.389846 14 PCDHGA3 4.37733 0.081062 54

CUX1 5.375193 0.383942 14 PCDHGB1 4.37733 0.082591 53

MSI2 7.013927 0.539533 13 PCDHGA4 4.37733 0.08583 51

GSE1 4.576023 0.352002 13 PCDHGB2 4.060944 0.082876 49

ADGRD1 5.774253 0.481188 12 PCDHGA5 4.060944 0.086403 47

ZC3H3 5.25217 0.437681 12 HDAC4 13.29829 0.359413 37

TBX4 5.04624 0.42052 12 PAX6 14.48402 0.413829 35

FBRSL1 4.714604 0.392884 12 RBFOX3 6.241908 0.17834 35

RASA3 4.589192 0.382433 12 DIP2C 8.787136 0.274598 32

CMIP 4.508705 0.375725 12 SOX2-OT 7.319255 0.252388 29

ZC3H12D 8.257341 0.750667 11 GALNT9 6.266872 0.232106 27

FGFR2 7.598239 0.690749 11 ADARB2 8.089442 0.311132 26 SHANK2 6.05991 0.233073 26 CLYBL 5.510349 0.423873 13 AGAP1 8.374534 0.334981 25 MYT1L 5.126763 0.394366 13 PDGFRA 5.01173 0.200469 25 ZC3H3 4.978083 0.41484 12 CAMTAI 4.487403 0.179496 25 ADGRD1 4.926207 0.410517 12 SATB2 8.753201 0.364717 24 CMIP 4.886292 0.407191 12 MEIS1 4.083446 0.170144 24 TNS3 4.622121 0.385177 12 RPTOR 9.988587 0.434286 23 FBRSL1 4.094101 0.341175 12 NCOR2 6.55577 0.285033 23 ZC3H12D 7.253406 0.659401 11 HOXB3 5.776906 0.25117 23 CCDC140 5.226762 0.47516 11 RIMBP2 5.376796 0.233774 23 VGLL4 4.634901 0.421355 11 NXN 4.613719 0.200596 23 ACOT7 5.075241 0.507524 10 PRKCZ 7.616692 0.346213 22 ATP11A 6.340161 0.704462 9 SKI 9.345082 0.445004 21 SND1 6.218579 0.690953 9 ZIC4 5.183042 0.246812 21 RUNX1 4.884786 0.542754 9 SDK1 7.884654 0.394233 20 SLC22A18 4.190517 0.465613 9 FRMD4A 6.143578 0.307179 20 ADAMTS2 4.018914 0.446546 9 ABR 5.536943 0.276847 20 CACNA2D4 3.999655 0.444406 9 MAD1L1 11.41524 0.600802 19 MSRA 4.921088 0.615136 8 ZNF423 8.664317 0.456017 19 LMX1B 4.536467 0.567058 8 CFAP46 5.304846 0.279202 19 PRDM6 4.518116 0.564764 8 SMG1P2 4.825948 0.253997 19 DLEU1 4.483962 0.560495 8 BOLA2 4.825948 0.253997 19 LINC00311 4.213928 0.526741 8

LOC613038 4.825948 0.253997 19 KIF26A 4.10704 0.51338 8 CASZ1 4.66076 0.245303 19 TENM3-AS1 4.767356 0.681051 7 KCNQ1 4.603715 0.242301 19 Clorf94 4.572866 0.653267 7 SEPTIN9 8.788187 0.488233 18 HOXB-AS3 4.288964 0.612709 7 FOXK1 6.147356 0.34152 18 NAVI 4.213013 0.601859 7 TBC1D16 5.28406 0.293559 18 DUSP6 4.100914 0.585845 7 PAX6-AS1 5.994998 0.352647 17 SATB2-AS1 4.979445 0.829907 6 RCN1 5.994998 0.352647 17 CNPY1 5.302207 1.060441 5 OPCML 5.742541 0.337797 17 TSNAX-DISC1 4.636077 0.927215 5 NAV2 5.608379 0.350524 16 PRR5L 4.341445 0.868289 5 EBF3 5.075295 0.317206 16 RUNDC3A 4.159525 0.831905 5 FOXP1 4.729745 0.295609 16 RBMS3 4.617734 1.154433 4 GLI2 7.42542 0.495028 15 SLC25A10 4.631508 2.315754 2 BAIAP2 4.950271 0.330018 15 ANKLE2 4.023139 2.01157 2 LRMDA 4.691617 0.312774 15 NFATC1 4.682222 0.312148 15 TABLE 43: Cancer Type EPN_PFB_1 KNDC1 4.580988 0.305399 15 Gene site imp sum imp mean n NHX 4.565442 0.304363 15 PTPRN2 14.34232 0.174906 82 EMX2OS 4.433473 0.295565 15 PRDM16 19.15675 0.269813 71 RPS6KA2 8.526582 0.609042 14 PCDHGB1 3.436915 0.064847 53 CUX1 6.091662 0.435119 14 PCDHGB2 3.436915 0.070141 49 PRKAG2 5.14939 0.367814 14 PCDHGA5 3.436915 0.073126 47 C7orf50 5.013166 0.358083 14 PCDHGB3 3.436915 0.079928 43 ARHGEF10 4.177837 0.298417 14 PCDHGA6 3.553808 0.088845 40 MSI2 6.653697 0.511823 13 HDAC4 9.949331 0.268901 37 KIF26B 5.568032 0.42831 13 PCDHGA7 3.870194 0.1046 37 PAX6 14.36937 0.410553 35 MIR548F5 3.520189 0.251442 14 RBFOX3 7.486781 0.213908 35 GSE1 5.18713 0.39901 13 PCDHGB4 3.870194 0.110577 35 MYT1L 4.935413 0.379647 13 PCDHGA8 3.870194 0.110577 35 MSI2 4.665388 0.358876 13 DIP2C 10.83914 0.338723 32 RFX4 4.204038 0.323388 13 SOX2-OT 8.596926 0.296446 29 KIF26B 4.056407 0.312031 13 GALNT9 6.171874 0.228588 27 CLYBL 3.462787 0.266368 13 SHANK2 7.985448 0.307133 26 ZC3H3 5.412723 0.45106 12 ADARB2 4.97106 0.191195 26 MIRLET7BHG 4.697784 0.391482 12 AGAP1 7.294908 0.291796 25 TNS3 4.627592 0.385633 12 CAMTAI 5.086112 0.203444 25 CMIP 4.384051 0.365338 12 SATB2 6.296275 0.262345 24 MAML3 3.565524 0.297127 12 RPTOR 8.152366 0.354451 23 RASA3 3.47917 0.289931 12 HOXB3 5.319994 0.231304 23 VGLL4 3.684002 0.334909 11 RIMBP2 4.533346 0.197102 23 ZC3H12D 3.516208 0.319655 11 INPP5A 4.21213 0.183136 23 ACOT7 4.804751 0.480475 10 NCOR2 3.666971 0.159434 23 SH3RF3 3.992997 0.3993 10 PRKCZ 4.701794 0.213718 22 NR2F1-AS1 3.661456 0.366146 10 SKI 7.691187 0.366247 21 AKAP13 3.469122 0.346912 10 ZIC4 6.763892 0.32209 21 SND1 6.320565 0.702285 9 SDK1 5.929341 0.296467 20 ATP11A 5.097164 0.566352 9 FRMD4A 5.310431 0.265522 20 ADAMTS2 4.443487 0.493721 9 MAD1L1 9.899326 0.521017 19 KAZN 3.745411 0.416157 9 ZNF423 8.938096 0.470426 19 IGF2BP1 3.449928 0.383325 9 CASZ1 5.802896 0.305416 19 RORA 5.794622 0.724328 8 SMG1P2 4.4044 0.231811 19 AFF3 4.712983 0.589123 8 BOLA2 4.4044 0.231811 19 LHX4 4.609795 0.576224 8 LOC613038 4.4044 0.231811 19 DLEU1 4.327767 0.540971 8 FOXK1 7.895185 0.438621 18 LINC00311 4.047595 0.505949 8 SEPTIN9 6.791985 0.377333 18 MSRA 4.01467 0.501834 8 ANKRD11 5.579274 0.30996 18 DUSP6 4.033287 0.576184 7 TBC1D16 5.542428 0.307913 18 RXRA 3.800895 0.542985 7 OPCML 5.939946 0.349409 17 SLC22A18AS 3.913593 0.652265 6 PAX6-AS1 5.00947 0.294675 17 MIR100HG 3.468902 0.57815 6 RCN1 5.00947 0.294675 17 TSNAX-DISC1 4.330247 0.866049 5 SIM1 3.834599 0.225565 17 RUNDC3A 4.179604 0.835921 5 FOXP1 5.652189 0.353262 16 PRR5L 3.888385 0.777677 5 NAV2 4.151343 0.259459 16 HOXB6 3.711412 0.742282 5 GLI2 10.6818 0.71212 15 BCAR1 3.496141 0.699228 5 BAIAP2 4.41495 0.29433 15 VOPP1 3.945338 0.986334 4 KNDC1 4.348924 0.289928 15 DTNA 3.457698 0.864424 4 ZBTB20 3.707767 0.247184 15 SLC25A10 4.417827 2.208914 2 RPS6KA2 6.415394 0.458242 14 ANKLE2 3.597331 1.798665 2 PRKAG2 5.699687 0.40712 14 CUX1 5.448411 0.389172 14 TABLE 44: Cancer Type EPN_PFB_2 IQSEC1 4.576315 0.32688 14 Gene site imp sum imp mean n C7orf50 4.191569 0.299398 14 PTPRN2 10.64422 0.129808 82 TBX5 3.814253 0.272447 14 PRDM16 20.94126 0.294947 71 PCDHGA1 4.625461 0.078398 59 FOXP1 4.178346 0.261147 16 PCDHGA2 4.309075 0.075598 57 GLI2 9.179313 0.611954 15 PCDHGA3 4.309075 0.079798 54 BAIAP2 4.301727 0.286782 15 PCDHGB1 4.309075 0.081303 53 KIRREL3 3.88947 0.259298 15 PCDHGA4 4.309075 0.084492 51 KNDC1 3.806155 0.253744 15 PCDHGB2 4.309075 0.08794 49 CUX1 5.473789 0.390985 14 PCDHGA5 4.309075 0.091682 47 RPS6KA2 4.773826 0.340988 14 PCDHGB3 4.309075 0.100211 43 IQSEC1 4.576661 0.326904 14 HDAC4 9.160366 0.247577 37 PRKAG2 4.566589 0.326185 14 PAX6 12.17926 0.347979 35 MSI2 6.194145 0.476473 13 RBFOX3 8.260381 0.236011 35 HOXC4 5.120243 0.393865 13 DIP2C 9.945545 0.310798 32 GSE1 4.956749 0.381288 13 SOX2-OT 7.444734 0.256715 29 CLYBL 4.880451 0.375419 13 GALNT9 3.714889 0.137588 27 RFX4 4.176726 0.321287 13 ADARB2 7.560911 0.290804 26 KIF26B 3.550082 0.273083 13 SHANK2 7.534784 0.289799 26 TNS3 4.732704 0.394392 12 AGAP1 6.452711 0.258108 25 ZC3H3 4.458258 0.371522 12 CAMTAI 6.356277 0.254251 25 MIRLET7BHG 3.820327 0.318361 12 SATB2 5.476687 0.228195 24 ADGRD1 3.795001 0.31625 12 MEIS1 3.786328 0.157764 24 MEIS2 3.703953 0.308663 12 RPTOR 10.50927 0.456925 23 CMIP 3.662126 0.305177 12 NCOR2 5.665408 0.246322 23 ZC3H12D 6.308579 0.573507 11 HOXB3 5.333976 0.231912 23 VGLL4 4.51024 0.410022 11 INPP5A 4.486591 0.195069 23 FGFR2 4.33965 0.394514 11 PRKCZ 4.776667 0.217121 22 ACOT7 5.294485 0.529449 10 SKI 7.723905 0.367805 21 SH3RF3 3.765773 0.376577 10 ZIC4 5.592501 0.26631 21 SND1 6.409491 0.712166 9 HOXA-AS3 3.813032 0.181573 21 ATP11A 5.546325 0.616258 9 SIM2 3.689477 0.175689 21 ADAMTS2 5.51799 0.61311 9 ABR 5.02434 0.251217 20 RUNX1 4.372101 0.485789 9 SDK1 4.985671 0.249284 20 GPC6 4.279437 0.475493 9 FRMD4A 3.900745 0.195037 20 SLC22A18 4.057507 0.450834 9 MAD1L1 10.37438 0.54602 19 TSPAN9 3.972817 0.441424 9 ZNF423 7.72118 0.406378 19 DLEU1 5.051633 0.631454 8 CASZ1 6.641635 0.34956 19 LINC00311 3.977828 0.497228 8 SMG1P2 4.909237 0.258381 19 TRAPPC9 3.716779 0.464597 8 BOLA2 4.909237 0.258381 19 LHX4 3.668568 0.458571 8 LOC613038 4.909237 0.258381 19 NAVI 5.538133 0.791162 7 CFAP46 3.84721 0.202485 19 RXRA 4.146925 0.592418 7 FOXK1 6.551795 0.363989 18 CXXC5 3.870947 0.552992 7 TBC1D16 5.057483 0.280971 18 HOXB-AS3 3.664901 0.523557 7 SEPTIN9 4.458712 0.247706 18 PRR5L 4.522524 0.904505 5 HOXA3 4.091188 0.227288 18 RUNDC3A 4.510368 0.902074 5 OPCML 6.375074 0.375004 17 HOXB6 3.934206 0.786841 5 PAX6-AS1 4.454477 0.262028 17 BCAR1 3.763479 0.752696 5 RCN1 4.454477 0.262028 17 RBMS3 5.001333 1.250333 4 EBF3 4.717717 0.294857 16 VOPP1 3.885374 0.971344 4 NAV2 4.486574 0.280411 16 DTNA 3.583953 0.895988 4 SLC25A10 4.433538 2.216769 2 OPCML 4.277913 0.251642 17 ANKLE2 3.768291 1.884146 2 NAV2 3.630784 0.226924 16 FOXP1 3.541174 0.221323 16

TABLE 45: Cancer Type EPN_PFB_3 GLI2 8.564224 0.570948 15 Gene site imp sum imp mean n BAIAP2 6.205878 0.413725 15 PTPRN2 11.25944 0.13731 82 NHX 4.487322 0.299155 15 PRDM16 10.83668 0.152629 71 SLX1B- SULT1A4 3.45221 0.230147 15 PCDHGA1 4.725174 0.080088 59 SLX1A 3.45221 0.230147 15 PCDHGA2 4.725174 0.082898 57 LOC606724 3.45221 0.230147 15 PCDHGA3 4.725174 0.087503 54 COL23A1 3.392095 0.22614 15 PCDHGB1 4.725174 0.089154 53 RPS6KA2 5.650107 0.403579 14 PCDHGA4 4.725174 0.09265 51 CUX1 4.699737 0.335695 14 PCDHGB2 4.725174 0.096432 49 PRKAG2 4.307095 0.30765 14 PCDHGA5 4.725174 0.100536 47

IQSEC1 3.413923 0.243852 14 PCDHGB3 4.420877 0.102811 43 CACNA1H 3.359408 0.239958 14 HDAC4 10.7486 0.290503 37 MSI2 4.910911 0.377762 13 RBFOX3 7.590022 0.216858 35 GSE1 4.885898 0.375838 13 PAX6 3.458426 0.098812 35 MYT1L 4.069793 0.313061 13 DIP2C 10.01905 0.313095 32 KIF26B 3.833933 0.294918 13 SOX2-OT 3.796971 0.13093 29 MIRLET7BHG 4.778635 0.39822 12 GALNT9 5.113729 0.189397 27 ZC3H3 4.647555 0.387296 12 SHANK2 7.883804 0.303223 26 CMIP 4.240018 0.353335 12 ADARB2 3.627241 0.139509 26 ADGRD1 3.971306 0.330942 12 AGAP1 7.079758 0.28319 25 MAML3 3.603578 0.300298 12 CAMTAI 6.428511 0.25714 25 RASA3 3.385833 0.282153 12 PDGFRA 4.221835 0.168873 25 CTNNA2 3.281086 0.273424 12 RPTOR 9.645559 0.419372 23 VGLL4 4.388228 0.39893 11 NCOR2 6.617263 0.287707 23 TBCD 3.611524 0.32832 11 HOXB3 3.617653 0.157289 23 SPON2 3.420958 0.310996 11 PRKCZ 3.516451 0.159839 22 CTBP2 3.387802 0.307982 11 SKI 8.472504 0.403453 21 RAD51B 3.273957 0.297632 11 ZIC4 4.417608 0.210362 21 AUTS2 4.1794 0.41794 10 ABR 6.032617 0.301631 20 ACOT7 3.657825 0.365783 10 FRMD4A 5.405346 0.270267 20 ATP11A 5.797338 0.644149 9 SDK1 4.210196 0.21051 20 SND1 5.407752 0.600861 9 MAD1L1 9.027777 0.475146 19 RUNX1 4.813294 0.53481 9 ZNF423 8.819575 0.464188 19 TSPAN9 3.624113 0.402679 9 CASZ1 7.223512 0.380185 19 CACNA2D4 3.508756 0.389862 9 SMG1P2 4.699263 0.24733 19 KAZN 3.459391 0.384377 9 BOLA2 4.699263 0.24733 19 ADAMTS2 3.387526 0.376392 9 LOC613038 4.699263 0.24733 19 DLEU1 5.128955 0.641119 8 KCNQ1 3.721046 0.195845 19 RORA 4.728827 0.591103 8 FOXK1 6.592575 0.366254 18 LHX4 4.710325 0.588791 8 TBC1D16 5.752235 0.319569 18 AFF3 4.070909 0.508864 8 SEPTIN9 5.262248 0.292347 18 MSRA 3.470129 0.433766 8 MCF2L 3.453333 0.191852 18 RXRA 4.622069 0.660296 7 PAX6-AS1 4.768791 0.280517 17 NAVI 3.295544 0.470792 7 RCN1 4.768791 0.280517 17 LHX2 3.277039 0.468148 7 TBX15 2.864971 0.168528 17 RUNDC3A 4.323891 0.864778 5 HBG2 2.761104 0.162418 17 TSN AX-DISCI 3.718752 0.74375 5 FOXP1 4.56657 0.285411 16 IFT80 3.555972 0.711194 5 EBF3 3.552658 0.222041 16 BCAR1 3.455726 0.691145 5 NAV2 3.185503 0.199094 16 PRR5L 3.445009 0.689002 5 SORBS2 3.165694 0.197856 16

VOPP1 3.63351 0.908377 4 GLI2 7.900942 0.526729 15 RBMS3 3.507961 0.87699 4 BAIAP2 4.598937 0.306596 15 SLC25A10 4.224294 2.112147 2 NHX 4.032807 0.268854 15 ANKLE2 3.376797 1.688399 2 SLX1B-

SULT1A4 3.487957 0.23253 15

TABLE 46: Cancer Type EPN_PFB_4 SLX1A 3.487957 0.23253 15

LOC606724 3.487957 imp sum im 0.23253 15

Gene site p mean n

KIRREL3 3.232994 0.215533 15 PTPRN2 9.754981 0.118963 82

LRMDA 3.152952 0.210197 15 PRDM16 13.42466 0.18908 71

RPS6KA2 5.767982 0.411999 14 HDAC4 9.67948 0.261608 37

CUX1 4.361508 0.311536 14 RBFOX3 5.7616 0.164617 35

C7orf50 3.691524 0.26368 14 PAX6 4.761217 0.136035 35

ARHGEF10 3.185612 0.227544 14 DIP2C 8.308398 0.259637 32

PRKAG2 3.118083 0.22272 14

SOX2-OT 3.781182 0.130386 29

MSI2 5.350692 0.411592 13 GALNT9 3.659274 0.135529 27

GSE1 4.20783 0.323679 13 SHANK2 4.397145 0.169121 26

HOXC4 4.020625 0.309279 13 AGAP1 7.614459 0.304578 25

RFX4 3.932162 0.302474 13 CAMTAI 4.517669 0.180707 25

KIF26B 3.425524 0.263502 13 PDGFRA 3.958665 0.158347 25

CLYBL 3.13253 0.240964 13 RPTOR 9.507141 0.413354 23

ADGRD1 3.799622 0.316635 12

NCOR2 7.694682 0.334551 23

ZC3H3 3.778042 0.314837 12 HOXB3 4.353924 0.189301 23

TNS3 3.47612 0.289677 12 NXN 3.725682 0.161986 23

RASA3 3.461626 0.288469 12 RIMBP2 2.932044 0.12748 23

MIRLET7BHG 3.280793 0.273399 12 PRKCZ 4.460639 0.202756 22

CMIP 3.237477 0.26979 12 SKI 7.174851 0.34166 21

MEGF6 3.108194 0.259016 12 ABR 3.173557 0.158678 20

LRBA 2.98263 0.248553 12

MAD1L1 8.136349 0.428229 19

RAD51B 4.407876 0.400716 11 ZNF423 7.654871 0.402888 19

VGLL4 3.312149 0.301104 11 CASZ1 5.117286 0.269331 19

SPON2 3.021012 0.274637 11 SMG1P2 3.870562 0.203714 19

ACOT7 4.363092 0.436309 10 BOLA2 3.870562 0.203714 19

ADGRA1 3.005443 0.300544 10 LOC613038 3.870562 0.203714 19

ANKS1B 2.745824 0.274582 10 KCNQ1 2.770132 0.145796 19

SND1 6.445786 0.716198 9 SEPTIN9 5.085943 0.282552 18

ATP11A 3.993485 0.443721 9

FOXK1 3.982923 0.221274 18

RUNX1 3.668851 0.40765 9 ANKRD11 3.179216 0.176623 18

ADAMTS2 3.344618 0.371624 9 RBFOX1 2.833921 0.15744 18

TSPAN9 3.203848 0.355983 9 PAX6-AS1 6.991442 0.411261 17

DLEU1 4.281097 0.535137 8 RCN1 6.991442 0.411261 17

MSRA 4.095142 0.511893 8 OPCML 5.525355 0.325021 17

LHX4 3.732792 0.466599 8 SIM1 3.239462 0.190557 17 LINC00311 3.48934 0.436167 8 GLI2 3.249755 0.21665 15 AFF3 3.192509 0.399064 8 BAIAP2 2.020019 0.134668 15 MACROD1 3.049246 0.381156 8 CUX1 2.880317 0.205737 14 ESRRG 2.782387 0.347798 8 RPS6KA2 2.432854 0.173775 14 RXRA 3.991019 0.570146 7 MIR548F5 1.87059 0.133614 14 PRKCA 2.732046 0.390292 7 KIF26B 2.463411 0.189493 13 SLC22A18AS 3.21655 0.536092 6 RFX4 2.459586 0.189199 13 FAM181A 3.132089 0.522015 6 MSI2 1.943557 0.149504 13 CRADD 2.986429 0.497738 6 MYT1L 1.687426 0.129802 13 PRR5L 4.220856 0.844171 5 ADGRD1 2.522095 0.210175 12 RUNDC3A 3.896503 0.779301 5 FBRSL1 2.208379 0.184032 12 TSN AX-DISCI 3.804691 0.760938 5 ZC3H3 2.008673 0.167389 12 IFT80 2.761688 0.552338 5 MIRLET7BHG 1.691575 0.140965 12 CRB2 3.298683 0.824671 4 MAML3 1.58193 0.131827 12

VOPP1 2.967116 0.741779 4 ZC3H12D 2.923161 0.265742 11 GRIN2B 2.872451 0.957484 3 CTBP2 2.005876 0.182352 11 DAGLB 2.752212 0.917404 3 SH3RF3 2.498859 0.249886 10 SLC25A10 4.463499 2.23175 2 ACOT7 2.319072 0.231907 10

WT1 2.219465 0.221947 10

TABLE 47: Cancer Type EPN_PFB_5 BCL11B 2.138854 0.213885 10 Gene site imp sum imp mean n AKAP13 1.704292 0.170429 10

PTPRN2 4.46851 0.054494 82 SLC22A18 3.499743 0.38886 9 PRDM16 6.483381 0.091315 71 SND1 3.135897 0.348433 9 HDAC4 9.176118 0.248003 37 ATP11A 3.070559 0.341173 9 PAX6 4.84465 0.138419 35 TSPAN9 2.635775 0.292864 9 RBFOX3 3.097536 0.088501 35 ADAMTS2 1.980199 0.220022 9 DIP2C 2.944686 0.092021 32 AXIN2 1.953341 0.217038 9 SOX2-OT 2.029367 0.069978 29 CACNA2D4 1.774977 0.19722 9 ADARB2 3.327972 0.127999 26 RORA 2.616519 0.327065 8 AGAP1 3.839349 0.153574 25 MECOM 2.340984 0.292623 8 CAMTAI 2.746112 0.109844 25 DLEU1 1.911684 0.23896 8

PDGFRA 2.255211 0.090208 25 NAVI 3.138964 0.448423 7 MEIS1 1.598138 0.066589 24 ITPK1 1.998211 0.285459 7 INPP5A 2.40014 0.104354 23 PITPNC1 1.855749 0.265107 7 RPTOR 2.183762 0.094946 23 TACC2 1.700116 0.242874 7 RIMBP2 1.61115 0.07005 23 LHX2 1.65945 0.237064 7 PRKCZ 2.212583 0.100572 22 TAFA2 1.624949 0.232136 7 SKI 5.060951 0.240998 21 Clorf94 1.615256 0.230751 7 ZIC4 1.665744 0.079321 21 FBXL18 2.981045 0.496841 6 SDK1 4.455208 0.22276 20 LRRFIP1 2.215662 0.369277 6 FRMD4A 2.685163 0.134258 20 SLC22A18AS 1.734773 0.289129 6 MAD1L1 5.054423 0.266022 19 DENND3 1.704292 0.284049 6

ZNF423 3.168039 0.166739 19 FAM181A 1.650962 0.27516 6 FOXK1 3.297041 0.183169 18 PTPRG 1.649666 0.274944 6 RBFOX1 2.862815 0.159045 18 PRR5L 2.98927 0.597854 5 SEPTIN9 2.798426 0.155468 18 RUNDC3A 2.945389 0.589078 5 OPCML 2.522854 0.148403 17 AP2A2 2.327598 0.46552 5 NAV2 3.636725 0.227295 16 TSNAX-DISC1 2.19047 0.438094 5 NRCAM 1.974293 0.394859 5 AGAP1 9.977599 0.399104 25 VAV2 1.700459 0.340092 5 CAMTAI 7.307139 0.292286 25 TENM4 3.56457 0.891143 4 PDGFRA 5.039539 0.201582 25 CRB2 2.560978 0.640245 4 SATB2 6.869827 0.286243 24 VOPP1 2.342273 0.585568 4 MEIS1 4.057547 0.169064 24 HK1 1.730885 0.432721 4 RPTOR 10.63516 0.462398 23 GCK 3.08857 1.029523 3 NCOR2 8.031995 0.349217 23 PLXNC1 2.249994 0.749998 3 RIMBP2 6.227338 0.270754 23 LRP2 2.090608 0.696869 3 INPP5A 4.07289 0.177082 23 SLC6A9 1.844057 0.614686 3 PRKCZ 6.578606 0.299028 22

ZNF536 1.688754 0.562918 3 SKI 12.54878 0.597561 21 GRIN2B 1.622195 0.540732 3 FRMD4A 6.162069 0.308103 20 DAGLB 1.612261 0.53742 3 ABR 5.331761 0.266588 20 SLC25A10 3.804738 1.902369 2 CASZ1 12.44942 0.655233 19 CHTF18 1.796626 0.898313 2 ZNF423 10.80291 0.568574 19 ANKLE2 1.796345 0.898173 2 MAD1L1 10.12687 0.532993 19 PDE4D 1.670607 0.835304 2 SMG1P2 5.095489 0.268184 19 MLLT1 1.607184 0.803592 2 BOLA2 5.095489 0.268184 19 ZIC5 1.603097 0.801548 2 LOC613038 5.095489 0.268184 19

RABGAP1L 2.233425 2.233425 1 FOXK1 5.739273 0.318849 18 RNF4 2.181539 2.181539 1 SEPTIN9 5.347458 0.297081 18 ACAD10 2.071929 2.071929 1 ANKRD11 4.533038 0.251835 18 C10orfl05 1.897344 1.897344 1 TBC1D16 4.319345 0.239964 18 GRTP1 1.739101 1.739101 1 OPCML 7.479851 0.439991 17 DPY19L1P1 1.654306 1.654306 1 PAX6-AS1 4.50606 0.265062 17

RCN1 4.50606 0.265062 17

TABLE 48: Cancer Type FOXP1 5.290618 0.330664 16 EPN_RELA_Like_A

EBF3 4.42745 0.276716 16

Gene site imp sum imp mean n GLI2 8.995217 0.599681 15 PTPRN2 18.96264 0.231252 82 BAIAP2 5.866399 0.391093 15 PRDM16 17.17107 0.241846 71 KIRREL3 5.358859 0.357257 15 PCDHGA1 5.310824 0.090014 59 NHX 5.357149 0.357143 15 PCDHGA2 5.310824 0.093172 57 ZBTB20 4.91273 0.327515 15 PCDHGA3 5.310824 0.098349 54 CUX1 5.73646 0.409747 14 PCDHGB1 5.310824 0.100204 53 RPS6KA2 5.57806 0.398433 14 PCDHGA4 5.310824 0.104134 51 C7orf50 4.361648 0.311546 14 PCDHGB2 5.30142 0.108192 49 MSI2 6.655446 0.511957 13 PCDHGA5 4.567029 0.097171 47 MYT1L 4.804576 0.369583 13

PCDHGB3 4.250643 0.098852 43 KIF26B 4.424066 0.340313 13 HDAC4 10.24476 0.276885 37 CLYBL 4.212438 0.324034 13 PAX6 11.80595 0.337313 35 GSE1 3.963453 0.304881 13 RBFOX3 6.523044 0.186373 35 ZC3H3 6.297857 0.524821 12 DIP2C 8.912799 0.278525 32 CMIP 5.610394 0.467533 12 PCDHGA9 4.120281 0.132912 31 TNS3 5.516677 0.459723 12 SOX2-OT 6.262955 0.215964 29

MIRLET7BHG 5.302931 0.441911 12 GALNT9 4.782744 0.177139 27

MAML3 4.779313 0.398276 12 ADARB2 6.83138 0.262745 26

ZC3H12D 5.434465 0.494042 11 SHANK2 5.402521 0.207789 26

SPON2 4.314227 0.392202 11 CTBP2 4.268214 0.388019 11 DIP2C 8.212701 0.256647 32 ACOT7 5.073081 0.507308 10 SHANK2 3.83591 0.147535 26 AKAP13 4.530778 0.453078 10 AGAP1 5.482431 0.219297 25 IGF1R 3.911352 0.391135 10 PDGFRA 3.144052 0.125762 25 SND1 5.767781 0.640865 9 CAMTAI 2.660756 0.10643 25 ATP11A 5.572346 0.61915 9 RPTOR 7.607041 0.330741 23 ASAP1 5.194917 0.577213 9 NXN 4.617392 0.200756 23 KCNH2 5.12592 0.569547 9 NCOR2 4.450173 0.193486 23 ADAMTS2 4.690699 0.521189 9 RIMBP2 3.898763 0.169511 23 KAZN 4.625799 0.513978 9 INPP5A 2.501373 0.108755 23 GPC6 4.588583 0.509843 9 SKI 6.831676 0.325318 21 SLC22A18 4.576435 0.508493 9 FRMD4A 4.473011 0.223651 20 TSPAN9 4.438942 0.493216 9 MAD1L1 7.081564 0.372714 19 NOTCH 1 4.239227 0.471025 9 ZNF423 3.653806 0.192306 19 PACS2 4.130017 0.458891 9 SMG1P2 3.167207 0.166695 19 TRAPPCI 2 4.122235 0.458026 9 BOLA2 3.167207 0.166695 19 LHX4 5.440953 0.680119 8 LOC613038 3.167207 0.166695 19 DLEU1 5.334289 0.666786 8 CASZ1 2.518759 0.132566 19 MSRA 4.729411 0.591176 8 ANKRD11 3.711528 0.206196 18 LINC00311 4.337721 0.542215 8 FOXK1 3.303989 0.183555 18 NRXN1 3.917334 0.489667 8 SEPTIN9 2.365648 0.131425 18 PPP2R2B 3.884981 0.485623 8 TBX15 3.629988 0.213529 17 KDM4B 4.077813 0.679636 6 OPCML 2.793909 0.164348 17 SLC22A18AS 3.993219 0.665537 6 FOXP1 3.316318 0.20727 16 RUNDC3A 4.824861 0.964972 5 SORBS2 3.060689 0.191293 16 KLHL25 4.759709 0.951942 5 NAV2 2.448398 0.153025 16 ARHGEF7 4.23351 0.846702 5 BAIAP2 6.727984 0.448532 15 TSN AX-DISCI 4.134782 0.826956 5 GLI2 5.852392 0.390159 15 CACNA1I 4.067336 0.813467 5 LRMDA 4.396956 0.29313 15 RAPGEF4 3.934423 0.786885 5 SLX1B- SULT1A4 2.601392 0.173426 15 NDST1 4.171788 1.042947 4

SLX1A 2.601392 0.173426 15 RBMS3 4.018327 1.004582 4 ANKLE2 3.901795 1.950898 2 LOC606724 2.601392 0.173426 15

RPS6KA2 4.913033 0.350931 14 BLE 49: Can C7orf50 3.631155 0.259368 14

TA cer Type

EPN_RELA_Like_B PRKAG2 3.252456 0.232318 14

Gene site imp sum imp mean n IQSEC1 3.004048 0.214575 14 PTPRN2 8.75796 0.106804 82 MOB2 2.55029 0.182164 14 PRDM16 7.915152 0.111481 71 ARHGEF10 2.468803 0.176343 14 PCDHGA1 3.231128 0.054765 59 MYT1L 3.298473 0.253729 13 PCDHGA2 2.914742 0.051136 57 MSI2 3.141991 0.241692 13 PCDHGA3 2.598356 0.048118 54 MIR9-3HG 2.575378 0.198106 13 PCDHGB1 2.598356 0.049026 53 GSE1 2.523138 0.194088 13 PCDHGB2 2.598356 0.053028 49 MIRLET7BHG 4.024739 0.335395 12 PCDHGA5 2.598356 0.055284 47 ZC3H3 3.214761 0.267897 12 HDAC4 7.807278 0.211008 37 GNA12 3.054646 0.254554 12 PAX6 4.474432 0.127841 35 CTNNA2 2.852575 0.237715 12 RBFOX3 3.785148 0.108147 35 RAD51B 3.708749 0.337159 11 FGFR2 3.340094 0.303645 11 HDAC4 9.792911 0.264673 37 COL4A1 2.802232 0.254748 11 RBFOX3 6.142056 0.175487 35 ZC3H12D 2.63323 0.239385 11 PAX6 3.959655 0.113133 35 VGLL4 2.392066 0.217461 11 DIP2C 4.684374 0.146387 32 TSPAN4 3.385127 0.338513 10 ADARB2 4.939766 0.189991 26

NR2F1-AS1 3.342812 0.334281 10 SHANK2 4.853133 0.186659 26

FMN1 2.947547 0.294755 10 AGAP1 6.329685 0.253187 25

MAML2 2.479743 0.247974 10 CAMTAI 4.983153 0.199326 25

BCL11B 2.467026 0.246703 10 PDGFRA 2.555145 0.102206 25

CHST11 2.421051 0.242105 10 SATB2 3.113167 0.129715 24

AXIN2 4.265368 0.47393 9 MEIS1 3.111077 0.129628 24

SND1 3.882986 0.431443 9 NXN 5.39116 0.234398 23

ADAMTS2 3.725764 0.413974 9 NCOR2 5.355784 0.23286 23

TSPAN9 3.365121 0.373902 9 RPTOR 5.185304 0.225448 23 CACNA2D4 2.791138 0.310126 9 HOXB3 3.364127 0.146266 23 NOTCH 1 2.699126 0.299903 9 PRKCZ 3.297049 0.149866 22 MGMT 2.630872 0.292319 9 SKI 4.176808 0.198896 21 APBA2 2.434052 0.27045 9 FRMD4A 5.580653 0.279033 20

ASPSCR1 4.941479 0.617685 8 SDK1 3.758879 0.187944 20

MSRA 3.824307 0.478038 8 MAD1L1 7.932548 0.417503 19

LHX4 3.00188 0.375235 8 CASZ1 4.757279 0.250383 19 LINC00311 2.710199 0.338775 8 SMG1P2 3.795435 0.19976 19 DLEU1 2.410444 0.301305 8 BOLA2 3.795435 0.19976 19 LINC01140 3.236537 0.462362 7 LOC613038 3.795435 0.19976 19

PCCA 3.02946 0.43278 7 KCNQ1 2.95117 0.155325 19

GAK 2.700565 0.385795 7 CFAP46 2.64123 0.139012 19 C19orf25 2.678181 0.382597 7 FOXK1 5.958762 0.331042 18 LTF 2.474482 0.353497 7 TBC1D16 4.918471 0.273248 18

LHPP 2.438772 0.348396 7 RBFOX1 2.685368 0.149187 18

NAVI 2.355967 0.336567 7 OPCML 3.268202 0.192247 17

FBXL18 3.275702 0.54595 6 FOXP1 3.996994 0.249812 16 CCDC177 2.894281 0.48238 6 SORBS2 2.610353 0.163147 16 COLECI 1 2.46434 0.410723 6 GLI2 5.057189 0.337146 15 RUNDC3A 3.816148 0.76323 5 EMX2OS 3.637625 0.242508 15

KLHL25 2.869872 0.573974 5 BAIAP2 3.481082 0.232072 15

TK1 2.503038 0.500608 5 NHX 2.913399 0.194227 15

EXPH5 2.46519 0.493038 5 SLX1B- SULT1A4 2.7081 0.18054 15

DICER1 3.31541 1.105137 3 SLX1A 2.7081 0.18054 15

SLC6A9 2.771843 0.923948 3 LOC606724 2.7081 0.18054 15 SLC25A10 2.783936 1.391968 2 IQSEC1 4.665443 0.333246 14 CHTF18 2.668617 1.334309 2 ANKLE2 2.628808 1.314404 2 RPS6KA2 2.90531 0.207522 14 CUX1 2.806976 0.200498 14

Cancer Ty PRKAG2 2.420803 0.172914 14

TABLE 50: pe

EPN_RELA_Like_C GSE1 3.850896 0.296223 13

Gene site imp sum imp mean n KIF26B 2.904956 0.223458 13 PTPRN2 6.388245 0.077905 82 GNA12 4.711493 0.392624 12 PRDM16 10.31376 0.145264 71 TNS3 3.09073 0.257561 12 MAML3 3.052777 0.254398 12 EOGT 2.409254 1.204627 2 ZC3H3 2.892362 0.24103 12 ANKLE2 2.372184 1.186092 2 MEIS2 2.876291 0.239691 12 SLC25A10 2.366913 1.183456 2 CMIP 2.785533 0.232128

ADGRD1 2.366348 0.197196 12

TABLE 51: Cancer Type EPN_SPINE ZC3H12D 3.563105 0.323919 11 Gene site imp sum imp mean n RAD51B 3.110039 0.282731 11 PTPRN2 18.00703 0.219598 82 ANAPC16 2.658012 0.241637 11 PRDM16 21.20807 0.298705 71 VGLL4 2.586591 0.235145 11 HDAC4 13.01415 0.351734 37 GAS7 3.014548 0.301455 10 PAX6 7.995691 0.228448 35 NR2F1-AS1 3.000588 0.300059 10 RBFOX3 6.970477 0.199156 35 IGF1R 2.660186 0.266019 10 DIP2C 10.38479 0.324525 32 TSPAN4 2.382834 0.238283 10 SOX2-OT 6.360972 0.219344 29 SND1 4.833414 0.537046 9 GALNT9 5.328999 0.19737 27 AXIN2 3.035017 0.337224 9 SHANK2 5.135682 0.197526 26 TRAPPCI 2 2.80405 0.311561 9 AGAP1 8.123733 0.324949 25 KCNH2 2.51942 0.279936 9 CAMTAI 6.624628 0.264985 25 APBA2 2.505382 0.278376 9 SATB2 4.364773 0.181866 24 KAZN 2.494474 0.277164 9 NCOR2 8.301598 0.360939 23 KCNMA1 2.478381 0.275376 9 RPTOR 8.110635 0.352636 23 ADAMTS2 2.473136 0.274793 9 RIMBP2 4.596968 0.199868 23 LHX4 3.455697 0.431962 8 PRKCZ 4.078898 0.185404 22 DNMT3A 3.206566 0.400821 8 SKI 10.08525 0.48025 21 VRK2 2.814389 0.351799 8 ZIC4 3.955615 0.188363 21 TRAPPC9 2.399386 0.299923 8 SDK1 5.291577 0.264579 20 C19orf25 3.027249 0.432464 7 ABR 4.94313 0.247157 20 NAVI 2.631295 0.375899 7 FRMD4A 4.534796 0.22674 20 WWOX 2.430468 0.34721 7 MAD1L1 10.78786 0.567782 19 AGO2 2.369084 0.338441 7 CASZ1 7.839378 0.412599 19 FBXL18 3.322959 0.553826 6 ZNF423 7.19556 0.378714 19 SLC22A18AS 3.084762 0.514127 6 SMG1P2 5.068935 0.266786 19 STRA6 2.641257 0.440209 6 BOLA2 5.068935 0.266786 19 C10orf90 2.622107 0.437018 6 LOC613038 5.068935 0.266786 19 COQ8A 2.579869 0.429978 6 SEPTIN9 6.749913 0.374995 18 CCDC177 2.55317 0.425528 6 TBC1D16 6.521948 0.36233 18 COLECI 1 2.525162 0.42086 6 RBFOX1 5.168689 0.287149 18 STK10 2.519602 0.419934 6 FOXK1 3.802433 0.211246 18 NUMA1 2.423579 0.40393 6 ANKRD11 3.67135 0.203964 18 ARHGEF7 3.999335 0.799867 5 OPCML 8.390771 0.493575 17 CACNA1I 3.715944 0.743189 5 NAV2 5.952011 0.372001 16 TK1 2.677213 0.535443 5 FOXP1 5.76391 0.360244 16 SDK2 2.515765 0.503153 5 EBF3 4.427132 0.276696 16 DTNA 2.688749 0.672187 4 SORBS2 4.389494 0.274343 16 DICER1 2.758525 0.919508 3 BAIAP2 5.503545 0.366903 15 SLC6A9 2.689165 0.896388 3 GLI2 5.395369 0.359691 15 DAGLB 2.676754 0.892251 3 LRMDA 4.368683 0.291246 15 SLC25A22 2.467789 0.822596 3 NHX 4.022804 0.268187 15 SOXIO 2.415804 1.207902 2 SLX1B- LRRFIP1 3.599162 0.59986 6 SULT1A4 3.543156 0.23621 15

FAM181A 3.597879 0 .599647 6 SLX1A 3.543156 0.23621 15

DENND3 3.442411 0 .573735 6 LOC606724 3.543156 0.23621 15

TSNAX-DISC1 4.470584 0 .894117 5 RPS6KA2 7.164354 0.51174 14

BCAR1 4.298052 0 .85961 5 CUX1 6.139501 0.438536 14

RUNDC3A 3.826841 0 .765368 5 C7orf50 3.830526 0.273609 14

PRR5L 3.551702 0 .71034 5 MIR548F5 3.805653 0.271832 14

VOPP1 3.871078 0 .967769 4 IQSEC1 3.699906 0.264279 14

DINA 3.738399 0 .9346 4 MSI2 6.515431 0.501187 13

CCDC167 3.465475 1 .155158 3 GSE1 6.362104 0.489393 13

SLC25A10 4.701776 2 .350888 2 RFX4 5.069011 0.389924 13 ANKLE2 3.649509 1 .824755 2 KIF26B 4.02975 0.309981 13 CLYBL 3.93966 0.303051 13

TABLE 52: Cancer Type ZC3H3 5.204388 0.433699 12 EPN_SPINE_MYCN MEIS2 4.613977 0.384498 12 Gene site imp sum imp mean n FBRSL1 4.582972 0.381914 12 PTPRN2 6.492942 0.079182 82 MEGF6 4.143271 0.345273 12 PRDM16 7.7856 0.109656 71 TNS3 3.906942 0.325579 12 PCDHGA1 3.162956 0.053609 59 MAML3 3.574924 0.29791 12 PCDHGA2 3.162956 0.05549 57 TBX4 3.547774 0.295648 12 PCDHGA3 3.162956 0.058573 54 ZC3H12D 6.090734 0.553703 11 PCDHGB1 3.162956 0.059678 53 VGLL4 4.273837 0.388531 11 PCDHGA4 3.162956 0.062019 51 SPON2 3.617264 0.328842 11 PCDHGB2 2.84657 0.058093 49 AKAP13 5.145785 0.514579 10 PCDHGA5 2.530184 0.053834 47 TSPAN4 3.955269 0.395527 10 PCDHGB3 2.530184 0.058841 43 NR2F1-AS1 3.943816 0.394382 10 PCDHGA6 2.530184 0.063255 40 ADGRA1 3.563767 0.356377 10 HDAC4 5.62407 0.152002 37 GAS7 3.524642 0.352464 10 PCDHGA7 2.530184 0.068383 37 SH3RF3 3.482212 0.348221 10 PAX6 3.986653 0.113904 35 SND1 5.671606 0.630178 9 PCDHGB4 2.530184 0.072291 35 TSPAN9 5.566682 0.61852 9 PCDHGA8 2.530184 0.072291 35 ATP11A 4.94636 0.549596 9 DIP2C 4.775328 0.149229 32 CACNA2D4 4.67568 0.51952 9 PCDHGB5 2.213798 0.069181 32 ADAMTS2 4.189157 0.465462 9 PCDHGA9 2.213798 0.071413 31 KCNH2 3.889556 0.432173 9 GALNT9 3.892155 0.144154 27 AXIN2 3.837607 0.426401 9 AGAP1 3.423555 0.136942 25 NOTCH 1 3.620084 0.402232 9 CAMTAI 3.338662 0.133546 25 MGMT 3.598229 0.399803 9 SATB2 4.430963 0.184623 24 ASAP1 3.584188 0.398243 9 RPTOR 3.916326 0.170275 23 MSRA 5.061993 0.632749 8 SKI 5.889311 0.280443 21 LHX4 4.717822 0.589728 8 HOXA-AS3 3.973196 0.1892 21 MCC 4.073128 0.509141 8 ZIC4 3.270454 0.155736 21 DLEU1 3.720543 0.465068 8 SIM2 2.463619 0.117315 21 NAVI 4.946607 0.706658 7 ABR 2.43546 0.121773 20 CXXC5 3.859552 0.551365 7 FRMD4A 2.285651 0.114283 20 VPS 13D 3.741837 0.534548 7 MAD1L1 4.81219 0.253273 19 SLC22A18AS 4.24026 0.70671 6 SMG1P2 4.027895 0.211994 19 BOLA2 4.027895 0.211994 19 RXRA 2.105226 0.300747 7 LOC613038 4.027895 0.211994 19 ARHGAP45 3.123683 0.520614 6 ZNF423 3.885737 0.204512 19 FBXL18 2.962263 0.49371 6 FOXK1 2.547433 0.141524 18 SATB2-AS1 2.871803 0.478634 6 SIM1 5.801214 0.341248 17 LRRFIP1 2.353039 0.392173 6 OPCML 3.150464 0.185321 17 PRR5L 3.2963 0.65926 5 TBX15 2.737097 0.161006 17 BCAR1 2.164598 0.43292 5 FOXP1 2.73496 0.170935 16 RUNDC3A 2.065769 0.413154 5 NAV2 2.531088 0.158193 16 VOPP1 2.76606 0.691515 4 GLI2 4.935341 0.329023 15 OLFM1 2.67317 0.668293 4 EMX2OS 2.453844 0.16359 15 CRB2 2.620027 0.655007 4 SLX1B- GABRB3 2.614687 0.653672 4 SULT1A4 2.134165 0.142278 15

LAIR1 2.532069 0.633017 4

SLX1A 2.134165 0.142278 15

DINA 2.209257 0.552314 4 LOC606724 2.134165 0.142278 15

RBMS3 2.174902 0.543726 4 BAIAP2 2.043891 0.136259 15

NDST1 2.090472 0.522618 4 GNG7 2.455568 0.175398 14

BCAT1 2.182819 0.727606 3 RPS6KA2 2.230171 0.159298 14

SLC25A10 3.725949 1.862975 2 MSI2 2.807782 0.215983 13

HNF1B 2.452458 1.226229 2 CLYBL 2.577345 0.198257 13 ACMSD 2.983202 2.983202 1 MYT1L 2.571778 0.197829 13 AC ADI 0 2.098791 2.098791 1 FBRSL1 3.083854 0.256988 12 ZC3H3 2.408241 0.200687 12

TABLE 53: Cancer Type MAML3 2.168862 0.180739 12 EPN_SPINE_SE_A

ADGRD1 2.073695 0.172808 12 Gene site imp sum imp mean n ZC3H12D 4.179757 0.379978 11 PTPRN2 4.467313 0.054479 82 RAD51B 2.468987 0.224453 11 PRDM16 5.538834 0.078012 71 PITX2 3.85557 0.385557 10 HDAC4 7.257234 0.196141 37 ADGRA1 3.749056 0.374906 10 RBFOX3 2.086675 0.059619 35 TFAP2B 3.226117 0.322612 10 DIP2C 2.523885 0.078871 32 ACOT7 3.181729 0.318173 10 GALNT9 1.812383 0.067125 27 NR2F1-AS1 2.785737 0.278574 10 ADARB2 2.334298 0.089781 26 FMN1 2.142563 0.214256 10 AGAP1 3.980887 0.159235 25 ATP11A 3.665884 0.40732 9 CAMTAI 3.968255 0.15873 25 SND1 3.162725 0.351414 9 SATB2 4.287825 0.178659 24 SLC22A18 2.776862 0.30854 9 RPTOR 3.996189 0.173747 23

IGF2BP1 2.408029 0.267559 9 HOXB3 2.9538 0.128426 23 RUNX1 2.348507 0.260945 9 RIMBP2 2.65345 0.115367 23 AXIN2 2.162687 0.240299 9 NXN 2.034489 0.088456 23 TSPAN9 2.115413 0.235046 9 SKI 4.451828 0.211992 21 AFF3 2.968832 0.371104 8 HOXA-AS3 3.749579 0.178551 21 KIF26A 2.695016 0.336877 8 ZIC4 3.199024 0.152334 21 MSRA 2.471942 0.308993 8 SIM2 2.58685 0.123183 21 DLEU1 2.059775 0.257472 8 ZNF423 4.318873 0.227309 19 Clorf94 2.594722 0.370675 7 MAD1L1 4.199438 0.221023 19

DUSP6 2.369086 0.338441 7 SMG1P2 2.016115 0.106111 19 CLDN10 2.333333 0.333333 7 BOLA2 2.016115 0.106111 19 TRIM2 2.136209 0.305173 7 LOC613038 2.016115 0.106111 19 CASZ1 1.898316 0.099911 19 LINC00311 1.875494 0.234437 8

SEPTIN9 1.764349 0.098019 18 ESRRG 1.771343 0.221418 8

TBX15 2.619569 0.154092 17 DUSP6 2.976121 0.42516 7

OPCML 2.460219 0.144719 17 LHX2 2.213486 0.316212 7

FOXP1 3.242841 0.202678 16 NAVI 1.787504 0.255358 7

EBF3 1.824205 0.114013 16 FAM181A 2.321552 0.386925 6

GLI2 5.087536 0.339169 15 SLC22A18AS 1.888411 0.314735 6

DLX6-AS1 2.789193 0.185946 15 PRR5L 2.943148 0.58863 5

EMX2OS 2.767121 0.184475 15 RUNDC3A 2.868566 0.573713 5

BAIAP2 2.224255 0.148284 15 PDE4B 2.389689 0.477938 5

NFATC1 1.776564 0.118438 15 HOXB6 2.066272 0.413254 5

CUX1 2.87708 0.205506 14 GRIP1 1.946106 0.389221 5

RPS6KA2 2.122895 0.151635 14 KLHL25 1.92724 0.385448 5

IQSEC1 1.951216 0.139373 14 ARHGEF7 1.839995 0.367999 5

MSI2 2.877331 0.221333 13 MCPH1 1.778196 0.355639 5

MYT1L 2.671325 0.205487 13 CRB2 1.947448 0.486862 4

CLYBL 2.480584 0.190814 13 DINA 1.876086 0.469022 4

RFX4 2.104561 0.161889 13 GATA6 1.875189 0.468797 4

ZC3H3 2.345505 0.195459 12 PPM1H 1.766097 0.441524 4

MIRLET7BHG 2.116093 0.176341 12 GRIN2B 1.967055 0.655685 3

CMIP 2.115268 0.176272 12 DICER1 1.860287 0.620096 3

FBRSL1 2.040479 0.17004 12 SLC25A10 3.588723 1.794361 2

MEGF6 1.757851 0.146488 12 ANKLE2 2.019678 1.009839 2

RAD51B 1.854509 0.168592 11 SOXIO 2.000114 1.000057 2

NR2F1-AS1 2.721518 0.272152 10 ACMSD 3.011469 3.011469 1

ACOT7 2.542456 0.254246 10 GRTP1 2.569028 2.569028 1

EBF1 2.314205 0.23142 10 ACAD10 2.131225 2.131225 1

PITX2 2.081013 0.208101 10 C10orfl05 1.947394 1.947394 1

NR5A2 2.038764 0.203876 10 AK1 1.790931 1.790931 1

BCL11B 2.027634 0.202763 10

TFAP2B 1.983684 0.198368 10 TABLE 54: Cancer Type EPN_SPINE_SE_B

SPPL2B 1.761071 0.176107 10 Gene site imp sum imp mean n

ATP11A 3.269095 0.363233 9 PTPRN2 20.69829 0.252418 82

RUNX1 2.889558 0.321062 9 PRDM16 22.9618 0.323406 71

SLC22A18 2.832039 0.314671 9 PCDHGA1 4.918729 0.083368 59

SND1 2.665522 0.296169 9 PCDHGA2 4.918729 0.086293 57

KAZN 2.529525 0.281058 9 PCDHGA3 4.198881 0.077757 54

AXIN2 2.34763 0.260848 9 PCDHGB1 4.198881 0.079224 53

TRAPPCI 2 2.32492 0.258324 9 PCDHGA4 4.198881 0.082331 51

ADAMTS2 2.039645 0.226627 9 PCDHGB2 4.198881 0.085691 49

NOTCH 1 1.991879 0.22132 9 PCDHGA5 4.198881 0.089338 47

TSPAN9 1.824324 0.202703 9 HDAC4 14.31085 0.38678 37

GATA4 2.510676 0.313834 8 PAX6 13.13532 0.375295 35

AFF3 2.510149 0.313769 8 RBFOX3 9.970229 0.284864 35

MSRA 2.302983 0.287873 8 DIP2C 11.0602 0.345631 32

DLEU1 2.271895 0.283987 8 SOX2-OT 11.19802 0.386139 29

RORA 2.087459 0.260932 8 GALNT9 7.862966 0.291221 27

PPP2R2B 1.904162 0.23802 8 ADARB2 6.067064 0.233349 26 SHANK2 5.873276 0.225895 26 GSE1 4.684687 0.360361 13 AGAP1 10.57591 0.423036 25 ZC3H3 6.255098 0.521258 12 CAMTAI 6.217241 0.24869 25 MIRLET7BHG 5.826241 0.48552 12 SATB2 7.490794 0.312116 24 TNS3 5.309675 0.442473 12 MEIS1 4.165291 0.173554 24 CMIP 4.284207 0.357017 12 RPTOR 9.826889 0.427256 23 RASA3 4.172052 0.347671 12 NCOR2 9.548187 0.415139 23 ZC3H12D 6.618574 0.601689 11 INPP5A 5.536311 0.240709 23 SPON2 5.097684 0.463426 11 NXN 5.318387 0.231234 23 RAD51B 4.824109 0.438555 11 PRKCZ 6.740115 0.306369 22 GLUD1P2 4.273133 0.388467 11 SKI 13.02695 0.620331 21 VGLL4 4.240334 0.385485 11 ZIC4 4.943805 0.235419 21 ACOT7 4.858882 0.485888 10 ABR 7.6722 0.38361 20 NR2F1-AS1 4.800457 0.480046 10 FRMD4A 5.546184 0.277309 20 SH3RF3 4.580332 0.458033 10 SDK1 5.284686 0.264234 20 ADGRA1 4.305105 0.43051 10 MAD1L1 13.03724 0.686171 19 ATP11A 5.885514 0.653946 9 ZNF423 10.79861 0.568348 19 SND1 5.659362 0.628818 9 CASZ1 7.383993 0.388631 19 ADAMTS2 5.246017 0.582891 9 SMG1P2 5.64007 0.296846 19 CACNA2D4 4.926576 0.547397 9 BOLA2 5.64007 0.296846 19 RUNX1 4.681661 0.520185 9 LOC613038 5.64007 0.296846 19 TSPAN9 4.67902 0.519891 9 FOXK1 9.325676 0.518093 18 GPC6 4.339458 0.482162 9 SEPTIN9 8.261632 0.45898 18 LHX4 6.731641 0.841455 8 ANKRD11 5.545247 0.308069 18 MSRA 5.131543 0.641443 8

OPCML 7.962893 0.468405 17 ESRRG 4.630434 0.578804 8 TBX15 6.519563 0.383504 17 LINC00311 4.613139 0.576642 8 PAX6-AS1 5.623303 0.330783 17 DLEU1 4.323018 0.540377 8 RCN1 5.623303 0.330783 17 SHROOM3 4.278519 0.534815 8 SIM1 5.056549 0.297444 17 AFF3 4.217597 0.5272 8 FOXP1 4.704797 0.29405 16 DUSP6 6.489698 0.9271 7 NAV2 4.700894 0.293806 16 FBXL18 4.174415 0.695736 6 EBF3 4.510004 0.281875 16 RUNDC3A 4.871555 0.974311 5 GLI2 10.22611 0.68174 15 PRR5L 4.840978 0.968196 5 BAIAP2 6.583023 0.438868 15 ARHGEF7 4.4663 0.89326 5 KIRREL3 5.563956 0.37093 15 TSNAX-DISC1 4.405841 0.881168 5 ZBTB20 5.414394 0.36096 15 GRIN2B 4.152206 1.384069 3 NHX 4.357147 0.290476 15 SLC25A10 4.592854 2.296427 2 SLX1B- SULT1A4 4.258454 0.283897

TABLE 55: Cancer Type EPN_ST_ND_A SLX1A 4.258454 0.283897 15

Gene site imp sum imp mean n LOC606724 4.258454 0.283897 15

PTPRN2 12.57252 0.153323 82 RPS6KA2 7.391291 0.527949 14

PRDM16 14.33 0.201831 71 PRKAG2 5.627199 0.401943 14

HDAC4 6.155105 0.166354 37 IQSEC1 4.523432 0.323102 14

PAX6 7.762951 0.221799 35 CUX1 4.38091 0.312922 14

RBFOX3 7.065691 0.201877 35 MSI2 7.917794 0.609061 13

DIP2C 6.741237 0.210664 32 RFX4 4.921662 0.378589 13

SOX2-OT 5.040501 0.17381 29 CLYBL 4.856825 0.373602

GALNT9 4.874103 0.180522 27 SHANK2 7.288743 0.280336 26 KIF26B 4.176009 0.321231 13 ADARB2 3.906834 0.150263 26 MYT1L 3.563742 0.274134 13 AGAP1 8.912881 0.356515 25 RFX4 3.506892 0.269761 13 CAMTAI 6.869073 0.274763 25 CLYBL 3.150749 0.242365 13 SATB2 7.694246 0.320594 24 GSE1 2.986001 0.229692 13 RPTOR 6.571293 0.285708 23 ZC3H3 5.844641 0.487053 12 INPP5A 4.421946 0.192259 23 TBX4 4.403033 0.366919 12 HOXB3 4.358938 0.189519 23 CMIP 4.392077 0.366006 12 RIMBP2 4.322707 0.187944 23 MEIS2 4.044825 0.337069 12 NCOR2 3.852216 0.167488 23 ADGRD1 4.029529 0.335794 12 PRKCZ 3.722508 0.169205 22 MIRLET7BHG 3.374908 0.281242 12 SKI 10.07708 0.479861 21 CTNNA2 3.246693 0.270558 12 ZIC4 5.448838 0.259468 21 FBRSL1 3.180878 0.265073 12 SDK1 5.266742 0.263337 20 VGLL4 3.209122 0.291738 11 FRMD4A 4.473937 0.223697 20 CACNA1C 3.104733 0.282248 11 ZNF423 8.417935 0.443049 19 RAD51B 2.936205 0.266928 11 MAD1L1 7.875587 0.414505 19 ACOT7 4.858651 0.485865 10 CASZ1 5.26721 0.277222 19 TP73 4.164661 0.416466 10 SMG1P2 3.458359 0.182019 19 NR2F1-AS1 3.072003 0.3072 10 BOLA2 3.458359 0.182019 19 AKAP13 2.999263 0.299926 10 LOC613038 3.458359 0.182019 19 ATP11A 5.059425 0.562158 9 FOXK1 5.183399 0.287967 18 SLC22A18 3.856998 0.428555 9 SEPTIN9 3.899176 0.216621 18 SND1 3.819203 0.424356 9

TBC1D16 3.342098 0.185672 18 AS API 3.802296 0.422477 9 MCF2L 3.157815 0.175434 18 RUNX1 3.343484 0.371498 9 OPCML 6.224343 0.366138 17 KCNMA1 3.216977 0.357442 9 TBX15 4.049813 0.238224 17 GPC6 2.958499 0.328722 9 PAX6-AS1 3.515691 0.206805 17 NOTCH 1 2.939813 0.326646 9 RCN1 3.515691 0.206805 17 LHX4 4.780661 0.597583 8 FOXP1 5.086289 0.317893 16 DLEU1 3.942974 0.492872 8 NAV2 3.823901 0.238994 16 AFF3 3.384681 0.423085 8 SORBS2 3.076293 0.192268 16 RGS20 3.296919 0.412115 8 GLI2 9.932155 0.662144 15 NAVI 5.090853 0.727265 7 EMX2OS 5.669129 0.377942 15 RXRA 4.045082 0.577869 7 NHX 4.441511 0.296101 15 TBR1 2.920705 0.417244 7 KNDC1 4.432388 0.295493 15 FAM181A 3.697698 0.616283 6 COL23A1 3.876927 0.258462 15 SATB2-AS1 3.660097 0.610016 6 ZBTB20 3.583497 0.2389 15 FBXL18 3.282771 0.547129 6 NFATC1 3.473814 0.231588 15 PRR5L 3.713016 0.742603 5 BAIAP2 3.181375 0.212092 15 KLHL25 3.472346 0.694469 5 RPS6KA2 5.42418 0.387441 14 TSNAX-DISC1 3.321393 0.664279 5 CUX1 4.397781 0.314127 14 RAPGEF4 3.220098 0.64402 5 PRKAG2 4.248722 0.30348 14 PPM1H 3.137802 0.784451 4 IQSEC1 3.297421 0.23553 14 SLC25A10 3.874652 1.937326 2 ARHGEF10 3.263557 0.233111 14 C7orf50 3.069573 0.219255 14 TABLE 56: Cancer Type EPN_ST_SE MIR9-3HG 8.809367 0.677644 13 Gene site imp sum imp mean n MSI2 5.605766 0.431213 13 PTPRN2 14.78757 0.180336 82 PRDM16 21.56532 0.303737 71 MSI2 6.101907 0.469377 13 HDAC4 14.04352 0.379555 37 GSE1 4.626839 0.355911 13 PAX6 11.96461 0.341846 35 CLYBL 4.397876 0.338298 13 RBFOX3 8.961976 0.256056 35 MYT1L 3.996951 0.307458 13 DIP2C 9.608908 0.300278 32 ZC3H3 5.670165 0.472514 12 SOX2-OT 8.497969 0.293033 29 MIRLET7BHG 5.655803 0.471317 12 GALNT9 5.740494 0.212611 27 ADGRD1 5.166093 0.430508 12 SHANK2 7.465656 0.287141 26 TNS3 4.686673 0.390556 12 ADARB2 6.559737 0.252298 26 CMIP 4.095593 0.341299 12 AGAP1 10.25957 0.410383 25 MEGF6 3.732575 0.311048 12 CAMTAI 6.874727 0.274989 25 MEIS2 3.623368 0.301947 12 SATB2 5.351616 0.222984 24 ZC3H12D 6.265589 0.569599 11 RPTOR 9.819349 0.426928 23 RAD51B 4.404139 0.400376 11 NCOR2 7.440985 0.323521 23 ACOT7 5.071909 0.507191 10 RIMBP2 5.916542 0.257241 23 NR2F1-AS1 4.302561 0.430256 10 INPP5A 4.349603 0.189113 23 AKAP13 4.039456 0.403946 10 NXN 3.831029 0.166566 23 KLHL29 3.846141 0.384614 10 PRKCZ 7.157802 0.325355 22 ATP11A 6.082089 0.675788 9 SKI 11.11734 0.529397 21 SND1 5.90144 0.655716 9 ZIC4 7.164295 0.341157 21 ADAMTS2 4.881248 0.542361 9 FRMD4A 6.790834 0.339542 20 TRAPPCI 2 4.750428 0.527825 9 ABR 6.306589 0.315329 20 KAZN 4.663185 0.518132 9 SDK1 4.903644 0.245182 20 TSPAN9 4.24802 0.472002 9 MAD1L1 10.49204 0.552212 19 RUNX1 4.047814 0.449757 9

ZNF423 9.522139 0.501165 19 KCNH2 3.967067 0.440785 9 CASZ1 7.009732 0.368933 19 CACNA2D4 3.965026 0.440558 9 SMG1P2 5.475582 0.288189 19 AXIN2 3.583753 0.398195 9 BOLA2 5.475582 0.288189 19 LHX4 5.144101 0.643013 8 LOC613038 5.475582 0.288189 19 DLEU1 4.440199 0.555025 8 CFAP46 3.567633 0.18777 19 PPP2R2B 4.044065 0.505508 8 TBC1D16 6.460163 0.358898 18 AFF3 3.839932 0.479992 8 SEPTIN9 5.192761 0.288487 18 MACROD1 3.809808 0.476226 8 FOXK1 4.631213 0.25729 18 DNMT3A 3.694474 0.461809 8 ANKRD11 4.424428 0.245802 18 NAVI 4.94547 0.706496 7 MCF2L 4.332015 0.240667 18 LHX2 4.648095 0.664014 7 OPCML 6.841107 0.402418 17 RXRA 4.29991 0.614273 7 FOXP1 5.663108 0.353944 16 VPS13D 3.874879 0.553554 7 GLI2 9.954524 0.663635 15 PRKCA 3.697941 0.528277 7 BAIAP2 5.130543 0.342036 15 FBXL18 3.844834 0.640806 6 KIRREL3 4.407345 0.293823 15 FAM181A 3.81628 0.636047 6 ZBTB20 4.208394 0.28056 15 TSNAX-DISC1 4.644692 0.928938 5 NHX 4.099719 0.273315 15 BCAR1 4.280171 0.856034 5 KNDC1 3.923233 0.261549 15 PRR5L 4.19888 0.839776 5 RPS6KA2 7.055123 0.503937 14 ARHGEF7 4.134932 0.826986 5 CUX1 6.973338 0.498096 14 RUNDC3A 4.087877 0.817575 5 PRKAG2 4.477268 0.319805 14 RBMS3 3.986484 0.996621 4 ARHGEF10 3.99234 0.285167 14 DTNA 3.828958 0.957239 4 MIR548F5 3.628218 0.259158 14 PER2 3.731991 0.932998 4 GRIN2B 3.692311 1.23077 3 BAIAP2 5.11409 0.340939 15

SLC25A10 4.834545 2.417272 2 ZBTB20 4.871469 0.324765 15

ANKLE2 4.05317 2.026585 2 SLX1B- SULT1A4 4.493723 0.299582 15

TABLE 57: Cancer Type EPN_YAP SLX1A 4.493723 0.299582 15 LOC606724 ne site imp sum i 4.493723 0.299582 15

Ge mp mean n NFATC1 4.395464 0.293031 15 PTPRN2 17.23822 0.210222 82 RPS6KA2 7.801359 0.55724 14 PRDM16 23.42478 0.329926 71 CUX1 6.376211 0.455444 14 HDAC4 13.23857 0.357799 37 PRKAG2 4.412961 0.315211 14 PAX6 21.39254 0.611215 35 C7orf50 3.980788 0.284342 14 RBFOX3 8.512954 0.243227 35 MSI2 6.990942 0.537765 13 DIP2C 10.6073 0.331478 32 GSE1 5.00517 0.385013 13 SOX2-OT 8.977742 0.309577 29 KIF26B 4.903826 0.377217 13

GALNT9 4.167793 0.154363 27 MIR9-3HG 4.894567 0.376505 13 ADARB2 7.132454 0.274325 26 CLYBL 4.868188 0.374476 13 SHANK2 5.964185 0.229392 26 RFX4 4.587237 0.352864 13 AGAP1 9.353925 0.374157 25 HOXC4 4.101998 0.315538 13 CAMTAI 3.886305 0.155452 25 MYT1L 3.95988 0.304606 13 SATB2 4.927197 0.2053 24 ZC3H3 6.30355 0.525296 12 RPTOR 12.46487 0.541951 23 TNS3 6.209721 0.517477 12 NCOR2 6.643614 0.288853 23

MIRLET7BHG 5.903381 0.491948 12 NXN 6.426718 0.279423 23 CMIP 5.294991 0.441249 12 HOXB3 5.600506 0.2435 23 MEGF6 4.612604 0.384384 12 INPP5A 4.027074 0.17509 23 FBRSL1 4.480354 0.373363 12 PRKCZ 6.461544 0.293707 22 VGLL4 5.340595 0.485509 11 SKI 11.13937 0.530446 21 ZC3H12D 5.005365 0.455033 11 ZIC4 4.102191 0.195342 21 RAD51B 4.29177 0.390161 11 ABR 5.611227 0.280561 20 OTX1 6.165507 0.616551 10

FRMD4A 5.558851 0.277943 20 AKAP13 4.68413 0.468413 10 SDK1 5.420154 0.271008 20 TFAP2B 4.020065 0.402007 10 MAD1L1 12.4804 0.656863 19 SND1 7.518726 0.835414 9 ZNF423 9.830616 0.517401 19 ATP11A 6.410488 0.712276 9 CASZ1 6.916102 0.364005 19 ADAMTS2 5.174234 0.574915 9 SMG1P2 6.485867 0.341361 19 TSPAN9 4.678385 0.519821 9 BOLA2 6.485867 0.341361 19 AXIN2 4.671273 0.51903 9 LOC613038 6.485867 0.341361 19

TRAPPCI 2 4.393501 0.488167 9

FOXK1 6.577737 0.36543 18 KAZN 4.306895 0.478544 9 TBC1D16 6.131496 0.340639 18 KCNMA1 4.166196 0.462911 9 SEPTIN9 5.646419 0.31369 18 CACNA2D4 3.982821 0.442536 9 MCF2L 5.28324 0.293513 18 LHX4 6.347496 0.793437 8 ANKRD11 4.767949 0.264886 18 DLEU1 5.400273 0.675034 8 OPCML 7.682188 0.451893 17 MSRA 4.880909 0.610114 8 SIM1 4.439576 0.261152 17 SHROOM3 4.771151 0.596394 8 SORBS2 5.097365 0.318585 16 LINC00311 4.53473 0.566841 8

NAV2 4.854781 0.303424 16 DNMT3A 4.117827 0.514728 8 FOXP1 4.852911 0.303307 16 RORA 3.996121 0.499515 8 GLI2 9.641517 0.642768 15 AFF3 3.891856 0.486482 8 NHX 7.684088 0.512273 15 RBM20 5.579537 0.797077 7 HOXA-AS3 9.306106 0.443148 21 RXRA 4.454168 0.63631 7 ZIC4 5.248086 0.249909 21 IQCE 4.215459 0.602208 7 SIM2 3.686565 0.175551 21 VPS 13D 4.017275 0.573896 7 SDK1 8.255789 0.412789 20 TSN AX-DISCI 5.221409 1.044282 5 FRMD4A 6.886077 0.344304 20 RUNDC3A 4.872651 0.97453 5 MAD1L1 11.9611 0.629532 19 ARHGEF7 4.514927 0.902985 5 ZNF423 6.307225 0.331959 19 PRR5L 4.43245 0.88649 5 SMG1P2 5.356851 0.28194 19 RBMS3 4.741908 1.185477 4 BOLA2 5.356851 0.28194 19 SLC25A10 4.793721 2.39686 2 LOC613038 5.356851 0.28194 19 ANKLE2 4.123466 2.061733 2 CASZ1 4.610425 0.242654 19 KCNQ1 4.21475 0.221829 19

TABLE 58: Cancer Type ERMS FOXK1 8.336183 0.463121 18 Gene site imp sum imp mean n ANKRD11 6.822507 0.379028 18

PTPRN2 8.157355 0.09948 82 TBC1D16 5.973922 0.331885 18 PRDM16 11.39271 0.160461 71 HOXA3 5.746921 0.319273 18 PCDHGA1 8.984443 0.152279 59 NAV2 5.302418 0.331401 16 PCDHGA2 8.351671 0.146521 57 FOXP1 5.185945 0.324122 16 PCDHGA3 7.402513 0.137084 54 GLI2 8.091988 0.539466 15 PCDHGB1 7.402513 0.13967 53 BAIAP2 5.722795 0.38152 15 PCDHGA4 7.253907 0.142233 51 KIRREL3 5.255829 0.350389 15 PCDHGB2 6.868 0.140163 49 SLX1B- SULT1A4 3.715797 0.24772 15 PCDHGA5 6.868 0.146128 47 SLX1A 3.715797 0.24772 15 PCDHGB3 6.28895 0.146255 43 LOC606724 3.715797 0.24772 15 PCDHGA6 5.517177 0.137929 40 IQSEC1 5.40076 0.385769 14 HDAC4 16.77947 0.453499 37 PRKAG2 5.122928 0.365923 14 PCDHGA7 5.200791 0.140562 37

CUX1 4.153132 0.296652 14 RBFOX3 9.22086 0.263453 35 C7orf50 3.849158 0.27494 14 PCDHGB4 4.884405 0.139554 35 ARHGEF10 3.740174 0.267155 14 PCDHGA8 4.884405 0.139554 35 GSE1 6.060605 0.4662 13 PAX6 4.584193 0.130977 35

MSI2 5.450645 0.41928 13 DIP2C 9.570651 0.299083 32 SPTBN4 4.880376 0.375414 13 PCDHGB5 4.884405 0.152638 32 MYT1L 4.579967 0.352305 13 PCDHGA9 4.884405 0.157561 31

CMIP 6.003932 0.500328 12 PCDHGB6 4.390992 0.151414 29 ZC3H3 5.82491 0.485409 12 SOX2-OT 3.88906 0.134106 29 GNA12 4.70056 0.391713 12 PCDHGA10 4.074606 0.145522 28 MEGF6 4.407827 0.367319 12 SHANK2 5.327244 0.204894 26 ISLR2 4.095581 0.341298 12

ADARB2 3.73715 0.143737 26 FBRSL1 3.994867 0.332906 12 AGAP1 11.34168 0.453667 25 ADGRD1 3.92948 0.327457 12 CAMTAI 7.539154 0.301566 25

TBX4 3.823881 0.318657 12 PDGFRA 4.315036 0.172601 25 CCDC140 4.510849 0.410077 11 MEIS1 4.155988 0.173166 24 CTBP2 4.27095 0.388268 11 RPTOR 8.680668 0.37742 23 RAD51B 3.718315 0.338029 11 NCOR2 8.138623 0.353853 23 AKAP13 3.979031 0.397903 10 NXN 5.422937 0.23578 23 CHST11 3.892027 0.389203 10 HOXB3 3.751074 0.16309 23 SND1 7.687314 0.854146 9 SKI 9.53873 0.454225 21 ATP11A 6.268404 0.696489 9 ZNF423 5.361067 0.282161 19

ADAMTS2 4.508347 0.500927 9 CASZ1 3.016508 0.158764 19

ASAP1 4.452648 0.494739 9 FOXK1 5.642007 0.313445 18

CACNA2D4 4.447124 0.494125 9 TBC1D16 4.117118 0.228729 18

MGMT 4.085135 0.453904 9 ANKRD11 3.034833 0.168602 18

PACS2 3.727252 0.414139 9 HOXA3 2.922267 0.162348 18

MSRA 5.063556 0.632945 8 SEPTIN9 2.731261 0.151737 18

LINC00311 4.6416 0.5802 8 FOXP1 5.779804 0.361238 16

VRK2 4.482111 0.560264 8 SORBS2 2.893713 0.180857 16

SYNJ2 4.400104 0.550013 8 EBF3 2.811049 0.175691 16

GAK 5.050612 0.721516 7 GLI2 6.58701 0.439134 15

NAVI 4.685313 0.66933 7 ZBTB20 3.025428 0.201695 15

C19orf25 4.636755 0.662394 7 GNG7 4.644684 0.331763 14

VPS 13D 4.124844 0.589263 7 RPS6KA2 4.257981 0.304142 14

LHPP 3.768018 0.538288 7 CUX1 4.252716 0.303765 14

FBXL18 3.763178 0.627196 6 C7orf50 3.545082 0.25322 14

RUNDC3A 5.129459 1.025892 5 MIR548F5 3.302271 0.235876 14

ARHGEF7 3.999063 0.799813 5 IQSEC1 3.015585 0.215399 14

BACH2 3.821299 0.76426 5 PRKAG2 2.958187 0.211299 14 ARHGEF10 2.909369 0.207812 14

TABLE 59: Cancer Type ETMR_Atyp MSI2 4.641112 0.357009 13

Gene site imp sum imp mean GSE1 3.652304 0.280946 13

PTPRN2 12.61628 0.153857

CLYBL 3.521073 0.270852 13

PRDM16 8.961167 0.126214

MYT1L 3.206279 0.246637 13

HDAC4 11.88801 0.321298

RFX4 2.853878 0.219529 13

PAX6 6.177259 0.176493

ZC3H3 5.288032 0.440669 12

DIP2C 6.424846 0.200776

ADGRD1 4.848656 0.404055 12

SOX2-OT 7.087108 0.244383

CMIP 4.797575 0.399798 12

GALNT9 5.293596 0.196059

MAML3 3.145508 0.262126 12

SHANK2 6.11146 0.235056

FBRSL1 2.955564 0.246297 12

AGAP1 13.69025 0.54761

MIRLET7BHG 2.830016 0.235835 12

CAMTAI 5.010274 0.200411

RASA3 2.783277 0.23194 12

PDGFRA 2.953273 0.118131

VGLL4 3.948416 0.358947 11

SATB2 2.649146 0.110381

ZC3H12D 3.60369 0.327608 11

RPTOR 7.439347 0.32345

RAD51B 3.444522 0.313138 11

NCOR2 5.023833 0.218428

ACOT7 4.094276 0.409428 10

NXN 3.647387 0.158582

MAML2 3.252715 0.325271 10

HOXB3 3.556701 0.154639

TFAP2B 2.892127 0.289213 10

INPP5A 3.314745 0.144119

SH3RF3 2.647604 0.26476 10

PRKCZ 5.04235 0.229198

NR5A2 2.620498 0.26205 10

SKI 7.416808 0.353181

ATP11A 4.89493 0.543881 9

HOXA-AS3 3.437471 0.163689

SND1 4.120306 0.457812 9

ABR 2.671086 0.133554

KCNH2 3.600796 0.400088 9

MAD1L1 9.29656 0.489293

PACS2 3.458328 0.384259 9

KCNQ1 6.105397 0.321337 EGFR 3.39395 0.377106 9

SMG1P2 5.60813 0.295165 ADAMTS2 3.248628 0.360959 9

BOLA2 5.60813 0.295165 TSPAN9 3.036751 0.337417 9 LOC613038 5.60813 0.295165 PAX3 2.942869 0.326985 9 TRAPPCI 2 2.920628 0.324514 9 PCDHGB5 4.579852 0.14312 32 ASAP1 2.714372 0.301597 9 PCDHGA9 4.579852 0.147737 31 MGMT 2.68465 0.298294 9 SOX2-OT 6.083001 0.209759 29 APBA2 2.681638 0.29796 9 PCDHGB6 4.263466 0.147016 29

MACROD1 4.228081 0.52851 8 PCDHGA10 4.263466 0.152267 28 MSRA 3.444056 0.430507 8 SHANK2 4.651371 0.178899 26 LINC00311 2.834108 0.354264 8 AGAP1 11.70109 0.468043 25 RXRA 3.906314 0.558045 7 CAMTAI 4.196368 0.167855 25 NAVI 3.340597 0.477228 7 PCDHGB7 3.94708 0.164462 24 VPS 13D 3.241423 0.46306 7 MEIS1 3.693835 0.15391 24 LHPP 2.635364 0.376481 7 RPTOR 10.80714 0.469876 23

FBXL18 4.520193 0.753365 6 INPP5A 6.427313 0.279448 23

COQ8A 4.07088 0.67848 6 NCOR2 5.658868 0.246038 23 SRGAP3 2.948205 0.491368 6 NXN 4.955936 0.215475 23

TSN AX-DISCI 4.041131 0.808226 5 PCDHGA11 3.94708 0.171612 23 RUNDC3A 3.966069 0.793214 5 PRKCZ 3.545285 0.161149 22 PRR5L 2.998843 0.599769 5 SKI 9.076939 0.432235 21

TK1 2.828315 0.565663 5 ABR 4.939217 0.246961 20

ARHGEF7 2.778802 0.55576 5 FRMD4A 4.369024 0.218451 20

RBMS3 4.005949 1.001487 4 SDK1 4.277882 0.213894 20

NDST1 2.748946 0.687236 4 MAD1L1 11.05499 0.581842 19 DTNA 2.646445 0.661611 4 SMG1P2 6.12457 0.322346 19 FBXL17 2.852793 0.950931 3 BOLA2 6.12457 0.322346 19 SLC12A9 2.814091 0.93803 3 LOC613038 6.12457 0.322346 19

SOX10 2.739494 1.369747 2 CASZ1 4.750979 0.250052 19 ANKLE2 2.707781 1.35389 2 ZNF423 4.569673 0.240509 19

KCNQ1 3.698012 0.194632 19

TABLE 60: Cancer Type FOXK1 6.360468 0.353359 18 ETMR_C19MC

TBC1D16 4.769727 0.264985 18

Gene site imp sum imp mean n ANKRD11 4.507033 0.250391 18

PTPRN2 14.69382 0.179193 OO

HOXA3 3.678129 0.204341 18 PRDM16 11.10744 0.156443 71

TBX15 4.506908 0.265112 17 PCDHGA1 6.356544 0.107738 59

PAX6-AS1 4.470024 0.262943 17 PCDHGA2 5.723772 0.100417 57

RCN1 4.470024 0.262943 17

PCDHGA3 5.342987 0.098944 54

OPCML 3.520633 0.207096 17 PCDHGB1 5.342987 0.100811 53

FOXP1 6.167845 0.38549 16 PCDHGA4 5.342987 0.104764

⁵¹ NAV2 3.968529 0.248033 16 PCDHGB2 5.342987 0.109041 49

GLI2 4.877839 0.325189 15

PCDHGA5 5.342987 0.113681 ⁴⁷ SLX1B- PCDHGB3 5.026601 0.116898 43 SULT1A4 3.954447 0.26363 15 PCDHGA6 4.710215 0.117755 40 SLX1A 3.954447 0.26363 15 HDAC4 15.4495 0.417554 37 LOC606724 3.954447 0.26363 15

PCDHGA7 4.710215 0.127303 37 BAIAP2 3.6137 0.240913 15 RBFOX3 9.566525 0.273329 35 RPS6KA2 5.856751 0.418339 14 PAX6 4.715074 0.134716 35 CUX1 5.813019 0.415216 14 PCDHGB4 4.710215 0.134578 35 PRKAG2 4.374581 0.31247 14

PCDHGA8 4.710215 0.134578 35 IQSEC1 4.288222 0.306302 14 DIP2C 9.206258 0.287696 32 GNG7 4.028826 0.287773 14 ARHGEF10 3.516089 0.251149 14

MSI2 7 .052339 0.542488 13

GSE1 3 .640543 0.280042 13

CMIP 5 .66703 0.472252 12

TNS3 4 .297541 0.358128 12

ZC3H3 4 .223031 0.351919 12

FBRSL1 3 .979732 0.331644 12

TBCD 3 .690381 0.335489 11

RAD51B 3 .664451 0.333132 11

ACOT7 3 .998721 0.399872 10

AKAP13 3 .628019 0.362802 10

CHST11 3 .625287 0.362529 10

SND1 5 .409773 0.601086 9

ADAMTS2 5 .34484 0.593871 9

ATP11A 5 .303516 0.58928 9

TSPAN9 4 .594095 0.510455 9

KCNH2 4 .047686 0.449743 9

CACNA2D4 4 0.444444 9

AXIN2 3 .787404 0.420823 9

TXNRD1 3 .62526 0.402807 9

VRK2 7 .00172 0.875215 8

MSRA 4 .521701 0.565213 8

DNMT3A 4 .405394 0.550674 8

PPP2R2B 3 .55962 0.444953 8

VPS 13D 4 .918324 0.702618 7

C19orf25 4 .004237 0.572034 7

FBXL18 5 .120557 0.853426 6

COQ8A 3 .912035 0.652006 6

TSN AX-DISCI 4 .890718 0.978144 5

BCAR1 3 .714802 0.74296 5

TUBA1C 4 .909867 1.227467 4

RBMS3 4 .340008 1.085002 4

DAGLB 3 .488732 1.162911 3

CHTF18 3 .643517 1.821758 2

ANKLE2 3 .592778 1.796389 2

TABLE 61: Cancer Type MSI2 4.658842 0.358372 13 EVNCYT RFX4 4.112173 0.316321 13

Gene site imp sum imp mean n MYT1L 3.744602 0.288046 13 PTPRN2 14.8685 0.181323 82 GSE1 3.63194 0.27938 13 PRDM16 14.39248 0.202711 71 MIRLET7BHG 5.061852 0.421821 12 PCDHGA1 4.472651 0.075808 59 ZC3H3 4.551285 0.379274 12 PCDHGA2 4.472651 0.078468 57 CMIP 4.522782 0.376899 12 PCDHGA3 3.839879 0.071109 54 FBRSL1 3.102052 0.258504 12 PCDHGB1 3.839879 0.072451 53 FGFR2 4.091655 0.371969 11 PCDHGA4 3.523493 0.069088 51 VGLL4 3.826003 0.347818 11 PCDHGB2 3.523493 0.071908 49 RAD51B 3.599109 0.327192 11 HDAC4 11.31675 0.305858 37 CCDC140 3.500543 0.318231 11 PAX6 7.318088 0.209088 35 LBX1-AS1 3.794145 0.379414 10 RBFOX3 6.25787 0.178796 35 ACOT7 3.731201 0.37312 10 DIP2C 7.6996 0.240613 32 AKAP13 3.709336 0.370934 10 SOX2-OT 5.155114 0.177763 29 GAS7 3.353155 0.335316 10 SHANK2 3.287713 0.12645 26 GRID1 3.317649 0.331765 10 AGAP1 8.012474 0.320499 25 SH3RF3 3.218749 0.321875 10 CAMTAI 6.573397 0.262936 25 ADGRB1 5.758616 0.639846 9 PDGFRA 4.343741 0.17375 25 SND1 5.419146 0.602127 9 MEIS1 4.274311 0.178096 24 ATP11A 4.765904 0.529545 9 RPTOR 9.309927 0.404779 23 ADAMTS2 3.946421 0.438491 9 INPP5A 4.019441 0.174758 23 KCNMA1 3.728571 0.414286 9 PRKCZ 5.654279 0.257013 22 TRAPPCI 2 3.586849 0.398539 9 SKI 9.625199 0.458343 21 AXIN2 3.538599 0.393178 9 ZIC4 3.365632 0.160268 21 CACNA2D4 3.203809 0.355979 9 MAD1L1 8.895139 0.468165 19 NOTCH1 3.186872 0.354097 9 ZNF423 8.863756 0.466513 19 LINC00311 5.007689 0.625961 8 SMG1P2 4.795953 0.252419 19 MSRA 4.707704 0.588463 8 BOLA2 4.795953 0.252419 19 ESRRG 3.942771 0.492846 8 LOC613038 4.795953 0.252419 19 RORA 3.815378 0.476922 8 CASZ1 3.443674 0.181246 19 DPP6 3.11482 0.389352 8 ANKRD11 4.230042 0.235002 18 DUSP6 5.324422 0.760632 7 TBC1D16 4.141089 0.230061 18 LINC00461 4.77633 0.682333 7 FOXK1 3.781959 0.210109 18 NAVI 4.164379 0.594911 7 MCF2L 3.555992 0.197555 18 FHIT 4.122583 0.58894 7 RBFOX1 3.30935 0.183853 18 ITPKB 3.575263 0.510752 7 OPCML 6.346396 0.373317 17 FBXL18 4.33218 0.72203 6 FOXP1 4.952197 0.309512 16 FAM181A 3.378516 0.563086 6 SORBS2 3.853856 0.240866 16 SLC22A18AS 3.30902 0.551503 6 NAV2 3.128093 0.195506 16 RUNDC3A 5.339258 1.067852 5 GLI2 10.08569 0.672379 15 ARHGEF7 3.476753 0.695351 5 ZBTB20 3.560998 0.2374 15 THRB 3.433111 0.686622 5 NHX 3.498528 0.233235 15 CACNA1I 3.363004 0.672601 5 RPS6KA2 4.441852 0.317275 14 TK1 3.296835 0.659367 5 PRKAG2 4.119838 0.294274 14

TSN AX-DISCI 3.226471 0.645294 5 IQSEC1 3.75183 0.267988 14 STAP2 3.52695 0.881738 4 ARHGEF10 3.411972 0.243712 14 CORO2B 3.398559 0.84964 4 RBMS3 3.3878 0.84695 4 FOXP1 3.051477 0.190717 16 DTNA 3.265761 0.81644 4 GLI2 6.11288 0.407525 15 GRIN2B 4.117413 1.372471 3 ZBTB20 4.576408 0.305094 15 DAGLB 3.313942 1.104647 3 BAIAP2 3.671115 0.244741 15 DLL1 3.178605 1.059535 3 RPS6KA2 7.520536 0.537181 14 SOXIO 4.950309 2.475154 2 IQSEC1 5.627224 0.401945 14 SLC25A10 3.296161 1.64808 2 PRKAG2 4.309802 0.307843 14 C7orf50 4.272252 0.305161 14

TABLE 62: Cancer Type EWS PPP2R2A 3.526415 0.251887 14 Gene site imp sum imp mean n MIR548F5 3.246979 0.231927 14 PTPRN2 6.594466 0.08042 82 CUX1 3.035699 0.216836 14 PRDM16 8.444389 0.118935 71 MSI2 5.781775 0.444752 13 PCDHGA1 3.163841 0.053624 59 GSE1 3.471098 0.267008 13 PCDHGA2 3.163841 0.055506 57 MYT1L 3.140211 0.241555 13 PCDHGA3 3.163841 0.05859 54 HOXC4 3.032424 0.233263 13 PCDHGB1 3.163841 0.059695 53 FBRSL1 5.401794 0.450149 12 PCDHGA4 3.163841 0.062036 51 CMIP 4.698304 0.391525 12 PCDHGB2 3.163841 0.064568 49 ADGRD1 4.340891 0.361741 12 PCDHGA5 3.163841 0.067316 47 GNA12 3.997858 0.333155 12 PCDHGB3 3.480227 0.080936 43 MEGF6 3.603341 0.300278 12 HDAC4 8.759235 0.236736 37 RAD51B 3.352156 0.304741 11 RBFOX3 4.953792 0.141537 35 CTBP2 3.021814 0.27471 11 PAX6 4.909085 0.14026 35 BCL11B 5.072798 0.50728 10 DIP2C 7.742061 0.241939 32 AKAP13 4.448844 0.444884 10 GALNT9 3.783787 0.14014 27 CHST11 3.972987 0.397299 10 SHANK2 4.063047 0.156271 26 FMN1 3.795513 0.379551 10 AGAP1 10.22599 0.40904 25 ACOT7 3.710255 0.371026 10 CAMTAI 5.832812 0.233312 25 KLHL29 3.646232 0.364623 10 SATB2 3.268379 0.136182 24 GAS7 2.932857 0.293286 10 MEIS1 3.164186 0.131841 24 RGS12 2.919996 0.292 10 RPTOR 10.08952 0.438675 23 IGF1R 2.912019 0.291202 10 INPP5A 6.721586 0.292243 23 ATP11A 6.351578 0.705731 9 NCOR2 5.642924 0.245345 23 SND1 4.100007 0.455556 9 PRKCZ 3.891859 0.176903 22 MGMT 3.877867 0.430874 9 SKI 8.625603 0.410743 21 TSPAN9 3.575338 0.39726 9 FRMD4A 3.761279 0.188064 20 TRAPPCI 2 3.24621 0.36069 9 ABR 3.155898 0.157795 20 ADAMTS2 2.987984 0.331998 9 SMG1P2 5.64643 0.297181 19 PACS2 2.966455 0.329606 9 BOLA2 5.64643 0.297181 19 DNMT3A 4.267716 0.533464 8 LOC613038 5.64643 0.297181 19 VRK2 3.403344 0.425418 8 ZNF423 5.470921 0.287943 19 DLEU1 3.081545 0.385193 8 CASZ1 4.319583 0.227346 19 SHROOM3 3.073762 0.38422 8 MAD1L1 3.31708 0.174583 19 GRIK2 3.050294 0.381287 8 ANKRD11 4.595043 0.25528 18 MSRA 3.047019 0.380877 8 SEPTIN9 4.030671 0.223926 18 C19orf25 5.392038 0.770291 7 TBC1D16 3.436328 0.190907 18 NAVI 4.318606 0.616944 7 OPCML 3.748227 0.220484 17 PTPN20 2.995701 0.427957 7 EBF3 3.578374 0.223648 16 KCNAB2 2.943331 0.420476 7 FBXL18 3.959427 0.659904 6 IQSEC1 2.230874 0.159348 14

CRADD 3.504241 0.58404 6 TBX5 1.898316 0.135594 14

CCDC177 3.049015 0.508169 6 PRKAG2 1.407036 0.100503 14

PAX1 3.010346 0.501724 6 MYT1L 1.690763 0.130059 13

RUNDC3A 4.677137 0.935427 5 MIR9-3HG 1.58193 0.121687 13

ARHGEF7 4.245753 0.849151 5 SPTBN4 1.521369 0.117028 13

TSN AX-DISCI 4.070083 0.814017 5 MIRLET7BHG 3.578224 0.298185 12

IDE 3.253075 0.650615 5 TBX4 2.297014 0.191418 12

KLHL25 3.148576 0.629715 5 CMIP 1.565499 0.130458 12

DONSON 3.503814 1.167938 3 MAML3 1.411568 0.117631 12

DAGLB 3.381121 1.12704 3 ADGRD1 1.391228 0.115936 12

DICER1 3.264558 1.088186 3 CCDC140 2.278284 0.207117 11

CHTF18 3.388371 1.694186 2 VGLL4 1.650163 0.150015 11 SLC25A10 3.07935 1.539675 2 GLUD1P2 1.584414 0.144038 11 LBX1-AS1 3.359792 0.335979 10

TABLE 63: Cancer Type GBM_CBM TSPAN4 2.514486 0.251449 10

Gene site imp sum imp mean n OTX1 2.43546 0.243546 10

PTPRN2 4.219585 0.051458 82 ACOT7 2.384765 0.238476 10

PRDM16 5.223884 0.073576 71 NR2F1-AS1 1.584295 0.15843 10

HDAC4 4.968689 0.134289 37 TFAP2A 1.529399 0.15294 10

PAX6 3.924259 0.112122 35 ATP11A 2.97819 0.33091 9

RBFOX3 2.757375 0.078782 35 RUNX1 1.959724 0.217747 9

DIP2C 2.740991 0.085656 32 TSPAN9 1.787018 0.198558 9

SOX2-OT 3.59743 0.124049 29 ZNF833P 1.704292 0.189366 9

PDGFRA 2.993496 0.11974 25 ADGRB1 1.656848 0.184094 9

AGAP1 2.315682 0.092627 25 NOTCH1 1.622247 0.18025 9

CAMTAI 1.729468 0.069179 25 GPC6 1.497155 0.166351 9

SATB2 4.371957 0.182165 24 APBA2 1.495026 0.166114 9

RPTOR 4.110492 0.178717 23 SND1 1.401819 0.155758 9

NCOR2 1.999787 0.086947 23 GRIK2 2.572652 0.321582 8

INPP5A 1.963659 0.085376 23 MSRA 2.151155 0.268894 8

PRKCZ 2.262767 0.102853 22 MACROD1 1.432446 0.179056 8

SIM2 2.459611 0.117124 21 RORA 1.422077 0.17776 8

FRMD4A 1.52728 0.076364 20 NR2E1 1.392098 0.174012 8

MAD1L1 3.476252 0.182961 19 DLEU1 1.384263 0.173033 8

ZNF423 1.960736 0.103197 19 TACC2 2.292627 0.327518 7

CASZ1 1.76757 0.09303 19 NAVI 2.179499 0.311357 7

FOXK1 4.095143 0.227508 18 LINC00461 2.036008 0.290858 7

SEPTIN9 2.346494 0.130361 18 RBM20 1.829679 0.261383 7

ANKRD11 2.23996 0.124442 18 DUSP6 1.605871 0.22941 7

TBX15 2.832461 0.166615 17 FBXL18 2.118706 0.353118 6

OPCML 2.132107 0.125418 17 FAM181A 2.084256 0.347376 6

FOXP1 2.240363 0.140023 16 VAX2 1.780092 0.296682 6

NAV2 1.612654 0.100791 16 SATB2-AS1 1.739164 0.289861 6

BAIAP2 2.22624 0.148416 15 TRAK1 1.680758 0.280126 6

GLI2 1.827958 0.121864 15 FMNL2 1.58193 0.263655 6

PPP2R2A 2.990556 0.213611 14 SLC22A18AS 1.512488 0.252081 6

CUX1 2.290274 0.163591 14 MYO 16 1.493941 0.24899 6 LRRFIP1 1.420423 0.236737 6 ADARB2 5.055486 0.194442 26

RUNDC3A 2.517392 0.503478 5 AGAP1 8.358635 0.334345 25

LOC100132215 2.087629 0.417526 5 CAMTAI 8.271489 0.33086 25

CACNA1I 1.661515 0.332303 5 PDGFRA 6.358749 0.25435 25

KLHL25 1.518958 0.303792 5 SATB2 9.115991 0.379833 24

ARHGEF7 1.457993 0.291599 5 MEIS1 6.622748 0.275948 24 RAPGEF4 1.396595 0.279319 5 PCDHGB7 4.126915 0.171955 24 STAP2 2.002651 0.500663 4 RPTOR 9.815838 0.426776 23 RBMS3 1.956574 0.489144 4 INPP5A 6.802927 0.295779 23 DINA 1.634875 0.408719 4 NCOR2 5.704298 0.248013 23 TUBA1C 1.486045 0.371511 4 PRKCZ 6.501686 0.295531 22 FRMPD2 1.396595 0.349149 4 SKI 7.97741 0.379877 21 TTC12 1.91512 0.638373 3 FRMD4A 4.932228 0.246611 20 LOXL3 1.633668 0.544556 3 MAD1L1 11.39993 0.599996 19

MET API D 1.453821 0.484607 3 SMG1P2 8.181284 0.430594 19 SLC25A22 1.429634 0.476545 3 BOLA2 8.181284 0.430594 19 SLC4A8 1.389227 0.463076 3 LOC613038 8.181284 0.430594 19 SOXIO 2.79619 1.398095 2 ZNF423 7.825319 0.411859 19 SLC25A10 1.591924 0.795962 2 CASZ1 4.801215 0.252696 19 ANKLE2 1.498168 0.749084 2 CFAP46 4.05837 0.213598 19 PHF19 1.38402 0.69201 2 KCNQ1 3.952958 0.20805 19

FOXK1 6.041595 0.335644 18

TABLE 64: Cancer Type GBM_G34 SEPTIN9 5.392621 0.29959 18

Gene site imp sum imp mean n MCF2L 3.795464 0.210859 18 PTPRN2 19.89897 0.24267 82 OPCML 6.854193 0.403188 17 PRDM16 14.43818 0.203355 71 TBX15 4.596928 0.270408 17 PCDHGA1 7.473345 0.126667 59 FOXP1 5.462874 0.34143 16 PCDHGA2 7.473345 0.131111 57 EBF3 4.291585 0.268224 16 PCDHGA3 7.473345 0.138395 54 GLI2 8.64613 0.576409 15 PCDHGB1 7.473345 0.141007 53 EMX2OS 4.110559 0.274037 15 PCDHGA4 7.473345 0.146536 51 ZBTB20 3.936085 0.262406 15 PCDHGB2 7.460566 0.152256 49 RPS6KA2 5.824749 0.416053 14 PCDHGA5 7.096471 0.150989 47 CUX1 5.076042 0.362574 14 PCDHGB3 6.379765 0.148367 43 IQSEC1 4.752308 0.339451 14 PCDHGA6 5.796002 0.1449 40 MYT1L 5.537306 0.425947 13 HDAC4 11.11229 0.300332 37 MSI2 4.925849 0.378911 13 PCDHGA7 5.322527 0.143852 37 KIF26B 3.854842 0.296526 13 RBFOX3 11.3611 0.324603 35 MIRLET7BHG 5.363207 0.446934 12 PAX6 7.229973 0.206571 35 TNS3 4.797854 0.399821 12 PCDHGB4 5.322527 0.152072 35 CMIP 4.72135 0.393446 12 PCDHGA8 5.322527 0.152072 35 TBX4 4.663755 0.388646 12 DIP2C 6.139385 0.191856 32 ZC3H12D 5.092555 0.46296 11 PCDHGB5 5.006141 0.156442 32 ANAPC16 4.847683 0.440698 11 PCDHGA9 5.006141 0.161488 31 SORCS2 3.899277 0.35448 11 SOX2-OT 10.3488 0.356855 29 SH3RF3 4.611812 0.461181 10 PCDHGB6 4.443301 0.153217 29 ACOT7 4.229379 0.422938 10 PCDHGA10 4.443301 0.158689 28 GRID1 4.011402 0.40114 10 SHANK2 5.9963 0.230627 26 ATP11A 7.379091 0.819899 9 SND1 5.619648 0.624405 9 RIMBP2 2.708516 0.117762 23 AXIN2 4.860437 0.540049 9 INPP5A 2.477152 0.107702 23 TSPAN9 4.587217 0.509691 9 PRKCZ 2.809272 0.127694 22 ADAMTS2 3.97508 0.441676 9 SKI 4.27164 0.203411 21 TRAPPCI 2 3.971414 0.441268 9 FRMD4A 3.782863 0.189143 20 LINC00311 4.648027 0.581003 8 SDK1 3.117634 0.155882 20 DNMT3A 4.303718 0.537965 8 ABR 2.594189 0.129709 20 MSRA 4.272627 0.534078 8 MAD1L1 7.157094 0.376689 19 LHX2 4.517777 0.645397 7 ZNF423 3.094365 0.162861 19 GDNF 4.454709 0.636387 7 SMG1P2 2.938503 0.154658 19 LINC00461 4.403363 0.629052 7 BOLA2 2.938503 0.154658 19 CDYL 4.356803 0.6224 7 LOC613038 2.938503 0.154658 19 DUSP6 4.317444 0.616778 7 KCNQ1 2.773978 0.145999 19 GLI3 3.890153 0.555736 7 CASZ1 2.31299 0.121736 19 LYPD1 5.224149 0.870691 6 ANKRD11 5.108699 0.283817 18 FBXL18 4.850918 0.808486 6 FOXK1 4.541819 0.252323 18 SATB2-AS1 4.373285 0.728881 6 TBC1D16 3.514847 0.195269 18 FAM181A 4.254141 0.709023 6 SEPTIN9 3.070805 0.1706 18 ARHGEF7 4.251735 0.850347 5 PAX6-AS1 3.440958 0.202409 17 CASC15 4.150169 0.830034 5 RCN1 3.440958 0.202409 17 ATP2B4 3.866019 0.773204 5 TBX15 2.685264 0.157957 17 IGSF21 4.438775 1.109694 4 FOXP1 3.750495 0.234406 16 STAP2 4.287031 1.071758 4 EBF3 3.173477 0.198342 16 DTNA 3.80024 0.95006 4 NAV2 3.098199 0.193637 16

ARHGAP23 4.72853 1.576177 3 SORBS2 2.531166 0.158198 16 SRRM3 3.870911 1.290304 3 GLI2 4.379243 0.29195 15 OLIG2 4.617618 2.308809 2 BAIAP2 2.897856 0.19319 15 SOXIO 3.854589 1.927294 2 NFIX 2.366496 0.157766 15

KNDC1 2.361173 0.157412 15

TABLE 65: Cancer Type PRKAG2 4.298671 0.307048 14 GBM_MES_Atyp RPS6KA2 4.264028 0.304573 14

Gene site imp sum imp mean n CUX1 3.672298 0.262307 14 PTPRN2 8.433087 0.102843 82

ARHGEF10 2.952753 0.210911 14 PRDM16 6.780965 0.095507 71

IQSEC1 2.923624 0.20883 14 HDAC4 7.186587 0.194232 37 MIR548F5 2.413638 0.172403 14 PAX6 4.903338 0.140095 35 TBX5 2.300758 0.16434 14 RBFOX3 3.107038 0.088773 35

MSI2 3.096241 0.238172 13 DIP2C 6.504262 0.203258 32

CMIP 4.54422 0.378685 12 SOX2-OT 2.911348 0.100391 29

MAML3 3.456282 0.288023 12 SHANK2 3.576954 0.137575 26

ADGRD1 3.143607 0.261967 12 ADARB2 2.697827 0.103763 26

FBRSL1 2.786601 0.232217 12 CAMTAI 5.335488 0.21342 25

CTNNA2 2.585921 0.215493 12 PDGFRA 4.368188 0.174728 25 SORCS2 2.993974 0.272179 11 AGAP1 3.977791 0.159112 25 ANAPC16 2.493181 0.226653 11 SATB2 4.514819 0.188117 24

SLC38A10 2.475242 0.225022 11 MEIS1 3.689164 0.153715 24 VGLL4 2.324173 0.211288 11

RPTOR 7.619219 0.33127 23 SH3RF3 3.976811 0.397681 10 NCOR2 4.33597 0.18852 23 TSPAN4 3.618876 0.361888 10 AKAP13 3.113099 0.31131 10 PCDHGA5 8.422682 0.179206 47 BCL11B 3.084883 0.308488 10 PCDHGB3 7.473524 0.173803 43 GAS7 2.404786 0.240479 10 PCDHGA6 6.709864 0.167747 40 FMN1 2.334601 0.23346 10 HDAC4 15.91135 0.430036 37 SND1 3.742607 0.415845 9 PCDHGA7 6.393478 0.172797 37 AXIN2 3.283112 0.36479 9 PAX6 11.66539 0.333297 35 RUNX1 3.232644 0.359183 9 RBFOX3 7.368602 0.210531 35 ADAMTS2 3.075889 0.341765 9 PCDHGB4 6.288867 0.179682 35 TRAPPCI 2 2.869826 0.31887 9 PCDHGA8 6.288867 0.179682 35 NOTCH 1 2.600302 0.288922 9 DIP2C 10.53407 0.32919 32 ASAP1 2.478045 0.275338 9 PCDHGB5 6.288867 0.196527 32 ADGRB1 2.463991 0.273777 9 PCDHGA9 6.288867 0.202867 31 MCC 3.597978 0.449747 8 SOX2-OT 7.7339 0.266686 29 LINC00311 2.817797 0.352225 8 PCDHGB6 5.473696 0.188748 29 LHX4 2.589797 0.323725 8 PCDHGA10 5.473696 0.195489 28 DNMT3A 2.573273 0.321659 8 SHANK2 6.019655 0.231525 26 MSRA 2.46484 0.308105 8 ADARB2 5.68846 0.218787 26 DLEU1 2.384677 0.298085 8 AGAP1 10.45411 0.418164 25 AFF3 2.306736 0.288342 8 CAMTAI 7.166051 0.286642 25 MACROD1 2.300575 0.287572 8 PDGFRA 6.769784 0.270791 25 C19orf25 3.355938 0.47942 7 MEIS1 6.634768 0.276449 24

ITPK1 3.117822 0.445403 7 SATB2 6.269327 0.261222 24 CDYL 2.714648 0.387807 7 PCDHGB7 5.802861 0.241786 24 NAVI 2.690872 0.38441 7 RPTOR 12.93967 0.562595 23 SLC22A18AS 3.300956 0.550159 6 RIMBP2 6.255045 0.271958 23 MIR100HG 2.84836 0.474727 6 NCOR2 6.172236 0.268358 23 LRRFIP1 2.530759 0.421793 6 NXN 6.129696 0.266509 23 FMNL2 2.440385 0.406731 6 PCDHGA11 5.561293 0.241795 23 MIR548G 2.379921 0.396653 6 INPP5A 4.458401 0.193844 23 KLHL25 3.043751 0.60875 5 PRKCZ 6.232476 0.283294 22 ARHGEF7 2.408625 0.481725 5 SKI 10.62373 0.505892 21 TUBA1C 2.46219 0.615547 4 HOXA-AS3 4.545824 0.216468 21 DAGLB 2.838583 0.946194 3 ZIC4 4.536976 0.216046 21 ACSL1 2.293654 0.764551 3 SIM2 4.451726 0.211987 21 SOXIO 2.94264 1.47132 2 FRMD4A 7.193395 0.35967 20 SLC25A10 2.649164 1.324582 2 ABR 5.89055 0.294528 20 SDK1 5.592627 0.279631 20

Cancer Type

TABLE 66: MAD1L1 12.94929 0.681541 19 GBM_MES_Typ ZNF423 7.647999 0.402526 19

Gene site imp sum imp mean n CASZ1 7.56223 0.398012 19 PTPRN2 23.49127 0.286479 82 SMG1P2 6.857681 0.360931 19 PRDM16 22.97156 0.323543 71 BOLA2 6.857681 0.360931 19 PCDHGA1 10.84728 0.183852 59 LOC613038 6.857681 0.360931 19 PCDHGA2 10.21451 0.179202 57 KCNQ1 5.770086 0.303689 19 PCDHGA3 9.06282 0.16783 54 ANKRD11 7.654705 0.425261 18 PCDHGB1 9.06282 0.170997 53 FOXK1 7.432383 0.41291 18 PCDHGA4 9.06282 0.177702 51 MCF2L 6.444539 0.35803 18 PCDHGB2 8.931951 0.182285 49 TBC1D16 5.943273 0.330182 18 RBFOX1 4.392738 0.244041 18 PTPRN2 4.660027 0.05683 82 PAX6-AS1 6.660589 0.391799 17 PRDM16 1.212407 0.017076 71 RCN1 6.660589 0.391799 17 PCDHGA1 1.98213 0.033595 59 OPCML 6.571515 0.38656 17 PCDHGA2 1.98213 0.034774 57 FOXP1 7.921231 0.495077 16 PCDHGA3 1.98213 0.036706 54 NAV2 6.202516 0.387657 16 PCDHGB1 1.98213 0.037399 53 EBF3 4.524319 0.28277 16 PCDHGA4 1.665744 0.032662 51 GLI2 9.308291 0.620553 15 PCDHGB2 1.665744 0.033995 49 KIRREL3 6.247889 0.416526 15 PCDHGA5 1.265544 0.026926 47

KNDC1 5.324119 0.354941 15 PCDHGB3 1.265544 0.029431 43 NHX 5.277974 0.351865 15 PCDHGA6 1.265544 0.031639 40 BAIAP2 4.98361 0.332241 15 HDAC4 2.229184 0.060248 37 ZBTB20 4.772255 0.31815 15 DIP2C 2.621531 0.081923 32 RPS6KA2 6.525278 0.466091 14 SOX2-OT 2.793191 0.096317 29 PRKAG2 6.037694 0.431264 14 SHANK2 1.387906 0.053381 26 C7orf50 5.965491 0.426107 14 ADARB2 1.080209 0.041547 26 MIR548F5 4.766224 0.340445 14 CAMTAI 2.15173 0.086069 25 GNG7 4.731929 0.337995 14 AGAP1 1.310034 0.052401 25 MSI2 8.116375 0.624337 13 SATB2 3.618022 0.150751 24 SPTBN4 5.54136 0.426258 13 RPTOR 1.304413 0.056714 23

MYT1L 5.028356 0.386797 13 RIMBP2 1.265544 0.055024 23 ZC3H3 5.032763 0.419397 12 PRKCZ 1.835344 0.083425 22 CMIP 4.971106 0.414259 12 SKI 2.527028 0.120335 21 FBRSL1 4.79912 0.399927 12 ZIC4 1.396595 0.066505 21 TNS3 4.75218 0.396015 12 ZNF423 2.805755 0.147671 19 SORCS2 4.921415 0.447401 11 MAD1L1 2.765159 0.145535 19 VGLL4 4.606674 0.418789 11 SMG1P2 2.46524 0.129749 19 COL4A1 4.48589 0.407808 11 BOLA2 2.46524 0.129749 19 ACOT7 5.030053 0.503005 10 LOC613038 2.46524 0.129749 19

AKAP13 4.56057 0.456057 10 CASZ1 2.132961 0.112261 19 SND1 7.281794 0.809088 9 FOXK1 2.36923 0.131624 18

ADAMTS2 5.153743 0.572638 9 SEPTIN9 1.792162 0.099565 18 TSPAN9 4.538052 0.504228 9 MCF2L 1.431384 0.079521 18 TRAPPCI 2 4.457085 0.495232 9 RBFOX1 1.38183 0.076768 18 SSBP3 4.353363 0.483707 9 TBX15 1.93143 0.113614 17 LINC00311 4.787609 0.598451 8 OPCML 1.606827 0.094519 17 DLEU1 4.646289 0.580786 8 PAX6-AS1 1.265544 0.074444 17 RGS20 4.459601 0.55745 8 RCN1 1.265544 0.074444 17 DUSP6 5.217385 0.745341 7 GLI2 2.853675 0.190245 15 LINC00461 4.68498 0.669283 7 LRMDA 1.58193 0.105462 15

NAVI 4.450325 0.635761 7 CUX1 2.424641 0.173189 14 FBXL18 4.963335 0.827222 6 RPS6KA2 2.029367 0.144955 14

TSN AX-DISCI 4.412335 0.882467 5 PRKAG2 1.396595 0.099757 14 SOX10 4.332658 2.166329 2 CLYBL 1.712981 0.131768 13 ADGRD1 1.743981 0.145332 12

TABLE 67: Cancer Type FBRSL1 1.298956 0.108246 12

GBM_ped_ND_A TBX4 1.265544 0.105462 12

Gene site imp sum imp mean n CMIP 1.180952 0.098413 12 ZC3H12D 2.137458 0.194314 11 DAGLB 1.703494 0.567831 3 RAD51B 1.387906 0.126173 11 SLC6A9 1.086286 0.362095 3 TBCD 1.080209 0.098201 11 TLX1NB 1.080209 0.36007 3 NTM 1.918556 0.191856 10 SLC25A10 2.125032 1.062516 2 TFAP2B 1.34721 0.134721 10 ACOT7 1.227596 0.12276 10 Cancer Type

TABLE 68: GBM_ped_ND_B BCL11B 1.206764 0.120676 10

Gene site imp sum imp mean n AUTS2 1.080209 0.108021 10 PTPRN2 12.14662 0.14813 82 ATP11A 1.929964 0.21444 9 PRDM16 6.993189 0.098496 71 AXIN2 1.916799 0.212978 9 PCDHGA1 5.102496 0.086483 59 SND1 1.197867 0.133096 9 PCDHGA2 5.102496 0.089517 57 TSPAN9 1.174158 0.130462 9 PCDHGA3 4.681997 0.086704 54 SYNJ2 1.650285 0.206286 8 PCDHGB1 4.681997 0.08834 53 RGS20 1.387906 0.173488 8 PCDHGA4 4.365611 0.0856 51 LHX4 1.265544 0.158193 8 PCDHGB2 4.365611 0.089094 49 NR2E1 1.080209 0.135026 8 PCDHGA5 3.843487 0.081776 47 LINC00311 1.080209 0.135026 8 PCDHGB3 3.527101 0.082026 43 CDYL 1.884525 0.269218 7 PCDHGA6 3.527101 0.088178 40 TRIM2 1.396595 0.199514 7 HDAC4 6.965959 0.188269 37 ITPKB 1.205787 0.172255 7 PCDHGA7 3.210715 0.086776 37 LHX2 1.193883 0.170555 7 PAX6 8.183288 0.233808 35 OTX2-AS1 1.080209 0.154316 7 RBFOX3 4.508841 0.128824 35 FBXL18 1.784169 0.297362 6 PCDHGB4 3.210715 0.091735 35 ACTR3C 1.704515 0.284086 6 PCDHGA8 3.210715 0.091735 35 SATB2-AS1 1.681932 0.280322 6 DIP2C 5.416515 0.169266 32 FAM181A 1.440657 0.24011 6 PCDHGB5 3.210715 0.100335 32 LRRFIP1 1.330824 0.221804 6 PCDHGA9 3.210715 0.103571 31 SLC22A18AS 1.202572 0.200429 6 PCDHGB6 2.584559 0.089123 29 LIMCH1 1.20248 0.200413 6 SHANK2 3.321582 0.127753 26 FMNL2 1.097757 0.18296 6 ADARB2 3.108278 0.119549 26 CDK6 1.080209 0.180035 6 AGAP1 5.4006 0.216024 25 JAKMIP1 1.080209 0.180035 6 PDGFRA 2.602431 0.104097 25 CELSR1 1.075712 0.179285 6 CAMTAI 2.419096 0.096764 25 TRABD2B 1.071723 0.178621 6 SATB2 7.161246 0.298385 24 CACNA2D3 1.07152 0.178587 6 MEIS1 3.279392 0.136641 24 MNX1 2.115646 0.423129 5 NCOR2 4.341474 0.18876 23 HLX 1.527647 0.305529 5 RPTOR 3.937426 0.171192 23 ARHGEF7 1.354671 0.270934 5 INPP5A 3.519542 0.153024 23 TMEM132C 1.160199 0.23204 5 SKI 5.30783 0.252754 21 SHOX2 1.119658 0.223932 5 SIM2 3.927126 0.187006 21 CPZ 1.07152 0.214304 5 ABR 2.58979 0.12949 20 NPHP4 1.06875 0.21375 5 FRMD4A 2.396029 0.119801 20 RBMS3 1.492019 0.373005 4 MAD1L1 6.393482 0.336499 19 IGF2BP3 1.432903 0.358226 4 ZNF423 6.385805 0.336095 19 VOPP1 1.354583 0.338646 4 SMG1P2 4.47727 0.235646 19 PPM1H 1.24313 0.310783 4 BOLA2 4.47727 0.235646 19 UNQ6494 1.241017 0.310254 4

LOC613038 4.47727 0.235646 19 LIPE-AS1 1.076052 0.269013 4 CASZ1 4.285857 0.225571 19 ACTR3C 2.897499 0.482916 6 MCF2L 4.109657 0.228314 18 FAM181A 2.66049 0.443415 6 ANKRD11 2.709106 0.150506 18 FBXL18 2.482766 0.413794 6 SEPTIN9 2.548878 0.141604 18 ARHGEF7 3.550002 0.71 5 SIM1 5.797883 0.341052 17 RUNDC3A 2.678331 0.535666 5 TBX15 4.177295 0.245723 17 TSN AX-DISCI 2.443815 0.488763 5 OPCML 4.163199 0.244894 17 RBMS3 2.902629 0.725657 4 FOXP1 2.710848 0.169428 16 UNQ6494 2.490583 0.622646 4 EBF3 2.326947 0.145434 16 CRB2 2.432396 0.608099 4 GLI2 7.550957 0.503397 15 SLC25A10 3.623653 1.811826 2 LRMDA 2.788694 0.185913 15 ANKLE2 2.456756 1.228378 2 CUX1 4.424777 0.316056 14 ACAD 10 2.40091 2.40091 1 C7orf50 2.990176 0.213584 14 RPS6KA2 2.923735 0.208838 14 TABLE 69: Cancer Type GBM_pedMYCN IQSEC1 2.848773 0.203484 14

Gene site imp sum imp mean n ARHGEF10 2.658323 0.18988 14 PTPRN2 13.7309 0.16745 82 PRKAG2 2.548134 0.18201 14 PRDM16 14.16247 0.199471 71 SPTBN4 3.758864 0.289143 13 PCDHGA1 8.092392 0.137159 59 MSI2 3.316939 0.255149 13 PCDHGA2 7.776006 0.136421 57 CLYBL 2.507192 0.192861 13 PCDHGA3 7.143234 0.132282 54 TBX4 3.229651 0.269138 12 PCDHGB1 7.45962 0.140748 53 ZC3H3 3.02575 0.252146 12 PCDHGA4 7.45962 0.146267 51 TNS3 2.80412 0.233677 12 PCDHGB2 7.143234 0.14578 49 CMIP 2.559671 0.213306 12 PCDHGA5 6.332692 0.134738 47 FBRSL1 2.511935 0.209328 12 PCDHGB3 5.629493 0.130918 43 ADGRD1 2.397913 0.199826 12 PCDHGA6 5.313107 0.132828 40 TBCD 3.339869 0.303624 11 HDAC4 6.570127 0.177571 37 RAD51B 3.175857 0.288714 11 PCDHGA7 4.996721 0.135047 37 ZC3H12D 2.811273 0.25557 11 PAX6 11.10894 0.317398 35 TFAP2B 3.53736 0.353736 10 RBFOX3 5.104816 0.145852 35 AKAP13 2.601768 0.260177 10 PCDHGB4 4.984633 0.142418 35 LBX1-AS1 2.447679 0.244768 10 PCDHGA8 4.984633 0.142418 35 ATP11A 4.313543 0.479283 9 DIP2C 8.089298 0.252791 32 SND1 3.356261 0.372918 9 PCDHGB5 4.809739 0.150304 32 ADAMTS2 2.928678 0.325409 9 PCDHGA9 4.493353 0.144947 31 RUNX1 2.889622 0.321069 9 SOX2-OT 3.57114 0.123143 29 TRAPPCI 2 2.864223 0.318247 9

PCDHGB6 3.322727 0.114577 29 NOTCH 1 2.764459 0.307162 9 ADARB2 4.248209 0.163393 26 ASAP1 2.588346 0.287594 9 SHANK2 4.211053 0.161964 26 LHX9 2.382751 0.26475 9 CAMTAI 8.757924 0.350317 25 DMRTA2 2.361578 0.262398 9 PDGFRA 5.018825 0.200753 25 LINC00311 3.544951 0.443119 8 AGAP1 4.881521 0.195261 25 NR2E1 2.462557 0.30782 8 SATB2 10.03345 0.41806 24 GRIK2 2.427412 0.303426 8 MEIS1 4.687863 0.195328 24 CDYL 3.880029 0.55429 7 NCOR2 5.290152 0.230007 23 LHX2 2.714765 0.387824 7 RPTOR 4.949618 0.215201 23 SATB2-AS1 3.780636 0.630106 6 RIMBP2 4.056962 0.17639 23 PAX1 3.657875 0.609646 6 INPP5A 3.448069 0.149916 23 KCNH2 3.411673 0.379075 9 SKI 6.838609 0.325648 21 SND1 3.364139 0.373793 9 HOXA-AS3 3.484558 0.165931 21 LINC00311 3.722809 0.465351 8 ABR 5.172557 0.258628 20 VEPH1 3.630206 0.453776 8 SDK1 4.600207 0.23001 20 DLEU1 3.626791 0.453349 8 MAD1L1 7.765849 0.408729 19 LRRC61 3.498689 0.437336 8 ZNF423 5.615465 0.295551 19 RGS20 3.332919 0.416615 8 CASZ1 4.149527 0.218396 19 AFF3 3.311203 0.4139 8 SMG1P2 3.848223 0.202538 19 DNMT3A 3.195727 0.399466 8 BOLA2 3.848223 0.202538 19 ASPSCR1 3.123 0.390375 8 LOC613038 3.848223 0.202538 19 CDYL 4.192885 0.598984 7

KCNQ1 3.783269 0.199119 19 LHX2 3.769659 0.538523 7 FOXK1 6.938724 0.385485 18 DUSP6 3.465504 0.495072 7 SEPTIN9 5.291204 0.293956 18 SATB2-AS1 4.890843 0.81514 6 RBFOX1 4.540653 0.252258 18 ACTR3C 3.265273 0.544212 6 TBC1D16 3.39222 0.188457 18 ATP2B4 4.18037 0.836074 5 OPCML 5.532892 0.325464 17 ARHGEF7 3.279377 0.655875 5 TBX15 5.350234 0.31472 17 SHOX2 3.12614 0.625228 5 PAX6-AS1 3.345572 0.196798 17 GRIN2B 3.309093 1.103031 3

RCN1 3.345572 0.196798 17 SOXIO 3.801967 1.900984 2 FOXP1 3.748139 0.234259 16 SLC25A10 3.604396 1.802198 2 GLI2 4.770497 0.318033 15 SLX1B- Cancer Type

TABLE 70: SULT1A4 4.051811 0.270121 15 GBM_pedRTKla SLX1A 4.051811 0.270121 15 Gene site imp sum imp mean n LOC606724 4.051811 0.270121 15 PTPRN2 24.80392 0.302487 82

RPS6KA2 4.640371 0.331455 14 PRDM16 16.05384 0.22611 71 CUX1 4.195972 0.299712 14 PCDHGA1 9.715844 0.164675 59 PRKAG2 3.509503 0.250679 14 PCDHGA2 9.715844 0.170453 57 MSI2 3.661582 0.28166 13 PCDHGA3 10.03223 0.185782 54 CLYBL 3.430496 0.263884 13 PCDHGB1 10.34862 0.195257 53 RFX4 3.323302 0.255639 13 PCDHGA4 10.34862 0.202914 51 ZC3H3 5.034914 0.419576 12 PCDHGB2 10.03223 0.204739 49

MIRLET7BHG 4.906574 0.408881 12 PCDHGA5 9.858888 0.209764 47 TBX4 4.513305 0.376109 12 PCDHGB3 9.516396 0.221312 43 TNS3 4.300972 0.358414 12 PCDHGA6 8.883624 0.222091 40 CMIP 3.897793 0.324816 12 HDAC4 11.62845 0.314282 37 SPON2 3.155581 0.286871 11 PCDHGA7 9.516396 0.2572 37 ZC3H12D 3.130966 0.284633 11 RBFOX3 10.31311 0.29466 35 TFAP2B 4.794462 0.479446 10 PCDHGB4 8.883624 0.253818 35 LBX1-AS1 4.313126 0.431313 10 PCDHGA8 8.883624 0.253818 35 OTX1 3.891279 0.389128 10 PAX6 8.730832 0.249452 35 NTM 3.878567 0.387857 10 DIP2C 12.67672 0.396148 32 ACOT7 3.560066 0.356007 10 PCDHGB5 8.567238 0.267726 32 NR5A2 3.307077 0.330708 10 PCDHGA9 8.136833 0.262478 31 ADGRA1 3.244699 0.32447 10 SOX2-OT 10.55781 0.364062 29 GAS7 3.133788 0.313379 10 PCDHGB6 7.482216 0.258007 29

ATP11A 5.134152 0.570461 9 PCDHGA10 7.482216 0.267222 28 SHANK2 4.574269 0.175933 26 VGLL4 5.44074 0.494613 11

AGAP1 9.283145 0.371326 25 RAD51B 4.763093 0.433008 11

PDGFRA 7.722014 0.308881 25 PCDHGC3 4.647271 0.422479 11

CAMTAI 6.438737 0.257549 25 FGFR2 4.172656 0.379332 11

SATB2 9.78528 0.40772 24 GLUD1P2 4.096334 0.372394 11

MEIS1 7.400244 0.308343 24 LBX1-AS1 6.643877 0.664388 10

PCDHGB7 7.16583 0.298576 24 ACOT7 4.245559 0.424556 10

RPTOR 8.582795 0.373165 23 GRID1 4.217946 0.421795 10

PCDHGA11 6.458615 0.280809 23 NR2F1-AS1 4.198591 0.419859 10

INPP5A 6.355389 0.276321 23 SH3RF3 4.070687 0.407069 10

PRKCZ 5.929783 0.269536 22 SND1 6.143544 0.682616 9

SKI 11.02648 0.52507 21 ATP11A 5.905842 0.656205 9

SIM2 6.157468 0.293213 21 ASAP1 5.312638 0.590293 9

ABR 6.332246 0.316612 20 ADGRB1 5.153716 0.572635 9

FRMD4A 5.456517 0.272826 20 TRAPPCI 2 4.912758 0.545862 9

SDK1 4.58138 0.229069 20 NOTCH1 4.490583 0.498954 9

MAD1L1 12.11511 0.637637 19 ADAMTS2 4.435597 0.492844 9

ZNF423 10.45062 0.550033 19 LINC00311 4.672861 0.584108 8

SMG1P2 6.194484 0.326025 19 NXPH1 4.415364 0.55192 8

BOLA2 6.194484 0.326025 19 DUSP6 6.259741 0.894249 7

LOC613038 6.194484 0.326025 19 VPS 13D 4.510809 0.644401 7

CASZ1 5.647379 0.29723 19 LHX2 4.153165 0.593309 7

FOXK1 7.159214 0.397734 18 FBXL18 4.458406 0.743068 6

MCF2L 4.622141 0.256786 18 RUNDC3A 5.156 1.0312 5

TBX15 5.525197 0.325012 17 ATP2B4 5.053604 1.010721 5

PAX6-AS1 4.792128 0.28189 17 STAP2 5.171362 1.292841 4

RCN1 4.792128 0.28189 17 RBMS3 4.424791 1.106198 4

OPCML 4.748257 0.279309 17 GRIN2B 4.55116 1.517053 3

NAV2 4.932821 0.308301 16 SOXIO 4.786496 2.393248 2

GLI2 9.602972 0.640198 15

ZBTB20 6.998146 0.466543 15

TABLE Cancer Type

LRMDA 4.327524 0.288502 15 71:

GBM_pedRTKlb

COL23A1 4.216483 0.281099 15 Gene site imp sum imp mean n

PCDHGA12 5.596429 0.399745 14 PTPRN2 16.8441 0.205416 82

RPS6KA2 5.49329 0.392378 14 PRDM16 8.257376 0.116301 71

PRKAG2 4.76182 0.34013 14 PCDHGA1 4.124719 0.06991 59

CUX1 4.426468 0.316176 14 PCDHGA2 4.124719 0.072363 57

TBX5 4.404041 0.314574 14 PCDHGA3 4.441105 0.082243 54

C7orf50 4.382117 0.313008 14 PCDHGB1 4.441105 0.083794 53

MSI2 6.547676 0.503667 13 PCDHGA4 4.124719 0.080877 51

MYT1L 5.523665 0.424897 13 PCDHGB2 4.2585 0.086908 49

RFX4 5.099645 0.39228 13 PCDHGA5 3.96155 0.084288 47

GSE1 4.415732 0.339672 13 PCDHGB3 3.923728 0.091249 43

CMIP 6.031148 0.502596 12 PCDHGA6 3.607342 0.090184 40

MEIS2 5.728016 0.477335 12 HDAC4 11.9522 0.323032 37

ZC3H3 5.622596 0.46855 12 PCDHGA7 3.607342 0.097496 37

TNS3 4.046921 0.337243 12 RBFOX3 11.41951 0.326272 35

FBRSL1 4.044398 0.337033 12 PAX6 7.858159 0.224519 35 PCDHGB4 3.607342 0.103067 35 KIF26B 3.910967 0.300844 13 PCDHGA8 3.607342 0.103067 35 MIRLET7BHG 5.539514 0.461626 12 DIP2C 8.969986 0.280312 32 CMIP 4.937353 0.411446 12 SOX2-OT 8.36321 0.288387 29 ZC3H3 4.925995 0.4105 12 ADARB2 4.170747 0.160413 26 FBRSL1 3.8216 0.318467 12 SHANK2 4.005233 0.154047 26 RAD51B 4.467921 0.406175 11 PDGFRA 7.265464 0.290619 25 GLUD1P2 4.168221 0.378929 11 AGAP1 7.15294 0.286118 25 VGLL4 4.132147 0.37565 11 CAMTAI 5.170631 0.206825 25 CCDC140 3.554233 0.323112 11 SATB2 5.301858 0.220911 24 LBX1-AS1 7.500434 0.750043 10 MEIS1 4.769272 0.19872 24 TSPAN4 4.208367 0.420837 10 RPTOR 10.70811 0.46557 23 NR2F1-AS1 4.171545 0.417154 10 INPP5A 5.238358 0.227755 23 SND1 5.542731 0.615859 9 NCOR2 3.92109 0.170482 23 ATP11A 5.413101 0.601456 9 PRKCZ 3.654551 0.166116 22 ZNF833P 4.763877 0.52932 9 SKI 6.381008 0.303858 21 ASAP1 4.354548 0.483839 9 FRMD4A 5.678504 0.283925 20 TRAPPCI 2 4.284941 0.476105 9 ABR 3.950629 0.197531 20 NOTCH1 3.817776 0.424197 9 SDK1 3.726003 0.1863 20 GRIK2 4.906423 0.613303 8 MAD1L1 6.973862 0.367045 19 LINC00311 4.410606 0.551326 8 ZNF423 6.674353 0.351282 19 DLEU1 3.741214 0.467652 8 SMG1P2 4.768102 0.250953 19 MSRA 3.471661 0.433958 8 BOLA2 4.768102 0.250953 19 NR2E1 3.463409 0.432926 8 LOC613038 4.768102 0.250953 19 SOX6 5.964048 0.852007 7 CASZ1 4.337346 0.228281 19 DUSP6 5.893848 0.841978 7 KCNQ1 3.811966 0.20063 19 NAVI 3.867131 0.552447 7 FOXK1 6.494021 0.360779 18 GALNT2 3.781673 0.540239 7 SEPTIN9 5.08708 0.282616 18 FBXL18 5.063272 0.843879 6 RBFOX1 5.08074 0.282263 18 CRACR2A 3.885679 0.647613 6 TBC1D16 4.43256 0.246253 18 DNAJB6 3.881887 0.646981 6 MCF2L 3.741877 0.207882 18 HOXD4 3.774326 0.629054 6 OPCML 5.119027 0.301119 17 VAX2 3.636933 0.606156 6 TBX15 4.511745 0.265397 17 RUNDC3A 5.488615 1.097723 5 PAX6-AS1 4.025643 0.236803 17 TSNAX-DISC1 4.149847 0.829969 5 RCN1 4.025643 0.236803 17 STAP2 3.883946 0.970986 4 FOXP1 4.976041 0.311003 16 GRIN2B 4.116633 1.372211 3 GLI2 10.24092 0.682728 15 SOXIO 5.368365 2.684182 2 ZBTB20 6.312985 0.420866 15 NHX 3.690353 0.246024 ¹⁵ TABLE 72: Cancer Type BAIAP2 3.667221 0.244481 15 GBM_pedRTKlc

3.462071 | , Gene site imp sum imp mean n KIRREL3 0.230805

PTPRN2 21.58137 0.263187 82 NFATC1 3.451441 0.230096

PRDM16 15.24929 0.214779 71 CUX1 5.320574 0.380041

₁₄ PCDHGA1 13.78797 0.233694

4.8 59 C7orf50 1344 0.343817

PCDHGA2 13.78797 0.241894 57 RPS6KA2 4.752512 0.339465

0. QSEC1 0.324031 ₁₄ PCDHGA3 13.11033 242784 54 I 4.536437

PCDHGB1 13.11033 0.247365 53 MSI2 5.416546 0.416657

PCDHGA4 MYT1L 13.11033 0.257065 51

4.465743 0.343519 PCDHGB2 12.31356 0.251297 49 TBX15 6.876137 0.404479 17

PCDHGA5 11.60184 0.246848 47 OPCML 4.769349 0.28055 17

PCDHGB3 10.59932 0.246496 43 FOXP1 6.147698 0.384231 16

PCDHGA6 9.643087 0.241077 40 SORBS2 4.38522 0.274076 16

PCDHGA7 9.195832 0.248536 37 GLI2 11.62047 0.774698 15 HDAC4 8.502729 0.229803 37 ZBTB20 4.843045 0.32287 15 RBFOX3 9.03437 0.258125 35 NFIX 3.97448 0.264965 15 PCDHGB4 8.879446 0.253698 35 CUX1 5.937357 0.424097 14

PCDHGA8 8.879446 0.253698 35 RPS6KA2 4.826212 0.344729 14 PAX6 5.81069 0.16602 35 C7orf50 4.504922 0.32178 14 DIP2C 10.58032 0.330635 32 MYT1L 6.483682 0.498745 13 PCDHGB5 8.795632 0.274863 32 MSI2 5.743576 0.441814 13

PCDHGA9 8.479246 0.273524 31 RFX4 4.041167 0.310859 13 SOX2-OT 7.604816 0.262235 29 ADGRD1 5.342894 0.445241 12 PCDHGB6 7.51977 0.259302 29 MIRLET7BHG 4.044424 0.337035 12 PCDHGA10 7.51977 0.268563 28 CMIP 4.025176 0.335431 12

SHANK2 4.015922 0.154459 26 ZC3H3 3.947124 0.328927 12 CAMTAI 8.165847 0.326634 25 VGLL4 5.135635 0.466876 11 PDGFRA 7.934166 0.317367 25 RAD51B 4.162093 0.378372 11 AGAP1 6.960914 0.278437 25 LBX1-AS1 7.429146 0.742915 10

SATB2 9.605772 0.40024 24 GRID1 5.065253 0.506525 10 PCDHGB7 6.570612 0.273776 24 NR2F1-AS1 4.644931 0.464493 10 RPTOR 7.988668 0.347333 23 SH3RF3 4.491597 0.44916 10 INPP5A 6.102587 0.26533 23 AKAP13 3.898361 0.389836 10

PCDHGA11 6.042179 0.262703 23 ZNF833P 6.168989 0.685443 9

HOXB3 4.130717 0.179596 23 ATP11A 5.682604 0.6314 9 RIMBP2 4.09859 0.1782 23 ASAP1 5.017223 0.557469 9 NCOR2 4.009765 0.174338 23 SND1 4.822785 0.535865 9 PRKCZ 6.119476 0.278158 22 ADAMTS2 4.475024 0.497225 9

SKI 6.474332 0.308302 21 GPC6 4.225264 0.469474 9

SIM2 5.283129 0.251578 21 NEAT1 4.193073 0.465897 9

ZIC4 4.033671 0.19208 21 LINC00311 4.876328 0.609541 8

FRMD4A 6.903309 0.345165 20 GRIK2 4.622532 0.577816 8

ABR 5.841206 0.29206 20 NR2E1 4.620947 0.577618 8

SDK1 4.903843 0.245192 20 RORA 4.290015 0.536252 8

MAD1L1 9.776501 0.514553 19 PPP2R2B 4.121802 0.515225 8

ZNF423 6.705634 0.352928 19 DUSP6 5.596834 0.799548 7

SMG1P2 5.695178 0.299746 19 NAVI 5.228216 0.746888 7

BOLA2 5.695178 0.299746 19 ITPKB 4.251161 0.607309 7 LOC613038 5.695178 0.299746 19 LHX2 4.084893 0.583556 7 CASZ1 5.679773 0.298935 19 RUNDC3A 4.809346 0.961869 5 KCNQ1 5.066135 0.266639 19 ARHGEF7 3.962916 0.792583 5

FOXK1 6.058288 0.336572 18 STAP2 4.016796 1.004199 4 TBC1D16 5.42445 0.301358 18 GRIN2B 4.484791 1.49493 3 MCF2L 5.130007 0.285 18 SOX10 5.491093 2.745546 2 ANKRD11 4.908939 0.272719 18

RBFOX1 3.99222 0.22179 ¹⁸ Cancer Type TABLE 73: SEPTIN9 3.846863 0.213715 18 GBM_pedRTK2a Gene site imp sum imp mean n LOC613038 6.95649 0.366131 19 PTPRN2 28.1641 0.343465 82 CFAP46 4.887135 0.257218 19 PRDM16 21.49848 0.302796 71 FOXK1 7.572598 0.4207 18 PCDHGA1 10.54351 0.178704 59 TBC1D16 5.958143 0.331008 18 PCDHGA2 9.783748 0.171645 57 ANKRD11 4.911447 0.272858 18 PCDHGA3 9.467362 0.175322 54 MCF2L 4.815097 0.267505 18 PCDHGB1 9.467362 0.178629 53 OPCML 6.655209 0.391483 17 PCDHGA4 9.783748 0.191838 51 PAX6-AS1 5.534909 0.325583 17 PCDHGB2 9.807264 0.200148 49 RCN1 5.534909 0.325583 17 PCDHGA5 9.379782 0.19957 47 TBX15 5.496154 0.323303 17 PCDHGB3 8.128167 0.189027 43 HBG2 4.603913 0.270818 17 PCDHGA6 8.128167 0.203204 40 NAV2 5.046775 0.315423 16 HDAC4 12.80694 0.346133 37 FOXP1 4.916724 0.307295 16 PCDHGA7 8.030161 0.217031 37 GLI2 9.048138 0.603209 15 PAX6 13.01784 0.371938 35 ZBTB20 5.031503 0.335434 15 RBFOX3 9.143529 0.261244 35 BAIAP2 4.611745 0.30745 15 PCDHGB4 8.278399 0.236526 35 RPS6KA2 7.976409 0.569744 14 PCDHGA8 8.278399 0.236526 35 CUX1 6.534683 0.466763 14 DIP2C 10.34585 0.323308 32 PRKAG2 5.038111 0.359865 14 PCDHGB5 7.731808 0.241619 32 MSI2 7.113944 0.547226 13 PCDHGA9 7.731808 0.249413 31 MYT1L 6.357355 0.489027 13 SOX2-OT 12.30557 0.42433 29 CLYBL 4.987379 0.383645 13 PCDHGB6 7.002877 0.241479 29 RFX4 4.929773 0.379213 13 PCDHGA10 7.002877 0.250103 28 SPTBN4 4.556067 0.350467 13 GALNT9 6.062111 0.224523 27 MIRLET7BHG 6.349523 0.529127 12 ADARB2 8.59359 0.330523 26 CMIP 5.979667 0.498306 12 SHANK2 6.854793 0.263646 26 TBX4 5.161419 0.430118 12 AGAP1 8.555426 0.342217 25 TNS3 5.124884 0.427074 12 CAMTAI 8.267775 0.330711 25 ZC3H12D 7.204827 0.654984 11 PDGFRA 7.508267 0.300331 25 RAD51B 5.065146 0.460468 11 SATB2 11.18465 0.466027 24 AKAP13 4.664413 0.466441 10 MEIS1 6.644202 0.276842 24 NR5A2 4.618816 0.461882 10 PCDHGB7 6.555907 0.273163 24 ATP11A 7.175656 0.797295 9 RPTOR 11.37789 0.494691 23 SND1 6.615857 0.735095 9 NCOR2 7.036286 0.305925 23 ADAMTS2 6.153891 0.683766 9 PCDHGA11 6.047336 0.262928 23 TSPAN9 5.140176 0.571131 9 RIMBP2 4.742972 0.206216 23 KCNH2 5.02434 0.55826 9 PRKCZ 5.937995 0.269909 22 TRAPPCI 2 4.909557 0.545506 9 SKI 12.07293 0.574901 21 ADGRB1 4.550072 0.505564 9 HOXA-AS3 4.937572 0.235122 21 LINC00311 5.588942 0.698618 8 FRMD4A 6.668317 0.333416 20 DUSP6 6.487949 0.92685 7 SDK1 6.323901 0.316195 20 LINC01551 5.202681 0.74324 7 ABR 5.327392 0.26637 20 CDYL 4.932539 0.704648 7 MAD1L1 11.44563 0.602402 19 FBXL18 5.24403 0.874005 6 ZNF423 9.669321 0.508912 19 FAM181A 4.729407 0.788235 6 CASZ1 7.624501 0.40129 19 SATB2-AS1 4.656288 0.776048 6 SMG1P2 6.95649 0.366131 19 ATP2B4 5.000298 1.00006 5 BOLA2 6.95649 0.366131 19 RUNDC3A 4.913925 0.982785 5 ARHGEF7 4.639519 0.927904 5 SMG1P2 4.421746 0.232723 19

STAP2 4.549002 1.137251 4 BOLA2 4.421746 0.232723 19

METAP1D 5.050681 1.68356 3 LOC613038 4.421746 0.232723 19

OLIG2 5.738568 2.869284 2 CASZ1 3.503762 0.184409 19

SOXIO 4.568744 2.284372 2 FOXK1 3.481892 0.193438 18 SEPTIN9 2.899965 0.161109 18

TABLE 74: Cancer Type MCF2L 2.827648 0.157092 18

GBM_pedRTK2b TBX15 4.863068 0.286063 17

Gene site imp sum imp mean n OPCML 4.147014 0.243942 17 PTPRN2 11.04087 0.134645 82 NAV2 3.194876 0.19968 16 PRDM16 8.286591 0.116713 71 GLI2 6.363292 0.424219 15 PCDHGA1 5.501873 0.093252 59 LRMDA 2.353996 0.156933 15 PCDHGA2 5.501873 0.096524 57 RPS6KA2 3.202018 0.228716 14 PCDHGA3 5.818259 0.107746 54 PRKAG2 2.939428 0.209959 14 PCDHGB1 5.818259 0.109778 53 PCDHGA12 2.755248 0.196803 14 PCDHGA4 5.818259 0.114084 51 CUX1 2.690397 0.192171 14 PCDHGB2 5.818259 0.11874 49 MOB2 2.625962 0.187569 14 PCDHGA5 5.376909 0.114402 47 MIR548F5 2.444184 0.174585 14 PCDHGB3 4.744137 0.110329 43 MSI2 3.469356 0.266874 13 PCDHGA6 5.060523 0.126513 40 CLYBL 3.086376 0.237414 13 HDAC4 8.161755 0.220588 37 RFX4 2.839211 0.218401 13 PCDHGA7 5.376909 0.145322 37 ADGRD1 3.516245 0.29302 12 PAX6 6.948512 0.198529 35 MEGF6 2.874126 0.239511 12 PCDHGB4 5.376909 0.153626 35 TNS3 2.58521 0.215434 12 PCDHGA8 5.376909 0.153626 35 MIRLET7BHG 2.470717 0.205893 12 RBFOX3 4.667296 0.133351 35 ZC3H3 2.325213 0.193768 12 PCDHGB5 5.060523 0.158141 32 ANAPC16 3.200292 0.290936 11 DIP2C 3.099325 0.096854 32 VGLL4 2.922761 0.265706 11 PCDHGA9 4.744137 0.153037 31 RAD51B 2.753059 0.250278 11 SOX2-OT 5.636251 0.194353 29 ZC3H12D 2.366425 0.21513 11 PCDHGB6 3.986475 0.137465 29 AKAP13 3.046862 0.304686 10 PCDHGA10 3.986475 0.142374 28 NR2F1-AS1 2.926888 0.292689 10 GALNT9 2.756451 0.102091 27 ACOT7 2.851214 0.285121 10 SHANK2 3.6576 0.140677 26 TFAP2B 2.488557 0.248856 10 AGAP1 4.791877 0.191675 25 BCL11B 2.409928 0.240993 10 CAMTAI 4.553356 0.182134 25 AUTS2 2.313751 0.231375 10 SATB2 7.4428 0.310117 24 ATP11A 3.937952 0.43755 9 PCDHGB7 4.020792 0.167533 24 KCNH2 3.612538 0.401393 9

INPP5A 4.580119 0.199136 23 TSPAN9 3.420374 0.380042 9 RPTOR 4.330385 0.188278 23 SND1 2.556235 0.284026 9 PCDHGA11 4.020792 0.174817 23 TRAPPCI 2 2.513835 0.279315 9 NCOR2 3.21157 0.139633 23 JPH3 2.327109 0.258568 9 RIMBP2 2.324574 0.101068 23 ESRRG 3.313599 0.4142 8 PRKCZ 4.457482 0.202613 22 MCC 2.724467 0.340558 8 SKI 6.52049 0.3105 21 ANK1 2.680844 0.335106 8 ABR 3.258228 0.162911 20 MBP 2.522589 0.315324 8 MAD1L1 5.960424 0.313707 19 CDYL 3.198895 0.456985 7 ZNF423 5.395207 0.283958 19 DUSP6 2.847414 0.406773 7 RBM20 2.626792 0.375256 7 ZIC4 3.52581 0.167896 21 SATB2-AS1 4.193875 0.698979 6 FRMD4A 2.726461 0.136323 20 FAM181A 3.402264 0.567044 6 ABR 2.57363 0.128681 20 FBXL18 3.214322 0.53572 6 ZNF423 5.300859 0.278993 19 COL26A1 2.824265 0.470711 6 SMG1P2 3.971251 0.209013 19 ATP2B4 3.761507 0.752301 5 BOLA2 3.971251 0.209013 19 RUNDC3A 2.668414 0.533683 5 LOC613038 3.971251 0.209013 19 ARHGEF7 2.51674 0.503348 5 MAD1L1 3.297692 0.173563 19 RBMS3 3.232868 0.808217 4 CASZ1 2.676858 0.140887 19 STAP2 2.547391 0.636848 4 FOXK1 5.10052 0.283362 18 SASH1 2.332012 0.583003 4 SEPTIN9 2.752116 0.152895 18 SOX10 2.350214 1.175107 2 OPCML 4.969368 0.292316 17 SLC25A10 2.344204 1.172102 2 TBX15 4.693877 0.27611 17

SIM1 3.561681 0.209511 17

TABLE 75: Cancer Type GBM_PNC PAX6-AS1 3.028129 0.178125 17 Gene site imp sum imp mean n RCN1 3.028129 0.178125 17

PTPRN2 6.423062 0.07833 82 NAV2 4.319793 0.269987 16 PRDM16 2.898317 0.040821 71 FOXP1 4.003332 0.250208 16 PCDHGA1 6.790692 0.115096 59 GLI2 5.244434 0.349629 15 PCDHGA2 7.107078 0.124686 57 EMX2OS 3.026256 0.20175 15 PCDHGA3 6.027488 0.11162 54 SLX1B-

SULT1A4 2.81556 0.187704 15 PCDHGB1 6.027488 0.113726 53

SLX1A 2.81556 0.187704 15 PCDHGA4 6.027488 0.118186 51

LOC606724 2.81556 0.187704 15 PCDHGB2 5.394716 0.110096 49

ZBTB20 2.709311 0.180621 15 PCDHGA5 5.07833 0.10805 47

KNDC1 2.633055 0.175537 15 PCDHGB3 5.394716 0.125459 43

RPS6KA2 3.433396 0.245243 14 PCDHGA6 5.711102 0.142778 40

IQSEC1 2.953314 0.210951 14 HDAC4 6.431201 0.173816 37

CUX1 2.779521 0.198537 14 PCDHGA7 5.272353 0.142496 37

PRKAG2 2.722693 0.194478 14 PAX6 6.287148 0.179633 35

PCDHGA12 2.612582 0.186613 14 PCDHGB4 5.272353 0.150639 35

SPTBN4 5.213154 0.401012 13 PCDHGA8 5.272353 0.150639 35

MSI2 4.418725 0.339902 13 RBFOX3 3.210057 0.091716 35

MYT1L 2.901417 0.223186 13

PCDHGB5 4.955967 0.154874 32

MIRLET7BHG 3.722823 0.310235 12 DIP2C 3.948744 0.123398 32

TNS3 3.361623 0.280135 12 PCDHGA9 4.955967 0.15987 31

FBRSL1 3.348249 0.279021 12 PCDHGB6 4.114368 0.141875 29

ZC3H3 3.273033 0.272753 12 PCDHGA10 4.114368 0.146942 28

TBX4 2.979561 0.248297 12 AGAP1 6.176979 0.247079 25

ZC3H12D 3.001307 0.272846 11 CAMTAI 4.11764 0.164706 25

SKOR1 3.370501 0.33705 10 PDGFRA 3.491247 0.13965 25

LBX1-AS1 2.881476 0.288148 10 SATB2 3.653961 0.152248 24

OBI1-AS1 2.81532 0.281532 10 PCDHGB7 3.352383 0.139683 24

KLHL29 2.755884 0.275588 10 RPTOR 6.97111 0.303092 23

ACOT7 2.743144 0.274314 10 NCOR2 3.367096 0.146395 23

ATP11A 5.442526 0.604725 9 PCDHGA11 2.905452 0.126324 23

ASAP1 3.670701 0.407856 9 PRKCZ 4.050971 0.184135 22

SND1 3.64327 0.404808 9 SKI 5.862098 0.279148 21 TSPAN9 3.445264 0.382807 9 ADARB2 7.490435 0.288094 26

CACNA2D4 3.278697 0.3643 9 SHANK2 5.723285 0.220126 26

ADAMTS2 3.192983 0.354776 9 AGAP1 9.620862 0.384834 25

KCNH2 3.135611 0.348401 9 CAMTAI 7.42271 0.296908 25

AXIN2 2.668943 0.296549 9 PDGFRA 6.579112 0.263164 25

LINC00311 3.914549 0.489319 8 MEIS1 9.833254 0.409719 24 DNMT3A 3.17287 0.396609 8 PCDHGB7 6.762717 0.28178 24 PRDM6 2.737071 0.342134 8 RPTOR 10.48325 0.455793 23 RORA 2.6248 0.3281 8 INPP5A 7.884809 0.342818 23 MCC 2.578296 0.322287 8 RIMBP2 6.547014 0.284653 23 TRAPPC9 2.557848 0.319731 8 PCDHGA11 6.150797 0.267426 23 NAVI 3.346483 0.478069 7 NCOR2 5.982639 0.260115 23 CDYL 2.959039 0.42272 7 PRKCZ 8.395716 0.381623 22 MIR548H4 2.759179 0.394168 7 SKI 10.55635 0.502683 21 FBXL18 4.388585 0.731431 6 SIM2 6.320251 0.300964 21 STK10 2.64978 0.44163 6 HOXA-AS3 5.76096 0.274331 21 RUNDC3A 4.488731 0.897746 5 FRMD4A 7.221386 0.361069 20 DAGLB 2.714242 0.904747 3 ABR 5.131695 0.256585 20 DICER1 2.613815 0.871272 3 SDK1 5.003845 0.250192 20 SOXIO 2.866816 1.433408 2 MAD1L1 12.34894 0.649944 19 SLC25A10 2.644748 1.322374 2 ZNF423 9.475006 0.498685 19 CASZ1 6.441073 0.339004 19

TABLE 76: Cancer Type GBM_RTK1 SMG1P2 6.064086 0.319162 19

Gene site imp sum imp mean n BOLA2 6.064086 0.319162 19 PTPRN2 28.44191 0.346853 82 LOC613038 6.064086 0.319162 19 PRDM16 20.84059 0.293529 71 FOXK1 8.450817 0.46949 18 PCDHGA1 12.39232 0.210039 59 SEPTIN9 5.128235 0.284902 18 PCDHGA2 12.07594 0.211859 57 PAX6-AS1 6.095445 0.358556 17 PCDHGA3 11.12678 0.206051 54 RCN1 6.095445 0.358556 17 PCDHGB1 11.12678 0.209939 53 TBX15 5.67349 0.333735 17 PCDHGA4 11.12678 0.218172 51 OPCML 5.359936 0.31529 17 PCDHGB2 10.49401 0.214163 49 FOXP1 5.657345 0.353584 16 PCDHGA5 9.753145 0.207514 47 NAV2 5.487794 0.342987 16 PCDHGB3 9.449537 0.219757 43 SORBS2 4.740616 0.296288 16 PCDHGA6 9.133151 0.228329 40 GLI2 10.48235 0.698823 15 HDAC4 12.30965 0.332693 37 BAIAP2 6.399272 0.426618 15 PCDHGA7 9.14593 0.247187 37 ZBTB20 6.189875 0.412658 15 RBFOX3 12.19811 0.348517 35 SLX1B- SULT1A4 4.983773 0.332252 15 PAX6 11.04636 0.31561 35 SLX1A 4.983773 0.332252 15 PCDHGB4 9.14593 0.261312 35

LOC606724 4.983773 0.332252 15 PCDHGA8 9.14593 0.261312 35 RPS6KA2 7.065162 0.504654 14 DIP2C 9.579993 0.299375 32 CUX1 6.693427 0.478102 14 PCDHGB5 8.597751 0.26868 32 PRKAG2 5.619134 0.401367 14 PCDHGA9 8.281365 0.267141 31 C7orf50 5.0646 0.361757 14 SOX2-OT 11.87077 0.409337 29 IQSEC1 4.856892 0.346921 14 PCDHGB6 7.520914 0.259342 29 MYT1L 6.962197 0.535554 13 PCDHGA10 7.520914 0.268604 28 SPTBN4 5.549333 0.426872 13

GALNT9 4.804374 0.17794 27 MSI2 5.429656 0.417666 13 PCDHGB4 8.268738 0.23625 35 GSE1 5.293354 0.407181 13 PCDHGA8 8.268738 0.23625 35 HOXA10- PCDHGB5 8.268738 0.258398 32 HOXA9 4.962239 0.381711 13 DIP2C 7.532289 0.235384 32 CMIP 5.283035 0.440253 12 PCDHGA9 8.268738 0.266733 31 ZC3H3 4.974593 0.414549 12 SOX2-OT 8.995953 0.310205 29 MAML3 4.830029 0.402502 12 PCDHGB6 7.071556 0.243847 29 VGLL4 5.27689 0.479717 11 PCDHGA10 7.071556 0.252556 28 RAD51B 5.159886 0.469081 11 SHANK2 6.280585 0.241561 26 ZC3H12D 4.848675 0.440789 11 ADARB2 4.727206 0.181816 26 LBX1-AS1 6.275327 0.627533 10 AGAP1 9.622224 0.384889 25 ACOT7 5.334854 0.533485 10 CAMTAI 7.433597 0.297344 25 SH3RF3 5.188231 0.518823 10 PDGFRA 5.709785 0.228391 25 AKAP13 4.795264 0.479526 10 SATB2 10.64248 0.443437 24 SND1 6.244847 0.693872 9 MEIS1 7.688916 0.320371 24 ATP11A 5.774312 0.64159 9 PCDHGB7 6.307733 0.262822 24 TSPAN9 5.127746 0.56975 9 RPTOR 12.03723 0.523358 23 ASAP1 5.047038 0.560782 9 NCOR2 8.962317 0.389666 23 AXIN2 4.911017 0.545669 9 NXN 6.297388 0.273799 23 ADAMTS2 4.847841 0.538649 9 HOXB3 5.798086 0.252091 23 ADGRB1 4.763301 0.529256 9 PCDHGA11 5.732892 0.249256 23

LINC00311 5.827952 0.728494 8 INPP5A 5.068774 0.220381 23 DUSP6 6.547525 0.935361 7 PRKCZ 6.517701 0.296259 22 LINC00461 5.023787 0.717684 7 SKI 10.8709 0.517662 21 NAVI 4.941564 0.705938 7 HOXA-AS3 5.410938 0.257664 21 FBXL18 5.08984 0.848307 6 ZIC4 4.773249 0.227298 21 RUNDC3A 5.299092 1.059818 5 ABR 8.490465 0.424523 20 STAP2 5.035557 1.258889 4 FRMD4A 5.641957 0.282098 20 GRIN2B 4.823391 1.607797 3 SDK1 4.713856 0.235693 20 SOXIO 5.5945 2.79725 2 MAD1L1 11.38517 0.599219 19

ZNF423 8.122477 0.427499 19

TABLE 77: Cancer Type GBM_RTK2

SMG1P2 6.862892 0.361205 19 Gene site imp sum imp mean n BOLA2 6.862892 0.361205 19 PTPRN2 19.6513 0.23965 82

LOC613038 6.862892 0.361205 19 PRDM16 18.95536 0.266977 71 CASZ1 5.755603 0.302926 19 PCDHGA1 13.06176 0.221386 59 ANKRD11 7.390952 0.410608 18 PCDHGA2 12.74538 0.223603 57 FOXK1 7.256107 0.403117 18 PCDHGA3 11.57895 0.214425 54 SEPTIN9 5.800177 0.322232 18 PCDHGB1 11.57895 0.218471 53 MCF2L 5.699282 0.316627 18 PCDHGA4 10.94618 0.214631 51 OPCML 6.8263 0.401547 17 PCDHGB2 10.94618 0.223391 49 TBX15 6.010796 0.353576 17 PCDHGA5 10.46056 0.222565 47 PAX6-AS1 4.937854 0.290462 17 PCDHGB3 9.740708 0.226528 43 RCN1 4.937854 0.290462 17 PCDHGA6 9.107936 0.227698 40 FOXP1 6.393653 0.399603 16 HDAC4 14.32705 0.387217 37

NAV2 5.910149 0.369384 16 PCDHGA7 8.79155 0.237609 37 GLI2 10.11008 0.674006 15 RBFOX3 11.54054 0.32973 35 LRMDA 5.176466 0.345098 15 PAX6 11.22163 0.320618 35 BAIAP2 5.016139 0.334409 15 SLX1B- PCDHGA4 2.974028 0.058314 51 SULT1A4 4.974586 0.331639 15 PCDHGB2 2.974028 0.060694 49 SLX1A 4.974586 0.331639 15 PCDHGA5 2.974028 0.063277 47 LOC606724 4.974586 0.331639 15 PCDHGB3 2.974028 0.069163 43 RPS6KA2 7.413005 0.5295 14 PCDHGA6 2.341256 0.058531 40 IQSEC1 5.770443 0.412175 14 HDAC4 10.80254 0.291961 37 CUX1 5.510593 0.393614 14 PCDHGA7 2.341256 0.063277 37 MSI2 8.266631 0.635895 13 PAX6 4.725962 0.135027 35 MYT1L 5.590905 0.43007 13 RBFOX3 3.270117 0.093432 35 SPTBN4 5.57676 0.428982 13 PCDHGB4 2.341256 0.066893 35 GSE1 5.208374 0.400644 13 PCDHGA8 2.341256 0.066893 35 ZC3H3 6.33547 0.527956 12 DIP2C 4.662698 0.145709 32

MIRLET7BHG 6.225246 0.518771 12 SOX2-OT 2.662139 0.091798 29 TNS3 5.933709 0.494476 12 AGAP1 6.21563 0.248625 25 CMIP 5.395776 0.449648 12 CAMTAI 2.834911 0.113396 25 ADGRD1 4.719988 0.393332 12 SATB2 2.57308 0.107212 24 VGLL4 4.902711 0.445701 11 RPTOR 6.923574 0.301025 23 SH3RF3 5.296941 0.529694 10 INPP5A 3.284902 0.142822 23 LBX1-AS1 5.044842 0.504484 10 NCOR2 2.447521 0.106414 23 NR2F1-AS1 4.856945 0.485695 10 SKI 6.513082 0.310147 21 ATP11A 6.945317 0.771702 9 ZIC4 3.927683 0.187033 21 SND1 6.758195 0.750911 9 SDK1 2.463619 0.123181 20 AXIN2 5.341699 0.593522 9 MAD1L1 4.754699 0.250247 19 ADAMTS2 5.162272 0.573586 9 SMG1P2 2.985947 0.157155 19 TRAPPCI 2 5.068641 0.563182 9 BOLA2 2.985947 0.157155 19 ASAP1 4.919802 0.546645 9 LOC613038 2.985947 0.157155 19 TSPAN9 4.90379 0.544866 9 ZNF423 2.952116 0.155375 19 DMRTA2 4.730823 0.525647 9 CASZ1 2.272645 0.119613 19 LINC00311 5.353218 0.669152 8 TBC1D16 4.224717 0.234707 18 DLEU1 5.160303 0.645038 8 FOXK1 3.763196 0.209066 18 PPP2R2B 4.979109 0.622389 8 SEPTIN9 3.473925 0.192996 18 MBP 4.625602 0.5782 8 ANKRD11 3.207682 0.178205 18 DUSP6 6.155281 0.879326 7 OPCML 3.696412 0.217436 17 NAVI 5.179188 0.739884 7 NAV2 3.016295 0.188518 16 CDYL 5.115782 0.730826 7 FOXP1 2.315913 0.144745 16

TSN AX-DISCI 4.793222 0.958644 5 NFIX 3.557653 0.237177 15 ARHGEF7 4.65138 0.930276 5 SLX1B- SOX10 5.000024 2.500012 2 SULT1A4 3.248136 0.216542 15 SLX1A 3.248136 0.216542 15

TABLE 78: Cancer Type LOC606724 3.248136 0.216542 15 GCT_GERM_A ZBTB20 2.616493 0.174433 15

Gene site imp sum imp mean n GLI2 2.424166 0.161611 15 PTPRN2 9.101473 0.110994 82 NFATC1 2.332888 0.155526 15 PRDM16 8.043839 0.113294 71 RPS6KA2 5.447932 0.389138 14 PCDHGA1 2.974028 0.050407 59 MIR548F5 3.459727 0.247123 14 PCDHGA2 2.974028 0.052176 57 IQSEC1 3.360687 0.240049 14 PCDHGA3 2.974028 0.055075 54 GNG7 2.978525 0.212752 14 PCDHGB1 2.974028 0.056114 53 C7orf50 2.941557 0.210111 14 CUX1 2.854477 0.203891 14 TABLE 79: Cancer Type GCT_GERM_B PRKAG2 2.623327 0.187381 14

Gene site imp sum imp mean n ARHGEF10 2.531088 0.180792 14 PTPRN2 19.31761 0.235581 82 MSI2 3.74575 0.288135 13 PRDM16 18.84404 0.265409 71 MYT1L 3.317276 0.255175 13 PCDHGA1 11.03441 0.187024 59 GSE1 2.318814 0.17837 13 PCDHGA2 10.71803 0.188036 57 CMIP 3.895785 0.324649 12 PCDHGA3 9.768867 0.180905 54 ADGRD1 2.926081 0.24384 12 PCDHGB1 9.452481 0.178349 53 ZC3H3 2.88618 0.240515 12 PCDHGA4 9.452481 0.185343 51 GNA12 2.495305 0.207942 12 PCDHGB2 9.136095 0.186451 49 ISLR2 2.4525 0.204375 12 PCDHGA5 8.819709 0.187653 47 TBX4 2.325781 0.193815 12 PCDHGB3 8.396513 0.195268 43 ZC3H12D 2.609661 0.237242 11 PCDHGA6 8.080127 0.202003 40 WNT5A 2.549258 0.231751 11 HDAC4 13.99709 0.3783 37 ACOT7 3.651995 0.365199 10 PCDHGA7 7.447355 0.20128 37 NR2F1-AS1 2.902074 0.290207 10 RBFOX3 9.974812 0.284995 35

BCL11B 2.337443 0.233744 10 PAX6 7.738563 0.221102 35

TSPAN4 2.231434 0.223143 10 PCDHGB4 7.130969 0.203742 35 SND1 4.244156 0.471573 9 PCDHGA8 7.130969 0.203742 35 TRAPPCI 2 3.440843 0.382316 9 DIP2C 10.74588 0.335809 32

ATP11A 3.195474 0.355053 9 PCDHGB5 6.217327 0.194291 32

SSBP3 3.053275 0.339253 9 PCDHGA9 6.217327 0.200559 31 CACNA2D4 2.342664 0.260296 9 SOX2-OT 7.245309 0.249838 29 KCNH2 2.310504 0.256723 9 PCDHGB6 5.584555 0.192571 29

MSRA 3.189972 0.398746 8 PCDHGA10 5.584555 0.199448 28

SYNJ2 2.727817 0.340977 8 GALNT9 5.550231 0.205564 27

DLEU1 2.683196 0.3354 8 ADARB2 7.655789 0.294453 26

CDH4 2.395494 0.299437 8 SHANK2 6.731637 0.258909 26 DNMT3A 2.392436 0.299055 8 AGAP1 9.790224 0.391609 25 TENM2 2.339529 0.292441 8 CAMTAI 7.134214 0.285369 25

SHROOM3 2.255323 0.281915 8 PDGFRA 6.733768 0.269351 25 C19orf25 4.052101 0.578872 7 SATB2 6.469941 0.269581 24 GAK 2.852329 0.407476 7 PCDHGB7 5.165607 0.215234 24

MIR548H4 2.571419 0.367346 7 RPTOR 11.22884 0.488211 23 VPS 13D 2.460848 0.35155 7 NCOR2 7.659987 0.333043 23 STK10 3.198507 0.533084 6 NXN 6.652723 0.289249 23

RADIL 3.066899 0.51115 6 INPP5A 6.507467 0.282933 23 FBXL18 2.858599 0.476433 6 HOXB3 4.777145 0.207702 23 CCDC177 2.672475 0.445412 6 PCDHGA11 4.723644 0.205376 23

RUNDC3A 3.376471 0.675294 5 PRKCZ 8.538897 0.388132 22 CCR6 3.187996 0.637599 5 SKI 7.434102 0.354005 21

TSN AX-DISCI 3.062186 0.612437 5 ZIC4 4.447648 0.211793 21 ARHGEF7 2.892383 0.578477 5 HOXA-AS3 4.239343 0.201873 21 AP2A2 2.57388 0.514776 5 ABR 5.742782 0.287139 20 ARHGAP26 2.557087 0.511417 5 FRMD4A 5.473026 0.273651 20 DTNA 2.421815 0.605454 4 SDK1 4.769503 0.238475 20 TBC1D7 3.196945 1.065648 3 MAD1L1 10.94435 0.576018 19 ZNF423 6.837359 0.359861 19 DLEU1 3.93938 0.492422 8 CASZ1 5.715937 0.300839 19 PPP2R2B 3.861173 0.482647 8 SMG1P2 4.984773 0.262356 19 NAVI 4.691965 0.670281 7 BOLA2 4.984773 0.262356 19 DUSP6 4.203916 0.600559 7 LOC613038 4.984773 0.262356 19 GAK 3.708546 0.529792 7 SEPTIN9 7.182444 0.399025 18 FBXL18 3.708094 0.618016 6 TBC1D16 6.250265 0.347237 18 TSNAX-DISC1 4.859345 0.971869 5 ANKRD11 6.040036 0.335558 18 RUNDC3A 3.725693 0.745139 5 HOXA3 4.762563 0.264587 18 FOXK1 4.554552 0.253031 18 TABLE 80: Cancer Type GCT TERA MCF2L 3.738665 0.207704 18 Gene site imp sum imp mean n OPCML 7.256601 0.426859 17 PTPRN2 17.87431 0.217979 82 PAX6-AS1 3.966804 0.233341 17 PRDM16 15.90775 0.224053 71 RCN1 3.966804 0.233341 17 PCDHGA1 4.154031 0.070407 59 FOXP1 5.738304 0.358644 16 PCDHGA2 4.470417 0.078428 57 GLI2 8.145145 0.54301 15 PCDHGA3 4.018183 0.074411 54 KNDC1 4.826227 0.321748 15 PCDHGB1 4.018183 0.075815 53 SLX1B- PCDHGA4 4.018183 0.078788 51 SULT1A4 3.946591 0.263106 15 PCDHGB2 4.018183 0.082004 49 SLX1A 3.946591 0.263106 15 PCDHGA5 4.295947 0.091403 47 LOC606724 3.946591 0.263106 15 HDAC4 17.99685 0.486401 37 RPS6KA2 7.147811 0.510558 14 RBFOX3 9.614951 0.274713 35 CUX1 6.450239 0.460731 14 PAX6 9.381022 0.268029 35 PRKAG2 5.410973 0.386498 14 DIP2C 9.374361 0.292949 32 CACNA1H 4.776324 0.341166 14 SOX2-OT 4.59365 0.158402 29 MOB2 4.437151 0.316939 14 GALNT9 4.846109 0.179486 27 IQSEC1 4.235867 0.302562 14 SHANK2 5.967514 0.22952 26 MYT1L 5.984541 0.460349 13 AGAP1 10.10199 0.40408 25 MSI2 5.847016 0.44977 13 CAMTAI 9.78895 0.391558 25 RFX4 4.210517 0.323886 13 PDGFRA 6.632905 0.265316 25 KIF26B 3.750733 0.288518 13 MEIS1 6.378497 0.265771 24 FBRSL1 4.946713 0.412226 12 SATB2 5.7202 0.238342 24 ADGRD1 4.94334 0.411945 12 RPTOR 13.34639 0.580278 23 MAML3 3.926583 0.327215 12 NCOR2 9.562957 0.415781 23 RASA3 3.885229 0.323769 12 NXN 5.929367 0.257799 23 CMIP 3.883371 0.323614 12 PRKCZ 6.904633 0.313847 22 ZC3H3 3.800354 0.316696 12 SKI 8.750163 0.416674 21 ZC3H12D 4.564659 0.414969 11 FRMD4A 7.225964 0.361298 20 TBCD 3.721824 0.338348 11 SDK1 6.522443 0.326122 20 TRAPPCI 2 5.592923 0.621436 9 MAD1L1 11.14816 0.586745 19 SND1 5.51222 0.612469 9 CASZ1 7.408534 0.389923 19 ATP11A 4.466621 0.496291 9 ZNF423 6.4314 0.338495 19 RUNX1 4.068281 0.452031 9 SMG1P2 6.203487 0.326499 19 AXIN2 3.879315 0.431035 9 BOLA2 6.203487 0.326499 19 TSPAN9 3.811645 0.423516 9 LOC613038 6.203487 0.326499 19 CACNA2D4 3.738174 0.415353 9 TBC1D16 7.737052 0.429836 18 VRK2 4.449892 0.556237 8 ANKRD11 6.607758 0.367098 18 AFF3 4.052908 0.506614 8 FOXK1 6.211291 0.345072 18 MCF2L 5.36849 0.298249 18 MGMT 4.018641 0.446516 9 RBFOX1 3.760071 0.208893 18 ASAP1 3.692621 0.410291 9 PAX6-AS1 5.905532 0.347384 17 DLEU1 5.903349 0.737919 8 RCN1 5.905532 0.347384 17 MSRA 4.837281 0.60466 8 OPCML 3.974252 0.23378 17 LHX4 4.117693 0.514712 8 FOXP1 5.642803 0.352675 16 DNMT3A 3.852664 0.481583 8 SORBS2 5.266291 0.329143 16 RORA 3.836926 0.479616 8 NAV2 4.03055 0.251909 16 VRK2 3.678644 0.45983 8 BAIAP2 5.289703 0.352647 15 NAVI 4.495543 0.64222 7 GLI2 4.695861 0.313057 15 GAK 4.323294 0.617613 7 KIRREL3 4.319986 0.287999 15 FBXL18 5.140929 0.856822 6 NHX 4.191253 0.279417 15 RUNDC3A 5.185511 1.037102 5 ZBTB20 4.018173 0.267878 15 ARHGEF7 4.182116 0.836423 5 COL23A1 3.724193 0.24828 15 BCAR1 3.830265 0.766053 5 RPS6KA2 6.902618 0.493044 14 TSNAX-DISC1 3.757437 0.751487 5

ARHGEF10 5.265573 0.376112 14 PRKAG2 4.694434 0.335317

C7orf50 4.404999 0.314643 14

TABLE 81: Cancer Type GCT_YOLKSAC MOB2 4.213358 0.300954 14 Gene site imp sum imp mean n TBX5 4.202229 0.300159 14 PTPRN2 4.99835 0.060955 82 MSI2 5.703035 0.438695 13 PRDM16 5.801836 0.081716 71 MIR9-3HG 4.989205 0.383785 13 PCDHGA3 2.502743 0.046347 54 SPTBN4 4.626165 0.355859 13 PCDHGB1 2.502743 0.047222 53 RFX4 4.49132 0.345486 13 PCDHGA4 2.502743 0.049073 51 MYT1L 4.371628 0.336279 13 HDAC4 11.14682 0.301266 37 GSE1 3.778988 0.290691 13 RBFOX3 4.511934 0.128912 35 ZC3H3 5.296445 0.44137 12 PAX6 3.453012 0.098657 35 LRBA 5.208066 0.434005 12 DIP2C 6.987383 0.218356 32 ADGRD1 4.681358 0.390113 12 SHANK2 3.496089 0.134465 26 CMIP 4.38781 0.365651 12 AGAP1 7.738 0.30952 25 TNS3 4.246333 0.353861 12 CAMTAI 4.366767 0.174671 25 FBRSL1 4.013577 0.334465 12 PDGFRA 2.790693 0.111628 25

MIRLET7BHG 3.678431 0.306536 12 RPTOR 8.298155 0.360789 23 ANAPC16 4.975722 0.452338 11 NCOR2 6.257643 0.272071 23 CTBP2 4.523532 0.41123 11 NXN 5.325576 0.231547 23 ZC3H12D 4.448097 0.404372 11 PRKCZ 3.769165 0.171326 22 TBCD 4.235393 0.385036 11 SKI 6.772891 0.322519 21 RAD51B 4.092926 0.372084 11 ABR 2.749045 0.137452 20 CCDC140 3.835235 0.348658 11 SDK1 2.517911 0.125896 20 PCDHGC3 3.759371 0.341761 11 MAD1L1 8.328458 0.43834 19 TP73 5.395891 0.539589 10 CASZ1 3.960116 0.208427 19 KLHL29 4.211312 0.421131 10 KCNQ1 3.539228 0.186275 19 RGS12 4.028726 0.402873 10 SMG1P2 2.739954 0.144208 19 SH3RF3 3.735144 0.373514 10 BOLA2 2.739954 0.144208 19 SND1 5.405361 0.600596 9 LOC613038 2.739954 0.144208 19 ATP11A 5.190054 0.576673 9 FOXK1 6.469351 0.359408 18 ADAMTS2 4.816169 0.53513 9 TBC1D16 4.904479 0.272471 18 CACNA2D4 4.7708 0.530089 9 SEPTIN9 2.749956 0.152775 18 SYNJ2 2.540975 0.317622 8 PAX6-AS1 2.751846 0.161873 17 RXRA 3.981993 0.568856 7 RCN1 2.751846 0.161873 17 CXXC5 3.667471 0.523924 7 FOXP1 4.98589 0.311618 16 MIR548H4 2.932401 0.418914 7 EBF3 3.495841 0.21849 16 OTX2-AS1 2.720198 0.3886 7

GLI2 3.827065 0.255138 15 CRADD 4.975217 0.829203 6 SLX1B- FBXL18 4.219659 0.703277 6 SULT1A4 3.01742 0.201161 15 TRAK1 3.020541 0.503424 6 SLX1A 3.01742 0.201161 15 MYO 16 2.676559 0.446093 6

LOC606724 3.01742 0.201161 15 FMNL2 2.639078 0.439846 6

PRKAG2 4.033346 0.288096 14 RUNDC3A 3.406303 0.681261 5

CUX1 3.696431 0.264031 14 ARHGEF7 3.345842 0.669168 5

C7orf50 3.680209 0.262872 14 BCAR1 2.989539 0.597908 5

IQSEC1 3.21317 0.229512 14 FAM53B 2.816195 0.563239 5 ARHGEF10 2.563992 0.183142 14 AP2A2 2.728119 0.545624 5 RPS6KA2 2.556839 0.182631 14 ZMIZ1 2.638003 0.659501 4 MSI2 5.590865 0.430067 13 DUSP5 2.553512 0.638378 4 GSE1 5.181869 0.398605 13

LPP 2.466561 0.61664 4

CMIP 5.62367 0.468639 12 DINA 2.44406 0.611015 4

ZC3H3 3.288293 0.274024 12 SLC6A9 2.449393 0.816464 3

GNA12 3.222275 0.268523 12 RALGAPA2 3.817503 1.908751 2

RASA3 2.940557 0.245046 12 RAB11FIP3 2.739979 1.36999 2

MEIS2 2.75406 0.229505 12 ERI3 2.704809 1.352405 2

TBX4 2.530601 0.210883 12

TRIM65 2.64978 1.32489 2

ADGRD1 2.482515 0.206876 12 KCNV2 2.795229 2.795229 1

GLUD1P2 4.223631 0.383966 11

RAD51B 3.500001 0.318182 11 Cancer Type

TABLE 82:

CTBP2 3.119431 0.283585 11 GG

VGLL4 2.636152 0.23965 11 Gene site imp sum imp mean n

ZC3H12D 2.51935 0.229032 11 PTPRN2 25.34553 0.309092 82

FGFR2 2.458012 0.223456 11 PRDM16 29.31827 0.412933 71

TSPAN4 3.917092 0.391709 10 PCDHGA1 9.194939 0.155846 59

ACOT7 3.318852 0.331885 10 PCDHGA2 8.878553 0.155764 57

AKAP13 3.17245 0.317245 10 PCDHGA3 9.194939 0.170277 54

CHST11 2.657571 0.265757 10 PCDHGB1 9.194939 0.173489 53

SH3RF3 2.64241 0.264241 10 PCDHGA4 9.194939 0.180293 51

KLHL29 2.557927 0.255793 10 PCDHGB2 9.511325 0.194109 49 SND1 5.450853 0.60565 9 PCDHGA5 8.625482 0.183521 47 ATP11A 5.3636 0.595956 9 PCDHGB3 7.99271 0.185877 43 MGMT 3.116205 0.346245 9 PCDHGA6 7.676324 0.191908 40

AXIN2 2.804539 0.311615 9 HDAC4 17.47931 0.472414 37 TSPAN9 2.737463 0.304163 9 PCDHGA7 7.043552 0.190366 37 MACROD1 3.046225 0.380778 8 PAX6 15.42818 0.440805 35 TRIM71 2.748109 0.343514 8 RBFOX3 13.02851 0.372243 35

DNMT3A 2.729414 0.341177 8 PCDHGB4 6.813347 0.194667 35 LINC00311 2.713531 0.339191 8 PCDHGA8 6.813347 0.194667 35 DLEU1 2.630831 0.328854 8 DIP2C 13.69455 0.427955 32 TRAPPC9 2.590092 0.323761 8 PCDHGB5 6.813347 0.212917 32 PCDHGA9 6.813347 0.219785 31 PRKAG2 5.297489 0.378392 14 SOX2-OT 12.0816 0.416607 29 MSI2 8.236219 0.633555 13 PCDHGB6 5.734782 0.197751 29 GSE1 6.344139 0.488011 13 PCDHGA10 5.734782 0.204814 28 KIF26B 5.955905 0.458147 13 SHANK2 8.764206 0.337085 26 RFX4 5.736025 0.441233 13 ADARB2 7.918374 0.304553 26 MYT1L 5.609456 0.431497 13 AGAP1 10.13629 0.405452 25 SPTBN4 5.601586 0.430891 13 CAMTAI 8.46813 0.338725 25 ZC3H3 6.473633 0.539469 12 PDGFRA 6.710821 0.268433 25 TNS3 6.410599 0.534217 12 SATB2 8.66246 0.360936 24 CMIP 5.938821 0.494902 12 MEIS1 7.857727 0.327405 24 TBX4 5.883156 0.490263 12 RPTOR 13.73371 0.597118 23 MIRLET7BHG 5.653598 0.471133 12 NCOR2 8.935001 0.388478 23 ADGRD1 5.444733 0.453728 12 HOXB3 7.16603 0.311567 23 MEGF6 5.315412 0.442951 12 INPP5A 7.097167 0.308572 23 MAML3 5.176265 0.431355 12 NXN 6.273458 0.272759 23 ZC3H12D 7.116886 0.64699 11 RIMBP2 5.900218 0.256531 23 VGLL4 5.735209 0.521383 11 PRKCZ 7.814398 0.3552 22 RAD51B 5.701272 0.518297 11 SKI 13.60206 0.647717 21 SPON2 5.238344 0.476213 11 SIM2 7.465344 0.355493 21 ACOT7 5.762192 0.576219 10 ZIC4 6.097637 0.290364 21 OTX1 5.712617 0.571262 10 ABR 9.118534 0.455927 20 IGF1R 5.600259 0.560026 10 FRMD4A 8.819341 0.440967 20 SND1 6.771402 0.752378 9 SDK1 5.901252 0.295063 20 ATP11A 6.746126 0.74957 9 MAD1L1 13.06315 0.687534 19 ASAP1 5.982675 0.664742 9 ZNF423 10.99371 0.578616 19 AXIN2 5.952509 0.66139 9 CASZ1 8.968132 0.472007 19 TSPAN9 5.389787 0.598865 9 SMG1P2 6.003089 0.315952 19 LHX4 6.42543 0.803179 8 BOLA2 6.003089 0.315952 19 LINC00311 5.699983 0.712498 8 LOC613038 6.003089 0.315952 19 ASPSCR1 5.1551 0.644388 8 FOXK1 8.454174 0.469676 18 DUSP6 6.819031 0.974147 7 ANKRD11 6.82245 0.379025 18 LINC00461 5.320591 0.760084 7 MCF2L 6.745235 0.374735 18 FBXL18 5.134469 0.855745 6 SEPTIN9 5.530185 0.307233 18 TBC1D16 5.445359 0.30252 18 TABLE 83: Cancer Type GNT_ND OPCML 8.474402 0.498494 17 Gene site imp sum imp mean n TBX15 7.334093 0.431417 17 PTPRN2 11.49988 0.140242 82 PAX6-AS1 5.324681 0.313217 17 PRDM16 10.36101 0.14593 71 RCN1 5.324681 0.313217 17 PCDHGA1 2.628587 0.044552 59 FOXP1 8.364046 0.522753 16 PCDHGA2 2.628587 0.046116 57 NAV2 5.598424 0.349902 16 PCDHGA3 2.944973 0.054537 54 GLI2 12.02527 0.801685 15 PCDHGB1 2.944973 0.055566 53 ZBTB20 6.936051 0.462403 15 PCDHGA4 2.628587 0.051541 51 NHX 5.624007 0.374934 15 PCDHGA5 2.628587 0.055927 47 LRMDA 5.508807 0.367254 15 PCDHGB3 3.577745 0.083203 43 RPS6KA2 7.775948 0.555425 14 PCDHGA6 2.807114 0.070178 40 C7orf50 6.54699 0.467642 14 HDAC4 9.886463 0.267202 37 CUX1 6.333639 0.452403 14 RBFOX3 7.509306 0.214552 35 PAX6 5.357323 0.153066 35 ADGRB1 5.36614 0.596238 9

DIP2C 8.208457 0.256514 32 ATP11A 5.061607 0.562401 9

SOX2-OT 8.728742 0.300991 29 SND1 3.843547 0.427061 9

ADARB2 3.267447 0.125671 26 ASAP1 3.648247 0.405361 9

CAMTAI 6.086511 0.24346 25 NOTCH1 3.530086 0.392232 9

PDGFRA 5.626339 0.225054 25 ZNF833P 3.196336 0.355148 9

AGAP1 5.331453 0.213258 25 RUNX1 3.10094 0.344549 9

PCDHGB7 2.759321 0.114972 24 TRAPPCI 2 3.083809 0.342645 9

RPTOR 6.427431 0.279454 23 KCNMA1 2.976558 0.330729 9

NCOR2 4.320246 0.187837 23 AXIN2 2.910305 0.323367 9

RIMBP2 3.703202 0.161009 23 TSPAN9 2.775641 0.308405 9

NXN 2.693327 0.117101 23 ADAMTS2 2.645081 0.293898 9

PRKCZ 4.280145 0.194552 22 GPC6 2.638177 0.293131 9

SKI 8.403006 0.400143 21 LINC00311 2.95298 0.369122 8

FRMD4A 5.84965 0.292483 20 GRIK2 2.800493 0.350062 8

ZNF423 7.989529 0.420502 19 ESRRG 2.688412 0.336052 8

MAD1L1 6.742287 0.354857 19 MSRA 2.685591 0.335699 8

SMG1P2 5.493399 0.289126 19 DUSP6 4.29677 0.613824 7

BOLA2 5.493399 0.289126 19 LINC00461 3.812362 0.544623 7

LOC613038 5.493399 0.289126 19 NAVI 3.484596 0.497799 7

CASZ1 3.400766 0.178988 19 SOX6 3.175191 0.453599 7

MCF2L 4.583134 0.254619 18 FHIT 2.908577 0.415511 7

FOXK1 3.13369 0.174094 18 LHX2 2.781746 0.397392 7

SEPTIN9 2.728531 0.151585 18 LINC01140 2.688698 0.3841 7

TBX15 4.164269 0.244957 17 CXXC5 2.684351 0.383479 7

OPCML 4.137456 0.24338 17 FBXL18 3.966795 0.661133 6

FOXP1 4.774371 0.298398 16 FAM181A 3.321132 0.553522 6

GLI2 9.168754 0.61125 15 MYO 16 3.110838 0.518473 6

KIRREL3 3.814096 0.254273 15 RUNDC3A 4.597805 0.919561 5

LRMDA 3.771615 0.251441 15 PRR5L 3.231042 0.646208 5

ZBTB20 3.032585 0.202172 15 TSN AX-DISCI 3.12277 0.624554 5

CUX1 3.015518 0.215394 14 ARHGEF7 3.056062 0.611212 5

MYT1L 4.276737 0.32898 13 THRB 2.723247 0.544649 5

MSI2 3.848376 0.296029 13 RBMS3 3.544672 0.886168 4

RFX4 2.894789 0.222676 13 STAP2 3.066668 0.766667 4

SPTBN4 2.681901 0.2063 13 LINC00856 2.744387 0.686097 4

CMIP 5.471674 0.455973 12 GRIN2B 3.331979 1.11066 3

ZC3H3 3.35249 0.279374 12 DAGLB 2.993153 0.997718 3

MIRLET7BHG 2.987944 0.248995 12 SOXIO 4.597364 2.298682 2

ADGRD1 2.913691 0.242808 12 SLC25A10 2.817577 1.408788 2

TNS3 2.719485 0.226624 12

TBX4 2.638051 0.219838 12 TABLE 84: Cancer Type HGAP

FGFR2 4.110203 0.373655 11 Gene site imp sum imp mean n

RAD51B 3.301774 0.300161 11 PTPRN2 24.4535 0.298213 82

VGLL4 3.180565 0.289142 11 PRDM16 19.6305 0.276486 71

LBX1-AS1 5.781882 0.578188 10 PCDHGA1 12.32161 0.208841 59

ACOT7 4.055634 0.405563 10 PCDHGA2 12.00523 0.210618 57

SH3RF3 3.706206 0.370621 10 PCDHGA3 10.70417 0.198225 54 PCDHGB1 10.70417 0.201966 53 TBX15 6.224702 0.366159 17 PCDHGA4 10.56268 0.207111 51 PAX6-AS1 5.918508 0.348148 17 PCDHGB2 9.92991 0.202651 49 RCN1 5.918508 0.348148 17 PCDHGA5 9.267611 0.197183 47 NAV2 5.629729 0.351858 16 PCDHGB3 8.503787 0.197762 43 SORBS2 5.629435 0.35184 16 PCDHGA6 8.318986 0.207975 40 FOXP1 5.595224 0.349701 16 HDAC4 14.08724 0.380736 37 GLI2 10.32721 0.688481 15 PCDHGA7 7.686214 0.207736 37 BAIAP2 6.495956 0.433064 15 PAX6 12.7288 0.36368 35 SLX1B- SULT1A4 5.023958 0.334931 15 RBFOX3 10.1735 0.290671 35 SLX1A 5.023958 0.334931 15 PCDHGB4 7.686214 0.219606 35 LOC606724 5.023958 0.334931 15 PCDHGA8 7.686214 0.219606 35 COL23A1 4.715474 0.314365 15 DIP2C 12.73497 0.397968 32 LRMDA 4.577949 0.305197 15 PCDHGB5 7.053442 0.22042 32 C7orf50 5.186346 0.370453 14 PCDHGA9 7.053442 0.22753 31 RPS6KA2 5.142733 0.367338 14 SOX2-OT 11.2276 0.387159 29 MSI2 6.845577 0.526583 13 PCDHGB6 6.465793 0.222958 29 MYT1L 5.449512 0.419193 13 PCDHGA10 6.149407 0.219622 28 SPTBN4 5.417407 0.416724 13 SHANK2 5.314729 0.204413 26 RFX4 5.175823 0.39814 13 CAMTAI 9.327048 0.373082 25 MIR9-3HG 4.57855 0.352196 13 AGAP1 8.252622 0.330105 25

ZC3H3 7.215793 0.601316 12 PDGFRA 5.639808 0.225592 25 CMIP 5.485913 0.457159 12 SATB2 7.592696 0.316362 24 MIRLET7BHG 4.868696 0.405725 12 MEIS1 7.443434 0.310143 24 ADGRD1 4.826819 0.402235 12 PCDHGB7 5.833021 0.243043 24 VGLL4 5.635849 0.51235 11 RPTOR 9.629501 0.418674 23 FGFR2 5.206888 0.473353 11 NCOR2 7.868925 0.342127 23 RAD51B 5.131747 0.466522 11 INPP5A 6.119086 0.266047 23 LBX1-AS1 6.227283 0.622728 10 NXN 5.884705 0.255857 23 AKAP13 4.714159 0.471416 10 RIMBP2 5.5258 0.240252 23 CHST11 4.491095 0.449109 10 PCDHGA11 5.385766 0.234164 23 OTX1 4.46974 0.446974 10 PRKCZ 6.311547 0.286888 22

ADGRB1 5.954707 0.661634 9 SKI 10.8685 0.517548 21 ATP11A 5.724009 0.636001 9 SIM2 7.261332 0.345778 21 TSPAN9 5.3629 0.595878 9 HOXA-AS3 4.630083 0.22048 21 ASAP1 4.940787 0.548976 9 FRMD4A 5.211017 0.260551 20 NEAT1 4.925836 0.547315 9 ABR 4.617089 0.230854 20 ADAMTS2 4.535995 0.503999 9 MAD1L1 12.86645 0.677181 19

KCNH2 4.42098 0.49122 9 ZNF423 9.219133 0.485218 19 LINC00311 4.67027 0.583784 8 SMG1P2 7.153844 0.376518 19 ASPSCR1 4.437863 0.554733 8 BOLA2 7.153844 0.376518 19 DUSP6 6.379713 0.911388 7 LOC613038 7.153844 0.376518 19 LINC00461 5.629928 0.804275 7 KCNQ1 6.379811 0.33578 19

NAVI 5.075466 0.725067 7 SEPTIN9 5.374577 0.298588 18 FAM181A 4.627874 0.771312 6 FOXK1 5.363211 0.297956 18 RUNDC3A 4.993431 0.998686 5 MCF2L 5.30329 0.294627 18 TSNAX-DISC1 4.881004 0.976201 5 ANKRD11 4.888001 0.271556 18 STAP2 4.615269 1.153817 4 OPCML 7.865005 0.462647 17 PPP2R2A 2.952354 0.210882 14

TABLE 85: Cancer Type RPS6KA2 2.287237 0.163374 14

HGNET_BCOR_Fus

MYT1L 2.76295 0.212535 13

Gene site imp sum imp mean n

GSE1 2.657642 0.204434 13 PTPRN2 8.695059 0.106037 82

MSI2 2.521813 0.193986 13 PRDM16 9.086343 0.127977 71

CMIP 3.396818 0.283068 12 HDAC4 6.063527 0.163879 37

MIRLET7BHG 2.83575 0.236313 12 RBFOX3 9.137664 0.261076 35

TNS3 2.352031 0.196003 12 PAX6 7.319629 0.209132 35

RAD51B 2.971816 0.270165 11 DIP2C 3.760208 0.117507 32

SLC9A3 2.200231 0.200021 11 SOX2-OT 4.818061 0.16614 29

ACOT7 3.51291 0.351291 10 GALNT9 2.927062 0.10841 27

GRID1 2.803667 0.280367 10 ADARB2 2.991152 0.115044 26

FMN1 2.770846 0.277085 10 SHANK2 2.288362 0.088014 26

LBX1-AS1 2.677234 0.267723 10 CAMTAI 5.018457 0.200738 25

NR2F1-AS1 2.490698 0.24907 10 AGAP1 4.193611 0.167744 25

NR5A2 2.331394 0.233139 10 PDGFRA 3.368038 0.134722 25

ATP11A 4.224137 0.469349 9 SATB2 4.754808 0.198117 24

SND1 3.840995 0.426777 9 RPTOR 5.570949 0.242215 23

AXIN2 2.695001 0.299445 9 NCOR2 4.164385 0.18106 23

ASAP1 2.500677 0.277853 9 NXN 3.100555 0.134807 23

RUNX1 2.490049 0.276672 9 RIMBP2 2.694694 0.117161 23

TSPAN9 2.272582 0.252509 9 PRKCZ 3.816924 0.173497 22

LHX4 10.54497 1.318122 8 SKI 7.813392 0.372066 21

DLEU1 3.431598 0.42895 8 ZIC4 2.983964 0.142094 21

ESRRG 2.898586 0.362323 8 SIM2 2.501738 0.11913 21

NR2E1 2.736802 0.3421 8 FRMD4A 3.927263 0.196363 20

LINC00311 2.598862 0.324858 8 SDK1 3.048273 0.152414 20

MSRA 2.415583 0.301948 8 MAD1L1 7.395283 0.389225 19

AFF3 2.262456 0.282807 8 ZNF423 5.272324 0.277491 19

MCC 2.244619 0.280577 8 SMG1P2 2.630407 0.138442 19

LHX2 3.357274 0.479611 7 BOLA2 2.630407 0.138442 19

CDYL 3.302677 0.471811 7 LOC613038 2.630407 0.138442 19

DUSP6 3.1137 0.444814 7 CASZ1 2.241851 0.117992 19

EBF2 IH ITlb 0.393897 7 FOXK1 5.409171 0.30051 18

TACC2 2.218472 0.316925 7 ANKRD11 3.080524 0.17114 18

WNT6 3.408692 0.568115 6 SEPTIN9 2.188115 0.121562 18

SATB2-AS1 3.133854 0.522309 6 OPCML 4.080539 0.240032 17

PAX1 3.129753 0.521625 6 FOXP1 2.904035 0.181502 16

FAM181A 2.728883 0.454814 6 SORBS2 2.43297 0.152061 16

ROR1 2.626658 0.437776 6 EBF3 2.260408 0.141276 16

CALD1 2.603399 0.4339 6 GLI2 7.058917 0.470594 15

FBXL18 2.481157 0.413526 6 BAIAP2 3.808955 0.25393 15

VAX2 2.406599 0.4011 6 EMX2OS 3.695112 0.246341 15

AGAP2 3.285679 0.657136 5 ZBTB20 2.56894 0.171263 15

RUNDC3A 3.030862 0.606172 5 COL23A1 2.468116 0.164541 15

ARHGEF7 2.881976 0.576395 5 CUX1 3.79665 0.271189 14

MNX1 2.619473 0.523895 5 PRKAG2 3.465237 0.247517 14

CCR6 2.475371 0.495074 5 TSN AX-DISCI 2.4505 0.4901 5 RCN1 4.633497 0.272559 17 VAV2 2.274903 0.454981 5 EBF3 6.410784 0.400674 16 DTNA 2.296774 0.574194 4 NAV2 5.476947 0.342309 16 RBMS3 2.192891 0.548223 4 FOXP1 5.298004 0.331125 16 LHX5 2.424746 0.808249 3 GLI2 9.711034 0.647402 15 GRIN2B 2.213335 0.737778 3 ZBTB20 5.106156 0.34041 15 ICAM5 3.406453 1.703226 2 NFIX 4.639626 0.309308 15 SOXIO 2.655583 1.327791 2 RPS6KA2 6.94009 0.495721 14

CUX1 5.863901 0.41885 14

TABLE 86: Cancer Type PRKAG2 5.374298 0.383878 14

HGNET_BCOR_ITD

MOB2 4.099752 0.292839 14

Gene site imp sum imp mean n

C7orf50 4.038509 0.288465 14 PTPRN2 19.0009 0.231718 82

ARHGEF10 3.669185 0.262085 14 PRDM16 12.49491 0.175985 71

MSI2 5.523823 0.424909 13 HDAC4 9.725468 0.26285 37

KIF26B 4.504027 0.346464 13 RBFOX3 9.104029 0.260115 35

MYT1L 3.890961 0.299305 13 PAX6 5.207629 0.148789 35

GSE1 3.703098 0.284854 13 DIP2C 11.88098 0.371281 32

CMIP 5.333268 0.444439 12 SOX2-OT 5.587138 0.19266 29

MIRLET7BHG 5.215694 0.434641 12 GALNT9 5.693217 0.21086 27

ZC3H3 4.979102 0.414925 12 SHANK2 6.847045 0.263348 26

MEGF6 4.063235 0.338603 12 ADARB2 6.108296 0.234934 26

RASA3 3.849104 0.320759 12 AGAP1 7.80205 0.312082 25

FBRSL1 3.633048 0.302754 12 CAMTAI 6.115775 0.244631 25

WNT5A 5.464912 0.49681 11 PDGFRA 4.349081 0.173963 25

VGLL4 4.877535 0.443412 11 SATB2 8.841752 0.368406 24

GLUD1P2 4.790063 0.43546 11 RPTOR 12.31693 0.535519 23

ZC3H12D 4.175176 0.379561 11 NCOR2 6.809669 0.296073 23

RAD51B 4.078728 0.370793 11 RIMBP2 6.235802 0.271122 23

CTBP2 3.650224 0.331839 11 NXN 6.083741 0.26451 23

ACOT7 4.879087 0.487909 10 PRKCZ 6.466025 0.29391 22

TSPAN4 4.690828 0.469083 10 SKI 10.71215 0.510102 21 NR2F1-AS1 4.247365 0.424737 10 SIM2 4.336066 0.206479 21 SH3RF3 4.173414 0.417341 10 FRMD4A 6.19085 0.309543 20

ATP11A 7.025335 0.780593 9 ABR 4.797977 0.239899 20

SND1 6.735886 0.748432 9 SDK1 3.99684 0.199842 20

ADAMTS2 6.151802 0.683534 9 MAD1L1 12.21106 0.642687 19

AXIN2 5.23349 0.581499 9 ZNF423 7.681063 0.404266 19

TSPAN9 5.193186 0.577021 9 CASZ1 6.387941 0.336207 19

KAZN 4.72919 0.525466 9 SMG1P2 5.249663 0.276298 19

RUNX1 4.116735 0.457415 9 BOLA2 5.249663 0.276298 19

NOTCH1 3.965165 0.440574 9 LOC613038 5.249663 0.276298 19

CACNA2D4 3.931642 0.436849 9 FOXK1 6.729168 0.373843 18

ASAP1 3.624512 0.402724 9 TBC1D16 4.621618 0.256757 18

LHX4 10.1066 1.263325 8 SEPTIN9 4.199987 0.233333 18

RGS20 4.895937 0.611992 8 MCF2L 4.174511 0.231917 18

MSRA 4.804917 0.600615 8 OPCML 7.484302 0.440253 17 LINC00311 4.611899 0.576487 8 PAX6-AS1 4.633497 0.272559 17 DLEU1 4.342586 0.542823 8 PPP2R2B 3.963711 0.495464 8 MAD1L1 9.370734 0.493197 19 NAVI 4.892823 0.698975 7 ZNF423 6.724885 0.353941 19 DUSP6 4.672967 0.667567 7 KCNQ1 3.554033 0.187054 19 CPQ 3.824497 0.637416 6 CASZ1 3.474681 0.182878 19 CRADD 3.798747 0.633124 6 SMG1P2 3.135122 0.165006 19 MIR100HG 3.683164 0.613861 6 BOLA2 3.135122 0.165006 19 TSN AX-DISCI 5.490268 1.098054 5 LOC613038 3.135122 0.165006 19 ARHGEF7 4.757762 0.951552 5 SEPTIN9 5.11153 0.283974 18 RUNDC3A 3.670234 0.734047 5 FOXK1 3.764631 0.209146 18 RBMS3 3.732722 0.933181 4 RBFOX1 3.750624 0.208368 18 DTNA 3.700063 0.925016 4 TBC1D16 3.252347 0.180686 18 DAGLB 3.795143 1.265048 3 ANKRD11 2.628618 0.146034 18 GRIN2B 3.695401 1.2318 3 FOXP1 4.369803 0.273113 16 SOXIO 4.134033 2.067016 2 ZBTB20 4.816536 0.321102 15 SLC25A10 4.098196 2.049098 2 BAIAP2 4.473171 0.298211 15 ANKLE2 3.947945 1.973972 2 GLI2 3.099998 0.206667 15

CUX1 4.203818 0.300273 14

TABLE 87: Cancer Type HGNET_BEND2 RPS6KA2 4.011043 0.286503 14 Gene site inip suni imp_mean n PRKAG2 3.903697 0.278835 14 PTPRN2 11.65657 0.142153 82 IQSEC1 2.754749 0.196768 14 PRDM16 12.43657 0.175163 71 MSI2 5.503119 0.423317 13 PCDHGA1 4.072552 0.069026 59 RFX4 4.552872 0.350221 13 PCDHGA2 3.756166 0.065898 57 MYT1L 3.535586 0.271968 13 PCDHGA3 3.756166 0.069559 54 CLYBL 2.869046 0.220696 13 PCDHGB1 3.756166 0.070871 53 TNS3 6.128524 0.51071 12 PCDHGA4 3.756166 0.07365 51 CMIP 4.165661 0.347138 12 PCDHGB2 3.43978 0.0702 49 ADGRD1 3.521824 0.293485 12 PCDHGA5 3.43978 0.073187 47 MEGF6 3.426526 0.285544 12 PCDHGB3 3.123394 0.072637 43 GNA12 2.844038 0.237003 12 HDAC4 9.431211 0.254898 37 SPON2 4.630129 0.420921 11 PAX6 8.609217 0.245978 35 ZC3H12D 3.845786 0.349617 11 RBFOX3 5.355051 0.153001 35 TBCD 2.7058 0.245982 11 DIP2C 8.138146 0.254317 32 TSPAN4 3.978882 0.397888 10 SOX2-OT 4.38671 0.151266 29 CHST11 3.670097 0.36701 10 GALNT9 3.528299 0.130678 27 AUTS2 3.093549 0.309355 10 ADARB2 4.905996 0.188692 26 NR2F1-AS1 3.03703 0.303703 10 CAMTAI 5.38565 0.215426 25 ACOT7 3.002526 0.300253 10 AGAP1 5.293279 0.211731 25 LMF1 2.675611 0.267561 10 PDGFRA 5.028399 0.201136 25 ATP11A 5.470725 0.607858 9 SATB2 5.258892 0.219121 24 SND1 5.344849 0.593872 9 NXN 8.321484 0.361804 23 ADAMTS2 3.427103 0.380789 9 RPTOR 5.518358 0.239929 23 KAZN 3.061964 0.340218 9 RIMBP2 3.241004 0.140913 23 CACNA2D4 2.703012 0.300335 9 PRKCZ 6.480769 0.29458 22 AXIN2 2.666413 0.296268 9 SKI 4.733328 0.225397 21 TSPAN9 2.628527 0.292059 9

FRMD4A 5.660585 0.283029 20 DLEU1 4.043543 0.505443 8 ABR 5.165881 0.258294 20 LHX4 4.02215 0.502769 8 SDK1 2.552309 0.127615 20 DNMT3A 3.144953 0.393119 8 PPP2R2B 3.102758 0.387845 8 SKI 4.368183 0.208009 21 MACROD1 3.028968 0.378621 8 ABR 3.446806 0.17234 20 AFF3 2.958851 0.369856 8 SDK1 2.216034 0.110802 20 DLX5 2.698816 0.337352 8 MAD1L1 3.129601 0.164716 19 TRIM2 3.572583 0.510369 7 ZNF423 2.543212 0.133853 19 NAVI 3.009561 0.429937 7 KCNQ1 1.500359 0.078966 19 C19orf25 2.80825 0.401179 7 TBC1D16 2.402423 0.133468 18

LHX2 2.68695 0.38385 7 FOXK1 2.33257 0.129587 18 FAM181A 3.475105 0.579184 6 MCF2L 1.418762 0.07882 18 SATB2-AS1 3.344044 0.557341 6 SEPTIN9 1.396595 0.077589 18 CELSR1 3.153194 0.525532 6 OPCML 2.399511 0.141148 17 FMNL2 3.035279 0.50588 6 EBF3 2.809053 0.175566 16

DNAJC17 2.948643 0.491441 6 FOXP1 1.790086 0.11188 16 LRRFIP1 2.732459 0.45541 6 GLI2 2.267675 0.151178 15

TSN AX-DISCI 3.759786 0.751957 5 NFATC1 1.58193 0.105462 15 ARHGEF7 3.698422 0.739684 5 SLX1B- SULT1A4 1.518958 0.101264 15 BCAR1 2.750124 0.550025 5

SLX1A 1.518958 0.101264 15 NPHP4 2.647374 0.529475 5 LOC606724 1.518958 0.101264 15

VOPP1 2.754547 0.688637 4 RPS6KA2 2.496148 0.178296 14 EXT1 2.639232 0.659808 4

CUX1 1.532479 0.109463 14 GRIN2B 3.046582 1.015527 3

GSE1 2.132961 0.164074 13 DAGLB 2.845096 0.948365 3 SOX10 3.388138 1.694069 2 MSI2 2.033189 0.156399 13

MYT1L 1.337815 0.102909 13

TABLE 88: Cancer Type HGNET_CXXC5 CMIP 1.709623 0.142469 12

MIRLET7BHG 1.69019 0.140849 12 Gene site imp sum imp mean n

CSMD1 1.492681 0.12439 12 PTPRN2 2.49268 0.030399 82

FBRSL1 1.472528 0.122711 12 PRDM16 8.149901 0.114787 71

VGLL4 1.298956 0.118087 11 PCDHGA1 1.396595 0.023671 59

GRID1 1.743341 0.174334 10

PCDHGA2 1.396595 0.024502 57

GAS7 1.704292 0.170429 10 PCDHGA3 1.396595 0.025863 54

RGS12 1.392098 0.13921 10 PCDHGB1 1.396595 0.026351 53

ATP11A 1.748129 0.194237 9 PCDHGA4 1.396595 0.027384 51

ADAMTS2 1.58545 0.176161 9 PCDHGB2 1.396595 0.028502 49

ASAP1 1.516276 0.168475 9

PCDHGA5 1.396595 0.029715 47

TSPAN9 1.397071 0.15523 9 PCDHGB3 1.396595 0.032479 43

SND1 1.289476 0.143275 9 PCDHGA6 1.396595 0.034915 40

TRAPPCI 2 1.279397 0.142155 9 HDAC4 6.832493 0.184662 37

AFF3 1.779233 0.222404 8 PCDHGA7 1.396595 0.037746 37

SMAD3 1.598138 0.199767 8

PAX6 2.536371 0.072468 35

LINC00311 1.476182 0.184523 8 DIP2C 2.990636 0.093457 32

DLEU1 1.384494 0.173062 8 SOX2-OT 1.396595 0.048158 29

GAK 1.678084 0.239726 7 ADARB2 1.396595 0.053715 26

C19orf25 1.61115 0.230164 7 CAMTAI 3.160176 0.126407 25

CDYL 1.563736 0.223391 7

AGAP1 2.907156 0.116286 25

TBR1 1.392098 0.198871 7 NXN 2.882371 0.12532 23

KDM4B 2.065877 0.344313 6 INPP5A 2.303654 0.100159 23

PSD3 1.622697 0.270449 6 NCOR2 1.836913 0.079866 23 MIR100HG 1.529752 0.254959 6 PCDHGB5 2.223121 0.069473 32 SLC22A18AS 1.517593 0.252932 6 PCDHGA9 2.223121 0.071714 31 GPR39 1.482671 0.247112 6 SOX2-OT 4.323853 0.149098 29 LRRFIP1 1.396987 0.232831 6 SHANK2 2.334295 0.089781 26 CCDC177 1.387906 0.231318 6 AGAP1 4.297918 0.171917 25

MYO 16 1.381014 0.230169 6 PDGFRA 3.281884 0.131275 25 EPHB1 1.286386 0.214398 6 CAMTAI 3.235869 0.129435 25

TSN AX-DISCI 1.85738 0.371476 5 MEIS1 6.112786 0.254699 24 ARHGEF7 1.687341 0.337468 5 SATB2 3.003986 0.125166 24 CABLES 1 1.527647 0.305529 5 RPTOR 4.919096 0.213874 23 TK1 1.518958 0.303792 5 INPP5A 2.831969 0.123129 23

THRB 1.497338 0.299468 5 PRKCZ 3.058304 0.139014 22

RNLS 1.387906 0.277581 5 SKI 5.751564 0.273884 21 CASP8 1.387906 0.277581 5 FRMD4A 3.997671 0.199884 20 IE ADI 1.307323 0.261465 5 SDK1 2.415599 0.12078 20 MAPK8IP3 2.130606 0.532651 4 MAD1L1 5.24362 0.27598 19 EDNRB 1.881066 0.470266 4 ZNF423 4.461305 0.234806 19

FOXO1 1.546283 0.386571 4 SMG1P2 3.210425 0.16897 19 STOX2 1.40626 0.351565 4 BOLA2 3.210425 0.16897 19 LINC00856 1.396128 0.349032 4 LOC613038 3.210425 0.16897 19 VOPP1 1.383603 0.345901 4 CASZ1 2.630815 0.138464 19

NDST1 1.316257 0.329064 4 FOXK1 4.570601 0.253922 18 MYT1 1.303009 0.325752 4 MCF2L 3.630234 0.20168 18 EPAS1 1.841716 0.613905 3 SEPTIN9 3.195472 0.177526 18 DICER1 1.56627 0.52209 3 OPCML 6.284087 0.369652 17 SLC6A9 1.450121 0.483374 3 TBX15 4.028118 0.236948 17

DAGLB 1.305795 0.435265 3 NAV2 2.630111 0.164382 16 SLC25A10 1.882719 0.94136 2 FOXP1 2.224576 0.139036 16 SOXIO 1.72484 0.86242 2 GLI2 5.770352 0.38469 15 UFSP2 1.653183 0.826591 2 BAIAP2 4.016603 0.267774 15

DISCI I.330762 0.665381 2 ZBTB20 2.599909 0.173327 15 MIR548F5 3.480232 0.248588 14

Cancer Type

TABLE 89: RPS6KA2 2.764372 0.197455 14 HGNET_ND_B

CUX1 2.612496 0.186607 14

Gene site imp sum imp mean n PRKAG2 2.487324 0.177666 14

PTPRN2 II.57447 0.141152 82 MSI2 3.219484 0.247653 13 PRDM16 4.038915 0.056886 71 MYT1L 2.901918 0.223224 13 PCDHGA1 3.488665 0.05913 59 SPTBN4 2.502449 0.192496 13 PCDHGA2 3.488665 0.061205 57 CMIP 4.42819 0.369016 12 PCDHGA3 3.172279 0.058746 54

MIRLET7BHG 2.426833 0.202236 12

PCDHGB1 3.172279 0.059854 53

VGLL4 5.39493 0.490448 11 PCDHGA4 2.855893 0.055998 51

GLUD1P2 2.888543 0.262595 11 PCDHGB2 2.855893 0.058284 49

CACNA1C 2.659495 0.241772 11 PCDHGA5 2.223121 0.0473 47

ATP11A 3.97013 0.441126 9 HDAC4 6.376827 0.172347 37

ADAMTS2 3.669879 0.407764 9 PAX6 5.147216 0.147063 35

AXIN2 3.418227 0.379803 9 RBFOX3 4.611426 0.131755 35

RUNX1 3.347958 0.371995 9 DIP2C 6.615504 0.206735 32

SND1 3.134135 0.348237 9 NOTCH 1 2.670621 0.296736 9 DIP2C 4.067567 0.127111 32 ASAP1 2.47974 0.275527 9 SOX2-OT 5.505808 0.189855 29 CACNB2 2.449655 0.272184 9 ADARB2 4.089054 0.157271 26 TRAPPCI 2 2.194112 0.24379 9 SHANK2 3.054717 0.117489 26 GRIK2 6.924673 0.865584 8 AGAP1 6.291578 0.251663 25 LINC00311 2.704552 0.338069 8 CAMTAI 2.683617 0.107345 25 ASPSCR1 2.620689 0.327586 8 PDGFRA 2.01336 0.080534 25 MBP 2.552522 0.319065 8 SATB2 4.086095 0.170254 24 ESRRG 2.400991 0.300124 8 RPTOR 4.202601 0.182722 23 NR2E1 2.262175 0.282772 8 INPP5A 2.531088 0.110047 23 NAVI 3.393725 0.484818 7 NCOR2 2.446499 0.10637 23 DUSP6 2.942372 0.420339 7 PRKCZ 3.53049 0.160477 22 ADAMTS17 2.832985 0.404712 7 SKI 4.931482 0.234832 21

TBR1 2.483161 0.354737 7 FRMD4A 3.027059 0.151353 20 LINC00461 2.476002 0.353715 7 MAD1L1 5.636523 0.296659 19 VPS 13D 2.406937 0.343848 7 KCNQ1 2.847474 0.149867 19 SOX6 2.305773 0.329396 7 ZNF423 2.584632 0.136033 19

ITPKB 2.303968 0.329138 7 SEPTIN9 6.304789 0.350266 18 SLC22A18AS 2.76306 0.46051 6 FOXK1 2.265772 0.125876 18 FBXL18 2.717941 0.45299 6 RBFOX1 1.710245 0.095014 18

COQ8A 2.380451 0.396742 6 FOXP1 3.143963 0.196498 16 LIMCH1 2.275298 0.379216 6 SORBS2 2.899457 0.181216 16 FMNL2 2.225253 0.370876 6 EBF3 2.255027 0.140939 16

RUNDC3A 3.568779 0.713756 5 GLI2 3.951824 0.263455 15

GAREM2 2.432091 0.486418 5 ZBTB20 2.759924 0.183995 15

TK1 2.28875 0.45775 5 NFATC1 2.025083 0.135006 15

TSN AX-DISCI 2.244638 0.448928 5 CUX1 2.824382 0.201742 14 ARHGEF7 2.206025 0.441205 5 IQSEC1 2.472231 0.176588 14 NFIB 4.010649 1.002662 4 ARHGEF10 2.345753 0.167554 14 DTNA 3.340887 0.835222 4 RPS6KA2 2.249066 0.160648 14 ONECUT2 3.070253 0.767563 4 PRKAG2 2.103343 0.150239 14 STAP2 2.550313 0.637578 4 C7orf50 1.875874 0.133991 14 RBMS3 2.471495 0.617874 4 MYT1L 2.655877 0.204298 13 SASH1 2.315015 0.578754 4 MIR9-3HG 2.582158 0.198628 13 GRIN2B 3.638806 1.212935 3 KIF26B 1.803358 0.13872 13 DAGLB 2.323308 0.774436 3 MSI2 1.769056 0.136081 13 LOXL3 2.31565 0.771883 3 ZC3H3 3.083274 0.256939 12 TTC12 2.28273 0.76091 3 CMIP 3.058493 0.254874 12

MAP2K3 2.157708 1.078854 2 RASA3 2.896784 0.241399 12

TBX4 2.123167 0.176931 12

Cancer Type

TABLE 90: TNS3 1.935834 0.161319 12 HGNET_ND_C

CTNNA2 1.926265 0.160522 12

Gene site imp sum imp mean n

TBCD 2.487809 0.226164 11

PTPRN2 9.737831 0.118754 82

RAD51B 1.82219 0.165654 11

PRDM16 5.153874 0.07259 71

AKAP13 2.800951 0.280095 10 HDAC4 4.968572 0.134286 37

CHST11 I.TIQTI 0.272077 10 RBFOX3 5.080853 0.145167 35

BCL11B 2.6064 0.26064 10 PAX6 4.020079 0.114859 35

ACOT7 2.500636 0.250064 10 LBX1-AS1 2.461608 0.246161 10 TABLE 91: Cancer Type HGNET_ND_D

KLHL29 2.348852 0.234885 10

10 Gene site imp sum imp mean n

ETS1 1.756824 0.175682 9 PTPRN2 9.864449 0.120298 82

ATP11A 3.391144 0.376794 9 PRDM16 9.259115 0.13041 71

RUNX1 3.20046 0.355607 PCDHGA1 3.439024 0.058289 59

ASAP1 2.892005 0.321334

PCDHGA2 3.439024 0.060334 57

NOTCH 1 2.535825 0.281758

9 PCDHGA3 3.014547 0.055825 54

APBA2 1.926545 0.214061 PCDHGB1 3.014547 0.056878 53

PAX3 1.841296 0.204588

PCDHGA4 3.014547 0.059109 51

SND1 1.791119 0.199013

PCDHGB2 3.014547 0.061521 49

KCNMA1 1.738471 0.193163

3.014547 0.064139 4

SSBP3 1.731396 ₉ PCDHGA5 7

0.192377 PCDHGB3 3.014547 0.070106 43

AXIN2 1.717771 0.190863 PCDHGA6 3.014547 0.075364 40

MACROD1 3.289917 0.41124

HDAC4 6.917241 0.186952 37

MSRA 2.995025 0.374378

PCDHGA7 2.698161 0.072923 37

BAHCC1 2.627357 0.32842

6.524986 0.186428 35

LINC00311 2.386556 0.29832 8 ^PAX6 RBFOX3 3.342572 0.095502 35

VRK2 1.942024 0.242753

PCDHGB4 2.381775 0.068051 35

AFF3 1.797905 0.224738

PCDHGA8 2.381775 0.068051 35

TRAPPC9 1.776346 0.222043

DIP2C 7.027478 0.219609 32

SYNJ2 1.77498 0.221872

PCDHGB5 2.357303 0.073666 32

DUSP6 3.619583 0.517083

SOX2-OT 4.8712 0.167972 29

RBMS1 1.893431 0.27049

GALNT9 2.278284 0.084381 27

PRKCA 1.810447 0.258635

SHANK2 2.623623 0.100909 26

TRIM2 1.810327 0.258618

AGAP1 5.019296 0.200772 25

ZNF664-RFLNA 1.796796 0.256685

PDGFRA 4.475775 0.179031 25

CXXC5 1.764596 0.252085

_ CAMTAI 3.807931 0.152317 25

FBXL18 2.644251 0.440709 6 MEIS1 2.889276 0.120387 24

SLC22A18AS 2.461434 0.410239 6 _ SATB2 2.52298 0.105124 24

TG 2.157854 0.359642 6 _ RPTOR 5.902472 0.256629 23

SATB2-AS1 2.04251 0.340418 6 _ NCOR2 2.516777 0.109425 23

HOXD4 1.836799 0.306133 6 , PRKCZ 3.91809 0.178095 22

SRGAP3 1.739316 0.289886 6

5.236337 0.249349 21

ARHGEF7 2.395104 0.479021 5 ^SKI FRMD4A 4.373247 0.218662 20

BCAR1 1.866178 0.373236

MAD1L1 6.162868 0.324361 19

PRR5L 1.74963 0.349926

SMG1P2 3.647932 0.191996 19

SASH1 2.149578 0.537394 4

BOLA2 3.647932 0.191996 19

VOPP1 1.976727 0.494182

LOC613038 3.647932 0.191996 19

RBMS3 1.940124 0.485031 4

ZNF423 3.494232 0.183907 19

ACOX3 1.719939 0.429985

CASZ1 2.658986 0.139947 19

GRIN2B 3.178745 1.059582

SEPTIN9 3.617041 0.200947 18

DICER1 2.067448 0.689149

MCF2L 3.531901 0.196217 18

SLC6A9 1.970891 0.656964

FOXK1 3.184272 0.176904 18

IFFO1 1.811975 0.603992

ANKRD11 2.73067 0.151704 18

EPAS1 1.765209 0.588403 „ FOXP1 4.188119 0.261757 16

SOX10 3.343898 1.671949 2

ZBTB20 2.583813 0.172254 15

KIF21B 1.814686 0.907343

GLI2 2.570217 0.171348 15 RPS6KA2 4.104399 0.293171 14 THRA 3.34908 1.11636 3 CUX1 3.949712 0.282122 14 GRIN2B 2.508702 0.836234 3 C7orf50 3.928659 0.280619 14 DAGLB 2.390514 0.796838 3 IQSEC1 3.089471 0.220677 14 CNP 2.335046 0.778349 3 TBX5 2.692247 0.192303 14 SOXIO 3.171381 1.585691 2 GNG7 2.415288 0.172521 14 DENND11 2.860855 1.430428 2 MSI2 3.746287 0.288176 13 NR1D1 2.395607 1.197804 2 MYT1L 3.666093 0.282007 13 SPTBN4 2.632143 0.202473 Cancer Type

13 TABLE 92: HGNET_PATZ CMIP 3.366597 0.28055 12

Gene site imp sum imp mean n FBRSL1 2.872354 0.239363 12 PTPRN2 17.14336 0.209065 82 CTBP2 3.574491 0.324954 11 PRDM16 12.51692 0.176295 71 VGLL4 3.287994 0.298909 11 PCDHGA1 3.803542 0.064467 59 SPON2 2.968323 0.269848 11 PCDHGB2 3.487156 0.071166 49 GLUD1P2 2.744848 0.249532 11 PCDHGA5 3.933456 0.083691 47 ZC3H12D 2.33304 0.212095 11 PCDHGA6 3.648056 0.091201 40 RAD51B 2.318825 0.210802 11 HDAC4 13.10879 0.354292 37 CHST11 3.437065 0.343707 10 RBFOX3 9.455681 0.270162 35 ACOT7 3.064985 0.306499 10 PAX6 6.343311 0.181237 35 NTM 2.596398 0.25964 10 DIP2C 11.99765 0.374927 32 ATP11A 3.703061 0.411451 9 SOX2-OT 6.001773 0.206958 29 SND1 2.984411 0.331601 9 GALNT9 5.841 0.216333 27 PACS2 2.978543 0.330949 9 SHANK2 6.679166 0.256891 26 ADAMTS2 2.841953 0.315773 9 ADARB2 5.552845 0.213571 26 RUNX1 2.765462 0.307274 9 AGAP1 6.78803 0.271521 25 SSBP3 2.762278 0.30692 9 CAMTAI 5.56868 0.222747 25

TRAPPCI 2 2.469924 0.274436 9 SATB2 4.700124 0.195839 24 CACNA2D4 2.321527 0.257947 9 RPTOR 10.83693 0.471171 23 TSPAN9 2.300269 0.255585 9 NCOR2 7.807486 0.339456 23 NR2E1 2.795337 0.349417 8 RIMBP2 6.394636 0.278028 23 DLEU1 2.774543 0.346818 8 HOXB3 4.338576 0.188634 23 MSRA 2.533345 0.316668 8 NXN 3.893343 0.169276 23 ESRRG 2.476585 0.309573 8 PRKCZ 5.89598 0.267999 22 SYNJ2 2.466121 0.308265 8 SKI 10.8308 0.515752 21 LINC00461 3.138709 0.448387 7 SIM2 4.655035 0.221668 21 FBXL18 3.766261 0.62771 6 ZIC4 4.491162 0.213865 21 PVT1 2.654909 0.442485 6 HOXA-AS3 3.87165 0.184364 21 COQ8A 2.392082 0.39868 6 FRMD4A 6.736049 0.336802 20 FMNL2 2.28478 0.380797 6 ABR 6.342327 0.317116 20 RUNDC3A 3.898227 0.779645 5 SDK1 4.559424 0.227971 20 ARHGEF7 3.104328 0.620866 5 ZNF423 10.68511 0.562374 19 PRR5L 2.514518 0.502904 5 MAD1L1 7.895225 0.415538 19 TGFB3 2.282278 0.456456 5 CASZ1 6.119203 0.322063 19 STOX2 2.85666 0.714165 4 SMG1P2 3.883388 0.204389 19 RBMS3 2.687846 0.671961 4 BOLA2 3.883388 0.204389 19 VOPP1 2.621298 0.655325 4 LOC613038 3.883388 0.204389 19 SASH1 2.344511 0.586128 4 CFAP46 3.554014 0.187053 19 PRDM2 3.809634 1.269878 3 FOXK1 7.423561 0.41242 18 AXIN2 4.011781 0.445753 9 TBC1D16 5.663698 0.31465 18 CACNA2D4 3.763396 0.418155 9 ANKRD11 4.637601 0.257644 18 AS API 3.633912 0.403768 9

SEPTIN9 4.478314 0.248795 18 DLEU1 4.647124 0.58089 8

HOXA3 3.635087 0.201949 18 MSRA 4.295307 0.536913 8 OPCML 6.252435 0.36779 17 LHX4 4.246082 0.53076 8 PAX6-AS1 3.625755 0.21328 17 LINC00311 4.23156 0.528945 8

RCN1 3.625755 0.21328 17 SMAD3 4.093521 0.51169 8

FOXP1 5.479421 0.342464 16 RORA 3.917132 0.489642 8

NAV2 4.769675 0.298105 16 SHROOM3 3.593844 0.449231 8

EBF3 4.245607 0.26535 16 RGS20 3.52543 0.440679 8

GLI2 7.741287 0.516086 15 DUSP6 4.110106 0.587158 7

NFIX 6.705372 0.447025 15 RXRA 3.917573 0.559653 7

ZBTB20 4.563247 0.304216 15 LINC00461 3.617164 0.516738 7

SLX1B- TSN AX-DISCI 4.095521 0.819104 5 SULT1A4 4.240652 0.28271 ¹⁵ BOC 4.393729 1.098432 4 SLX1A 4.240652 0.28271 15

LOC606724 4.240652 0.28271 15

TABLE 93: Cancer Type

EMX2OS 4.236075 0.282405

HGNET_PLAG LRMDA 3.883948 0.25893 15 Gene site imp sum imp mean n KIRREL3 3.505505 0.2337 15 PTPRN2 4.705472 0.057384 82

RPS6KA2 5.780924 0.412923 14 PRDM16 4.475435 0.063034 71 ARHGEF10 4.310297 0.307878 14 PCDHGB1 1.291057 0.02436 53 MSI2 6.201837 0.477064 13 PCDHGA4 1.291057 0.025315 51 MYT1L 3.912213 0.300939 13 PCDHGB2 1.291057 0.026348 49 SPTBN4 3.634946 0.279611 13 PCDHGA5 1.291057 0.027469 47 CMIP 5.849535 0.487461 12 HDAC4 3.756115 0.101517 37 ZC3H3 5.205313 0.433776 12 PAX6 3.421466 0.097756 35

MIRLET7BHG 5.070343 0.422529 12 RBFOX3 1.806047 0.051601 35

TBX4 4.13671 0.344726 12 DIP2C 2.46776 0.077117 32

TNS3 4.118458 0.343205 12 AGAP1 1.871741 0.07487 25

RASA3 3.744375 0.312031 12 RPTOR 2.933618 0.127549 23

ADGRD1 3.661597 0.305133 12 INPP5A 2.125246 0.092402 23

ZC3H12D 5.857201 0.532473 11 NCOR2 1.764604 0.076722 23

FGFR2 4.244744 0.385886 11 SKI 6.178051 0.294193 21

RAD51B 3.665567 0.333233 11 SDK1 1.793089 0.089654 20

SPON2 3.504301 0.318573 11 MAD1L1 5.540848 0.291624 19 NR2F1-AS1 4.920196 0.49202 10 ZNF423 3.343331 0.175965 19 TSPAN4 4.170382 0.417038 10 KCNQ1 1.373634 0.072297 19

ACOT7 4.133986 0.413399 10 SEPTIN9 2.332394 0.129577 18

AKAP13 4.130958 0.413096 10 TBC1D16 1.768886 0.098271 18

MAML2 3.920929 0.392093 10 PAX6-AS1 1.383236 0.081367 17

ANKS1B 3.577294 0.357729 10 RCN1 1.383236 0.081367 17

BCL11B 3.50077 0.350077 10 EBF3 2.248947 0.140559 16

ATP11A 5.634833 0.626093 9 NFIX 2.486526 0.165768 15 TRAPPCI 2 4.970254 0.55225 9 KIRREL3 1.58193 0.105462 15 SND1 4.610867 0.512319 ₉ SLX1B-

„ SULT1A4 1.49431 0.099621 15

KCNH2 4.075902 0.452878 SLX1A 1.49431 0.099621 15 BACH2 2.027731 0.405546 5

LOC606724 1.49431 0.099621 15 CADM1 1.951302 0.39026 5

KNDC1 1.392098 0.092807 15 CUEDC1 1.497338 0.299468 5

RPS6KA2 2.950333 0.210738 14 HHEX 1.373634 0.274727 5

C7orf50 2.75936 0.197097 14 VOPP1 2.346855 0.586714 4

ARHGEF10 1.396595 0.099757 14 SASH1 1.683054 0.420763 4

MSI2 2.710296 0.208484 13 CRB2 1.568691 0.392173 4

MYT1L 2.21453 0.170348 13 STAP2 1.464166 0.366042 4

RFX4 1.991563 0.153197 13 DSE 1.416996 0.354249 4

CLYBL 1.962564 0.150966 13 TET1 1.392098 0.348025 4

SPTBN4 1.393859 0.10722 13 CSRNP1 1.380255 0.345064 4

CMIP 1.761756 0.146813 12 PPM1H 1.346451 0.336613 4

MEIS2 1.58193 0.131827 12 GNAS 1.675009 0.558336 3

TBX4 1.477921 0.12316 12 SLC6A9 1.553944 0.517981 3

VGLL4 1.716353 0.156032 11 FAM83E 1.457047 0.485682 3

TSPAN4 2.233461 0.223346 10 FEZ1 1.428745 0.476248 3

AKAP13 1.799866 0.179987 10 HDAC7 2.043894 1.021947 2

KLHL29 1.653739 0.165374 10 SOXIO 1.908056 0.954028 2

GAS7 1.456932 0.145693 10 CHTF18 1.6427 0.82135 2

AUTS2 1.444522 0.144452 10 EXT2 1.607093 0.803547 2

MAML2 1.387906 0.138791 10 ANKLE2 1.503659 0.75183 2

TSPAN9 3.080775 0.342308 9 TSC2 1.336326 0.668163 2

KCNH2 2.247511 0.249723 9 TTLL11 1.294769 0.647385 2

ATP11A 2.224048 0.247116 9 DDA1 1.729932 1.729932 1

EGFR 2.140931 0.237881 9 HMGCR 1.438004 1.438004 1

SND1 1.694061 0.188229 9

TRAPPCI 2 1.641374 0.182375 9 TABLE 94: Cancer Type HMB

ADAMTS2 1.608443 0.178716 9 Gene site imp sum imp mean n

KAZN 1.458536 0.16206 9 PTPRN2 26.46067 0.322691 82

ASAP1 1.416927 0.157436 9 PRDM16 24.26095 0.341704 71

CRISPLD2 2.200829 0.275104 8 PCDHGA1 14.03915 0.237952 59

DNMT3A 2.002498 0.250312 8 PCDHGA2 13.72276 0.24075 57

NR2E1 1.708484 0.213561 8 PCDHGA3 13.08999 0.242407 54

WWP2 1.690684 0.211335 8 PCDHGB1 12.7736 0.241011 53 GCSAML 1.58193 0.197741 8 PCDHGA4 12.7736 0.250463 51 DLEU1 1.363563 0.170445 8 PCDHGB2 12.45722 0.254229 49

C19orf25 1.875902 0.267986 7 PCDHGA5 11.75716 0.250152 47

RXRA 1.470451 0.210064 7 PCDHGB3 10.808 0.251349 43

SRGAP3 2.643332 0.440555 6 PCDHGA6 10.17523 0.254381 40

LYPD1 2.531618 0.421936 6 HDAC4 17.71129 0.478684 37

SLC22A18AS 2.126474 0.354412 6 PCDHGA7 9.858841 0.266455 37

NKD2 1.794592 0.299099 6 PAX6 9.923022 0.283515 35

LPIN1 1.712981 0.285497 6 PCDHGB4 9.106144 0.260176 35

CYBA 1.624883 0.270814 6 PCDHGA8 9.106144 0.260176 35

FBXL18 1.483841 0.247307 6 RBFOX3 6.361317 0.181752 35

CRACR2A 1.383259 0.230543 6 DIP2C 12.46961 0.389675 32

ROR1 1.368682 0.228114 6 PCDHGB5 8.473372 0.264793 32

RUNDC3A 2.833298 0.56666 5 PCDHGA9 8.026117 0.258907 31 SOX2-OT 9.70619 0.334696 29 IQSEC1 7.107435 0.507674 14

PCDHGB6 7.175401 0.247428 29 MIR548F5 6.86813 0.490581 14 PCDHGA10 6.859015 0.244965 28 CUX1 6.527938 0.466281 14 GALNT9 7.818675 0.289581 27 ARHGEF10 5.786339 0.41331 14 ADARB2 7.906132 0.304082 26 PRKAG2 5.056265 0.361162 14

SHANK2 7.070504 0.271942 26 PCDHGA12 5.014631 0.358188 14 AGAP1 11.75792 0.470317 25 MSI2 6.781856 0.521681 13 CAMTAI 9.840931 0.393637 25 MYT1L 5.974692 0.459592 13

PDGFRA 8.698086 0.347923 25 RFX4 5.591116 0.430086 13 MEIS1 7.999986 0.333333 24 ZC3H3 6.534405 0.544534 12 SATB2 7.40875 0.308698 24 CMIP 6.323173 0.526931 12 PCDHGB7 6.542629 0.27261 24 GNA12 6.002641 0.50022 12

RPTOR 13.45937 0.58519 23 TNS3 5.687364 0.473947 12

NCOR2 8.791106 0.382222 23 MAML3 5.634682 0.469557 12

INPP5A 7.765362 0.337624 23 RASA3 5.562932 0.463578 12

NXN 7.53317 0.327529 23 FBRSL1 5.349059 0.445755 12

RIMBP2 6.453255 0.280576 23 RAD51B 6.362292 0.57839 11

PCDHGA11 6.226243 0.270706 23 ANAPC16 5.819704 0.529064 11

PRKCZ 6.210195 0.282282 22 VGLL4 5.464586 0.496781 11

SKI 11.9245 0.567833 21 ACOT7 5.700837 0.570084 10

ZIC4 5.846935 0.278425 21 KLHL29 5.468208 0.546821 10

HOXA-AS3 5.081374 0.24197 21 SND1 6.468907 0.718767 9

FRMD4A 7.269919 0.363496 20 ATP11A 6.187374 0.687486 9

SDK1 7.083018 0.354151 20 ADAMTS2 5.936374 0.659597 9

MAD1L1 14.41389 0.758626 19 NOTCH1 5.540961 0.615662 9

ZNF423 10.48737 0.551967 19 ASAP1 5.087106 0.565234 9

CASZ1 9.066917 0.477206 19 LINC00311 5.411709 0.676464 8

SMG1P2 7.991465 0.420603 19 MCC 5.258706 0.657338 8

BOLA2 7.991465 0.420603 19 DLEU1 5.054179 0.631772 8 LOC613038 7.991465 0.420603 19 RXRA 5.195303 0.742186 7 KCNQ1 5.977189 0.314589 19 TSN AX-DISCI 5.815857 1.163171 5 FOXK1 8.730481 0.485027 18 ARHGEF7 5.557932 1.111586 5

TBC1D16 8.260186 0.458899 18

ANKRD11 7.878235 0.43768 18 TABLE 95: Cancer Type IDH_B

SEPTIN9 7.040446 0.391136 18 Gene site imp sum imp mean n

MCF2L 6.444229 0.358013 18 PTPRN2 17.16233 0.209297 82 OPCML 6.12059 0.360035 17 PRDM16 14.96187 0.210731 71 FOXP1 7.749369 0.484336 16 PCDHGA1 7.441341 0.126124 59 NAV2 7.034679 0.439667 16 PCDHGA2 6.8546 0.120256 57

SORBS2 6.588335 0.411771 16 PCDHGA3 6.407163 0.118651 54

GLI2 7.702327 0.513488 15 PCDHGB1 6.723549 0.126859 53

KIRREL3 7.493121 0.499541 15 PCDHGA4 6.723549 0.131834 51

ZBTB20 6.170748 0.411383 15 PCDHGB2 6.407163 0.130758 49

NHX 6.062316 0.404154 15 PCDHGA5 6.661662 0.141737 47

NFATC1 5.634395 0.375626 15 PCDHGB3 6.249674 0.145341 43

BAIAP2 5.418057 0.361204 15 PCDHGA6 5.82758 0.14569 40 LRMDA 5.416243 0.361083 15 HDAC4 12.51746 0.33831 37 RPS6KA2 7.766361 0.55474 14 PCDHGA7 6.143966 0.166053 37 RBFOX3 10.87678 0.310765 35 LOC606724 5.138637 0.342576 15 PAX6 10.19727 0.291351 35 BAIAP2 4.870926 0.324728 15 PCDHGB4 6.326632 0.180761 35 RPS6KA2 5.921087 0.422935 14 PCDHGA8 6.326632 0.180761 35 IQSEC1 5.061376 0.361527 14 DIP2C 11.28103 0.352532 32 C7orf50 4.660564 0.332897 14 PCDHGB5 6.326632 0.197707 32 PRKAG2 4.396865 0.314062 14 PCDHGA9 6.326632 0.204085 31 MSI2 7.087188 0.545168 13 SOX2-OT 11.9739 0.412893 29 MYT1L 5.322932 0.409456 13 PCDHGB6 5.935454 0.204671 29 RFX4 4.816791 0.370522 13 PCDHGA10 5.619068 0.200681 28 KIF26B 4.55746 0.350574 13 SHANK2 4.728557 0.181868 26 SPTBN4 4.447745 0.342134 13 ADARB2 4.06952 0.15652 26 ZC3H3 5.713517 0.476126 12 AGAP1 8.657739 0.34631 25 CMIP 5.59996 0.466663 12 PDGFRA 6.586265 0.263451 25 MIRLET7BHG 4.177948 0.348162 12 CAMTAI 4.939632 0.197585 25 ADGRD1 4.159606 0.346634 12 MEIS1 9.504702 0.396029 24 FBRSL1 4.056428 0.338036 12 SATB2 7.120392 0.296683 24 VGLL4 4.973961 0.452178 11 PCDHGB7 5.944949 0.247706 24 FGFR2 4.888569 0.444415 11

RPTOR 10.58695 0.460302 23 RAD51B 4.783315 0.434847 11 NCOR2 6.324306 0.27497 23 ZC3H12D 4.146117 0.37692 11 PCDHGA11 5.280202 0.229574 23 NR2F1-AS1 4.866915 0.486691 10 HOXB3 5.139593 0.223461 23 TSPAN4 4.645856 0.464586 10 INPP5A 4.571725 0.198771 23 SH3RF3 4.417072 0.441707 10 RIMBP2 4.272317 0.185753 23 OTX1 4.083172 0.408317 10 PRKCZ 6.585171 0.299326 22 ATP11A 5.676572 0.63073 9 SKI 9.910396 0.471924 21 SND1 5.130784 0.570087 9 ZIC4 4.254706 0.202605 21 ADGRB1 5.066377 0.562931 9 FRMD4A 7.877648 0.393882 20 TSPAN9 5.020702 0.557856 9 ABR 6.512398 0.32562 20 TRAPPCI 2 4.9894 0.554378 9

MAD1L1 12.08799 0.63621 19 ASAP1 4.983764 0.553752 9 ZNF423 6.7748 0.356568 19 AXIN2 4.974147 0.552683 9 SMG1P2 5.607138 0.295113 19 RUNX1 4.191813 0.465757 9 BOLA2 5.607138 0.295113 19 ADAMTS2 4.02287 0.446986 9 LOC613038 5.607138 0.295113 19 LINC00311 4.643322 0.580415 8 CASZ1 4.524491 0.238131 19 NR2E1 4.551273 0.568909 8 FOXK1 6.358935 0.353274 18 DLEU1 4.506299 0.563287 8 ANKRD11 5.770334 0.320574 18 LINC00461 4.90227 0.700324 7 TBC1D16 4.557065 0.25317 18 FBXL18 4.159063 0.693177 6 OPCML 8.756938 0.515114 17 RUNDC3A 5.22174 1.044348 5 PAX6-AS1 4.758871 0.279934 17 TSN AX-DISCI 4.071943 0.814389 5

RCN1 4.758871 0.279934 17 FOXP1 5.7259 0.357869 16 TABLE 96: Cancer Type IHG NAV2 5.209677 0.325605 16 Gene site imp sum imp mean n GLI2 9.20778 0.613852 15 PTPRN2 21.98894 0.268158 82 ZBTB20 6.166419 0.411095 15 PRDM16 16.68139 0.234949 71 SLX1B- PCDHGA1 6.820854 0.115608 59 SULT1A4 5.138637 0.342576 15

PCDHGA2 6.820854 0.119664 57 SLX1A 5.138637 0.342576

PCDHGA3 6.188082 0.114594 54 PCDHGB1 6.188082 0.116756 53 ZBTB20 6.714497 0.447633 15 PCDHGA4 5.871696 0.115131 51 BAIAP2 5.486625 0.365775 15 PCDHGB2 5.871696 0.119831 49 KIRREL3 5.185464 0.345698 15 PCDHGA5 6.318987 0.134447 47 SLX1B- SULT1A4 5.143539 0.342903 15 PCDHGB3 5.369829 0.12488 43 SLX1A 5.143539 0.342903 15 PCDHGA6 5.8623 0.146558 40 LOC606724 5.143539 0.342903 15 HDAC4 13.86379 0.374697 37 NFIX 4.968449 0.33123 15 PCDHGA7 5.229528 0.141339 37 TBX5 6.484428 0.463173 14 PAX6 10.71551 0.306157 35 RPS6KA2 6.099945 0.43571 14 RBFOX3 10.31778 0.294794 35 CUX1 5.931251 0.423661 14 PCDHGB4 5.229528 0.149415 35 IQSEC1 5.523478 0.394534 14 PCDHGA8 5.229528 0.149415 35 MIR548F5 5.231893 0.373707 14 DIP2C 10.44121 0.326288 32 ARHGEF10 4.882863 0.348776 14 PCDHGB5 4.596756 0.143649 32 PRKAG2 4.696947 0.335496 14 PCDHGA9 4.913142 0.158488 31 SPTBN4 9.679117 0.744547 13 SOX2-OT 9.404015 0.324276 29 MSI2 7.306432 0.562033 13 SHANK2 5.97329 0.229742 26 MYT1L 5.174987 0.398076 13 ADARB2 5.645742 0.217144 26 KIF26B 5.16254 0.397118 13 CAMTAI 8.361545 0.334462 25 RFX4 4.804251 0.369558 13 AGAP1 8.047109 0.321884 25 ZC3H3 6.167031 0.513919 12 PDGFRA 6.74722 0.269889 25 MIRLET7BHG 5.276484 0.439707 12 SATB2 7.958792 0.331616 24 CMIP 5.091453 0.424288 12 MEIS1 7.797292 0.324887 24 ZC3H12D 5.829639 0.529967 11 RPTOR 12.32278 0.535773 23 RAD51B 5.240653 0.476423 11 NCOR2 8.741034 0.380045 23 FGFR2 5.0712 0.461018 11 NXN 5.731381 0.24919 23 VGLL4 4.963901 0.451264 11 INPP5A 5.566191 0.242008 23 NR2F1-AS1 4.662872 0.466287 10 PRKCZ 7.299384 0.33179 22 AKAP13 4.618214 0.461821 10 SKI 9.972847 0.474897 21 CHST11 4.535589 0.453559 10 SIM2 5.724523 0.272596 21 GAS7 4.521014 0.452101 10 FRMD4A 9.398365 0.469918 20 ATP11A 7.610869 0.845652 9 ABR 6.182838 0.309142 20 SND1 6.161357 0.684595 9 SDK1 5.248318 0.262416 20 KCNH2 5.738954 0.637662 9 MAD1L1 11.70125 0.615855 19 AXIN2 4.982084 0.553565 9 ZNF423 7.967461 0.41934 19 ADAMTS2 4.935406 0.548378 9 CASZ1 7.888011 0.415158 19 TRAPPCI 2 4.645836 0.516204 9 SMG1P2 7.249408 0.381548 19 LINC00311 6.172338 0.771542 8 BOLA2 7.249408 0.381548 19 LHX4 5.191303 0.648913 8 LOC613038 7.249408 0.381548 19 DLEU1 4.549579 0.568697 8 FOXK1 7.589663 0.421648 18 MSRA 4.495601 0.56195 8 ANKRD11 5.668737 0.31493 18 RGS20 4.410543 0.551318 8 SEPTIN9 4.595294 0.255294 18 DUSP6 6.409854 0.915693 7 TBC1D16 4.550263 0.252792 18 LINC00461 4.952709 0.70753 7 OPCML 7.944399 0.467318 17 RUNDC3A 5.680512 1.136102 5 PAX6-AS1 5.215924 0.306819 17 TSNAX-DISC1 4.566497 0.913299 5 RCN1 5.215924 0.306819 17 ARHGEF7 4.427856 0.885571 5 FOXP1 6.734018 0.420876 16 RBMS3 4.727666 1.181917 4 GLI2 9.012125 0.600808 15 SLX1A 4.790424 0.319362 15

TABLE 97: Cancer Type IO_MEPL LOC606724 4.790424 0.319362 15 Gene site imp sum imp mean n ZBTB20 4.784376 0.318958 15 PTPRN2 19.57579 0.238729 82 KNDC1 4.672532 0.311502 15 PRDM16 13.39836 0.188709 71 KIRREL3 4.620311 0.308021 15 PCDHGA2 4.149727 0.072802 57 RPS6KA2 8.189835 0.584988 14 PCDHGB2 4.003444 0.081703 49 MIR548F5 7.255671 0.518262 14 PCDHGA5 4.003444 0.08518 47 C7orf50 7.003642 0.50026 14 PCDHGB3 4.089625 0.095108 43 PRKAG2 5.365647 0.38326 14 HDAC4 16.86061 0.455692 37 IQSEC1 5.328487 0.380606 14 PAX6 7.205322 0.205866 35 CUX1 4.289627 0.306402 14 RBFOX3 4.407359 0.125925 35 MYT1L 6.437381 0.495183 13 DIP2C 10.45506 0.32672 32 MSI2 5.673086 0.436391 13 SOX2-OT 4.188497 0.144431 29 CLYBL 5.020663 0.386205 13 SHANK2 8.032537 0.308944 26 GSE1 4.968646 0.382204 13 AGAP1 13.61969 0.544788 25 KIF26B 4.768748 0.366827 13 CAMTAI 7.258327 0.290333 25 RFX4 4.112628 0.316356 13 PDGFRA 5.787904 0.231516 25 CMIP 5.445062 0.453755 12 MEIS1 6.160598 0.256692 24 ZC3H3 4.716954 0.39308 12 RPTOR 12.21252 0.530979 23 RASA3 4.708674 0.392389 12 INPP5A 6.643328 0.28884 23 ADGRD1 4.495558 0.37463 12 NCOR2 6.618779 0.287773 23 TNS3 4.396419 0.366368 12 NXN 6.500086 0.282612 23 FBRSL1 4.377279 0.364773 12 RIMBP2 4.707864 0.20469 23 MAML3 4.352462 0.362705 12 PRKCZ 5.144966 0.233862 22 MEIS2 4.249655 0.354138 12 SKI 8.219151 0.391388 21 GNA12 4.139511 0.344959 12

HOXA-AS3 4.440806 0.211467 21 RAD51B 5.524138 0.502194 11 ZIC4 4.366666 0.207936 21 COL4A1 4.950854 0.450078 11 SDK1 7.048012 0.352401 20 CTBP2 4.565877 0.41508 11 FRMD4A 6.156727 0.307836 20 ZC3H12D 4.337687 0.394335 11 MAD1L1 13.30588 0.70031 19 VGLL4 4.291892 0.390172 11 ZNF423 6.364884 0.334994 19 CCDC140 4.286443 0.389677 11 SMG1P2 6.161088 0.324268 19 TBCD 4.031543 0.366504 11 BOLA2 6.161088 0.324268 19 AKAP13 5.403911 0.540391 10 LOC613038 6.161088 0.324268 19 NBEA 5.17908 0.517908 10 CASZ1 5.576216 0.293485 19 ACOT7 4.447818 0.444782 10 TBC1D16 6.783411 0.376856 18 TSPAN4 4.277334 0.427733 10 FOXK1 6.461694 0.358983 18 KLHL29 4.146967 0.414697 10 ANKRD11 6.360361 0.353353 18 SND1 7.456092 0.828455 9 SEPTIN9 4.582259 0.25457 18 ATP11A 6.698008 0.744223 9 HOXA3 4.03831 0.224351 18 TRAPPCI 2 5.537784 0.615309 9 FOXP1 6.464623 0.404039 16 AXIN2 4.177251 0.464139 9 NAV2 4.301849 0.268866 16 ADAMTS2 4.05797 0.450886 9 GLI2 7.666491 0.511099 15 MGMT 4.00896 0.44544 9 LRMDA 5.004471 0.333631 15 MSRA 4.976348 0.622043 8 BAIAP2 4.945349 0.32969 15 DNMT3A 4.668408 0.583551 8 SLX1B- SYNJ2 4.434465 0.554308 8 SULT1A4 4.790424 0.319362

¹⁵ DLEU1 4.089529 0.511191 8 PPP2R2B 3.981533 0.497692 8 NAV2 3.133991 0.195874 16 FBXL18 4.770152 0.795025 6 ZBTB20 4.881349 0.325423 15 CRADD 4.507778 0.751296 6 GLI2 3.960304 0.26402 15 SLC22A18AS 4.258436 0.709739 ₆ SLX1B-

, SULT1A4 3.591227 0.239415 15 FMNL2 4.222057 0.703676 6

₅ SLX1A 3.591227 0.239415 15

TSN AX-DISCI 5.281229 1.056246

₅ LOC606724 3.591227 0.239415 15 RUNDC3A 4.794213 0.958843 DAGLB 4.037646 1.345882 ₃ KIRREL3 3.000317 0.200021 15

BAIAP2 2.97861 0.198574 15

TABLE 98: Cancer Type LCH RPS6KA2 7.52999 0.537856 14

CUX1 6.183826 0.441702 14 Gene site imp sum imp mean n IQSEC1 5.523366 0.394526 14 PTPRN2 11.90058 0.145129 82 C7orf50 4.021076 0.28722 14 PRDM16 6.340229 0.089299 71 ARHGEF10 3.337458 0.23839 14 PCDHGA1 3.009323 0.051005 59 PRKAG2 2.977514 0.21268 14 PCDHGA2 3.009323 0.052795 57 MYT1L 4.479703 0.344593 13 HDAC4 12.67247 0.342499 37 MSI2 3.719446 0.286111 13 PAX6 9.410904 0.268883 35 CMIP 6.420494 0.535041 12 RBFOX3 4.406468 0.125899 35 FBRSL1 4.962752 0.413563 12 DIP2C 7.492757 0.234149 32 GNA12 4.548846 0.379071 12 SHANK2 3.588698 0.138027 26 ZC3H3 4.072548 0.339379 12 AGAP1 7.359122 0.294365 25 TNS3 3.455477 0.287956 12 PDGFRA 5.737538 0.229502 25 RAD51B 3.854406 0.350401 11 CAMTAI 4.250446 0.170018 25 TBCD 3.40743 0.309766 11 RPTOR 10.60541 0.461105 23 VGLL4 3.205608 0.291419 11 NCOR2 7.527478 0.327282 23 SLC38A10 3.191701 0.290155 11 INPP5A 6.155063 0.267611 23 ZC3H12D 3.102792 0.282072 11 NXN 3.886534 0.16898 23 ACOT7 3.965729 0.396573 10 PRKCZ 3.531157 0.160507 22 OTX1 3.463251 0.346325 10 SKI 7.320851 0.348612 21 ATP11A 7.207192 0.800799 9 ZIC4 2.952765 0.140608 21 SND1 7.086397 0.787377 9 FRMD4A 3.211158 0.160558 20 ADAMTS2 4.230769 0.470085 9 SDK1 2.99271 0.149636 20 CACNA2D4 3.479424 0.386603 9 MAD1L1 10.71262 0.563822 19 AXIN2 3.428441 0.380938 9 CASZ1 4.302664 0.226456 19 MGMT 3.317762 0.36864 9 SMG1P2 4.101114 0.215848 19 ASAP1 3.281916 0.364657 9 BOLA2 4.101114 0.215848 19 TSPAN9 3.259625 0.362181 9 LOC613038 4.101114 0.215848 19 LINC00311 4.744947 0.593118 8 ZNF423 3.863493 0.203342 19 DLEU1 4.301357 0.53767 8 KCNQ1 3.067512 0.161448 19 DNMT3A 3.273543 0.409193 8 TBC1D16 5.600008 0.311112 18 MSRA 2.985396 0.373175 8 ANKRD11 4.708597 0.261589 18 MACROD1 2.949414 0.368677 8 FOXK1 4.345205 0.2414 18 C19orf25 5.014216 0.716317 7 SEPTIN9 3.964328 0.22024 18 NAVI 3.454065 0.493438 7 PAX6-AS1 3.90826 0.229898 17 VPS 13D 3.358144 0.479735 7 RCN1 3.90826 0.229898 17 GAK 3.259103 0.465586 7 OPCML 3.510236 0.206484 17 MIR548H4 3.246832 0.463833 7 FOXP1 5.172432 0.323277 16 CXXC5 3.232364 0.461766 7 EBF3 3.530611 0.220663 16 RXRA 2.991782 0.427397 7 MEIS1 3.570653 0.148777 24

ITPK1 2.944881 0.420697 7 SATB2 3.25786 0.135744 24

RADIL 4.025201 0.670867 6 PCDHGB7 3.162728 0.13178 24

SLC22A18AS 3.415395 0.569233 6 RPTOR 10.27897 0.446912 23

FMNL2 3.35009 0.558348

NCOR2 7.796333 0.338971 23

FBXL18 3.110054 0.518342 6 INPP5A 5.31175 0.230946 23

CRADD 2.974569 0.495762 6 HOXB3 4.171853 0.181385 23

RUNDC3A 4.135282 0.827056 5 NXN 3.25688 0.141603 23

ARHGEF7 3.716845 0.743369 5 PCDHGA11 3.162728 0.13751 23

ARHGAP26 3.411236 0.682247 5 SKI 6.442682 0.306794 21

NHSL1 4.167055 1.041764 4 FRMD4A 5.439477 0.271974 20

NDST1 3.6207 0.905175 4 SDK1 3.382742 0.169137 20 DAGLB 4.221934 1.407311 3 MAD1L1 8.43267 0.443825 19 TBC1D7 3.805081 1.26836 3 CASZ1 5.143108 0.27069 19 DICER1 3.103681 1.03456 3 KCNQ1 4.759959 0.250524 19

SLC25A10 3.006057 1.503029 2 ZNF423 4.636213 0.244011 19

SMG1P2 4.465582 0.235031 19

Cancer Type

TABLE 99: BOLA2 4.465582 0.235031 19 LGG_DIG_DIA

LOC613038 4.465582 0.235031 19

Gene site imp sum imp mean n

ANKRD11 5.788786 0.321599 18

PTPRN2 14.23623 0.173613 82 TBC1D16 4.406929 0.244829 18

PRDM16 10.76981 0.151687 71 RBFOX1 3.568704 0.198261 18

PCDHGA1 4.428272 0.075055 59

MCF2L 3.335676 0.185315 18

PCDHGA2 4.111886 0.072138 57 OPCML 4.914005 0.289059 17

PCDHGA3 4.111886 0.076146 54 PAX6-AS1 4.370298 0.257076 17

PCDHGB1 4.111886 0.077583 53

RCN1 4.370298 0.257076 17

PCDHGA4 4.111886 0.080625 51

FOXP1 5.151974 0.321998 16

PCDHGB2 4.111886 0.083916 49

SORBS2 3.612279 0.225767 16

PCDHGA5 4.111886 0.087487 47

GLI2 6.246745 0.41645 15

PCDHGB3 3.7955 0.088267 43

BAIAP2 4.444424 0.296295 15

PCDHGA6 3.7955 0.094888 40

ZBTB20 4.000898 0.266727 15 HDAC4 12.09384 0.32686 37

KIRREL3 3.9588 0.26392 15 PCDHGA7 3.7955 0.102581 37

RPS6KA2 5.124608 0.366043 14 RBFOX3 4.315354 0.123296 35

CUX1 4.211117 0.300794 14

PAX6 4.097637 0.117075 35

MIR548F5 3.791066 0.27079 14

PCDHGB4 3.479114 0.099403 35

C7orf50 3.682449 0.263032 14

PCDHGA8 3.479114 0.099403 35

IQSEC1 3.563053 0.254504 14 DIP2C 9.29439 0.29045 32

PRKAG2 3.524436 0.251745 14 PCDHGB5 3.162728 0.098835 32

MSI2 4.173561 0.321043 13 SOX2-OT 4.772139 0.164557 29

CMIP 4.027842 0.335653 12

PCDHGB6 3.162728 0.10906 29

TNS3 3.867169 0.322264 12

PCDHGA10 3.162728 0.112955 28

FBRSL1 3.797234 0.316436 12

GALNT9 5.318422 0.196979 27

MIRLET7BHG 3.723759 0.310313 12

SHANK2 4.310182 0.165776 26

ZC3H3 3.464802 0.288733 12

ADARB2 3.611297 0.138896 26

ADGRD1 3.416781 0.284732 12

AGAP1 9.830515 0.393221 25

FGFR2 3.448292 0.313481 11

CAMTAI 6.399763 0.255991 25 ANAPC16 3.384766 0.307706 11 PDGFRA 4.316944 0.172678 25 SPON2 3.240691 0.294608 11 ACOT7 4.742578 0.474258 10 SOX2-OT 10.59281 0.365269 29

OBI1-AS1 3.735848 0.373585 10 PCDHGB6 5.434719 0.187404 29

FMN1 3.643263 0.364326 10 PCDHGA10 5.118333 0.182798 28

NBEA 3.290281 0.329028 10 SHANK2 8.876006 0.341385 26

GAS7 3.287849 0.328785 10 ADARB2 7.537938 0.289921 26

RGS12 3.21931 0.321931 10 AGAP1 8.40558 0.336223 25

SND1 6.119553 0.67995 9 CAMTAI 7.763566 0.310543 25

ATP11A 4.62361 0.513734 9 PDGFRA 6.514455 0.260578 25

TRAPPCI 2 4.425617 0.491735 9 SATB2 6.728159 0.28034 24

ADAMTS2 4.14856 0.460951 9 PCDHGB7 5.118333 0.213264 24

RUNX1 3.551438 0.394604 9 RPTOR 10.82497 0.470651 23

DLEU1 4.021061 0.502633 8 NCOR2 10.41423 0.452793 23

SYNJ2 3.405088 0.425636 8 INPP5A 7.025501 0.305457 23

MSRA 3.27781 0.409726 8 HOXB3 6.240577 0.271329 23

RXRA 3.946638 0.563805 7 NXN 5.796623 0.252027 23

VPS 13D 3.880843 0.554406 7 PCDHGA11 4.801947 0.20878 23

NAVI 3.73432 0.533474 7 PRKCZ 7.662516 0.348296 22

FBXL18 3.527527 0.587921 6 SKI 11.08687 0.527946 21

EMNL2 3.275497 0.545916 6 ABR 6.446972 0.322349 20

RUNDC3A 4.650154 0.930031 5 FRMD4A 5.1939 0.259695 20

ARHGEF7 3.309919 0.661984 5 SDK1 5.027435 0.251372 20

TSN AX-DISCI 3.267592 0.653518 5 ZNF423 11.04971 0.581564 19

KLHL25 3.181519 0.636304 5 MAD1L1 10.99 0.578421 19

ZAR1 3.916304 1.958152 2 CASZ1 8.386398 0.441389 19

SMG1P2 5.578786 0.29362 19

Cancer Type BOLA2 5.578786 0.29362 19

TABLE 100:

LGG_MYB_A LOC613038 5.578786 0.29362 19

Gene site imp sum imp mean n SEPTIN9 8.84043 0.491135 18 PTPRN2 26.29143 0.320627 82 FOXK1 6.569609 0.364978 18 PRDM16 21.21807 0.298846 71 MCF2L 5.792536 0.321808 18 PCDHGA1 8.16692 0.138422 59 TBC1D16 5.737236 0.318735 18 PCDHGA2 7.850534 0.137729 57 ANKRD11 5.543978 0.307999 18 PCDHGA3 7.534148 0.139521 54 OPCML 6.014814 0.353813 17 PCDHGB1 7.534148 0.142154 53 PAX6-AS1 4.947244 0.291014 17 PCDHGA4 7.217762 0.141525 51 RCN1 4.947244 0.291014 17 PCDHGB2 6.80743 0.138927 49 NAV2 6.648902 0.415556 16 PCDHGA5 6.857864 0.145912 47 FOXP1 6.374021 0.398376 16

PCDHGB3 6.446917 0.149928 43 GLI2 8.757455 0.58383 15 PCDHGA6 6.130531 0.153263 40 KIRREL3 6.571073 0.438072 15 HDAC4 15.14366 0.409288 37 NFIX 6.394432 0.426295 15 PCDHGA7 5.814145 0.157139 37 ZBTB20 6.005432 0.400362 15 PAX6 16.6925 0.476929 35 EMX2OS 5.739645 0.382643 15 RBFOX3 9.111279 0.260322 35 BAIAP2 4.842575 0.322838 15 PCDHGB4 6.130531 0.175158 35 SLX1B-SULT1A4 4.676131 0.311742 15 PCDHGA8 6.130531 0.175158 35 SLX1A 4.676131 0.311742 15 DIP2C 11.70908 0.365909 32 CUX1 7.265301 0.51895 14 PCDHGB5 6.130531 0.191579 32 RPS6KA2 7.2188 0.515629 14

PCDHGA9 5.814145 0.187553 31 PRKAG2 4.906524 0.350466 14 MSI2 8.707651 0.669819 3.935759 0.122992 32 MYT1L 6.635139 0.510395 7.260823 0.250373 29 RFX4 6.006326 0.462025 2.749789 0.105761 26 GSE1 4.928383 0.379106 4.89778 0.195911 25 CLYBL 4.744826 0.364987 3.840148 0.153606 25 ZC3H3 6.383408 0.531951 2.344615 0.093785 25 CMIP 6.246763 0.520564 3.259669 0.13582 24 MIRLET7BHG 5.445528 0.453794 2.465852 0.102744 24 MEGF6 5.170835 0.430903 3.341045 0.145263 23 CTNNA2 4.953259 0.412772 2.605574 0.113286 23 TBX4 4.845798 0.403817 3.737433 0.169883 22 SPON2 5.815477 0.52868 6.522588 0.310599 21 RAD51B 5.263935 0.47854 4.133611 0.206681 20 ZC3H12D 5.156671 0.468788 2.531088 0.126554 20 SH3RF3 5.703608 0.570361 2.127596 0.10638 20 AKAP13 5.229688 0.522969 6.182977 0.32542 19 IGF1R 4.676857 0.467686 4.378512 0.230448 19 ATP11A 6.235484 0.692832 2.828645 0.148876 19 ASAP1 5.628994 0.625444 2.828645 0.148876 19 RUNX1 5.502947 0.611439 2.828645 0.148876 19 TSPAN9 5.099097 0.566566 2.304714 0.121301 19 ADAMTS2 4.897346 0.54415 4.758755 0.264375 18 SND1 4.870835 0.541204 3.374229 0.187457 18 TRAPPCI 2 4.807277 0.534142 3.083552 0.171308 18 NOTCH 1 4.725715 0.525079 2.679801 0.148878 18 LHX4 5.078995 0.634874 2.166848 0.12038 18 MSRA 4.831149 0.603894 3.863382 0.227258 17 NAVI 5.405609 0.77223 2.84974 0.178109 16 RUNDC3A 4.967815 0.993563 2.480408 0.155026 16 TSN AX-DISCI 4.966489 0.993298 2.3211 0.145069 16 RBMS3 5.011413 1.252853 3.825441 0.255029 15 GRIN2B 4.981407 1.660469 3.097589 0.206506 15

3.096569 0.206438 15

TABLE 101: Cancer Type 2.12156 0.141437 15

LGG_MYB_B

2.933436 0.209531 14

Gene site imp sum imp mean

2.791658 0.199404 14 PTPRN2 9.894068 0.120659

2.648296 0.189164 14 PRDM16 6.835973 0.096281

2.51764 0.179831 14 PCDHGA1 2.924209 0.049563

2.319059 0.165647 14 PCDHGA2 2.924209 0.051302

4.075431 0.313495 13 PCDHGA3 2.291437 0.042434

2.98633 0.229718 13 PCDHGB1 2.291437 0.043235

2.643675 0.20336 13 PCDHGA4 2.291437 0.04493

3.343622 0.278635 12 PCDHGB2 2.291437 0.046764

3.160924 0.26341 12 PCDHGA5 2.291437 0.048754

3.052967 0.254414 12 HDAC4 6.1271 0.165597

2.769872 0.230823 12 PAX6 3.644378 0.104125

2.016728 0.168061 12 RBFOX3 2.273223 0.064949

4.614115 0.419465 11 ZC3H12D 3.119557 0.283596 11 PCDHGA3 4.60837 0.08534 54 RAD51B 2.623407 0.238492 11 PCDHGB1 4.60837 0.08695 53 SH3RF3 2.873823 0.287382 10 PCDHGA4 4.291984 0.084157 51 LBX1-AS1 2.521427 0.252143 10 PCDHGB2 3.975598 0.081135 49 OTX1 2.448407 0.244841 10 HDAC4 10.06805 0.272109 37 GRID1 2.340932 0.234093 10 PAX6 15.08031 0.430866 35 RGS12 2.322792 0.232279 10 RBFOX3 8.465599 0.241874 35 ANKS1B 2.208493 0.220849 10 DIP2C 10.80977 0.337805 32 MAML2 2.04004 0.204004 10 SOX2-OT 8.438282 0.290975 29 PAX3 3.636353 0.404039 9 GALNT9 4.570214 0.169267 27 ATP11A 2.872912 0.319212 9 SHANK2 5.741485 0.220826 26 RUNX1 2.672577 0.296953 9 ADARB2 3.994562 0.153637 26 NOTCH 1 2.525311 0.28059 9 AGAP1 8.791625 0.351665 25 KCNH2 2.463535 0.273726 9 CAMTAI 6.88132 0.275253 25 SND1 2.355613 0.261735 9 PDGFRA 5.659159 0.226366 25 KCNMA1 2.183475 0.242608 9 SATB2 7.35095 0.30629 24 GRIK2 2.694068 0.336759 8 RPTOR 11.96567 0.520246 23 MSRA 2.68183 0.335229 8 NCOR2 8.201648 0.356593 23 RGS20 2.265403 0.283175 8 HOXB3 4.853942 0.211041 23 MACROD1 2.045585 0.255698 8 INPP5A 4.841783 0.210512 23 ASPSCR1 2.018782 0.252348 8 RIMBP2 4.131433 0.179628 23 DUSP6 3.414944 0.487849 7 NXN 3.984332 0.173232 23 NAVI 2.56527 0.366467 7 PRKCZ 6.612622 0.300574 22 LINC00461 2.430513 0.347216 7 SKI 13.19454 0.628312 21 NRXN3 2.375256 0.339322 7 SDK1 6.583704 0.329185 20 RBMS1 2.336389 0.33377 7 ABR 4.146009 0.2073 20 FHIT 2.209925 0.315704 7 MAD1L1 11.45751 0.603027 19 VPS 13D 2.080121 0.29716 7 ZNF423 9.218881 0.485204 19 COQ8A 2.607277 0.434546 6 CASZ1 8.599236 0.452591 19 FBXL18 2.406226 0.401038 6 SMG1P2 4.843875 0.254941 19 SLC22A18AS 2.041973 0.340329 6 BOLA2 4.843875 0.254941 19 RUNDC3A 2.474362 0.494872 5 LOC613038 4.843875 0.254941 19 ARHGEF7 2.035714 0.407143 5 FOXK1 7.433257 0.412959 18 OSBPL3 2.712546 0.678136 4 SEPTIN9 6.898671 0.383259 18 RBMS3 2.537584 0.634396 4 MCF2L 6.080352 0.337797 18 GRIN2B 3.514634 1.171545 3 RBFOX1 4.847449 0.269303 18 DAGLB 2.513521 0.83784 3 TBC1D16 4.803718 0.266873 18 SLC6A9 2.240435 0.746812 3 ANKRD11 4.704091 0.261338 18 PRDM2 2.140595 0.713532 3 OPCML 6.136104 0.360947 17 SOXIO 2.748606 1.374303 2 PAX6-AS1 5.627414 0.331024 17

RCN1 5.627414 0.331024 17

Cancer Type

TABLE 102: TBX15 4.66374 0.274338 17 LGG_MYB_C

NAV2 5.587066 0.349192 16

Gene site imp sum imp mean n EBF3 5.346808 0.334175 16

PTPRN2 20.20583 0.246413 ⁸² FOXP1 4.619708 0.288732 16

PRDM16 17.28914 0.243509 71

GLI2 9.468685 0.631246 15

PCDHGA1 4.924756 0.08347 59

ZBTB20 5.256564 0.350438 15

PCDHGA2 4.924756 0.086399 57

BAIAP2 4.249964 0.283331 15 RPS6KA2 6.945756 0.496125 TBX5 4.767199 0.340514 14 TABLE 103: Cancer Type LGG_MYB_D CUX1 4.567314 0.326237 14

Gene site imp sum imp mean n IQSEC1 4.544448 0.324603 14 PTPRN2 23.27595 0.283853 82 C7orf50 4.426138 0.316153 14 PRDM16 16.21061 0.228318 71 MSI2 6.825236 0.525018 13 PCDHGA1 9.160539 0.155263 59 RFX4 5.374636 0.413434 13 PCDHGA2 9.160539 0.160711 57 MYT1L 5.10985 0.393065 13 PCDHGA3 7.852367 0.145414 54 GSE1 4.902216 0.377094 13 PCDHGB1 7.852367 0.148158 53 KIF26B 4.234824 0.325756 13 PCDHGA4 7.852367 0.153968 51 ZC3H3 5.958219 0.496518 12 PCDHGB2 7.405548 0.151134 49 CMIP 4.941409 0.411784 12 PCDHGA5 7.264056 0.154554 47 TNS3 4.795633 0.399636 12 PCDHGB3 5.979533 0.139059 43 FBRSL1 4.35464 0.362887 12 PCDHGA6 5.21626 0.130406 40 ADGRD1 3.98479 0.332066 12 HDAC4 16.02144 0.433012 37 ZC3H12D 6.127102 0.557009 11 PCDHGA7 5.21626 0.14098 37 RAD51B 5.338869 0.485352 11 PAX6 12.2992 0.351406 35 CACNA1C 4.235148 0.385013 11 RBFOX3 6.122396 0.174926 35 CCDC140 4.231694 0.384699 11 PCDHGB4 5.532646 0.158076 35 VGLL4 4.106819 0.373347 11 PCDHGA8 5.532646 0.158076 35 SPON2 4.059063 0.369006 11 DIP2C 9.983397 0.311981 32 ACOT7 4.827365 0.482737 10 PCDHGB5 5.349908 0.167185 32 SH3RF3 4.309594 0.430959 10 PCDHGA9 5.349908 0.172578 31 ANKS1B 4.241103 0.42411 10 SOX2-OT 9.671774 0.333509 29 SND1 6.994693 0.777188 9 PCDHGB6 4.458488 0.153741 29 CACNA2D4 5.266681 0.585187 9 ADARB2 5.588489 0.214942 26 ATP11A 4.720307 0.524479 9 AGAP1 9.213862 0.368554 25 GPC6 4.65542 0.517269 9 PDGFRA 7.30522 0.292209 25 RUNX1 4.628724 0.514303 9 CAMTAI 5.162567 0.206503 25 TRAPPCI 2 4.612695 0.512522 9 MEIS1 6.02547 0.251061 24 ADAMTS2 4.574137 0.508237 9 SATB2 5.621512 0.23423 24 SLC22A18 4.305448 0.478383 9 RPTOR 9.213192 0.400574 23 AXIN2 4.123233 0.458137 9 NCOR2 6.408949 0.27865 23 NOTCH 1 3.941099 0.4379 9 HOXB3 6.227616 0.270766 23 LHX4 5.258583 0.657323 8 INPP5A 5.694734 0.247597 23 LINC00311 4.611648 0.576456 8 PRKCZ 6.459749 0.293625 22 DLEU1 4.375936 0.546992 8 SKI 12.18697 0.580332 21 ASPSCR1 4.105064 0.513133 8 FRMD4A 6.85308 0.342654 20 MSRA 4.098997 0.512375 8 ABR 4.623305 0.231165 20 DUSP6 8.13263 1.161804 7 MAD1L1 10.44305 0.549634 19 NAVI 4.915243 0.702178 7 SMG1P2 6.689881 0.352099 19 LINC00461 4.07618 0.582311 7 BOLA2 6.689881 0.352099 19 FBXL18 4.502532 0.750422 6

LOC613038 6.689881 0.352099 19 FAM181A 4.098243 0.683041 6 CASZ1 5.910579 0.311083 19 RUNDC3A 5.333783 1.066757 5 ZNF423 5.371146 0.282692 19 TSN AX-DISCI 5.037322 1.007464 5 FOXK1 6.442305 0.357906 18 RBMS3 4.399929 1.099982 4 TBC1D16 6.239467 0.346637 18 GRIN2B 4.044385 1.348128 3 SEPTIN9 5.949188 0.33051 18 MSRA 4.107945 0.513493 8 RBFOX1 4.629235 0.25718 18 DUSP6 6.069555 0.867079 7 MCF2L 4.384944 0.243608 18 VPS13D 4.62615 0.660879 7 OPCML 8.442759 0.496633 17 GAK 4.356915 0.622416 7 TBX15 4.712791 0.277223 17 NAVI 4.354487 0.62207 7 NAV2 6.350364 0.396898 16 FAM181A 4.252081 0.70868 6 SORBS2 5.402963 0.337685 16 RUNDC3A 4.698262 0.939652 5

FOXP1 4.987706 0.311732 16 SOX10 4.990034 2.495017 2 GLI2 10.69003 0.712669 15 EMX2OS 6.133218 0.408881 15 TABLE 104: Cancer Type LIPN ZBTB20 5.331774 0.355452 15 Gene site imp sum imp mean n LRMDA 4.22455 0.281637 15 PTPRN2 6.731022 0.082086 82 TBX5 7.168475 0.512034 14 PRDM16 9.056435 0.127555 71 RPS6KA2 6.102015 0.435858 14 HDAC4 6.920459 0.187039 37

IQSEC1 5.481145 0.39151 14 RBFOX3 4.482568 0.128073 35 CUX1 5.281736 0.377267 14 PAX6 3.647653 0.104219 35 C7orf50 4.578078 0.327006 14 DIP2C 6.348889 0.198403 32 PRKAG2 4.47899 0.319928 14 SOX2-OT 3.711222 0.127973 29 MSI2 6.722419 0.517109 13 ADARB2 6.182806 0.2378 26 REX4 6.635387 0.510414 13 SHANK2 3.204426 0.123247 26 MYT1L 6.108125 0.469856 13 CAMTAI 6.741431 0.269657 25

KIF26B 4.407831 0.339064 13 AGAP1 4.874396 0.194976 25 CMIP 6.108514 0.509043 12 PDGFRA 3.166753 0.12667 25 MIRLET7BHG 5.178592 0.431549 12 SATB2 2.720432 0.113351 24 TBX4 5.011236 0.417603 12 RPTOR 10.67932 0.464318 23 ADGRD1 4.376792 0.364733 12 INPP5A 5.333496 0.231891 23 CCDC140 5.725205 0.520473 11 NCOR2 4.784433 0.208019 23 RAD51B 4.883983 0.443998 11 RIMBP2 3.878209 0.168618 23 ZC3H12D 4.397805 0.3998 11 PRKCZ 5.076408 0.230746 22 ANAPC16 4.3899 0.399082 11 SKI 11.38324 0.542059 21 GLUD1P2 4.13606 0.376005 11 ZIC4 2.675222 0.127392 21 TSPAN4 4.482039 0.448204 10 ABR 3.659858 0.182993 20 ACOT7 4.480182 0.448018 10 FRMD4A 3.233023 0.161651 20 NR2F1-AS1 4.411228 0.441123 10 MAD1L1 8.907459 0.468814 19 AKAP13 4.391567 0.439157 10 ZNF423 6.517334 0.343018 19 SH3RF3 4.192477 0.419248 10 SMG1P2 6.286146 0.33085 19 SND1 6.302178 0.700242 9 BOLA2 6.286146 0.33085 19

ATP11A 5.52456 0.61384 9 LOC613038 6.286146 0.33085 19 NOTCH 1 5.406527 0.600725 9 CASZ1 3.545162 0.186587 19 ADAMTS2 5.093618 0.565958 9 KCNQ1 2.803672 0.147562 19 ASAP1 4.613364 0.512596 9 ANKRD11 3.883679 0.21576 18 PACS2 4.499113 0.499901 9 FOXK1 3.581042 0.198947 18 RUNX1 4.160767 0.462307 9 TBC1D16 3.291546 0.182864 18 TRAPPCI 2 4.153276 0.461475 9 SEPTIN9 3.145068 0.174726 18 KCNMA1 4.103531 0.455948 9 OPCML 3.807395 0.223964 17 LINC00311 4.754081 0.59426 8 TBX15 3.227398 0.189847 17 ESRRG 4.166404 0.520801 8 GLI2 6.244295 0.416286 15 PPP2R2B 4.13858 0.517322 8 NFIX 3.842107 0.25614 15 SLX1B-SULT1A4 3.508078 0.233872 15 PRR5L 4.743315 0.948663 5

SLX1A 3.508078 0.233872 15 ARHGEF7 4.358366 0.871673 5

LOC606724 3.508078 0.233872 15 RUNDC3A 4.230101 0.84602 5

ZBTB20 3.393498 0.226233 15 TSN AX-DISCI 3.971266 0.794253 5

C7orf50 4.232753 0.302339 14 TK1 3.759883 0.751977 5

PRKAG2 3.853803 0.275272 14 BCAR1 2.967733 0.593547 5

MIR548F5 3.550057 0.253575 14 AP2A2 2.928493 0.585699 5

CUX1 3.514123 0.251009 14 TTLL10 2.856842 0.571368 5

GSE1 5.333509 0.41027 13 TOLLIP 2.67497 0.534994 5

MSI2 5.045792 0.388138 13 RBMS3 3.938709 0.984677 4

CLYBL 3.955888 0.304299 13 SLC25A22 3.14499 1.04833 3

MYT1L 3.641557 0.28012 13 ANKLE2 4.355455 2.177728 2

KIF26B 3.115991 0.239692 13 SLC25A10 3.758294 1.879147 2

MIR9-3HG 2.847369 0.219028 13 CHTF18 2.700933 1.350466 2

MAML3 4.351798 0.36265 12 ACMSD 2.670176 2.670176 1

ZC3H3 4.308123 0.35901 12

FBRSL1 3.596014 0.299668 12 TABLE 105: Cancer Type MB_G34_I

CMIP 3.053446 0.254454 12 Gene site imp sum imp mean n

MEIS2 3.037518 0.253126 12 PTPRN2 15.21477 0.185546 82

RASA3 2.849411 0.237451 12 PRDM16 13.99515 0.197115 71

MIRLET7BHG 2.720116 0.226676 12 HDAC4 15.05659 0.406935 37

ZC3H12D 5.705381 0.518671 11 PAX6 10.99121 0.314035 35

CACNA1C 3.106977 0.282452 11 RBFOX3 9.720497 0.277728 35

TBCD 2.993291 0.272117 11 DIP2C 5.267308 0.164603 32

ACOT7 4.5126 0.45126 10 GALNT9 11.235 0.416111 27

NR2F1-AS1 3.941043 0.394104 10 SHANK2 6.139225 0.236124 26

LMF1 2.876585 0.287659 10 ADARB2 4.713313 0.181281 26

RGS12 2.712554 0.271255 10 CAMTAI 8.61843 0.344737 25

ATP11A 5.393156 0.59924 9 AGAP1 8.576106 0.343044 25

ADAMTS2 5.08888 0.565431 9 PDGFRA 3.561496 0.14246 25

TRAPPCI 2 3.099343 0.344371 9 MEIS1 3.752097 0.156337 24

SND1 3.000188 0.333354 9 RPTOR 9.10743 0.395975 23

KAZN 2.994854 0.332762 9 RIMBP2 8.129395 0.353452 23

SLC22A18 2.898108 0.322012 9 INPP5A 7.378585 0.320808 23

SPECC1 2.848229 0.31647 9 NCOR2 7.302389 0.317495 23

BAHCC1 4.507579 0.563447 8 NXN 6.254104 0.271918 23

MSRA 3.594303 0.449288 8 PRKCZ 6.210744 0.282307 22

LINC00311 3.134488 0.391811 8 SKI 6.826888 0.32509 21

RORA 2.807937 0.350992 8 ABR 6.348683 0.317434 20

PPP2R2B 2.79953 0.349941 8 MAD1L1 15.24947 0.802603 19

MCC 2.725199 0.34065 8 CASZ1 7.148909 0.376258 19

GAK 4.535148 0.647878 7 SMG1P2 6.024295 0.317068 19

RXRA 3.758968 0.536995 7 BOLA2 6.024295 0.317068 19

DUSP6 3.5019 0.500271 7 LOC613038 6.024295 0.317068 19

ITPK1 2.882647 0.411807 7 ZNF423 5.916425 0.311391 19

COQ8A 3.96694 0.661157 6 KCNQ1 4.662981 0.24542 19

CRADD 3.5242 0.587367 6 CFAP46 3.287552 0.173029 19

FBXL18 3.099236 0.516539 6 RBFOX1 5.193119 0.288507 18 SEPTIN9 4.630468 0.257248 18 CACNA2D4 4.219973 0.468886 9 FOXK1 4.379068 0.243282 18 TSPAN9 4.196219 0.466247 9 ANKRD11 4.039479 0.224415 18 VRK2 9.809346 1.226168 8 SIM1 5.337274 0.313957 17 PPP2R2B 4.7627 0.595338 8 PAX6-AS1 5.125289 0.301488 17 DNMT3A 4.54865 0.568581 8 RCN1 5.125289 0.301488 17 MSRA 4.300761 0.537595 8 OPCML 4.841857 0.284815 17 TRAPPC9 3.895066 0.486883 8 TBX15 4.156233 0.244484 17 ASPSCR1 3.587117 0.44839 8 HBG2 3.582301 0.210724 17 AFF3 3.463264 0.432908 8 FOXP1 7.656811 0.478551 16 PLEC 3.469843 0.495692 7 NAV2 5.590731 0.349421 16 PITPNC1 3.27975 0.468536 7 KNDC1 5.127187 0.341812 15 TRAK1 3.699044 0.616507 6 ZBTB20 4.614327 0.307622 15 CRADD 3.603323 0.600554 6 NHX 4.566382 0.304425 15 KDM4B 3.299808 0.549968 6 GLI2 4.154642 0.276976 15 ARHGEF7 4.059958 0.811992 5 BAIAP2 4.089022 0.272601 15 TSNAX-DISC1 4.032988 0.806598 5 C7orf50 5.987317 0.427665 14 SNX29 3.323028 0.664606 5 IQSEC1 5.521955 0.394425 14 TK1 3.266153 0.653231 5 CUX1 4.926357 0.351883 14 EXT1 3.723467 0.930867 4 PRKAG2 4.728344 0.337739 14 SOGA1 3.214915 1.071638 3 RPS6KA2 4.723242 0.337374 14 CHTF18 4.254009 2.127004 2 MOB2 3.873649 0.276689 14 ANKLE2 4.207152 2.103576 2 ARHGEF10 3.870515 0.276465 14 MSI2 6.361069 0.489313 13 TABLE 106: Cancer Type MB_G34_II MYT1L 4.843261 0.372559 13 Gene site imp sum imp mean n RFX4 4.65499 0.358076 13 PTPRN2 12.74639 0.155444 82 CLYBL 3.8267 0.294362 13 PRDM16 13.53703 0.190662 71 FBRSL1 5.87314 0.489428 12 PCDHGA1 5.680155 0.096274 59 ZC3H3 3.999881 0.333323 12 PCDHGA2 6.101345 0.107041 57 CSMD1 3.899739 0.324978 12 PCDHGA3 5.346211 0.099004 54 CMIP 3.871864 0.322655 12 PCDHGB1 5.346211 0.100872 53 LRBA 3.42567 0.285473 12 PCDHGA4 5.662597 0.111031 51 ADGRD1 3.385211 0.282101 12 PCDHGB2 5.662597 0.115563 49 COL4A1 4.603563 0.418506 11 PCDHGA5 5.662597 0.120481 47 TBCD 3.949157 0.359014 11 PCDHGB3 5.346211 0.12433 43 RAD51B 3.597217 0.32702 11 PCDHGA6 5.029825 0.125746 40 AUTS2 4.614624 0.461462 10 HDAC4 13.47601 0.364216 37 AKAP13 4.399463 0.439946 10 PCDHGA7 5.029825 0.135941 37 SNTG2 4.025889 0.402589 10 RBFOX3 5.762697 0.164648 35 STK32C 3.914647 0.391465 10 PCDHGB4 4.397053 0.12563 35 CHST11 3.564208 0.356421 10 PCDHGA8 4.397053 0.12563 35 NBEA 3.471319 0.347132 10 DIP2C 5.296077 0.165502 32 LMF1 3.252013 0.325201 10 PCDHGB5 4.397053 0.137408 32 AXIN2 5.679917 0.631102 9 PCDHGA9 3.849052 0.124163 31 ADAMTS2 5.592358 0.621373 9 SOX2-OT 4.564477 0.157396 29 SND1 5.569503 0.618834 9 PCDHGB6 3.849052 0.132726 29 ATP11A 4.782212 0.531357 9 PCDHGA10 3.849052 0.137466 28 GPC6 4.612468 0.512496 9 GALNT9 12.32559 0.456503 27 ADARB2 5.603404 0.215516 26 GSE1 4.289678 0.329975 13 SHANK2 5.402806 0.2078 26 FBRSL1 5.542824 0.461902 12 AGAP1 7.424954 0.296998 25 ZC3H3 5.160154 0.430013 12 CAMTAI 6.513135 0.260525 25 CSMD1 3.343871 0.278656 12 MEIS1 4.4845 0.186854 24 GNA12 3.269859 0.272488 12 PCDHGB7 3.849052 0.160377 24 AKAP13 4.584297 0.45843 10 RPTOR 8.737766 0.379903 23 LBX1-AS1 3.278201 0.32782 10 INPP5A 6.413009 0.278826 23 ATP11A 5.039492 0.559944 9 RIMBP2 4.94583 0.215036 23 ADAMTS2 4.637422 0.515269 9 NCOR2 4.636315 0.201579 23 GPC6 4.451665 0.494629 9 NXN 4.272086 0.185743 23 AXIN2 4.177111 0.464123 9

PCDHGA11 3.532666 0.153594 23 SND1 4.06486 0.451651 9 PRKCZ 7.474125 0.339733 22 SSBP3 3.706025 0.411781 9 SKI 7.30758 0.34798 21 PDE6B 3.491528 0.387948 9 SDK1 4.802333 0.240117 20 KAZN 3.397258 0.377473 9 FRMD4A 4.44969 0.222485 20 CACNA2D4 3.344224 0.37158 9 MAD1L1 14.57738 0.767231 19 ASAP1 3.281443 0.364605 9 CASZ1 6.899549 0.363134 19 PPP2R2B 4.157437 0.51968 8 ZNF423 5.180383 0.272652 19 TRAPPC9 4.044266 0.505533 8 SMG1P2 4.077975 0.21463 19 MSRA 3.725977 0.465747 8 BOLA2 4.077975 0.21463 19 GAK 3.32362 0.474803 7

LOC613038 4.077975 0.21463 19 PITPNC1 3.300358 0.47148 7 ANKRD11 5.335731 0.29643 18 COLEC11 3.730471 0.621745 6 RBFOX1 4.681435 0.26008 18 ARHGEF7 3.733163 0.746633 5 MCF2L 4.295341 0.23863 18 TSNAX-DISC1 3.660898 0.73218 5 FOXK1 4.236444 0.235358 18 CPEB1-AS1 3.41389 0.682778 5 SEPTIN9 3.476701 0.19315 18 EML1 3.313764 0.828441 4 SIM1 6.314996 0.37147 17 LOC339874 3.749811 1.249937 3 TBX15 4.392645 0.258391 17 CHTF18 4.105874 2.052937 2 OPCML 3.71019 0.218246 17 FOXP1 7.802048 0.487628 16 TABLE 107: Cancer Type MB_G34_III NAV2 3.845894 0.240368 16 Gene site imp sum imp mean n KNDC1 6.151625 0.410108 15 PTPRN2 14.7975 0.180457 82 GLI2 5.423438 0.361563 15 PRDM16 17.7935 0.250613 71 ZBTB20 5.123853 0.34159 15 PCDHGA4 3.301688 0.064739 51 EMX2OS 4.663506 0.3109 15 PCDHGB2 3.301688 0.067381 49 KIRREL3 3.868391 0.257893 15 HDAC4 15.89626 0.429629 37 BAIAP2 3.340001 0.222667 15 RBFOX3 9.015163 0.257576 35 NFATC1 3.316835 0.221122 15 PAX6 6.048704 0.17282 35 NHX 3.292725 0.219515 15 DIP2C 7.277516 0.227422 32 IQSEC1 5.181107 0.370079 14 PCDHGB5 3.618074 0.113065 32 C7orf50 4.968043 0.35486 14 PCDHGA9 3.217874 0.103802 31 CUX1 4.661009 0.332929 14 GALNT9 13.00801 0.481778 27 MOB2 4.283801 0.305986 14 SHANK2 3.631955 0.139691 26 CACNA1H 3.670581 0.262184 14 ADARB2 3.273835 0.125917 26 GNG7 3.388734 0.242052 14 AGAP1 10.3121 0.412484 25 MSI2 5.854141 0.450319 13 CAMTAI 7.452654 0.298106 25 MYT1L 5.022727 0.386364 13 PDGFRA 4.596988 0.18388 25 SATB2 3.175686 0.13232 24 ANAPC16 3.40214 0.309285 11

RPTOR 7.891204 0.343096 23 TBCD 3.146873 0.286079 11

NCOR2 6.955156 0.302398 23 AKAP13 3.858257 0.385826 10

INPP5A 6.462027 0.280958 23 LBX1-AS1 3.738787 0.373879 10

RIMBP2 6.059287 0.263447 23 AUTS2 3.589948 0.358995 10

NXN 5.423888 0.235821 23 SPPL2B 3.508733 0.350873 10 PRKCZ 4.474655 0.203393 22 FMN1 3.484234 0.348423 10 SKI 5.782114 0.275339 21 STK32C 3.35421 0.335421 10

ABR 6.255236 0.312762 20 AXIN2 5.46599 0.607332 9

SDK1 4.515301 0.225765 20 ATP11A 4.793503 0.532611 9

MAD1L1 15.11224 0.795381 19 SND1 4.675186 0.519465 9

ZNF423 5.857209 0.308274 19 ADAMTS2 4.119619 0.457735 9

SMG1P2 5.796451 0.305076 19 ASAP1 4.117795 0.457533 9

BOLA2 5.796451 0.305076 19 GPC6 4.069688 0.452188 9 LOC613038 5.796451 0.305076 19 TSPAN9 3.990656 0.443406 9 CASZ1 5.064561 0.266556 19 CACNA2D4 3.902544 0.433616 9

FOXK1 4.875026 0.270835 18 SSBP3 3.601051 0.400117 9 ANKRD11 4.659964 0.258887 18 VRK2 7.800481 0.97506 8 TBC1D16 3.530374 0.196132 18 PPP2R2B 4.310545 0.538818 8

HBG2 4.828729 0.284043 17 DNMT3A 3.937105 0.492138 8 OPCML 4.282999 0.251941 17 TRAPPC9 3.643122 0.45539 8 TBX15 3.693365 0.217257 17 DLEU1 3.586537 0.448317 8

FOXP1 7.819508 0.488719 16 ASPSCR1 3.52984 0.44123 8

NAV2 4.755238 0.297202 16 MSRA 3.365206 0.420651 8

KNDC1 6.627559 0.441837 15 GAK 4.133953 0.590565 7

KIRREL3 5.070696 0.338046 15 PITPNC1 4.01956 0.574223 7

BAIAP2 4.656342 0.310423 15 TRAK1 4.188055 0.698009 6

ZBTB20 4.388692 0.292579 15 CRADD 3.633965 0.605661 6

C7orf50 5.919713 0.422837 14 MYO16 3.490851 0.581809 6

RPS6KA2 5.695952 0.406854 14 TSNAX-DISC1 4.541564 0.908313 5

MOB2 4.067668 0.290548 14 CPEB1-AS1 3.981913 0.796383 5

PRKAG2 4.057194 0.2898 14 ARHGEF7 3.487939 0.697588 5

IQSEC1 3.935264 0.28109 14 CASP8 3.234689 0.646938 5

CUX1 3.910719 0.279337 14 LOC339874 3.535577 1.178526 3

ARHGEF10 3.843161 0.274512 14 CHTF18 4.356605 2.178303 2

MIR548F5 3.551018 0.253644 14 ANKLE2 3.982797 1.991399 2

GNG7 3.269011 0.233501 14

MSI2 7.8038 0.600292 13 TABLE 108: Cancer Type MB_G34_IV

GSE1 5.661925 0.435533 13 Gene site imp sum imp mean n

MYT1L 4.991771 0.383982 13 PTPRN2 10.83321 0.132112 82

RFX4 3.282977 0.252537 13 PRDM16 11.50299 0.162014 71

FBRSL1 5.460206 0.455017 12 PCDHGA1 4.072054 0.069018 59

MAML3 4.796059 0.399672 12 PCDHGA2 3.755668 0.065889 57

ZC3H3 4.548573 0.379048 12 PCDHGA3 3.755668 0.069549 54

CMIP 4.470874 0.372573 12 PCDHGB1 3.755668 0.070862 53

ADGRD1 4.190215 0.349185 12 PCDHGA4 3.755668 0.073641 51

TNS3 3.750401 0.312533 12 PCDHGB2 3.755668 0.076646 49

RAD51B 4.047182 0.367926 11 PCDHGA5 3.755668 0.079908 47 PCDHGB3 3.755668 0.087341 43 BAIAP2 4.480791 0.298719 15

HDAC4 16.18352 0.437393 37 IQSEC1 6.483397 0.4631 14

PCDHGA7 3.755668 0.101505 37 CUX1 5.545862 0.396133 14

RBFOX3 10.2518 0.292908 35 PRKAG2 5.327675 0.380548 14

PAX6 7.480697 0.213734 35 C7orf50 4.800099 0.342864 14

PCDHGB4 3.755668 0.107305 35 ARHGEF10 4.360194 0.311442 14

PCDHGA8 3.755668 0.107305 35 MOB2 3.909447 0.279246 14

DIP2C 6.618094 0.206815 32 MSI2 6.564374 0.504952 13

PCDHGB5 3.755668 0.117365 32 MYT1L 5.843539 0.449503 13

SOX2-OT 10.14846 0.349947 29 GSE1 4.535568 0.34889 13

GALNT9 12.59928 0.46664 27 RFX4 3.880353 0.298489 13

ADARB2 6.984609 0.268639 26 CLYBL 3.800938 0.29238 13

SHANK2 3.576848 0.137571 26 ZC3H3 6.596127 0.549677 12

CAMTAI 10.48882 0.419553 25 FBRSL1 4.890676 0.407556 12

AGAP1 8.930669 0.357227 25 CMIP 4.778508 0.398209 12

SATB2 4.666845 0.194452 24 MAML3 4.161716 0.34681 12

MEIS1 4.566273 0.190261 24 RAD51B 4.162748 0.378432 11

RPTOR 9.535014 0.414566 23 SLC38A10 4.026507 0.366046 11

NCOR2 7.285519 0.316762 23 AKAP13 5.211785 0.521178 10

NXN 6.055428 0.263279 23 AUTS2 4.624032 0.462403 10

RIMBP2 5.965647 0.259376 23 TSPAN4 3.669455 0.366946 10

INPP5A 5.795189 0.251965 23 ATP11A 5.709846 0.634427 9

PRKCZ 9.59318 0.436054 22 AXIN2 5.384306 0.598256 9

SKI 8.127488 0.387023 21 SND1 5.220556 0.580062 9

FRMD4A 5.105498 0.255275 20 ADAMTS2 5.121794 0.569088 9

ABR 4.470979 0.223549 20 TSPAN9 4.387251 0.487472 9

SDK1 3.740007 0.187 20 ASAP1 4.383918 0.487102 9

MAD1L1 14.70789 0.774099 19 CACNA2D4 4.234207 0.470467 9

SMG1P2 6.317952 0.332524 19 GPC6 3.728289 0.414254 9

BOLA2 6.317952 0.332524 19 VRK2 11.87504 1.484379 8

LOC613038 6.317952 0.332524 19 PPP2R2B 5.757154 0.719644 8

CASZ1 5.713871 0.30073 19 POU6F2 4.305534 0.538192 8

ZNF423 4.570964 0.240577 19 DNMT3A 3.872041 0.484005 8

KCNQ1 4.169022 0.219422 19 DGKG 5.205547 0.74365 7

RBFOX1 5.531657 0.307314 18 TRAK1 4.530712 0.755119 6

ANKRD11 4.37895 0.243275 18 CRADD 4.031996 0.671999 6

TBC1D16 4.136146 0.229786 18 FBXL18 3.823593 0.637265 6

FOXK1 3.841966 0.213443 18 PBX1 3.588716 0.598119 6

PAX6-AS1 5.58225 0.328368 17 DNAJC17 3.582465 0.597078 6

RCN1 5.58225 0.328368 17 TSNAX-DISC1 4.381651 0.87633 5

OPCML 4.51488 0.265581 17 CPEB1-AS1 3.876459 0.775292 5

FOXP1 7.148465 0.446779 16 GSG1 3.838918 0.95973 4

NAV2 4.558608 0.284913 16 CHTF18 4.578998 2.289499 2

SORBS2 3.795118 0.237195 16

NHX 5.333722 0.355581 15 TABLE 109: Cancer Type MB_G34_V

ZBTB20 5.109232 0.340615 15 Gene site imp sum imp mean n

KNDC1 5.067394 0.337826 15 PTPRN2 15.02368 0.183216 82

GLI2 4.87772 0.325181 15 PRDM16 13.41886 0.188998 71 PCDHGA1 3.065898 0.051964 59 ZBTB20 3.68549 0.245699 15 PCDHGA2 3.065898 0.053788 57 BAIAP2 3.120851 0.208057 15 PCDHGA3 3.065898 0.056776 54 C7orf50 5.481035 0.391503 14 PCDHGB1 3.065898 0.057847 53 IQSEC1 4.496178 0.321156 14 PCDHGA4 3.16386 0.062036 51 PRKAG2 4.202065 0.300148 14 PCDHGB2 3.16386 0.064569 49 MIR548F5 3.680712 0.262908 14 PCDHGA5 3.16386 0.067316 47 ARHGEF10 3.628172 0.259155 14 HDAC4 17.31966 0.468099 37 CACNA1H 3.292159 0.235154 14 PAX6 8.428562 0.240816 35 RPS6KA2 2.986116 0.213294 14 RBFOX3 3.922926 0.112084 35 CUX1 2.968351 0.212025 14 DIP2C 2.991318 0.093479 32 MSI2 6.420698 0.4939 13 SOX2-OT 5.144005 0.177379 29 MYT1L 5.041558 0.387812 13 GALNT9 10.38172 0.384508 27 KIF26B 3.1165 0.239731 13 SHANK2 4.511083 0.173503 26 FBRSL1 5.282675 0.440223 12 ADARB2 3.52234 0.135475 26 CMIP 3.938958 0.328246 12 CAMTAI 8.519389 0.340776 25 GNA12 3.670793 0.305899 12 AGAP1 6.689657 0.267586 25 ADGRD1 3.100876 0.258406 12 PDGFRA 4.069607 0.162784 25 RAD51B 3.753865 0.34126 11 RPTOR 8.105897 0.35243 23 TBCD 3.148106 0.286191 11 NCOR2 6.317118 0.274657 23 AUTS2 5.178221 0.517822 10 RIMBP2 6.002695 0.260987 23 LMF1 3.745211 0.374521 10 INPP5A 5.458839 0.237341 23 KCNIP4 3.594265 0.359426 10 NXN 4.695211 0.20414 23 AKAP13 3.474133 0.347413 10 PRKCZ 4.592679 0.208758 22 STK32C 3.223752 0.322375 10 SKI 6.257065 0.297955 21 SNTG2 3.181238 0.318124 10 ZIC4 3.985685 0.189795 21 SND1 5.78745 0.64305 9 ABR 4.831867 0.241593 20 ATP11A 5.549079 0.616564 9 SDK1 4.369185 0.218459 20 ADAMTS2 5.070177 0.563353 9 MAD1L1 15.70623 0.826644 19 AXIN2 4.408033 0.489781 9 CASZ1 6.481234 0.341118 19 CACNA2D4 3.86314 0.429238 9 ZNF423 5.926592 0.311926 19 GPC6 3.508593 0.389844 9 SMG1P2 5.729856 0.301571 19 TRAPPCI 2 3.361639 0.373515 9 BOLA2 5.729856 0.301571 19 PACS2 3.019137 0.33546 9 LOC613038 5.729856 0.301571 19 APBA2 2.968133 0.329793 9 ANKRD11 4.661094 0.25895 18 VRK2 6.038017 0.754752 8 FOXK1 4.579725 0.254429 18 PPP2R2B 4.733325 0.591666 8 SEPTIN9 3.983606 0.221311 18 MSRA 3.190686 0.398836 8 RBFOX1 3.736773 0.207599 18 LHX4 3.165907 0.395738 8 SIM1 6.169379 0.362905 17 CACHD1 3.077802 0.384725 8 OPCML 6.068681 0.356981 17 PITPNC1 4.553902 0.650557 7 TBX15 3.817539 0.224561 17 TRAK1 3.987901 0.66465 6 PAX6-AS1 3.360435 0.197673 17 FBXL18 3.290669 0.548445 6 RCN1 3.360435 0.197673 17 TSN AX-DISCI 4.152699 0.83054 5 FOXP1 7.540532 0.471283 16 ARHGEF7 4.077908 0.815582 5 NAV2 3.306268 0.206642 16 RUNDC3A 3.27271 0.654542 5 GLI2 4.726612 0.315107 15 NPHP4 3.018238 0.603648 5 KIRREL3 4.323392 0.288226 15 EXT1 3.188407 0.797102 4 KNDC1 4.300115 0.286674 15 TUBA1C 3.059964 0.764991 4 SLC25A22 3.133245 1.044415 3 OPCML 4.070115 0.239419 17 ANKLE2 4.199293 2.099646 2 FOXP1 6.653114 0.41582 16

NAV2 3.306215 0.206638 16

TABLE 110: Cancer Type MB_G34_VI KNDC1 4.03037 0.268691 15 Gene site imp sum imp mean n BAIAP2 3.990254 0.266017 15 PTPRN2 10.17979 0.124144 82 GLI2 3.975502 0.265033 15 PRDM16 11.67227 0.164398 71 NFIX 3.914943 0.260996 15 PCDHGA1 3.480246 0.058987 59 COL23A1 3.42571 0.228381 15 PCDHGA2 3.480246 0.061057 57 ZBTB20 3.248796 0.216586 15 PCDHGA3 3.16386 0.05859 54 RPS6KA2 5.475875 0.391134 14 PCDHGB1 3.16386 0.059695 53 ARHGEF10 5.037701 0.359836 14 PCDHGA4 3.16386 0.062036 51 CUX1 4.752663 0.339476 14 PCDHGB2 3.16386 0.064569 49 IQSEC1 4.448833 0.317774 14 PCDHGB3 3.16386 0.073578 43 CACNA1H 4.134716 0.295337 14 HDAC4 9.744729 0.263371 37 PRKAG2 3.83548 0.273963 14 PAX6 8.568253 0.244807 35 C7orf50 3.718669 0.265619 14 RBFOX3 8.19285 0.234081 35 MSI2 5.875307 0.451947 13 DIP2C 6.787527 0.21211 32 MYT1L 4.451341 0.342411 13 SOX2-OT 4.234859 0.14603 29 RFX4 3.148192 0.242169 13 GALNT9 5.760336 0.213346 27 FBRSL1 4.537368 0.378114 12 SHANK2 3.358318 0.129166 26 ZC3H3 4.41386 0.367822 12 CAMTAI 9.053347 0.362134 25 CMIP 3.487553 0.290629 12 AGAP1 7.179943 0.287198 25 GNA12 3.230051 0.269171 12 PDGFRA 3.471099 0.138844 25 CSMD1 3.219359 0.26828 12 RPTOR 8.284353 0.360189 23 RAD51B 3.769786 0.342708 11 NXN 5.924414 0.257583 23 ANAPC16 3.663236 0.333021 11 NCOR2 5.889471 0.256064 23 AUTS2 5.381906 0.538191 10 INPP5A 5.523952 0.240172 23 FMN1 3.894166 0.389417 10 PRKCZ 5.805273 0.263876 22 AKAP13 3.674493 0.367449 10 SKI 8.221779 0.391513 21 SPPL2B 3.110089 0.311009 10 SIM2 3.126904 0.1489 21 ADAMTS2 5.97969 0.66441 9 FRMD4A 5.072868 0.253643 20 AXIN2 4.644633 0.51607 9 ABR 4.938759 0.246938 20 CACNA2D4 4.153062 0.461451 9 SDK1 3.616258 0.180813 20 TSPAN9 4.120196 0.4578 9 MAD1L1 15.08777 0.794093 19 ATP11A 3.821801 0.424645 9 CASZ1 7.558691 0.397826 19 SND1 3.648233 0.405359 9 SMG1P2 5.981934 0.314839 19 GPC6 3.634651 0.40385 9 BOLA2 5.981934 0.314839 19 ASAP1 3.191981 0.354665 9 LOC613038 5.981934 0.314839 19 SSBP3 3.112073 0.345786 9 ZNF423 3.774906 0.198679 19 VRK2 7.861226 0.982653 8 ANKRD11 5.977816 0.332101 18 PPP2R2B 4.859435 0.607429 8 FOXK1 5.126775 0.284821 18 CACHD1 3.485521 0.43569 8 RBFOX1 4.580482 0.254471 18 DLEU1 3.438064 0.429758 8 TBC1D16 4.244049 0.235781 18 SYNJ2 3.288412 0.411051 8 SEPTIN9 3.720862 0.206715 18 PITPNC1 4.366461 0.62378 7 HOXA3 3.192502 0.177361 18 MIR124-2HG 3.521223 0.503032 7 SIM1 4.398925 0.25876 17 NAVI 3.352132 0.478876 7 TBX15 4.300947 0.252997 17 TRAK1 4.023501 0.670584 6 FBXL18 3.697156 0.616193 6 SEPTIN9 4.576042 0.254225 18 MYO 16 3.325326 0.554221 6 FOXK1 4.219129 0.234396 18 KDM4B 3.154782 0.525797 6 SIM1 5.678352 0.334021 17 TSN AX-DISCI 4.513548 0.90271 5 PAX6-AS1 4.920365 0.289433 17 ARHGEF7 3.418039 0.683608 5 RCN1 4.920365 0.289433 17 PRR5L 3.178152 0.63563 5 OPCML 4.643683 0.273158 17 DAGLB 3.126133 1.042044 3 FOXP1 4.504852 0.281553 16 ANKLE2 4.27943 2.139715 2 NAV2 4.067416 0.254213 16 CHTF18 3.98205 1.991025 2 BAIAP2 4.327026 0.288468 15

KNDC1 4.062649 0.270843 15

TABLE 111: Cancer Type MB_G34_VII GLI2 3.871486 0.258099 15 Gene site imp sum imp mean n NFIX 3.67403 0.244935 15

PTPRN2 17.04639 0.207883 82 ZBTB20 3.399125 0.226608 15 PRDM16 11.53801 0.162507 71 RPS6KA2 6.006307 0.429022 14 PCDHGA4 3.418749 0.067034 51 MIR548F5 5.164099 0.368864 14 PCDHGB2 3.418749 0.06977 49 C7orf50 4.769793 0.340699 14 PCDHGA5 3.418749 0.072739 47 CUX1 4.451564 0.317969 14 PCDHGB3 3.418749 0.079506 43 PRKAG2 4.423848 0.315989 14 PCDHGA6 3.304608 0.082615 40 ARHGEF10 3.474581 0.248184 14 HDAC4 10.74833 0.290495 37 MOB2 3.466234 0.247588 14 PAX6 12.88611 0.368175 35 MSI2 6.747519 0.51904 13 RBFOX3 7.249014 0.207115 35 MYT1L 5.256997 0.404384 13 DIP2C 4.86296 0.151968 32 GSE1 3.864862 0.297297 13 SOX2-OT 4.555504 0.157086 29 RFX4 3.106126 0.238933 13 GALNT9 8.297959 0.307332 27 FBRSL1 6.470299 0.539192 12 SHANK2 6.660721 0.256182 26 ZC3H3 4.847501 0.403958 12 CAMTAI 8.372982 0.334919 25 CMIP 4.417143 0.368095 12 AGAP1 7.769507 0.31078 25 GNA12 3.368387 0.280699 12 SATB2 3.540531 0.147522 24 ADGRD1 3.31883 0.276569 12 RPTOR 8.809254 0.383011 23 COL4A1 3.846087 0.349644 11 NCOR2 6.919265 0.300838 23 TBCD 3.438012 0.312547 11 NXN 5.018761 0.218207 23 SLC38A10 3.411534 0.310139 11 INPP5A 4.405626 0.191549 23 AUTS2 5.079352 0.507935 10 RIMBP2 3.938363 0.171233 23 AKAP13 4.669508 0.466951 10 PRKCZ 6.917698 0.314441 22 CHST11 3.239795 0.323979 10 SKI 5.83892 0.278044 21 FMN1 3.198852 0.319885 10 ABR 5.347094 0.267355 20 ADAMTS2 6.019738 0.66886 9 FRMD4A 4.905347 0.245267 20 ASAP1 5.521091 0.613455 9 SDK1 4.260924 0.213046 20 SND1 4.862382 0.540265 9 MAD1L1 16.30223 0.858012 19 ATP11A 4.324377 0.480486 9 CASZ1 7.001381 0.368494 19 CACNA2D4 4.292604 0.476956 9 SMG1P2 6.849276 0.360488 19 AXIN2 4.107988 0.456443 9

BOLA2 6.849276 0.360488 19 TRAPPCI 2 3.656299 0.406255 9 LOC613038 6.849276 0.360488 19 TSPAN9 3.573777 0.397086 9 ZNF423 4.929504 0.259448 19 GPC6 3.34619 0.371799 9 KCNQ1 3.717288 0.195647 19 VRK2 9.410478 1.17631 8 ANKRD11 6.106655 0.339259 18 PPP2R2B 5.123059 0.640382 8 TBC1D16 4.832975 0.268499 18 AFF3 3.560936 0.445117 8 DNMT3A 3.505303 0.438163 8 CASZ1 4.483706 0.235985 19 MSRA 3.282092 0.410261 8 SEPTIN9 6.234626 0.346368 18 MACROD1 3.237537 0.404692 8 ANKRD11 5.754369 0.319687 18 GAK 5.338333 0.762619 7 TBC1D16 2.862671 0.159037 18 PITPNC1 4.266097 0.609442 7 PAX6-AS1 4.611682 0.271275 17 KDM4B 3.779773 0.629962 6 RCN1 4.611682 0.271275 17 TRAK1 3.520526 0.586754 6 OPCML 4.231436 0.248908 17 FBXL18 3.426544 0.571091 6 FOXP1 5.111416 0.319464 16 COLECI 1 3.399725 0.566621 6 NAV2 3.395159 0.212197 16 MYO 16 3.249272 0.541545 6 GLI2 3.746676 0.249778 15 TSN AX-DISCI 4.644008 0.928802 5 BAIAP2 3.302877 0.220192 15 VAV2 3.821501 0.7643 5 RPS6KA2 5.857572 0.418398 14 ARHGEF7 3.681206 0.736241 5 C7orf50 5.496564 0.392612 14 EXT1 3.487271 0.871818 4 CACNA1H 5.012177 0.358013 14 ANKLE2 4.23297 2.116485 2 CUX1 4.071116 0.290794 14 CHTF18 4.140574 2.070287 2 IQSEC1 3.653357 0.260954 14

ARHGEF10 3.255632 0.232545 14

TABLE 112: Cancer Type MIR548F5 3.212982 0.229499 14 MB_G34_VIII

PPP2R2A 3.103164 0.221655 14

Gene site imp sum imp mean n

PRKAG2 2.953235 0.210945 14 PTPRN2 12.41194 0.151365 82

MSI2 5.642549 0.434042 13 PRDM16 9.171053 0.12917 71

GSE1 4.189249 0.32225 13 PCDHGA5 2.847474 0.060585 47

MYT1L 3.556903 0.273608 13 HDAC4 19.15223 0.517628 37

FBRSL1 5.232895 0.436075 12 RBFOX3 7.021825 0.200624 35

ZC3H3 4.260363 0.35503 12 PAX6 5.771277 0.164894 35

TNS3 3.420782 0.285065 12 DIP2C 7.083844 0.22137 32

CMIP 3.288465 0.274039 12 GALNT9 8.220999 0.304481 27

RASA3 3.00796 0.250663 12 SHANK2 5.171992 0.198923 26

ADGRD1 2.850136 0.237511 12 ADARB2 3.869727 0.148836 26

LMF1 3.80437 0.380437 10 AGAP1 7.03262 0.281305 25

AUTS2 3.525084 0.352508 10 CAMTAI 6.648568 0.265943 25

KCNIP4 3.505412 0.350541 10 PDGFRA 3.818061 0.152722 25

AKAP13 3.444648 0.344465 10 RPTOR 8.99687 0.391168 23

SPPL2B 2.931918 0.293192 10 NCOR2 5.77417 0.251051 23

NBEA 2.856603 0.28566 10 INPP5A 5.710913 0.248301 23

ADAMTS2 5.84082 0.64898 9 NXN 4.916837 0.213776 23

ATP11A 5.816007 0.646223 9 RIMBP2 3.734723 0.162379 23

SND1 4.531475 0.503497 9 PRKCZ 4.604762 0.209307 22

ASAP1 4.478279 0.497587 9 SKI 8.27076 0.393846 21

TSPAN9 3.791592 0.421288 9 ABR 3.627221 0.181361 20

TRAPPCI 2 3.668458 0.407606 9 FRMD4A 3.288809 0.16444 20

AXIN2 3.446436 0.382937 9 MAD1L1 14.58326 0.76754 19

CACNA2D4 3.32849 0.369832 9 SMG1P2 6.073956 0.319682 19

MGMT 3.227272 0.358586 9 BOLA2 6.073956 0.319682 19

GPC6 3.028942 0.336549 9

LOC613038 6.073956 0.319682 19

KCNMA1 2.827183 0.314131 9 ZNF423 5.20503 0.273949 19

VRK2 6.931173 0.866397 8 KCNQ1 4.682545 0.24645 19

PPP2R2B 4.915064 0.614383 8 MSRA 3.678484 0.459811 8 NXN 3.259735 0.141728 23 DNMT3A 3.518789 0.439849 8 INPP5A 2.909853 0.126515 23 DLEU1 2.818785 0.352348 8 PRKCZ 2.154902 0.09795 22 GAK 3.810517 0.54436 7 SKI 4.584725 0.21832 21 PLEC 2.924521 0.417789 7 MAD1L1 8.264018 0.434948 19

COLECI 1 3.272893 0.545482 6 CASZ1 4.00511 0.210795 19

FBXL18 2.993867 0.498978 6 SMG1P2 3.599582 0.189452 19

TRAK1 2.924649 0.487442 6 BOLA2 3.599582 0.189452 19

TSN AX-DISCI 5.124489 1.024898 5 LOC613038 3.599582 0.189452 19

EXPH5 3.453852 0.69077 5 ZNF423 2.44059 0.128452 19

ARHGEF7 3.007099 0.60142 5 FOXK1 2.97292 0.165162 18

NPHP4 2.860235 0.572047 5 TBC1D16 2.75853 0.153252 18

KIAA1522 3.621688 0.905422 4 SEPTIN9 2.343916 0.130218 18

SLC25A22 3.098151 1.032717 3 TBX15 3.458112 0.203418 17

DAGLB 2.998599 0.999533 3 HBG2 2.126484 0.125087 17

RASGRP3 2.837226 0.945742 3 FOXP1 5.347217 0.334201 16

ANKLE2 4.310693 2.155347 2 NAV2 2.471203 0.15445 16

CHTF18 3.302048 1.651024 2 EBF3 1.916364 0.119773 16

UHRF1 3.16184 1.58092 2 KNDC1 3.930209 0.262014 15

KIF21B 2.987182 1.493591 2 BAIAP2 3.588691 0.239246 15

SLC25A10 2.855353 1.427677 2 ZBTB20 3.235695 0.215713 15

KCNV2 2.984463 2.984463 1 SYCP2L 5.723577 0.408827 14

DDT 2.922807 2.922807 1 IQSEC1 3.736916 0.266923 14 ARL6IP6 2.877777 2.877777 1 CUX1 3.402324 0.243023 14 C7orf50 3.341656 0.23869 14

TABLE 113: Cancer Type MB_MYO PRKAG2 2.335498 0.166821 14

Gene site imp sum imp mean n CACNA1H 2.27144 0.162246 14

PTPRN2 3.785764 0.046168 82 ARHGEF10 2.023279 0.14452 14

PRDM16 8.662869 0.122012 71 RPS6KA2 1.898316 0.135594 14

PCDHGA1 2.905979 0.049254 59 MYT1L 3.01092 0.231609 13

PCDHGA2 2.589593 0.045431 57 GSE1 2.81954 0.216888 13

PCDHGA3 2.273207 0.042096 54 RFX4 2.068167 0.15909 13

PCDHGB1 2.273207 0.042891 53 MEGF6 2.437108 0.203092 12

PCDHGA4 2.273207 0.044573 51 FBRSL1 2.331273 0.194273 12

PCDHGB2 2.273207 0.046392 49 GNA12 2.069569 0.172464 12

PCDHGA5 2.273207 0.048366 47 TFAP2B 3.312113 0.331211 10

PCDHGB3 1.956821 0.045507 43 AKAP13 3.253774 0.325377 10

HDAC4 8.08646 0.218553 37 NBEA 2.197763 0.219776 10

PAX6 3.196964 0.091342 35 FMN1 2.093129 0.209313 10

DIP2C 2.964181 0.092631 32 SND1 4.095694 0.455077 9 SOX2-OT 3.704058 0.127726 29 TSPAN9 3.126332 0.34737 9 AGAP1 4.754946 0.190198 25 CACNA2D4 3.069865 0.341096 9

CAMTAI 3.193667 0.127747 25 ADAMTS2 2.8411 0.315678 9

MEIS1 3.63319 0.151383 24 ATP11A 2.667221 0.296358 9

SATB2 1.996545 0.083189 24 AXIN2 2.122505 0.235834 9

RPTOR 6.711731 0.291814 23 DNMT3A 2.986323 0.37329 8

RIMBP2 4.09283 0.177949 23 PPP2R2B 2.506865 0.313358 8

NCOR2 3.267564 0.142068 23 RORA 2.125144 0.265643 8 LINC00311 2.025461 0.253183 8.916148 0.156424 57

ASPSCR1 2.025297 0.253162 7.587891 0.140517 54

SMAD3 1.887128 0.235891 7.51521 0.139171 54

RXRA 2.000038 0.28572 8.916148 0.165114 54

EBF2 1.964235 0.280605 7.587891 0.143168 53

ARHGAP18 3.172124 0.528687 7.51521 0.141796 53

MYO 16 2.570597 0.428433 8.916148 0.168229 53

COLECI 1 2.317683 0.386281 7.587891 0.148782 51

MIR548G 2.237624 0.372937 7.51521 0.147357 51

CRADD 2.050473 0.341746 8.916148 0.174826 51

CCDC177 1.921631 0.320272 7.503957 0.153142 49

TSN AX-DISCI 2.117145 0.423429 6.882438 0.140458 49

VAV2 1.991836 0.398367 8.283376 0.169048 49

SHOX2 1.910358 0.382072 6.543723 0.139228 47

CPE 2.202064 0.550516 5.867063 0.124831 47

IGSF21 1.982063 0.495516 7.370543 0.15682 47

LOC339874 2.815089 0.938363 6.227337 0.144822 43

WNT16 2.546151 0.848717 5.452341 0.126799 43

DICER1 2.539719 0.846573 6.251393 0.145381 43

CHID1 2.469217 0.823072 6.543723 0.163593 40

SLC1A7 2.178243 0.726081 5.321472 0.133037 40

BFSP2 1.97466 0.65822 5.935007 0.148375 40

ANKLE2 3.431389 1.715695 11.44288 0.309267 37

CHTF18 2.845109 1.422554 5.49864 0.148612 37

UTRN 2.535066 1.267533 9.878771 0.266994 37

DISCI 1.968634 0.984317 4.899938 0.132431 37

KIF21B 1.874384 0.937192 7.008804 0.189427 37

ARL6IP6 2.61283 2.61283 9.624697 0.260127 37

DDT 2.59893 2.59893 5.302235 0.143304 37

DNAJC27 2.220019 2.220019 8.400886 0.240025 35

DLG4 1.974717 1.974717 5.800011 0.165715 35

5.182254 0.148064 35

Cancer Type

TABLE 114: 5.182254 0.148064 35

MB_SHH_AD

8.114223 0.231835 35

Gene site imp sum imp mean

6.09272 0.174078 35

PTPRN2 15.1265 0.18447

4.583552 0.130959 35

PTPRN2 17.35912 0.211697

4.583552 0.130959 35

PTPRN2 12.58685 0.153498

9.113563 0.260388 35

PTPRN2 12.13645 0.148005

3.406611 0.097332 35

PRDM16 7.907722 0.111376

6.680933 0.190884 35

PRDM16 11.0177 0.155179

5.618621 0.160532 35

PRDM16 8.809374 0.124076

5.618621 0.160532 35

PRDM16 7.679804 0.108166

8.48953 0.265298 32

PCDHGA1 7.892189 0.133766

4.95066 0.154708 32

PCDHGA1 7.831596 0.132739

10.05901 0.314344 32

PCDHGA1 8.916148 0.151121

4.509287 0.140915 32

PCDHGA2 7.904277 0.138672

6.27013 0.195942 32

PCDHGA2 7.831596 0.137396

9.408016 0.294001 32 PCDHGB5 4.717386 0.147418 32 NXN 6.050887 0.263082 23 PCDHGA9 4.634274 0.149493 31 RPTOR 6.854974 0.298042 23 PCDHGA9 4.509287 0.145461 31 NCOR2 6.23076 0.270903 23 PCDHGA9 4.717386 0.152174 31 INPP5A 5.522711 0.240118 23 SOX2-OT 6.737024 0.232311 29 NXN 5.108222 0.222097 23 PCDHGB6 3.660059 0.126209 29 RIMBP2 4.095477 0.178064 23 SOX2-OT 8.048747 0.277543 29 RPTOR 10.69073 0.464814 23 SOX2-OT 5.334916 0.183963 29 RIMBP2 8.17955 0.355633 23 SOX2-OT 9.085177 0.313282 29 NCOR2 5.678988 0.246913 23 PCDHGB6 3.662255 0.126285 29 PCDHA3 4.270382 0.185669 23 PCDHGA10 3.660059 0.130716 28 PRKCZ 5.399293 0.245422 22 PCDHGA10 3.662255 0.130795 28 PRKCZ 6.940502 0.315477 22 GALNT9 2.999202 0.111082 27 PRKCZ 3.781779 0.171899 22 PCDHA2 4.903154 0.181598 27 PRKCZ 5.777631 0.26262 22 PCDHA1 4.903154 0.181598 27 SKI 8.213644 0.391126 21 SHANK2 4.211899 0.161996 26 ZIC4 4.004628 0.190697 21 ADARB2 3.815923 0.146766 26 SKI 8.808682 0.419461 21 ADARB2 6.355375 0.244438 26 ZIC4 5.049453 0.24045 21 SHANK2 5.581315 0.214666 26 SIM2 4.200894 0.200043 21 ADARB2 4.42342 0.170132 26 SKI 6.37787 0.303708 21 SHANK2 4.141465 0.159287 26 ZIC4 5.926099 0.282195 21 ADARB2 4.612131 0.17739 26 SKI 6.428761 0.306131 21 SHANK2 3.884978 0.149422 26 PCDHA4 3.953996 0.188286 21 CAMTAI 7.940562 0.317622 25 ZIC4 3.611484 0.171975 21 AGAP1 7.763085 0.310523 25 ABR 3.90796 0.195398 20 PDGFRA 3.817616 0.152705 25 SDK1 6.719232 0.335962 20 AGAP1 8.738619 0.349545 25 ABR 4.43783 0.221892 20 CAMTAI 8.134202 0.325368 25 FRMD4A 4.326176 0.216309 20 PDGFRA 5.017886 0.200715 25 ABR 3.132036 0.156602 20 CAMTAI 7.419743 0.29679 25 ABR 5.060822 0.253041 20 AGAP1 6.302105 0.252084 25 FRMD4A 4.066681 0.203334 20 PDGFRA 3.972671 0.158907 25 SMG1P2 8.884298 0.467595 19 CAMTAI 6.810666 0.272427 25 BOLA2 8.884298 0.467595 19 AGAP1 5.913963 0.236559 25 LOC613038 8.884298 0.467595 19 PDGFRA 4.733582 0.189343 25 ZNF423 7.177879 0.377783 19 SATB2 3.738509 0.155771 24 MAD1L1 6.614525 0.348133 19 MEIS1 6.011862 0.250494 24 KCNQ1 4.692295 0.246963 19 SATB2 5.746239 0.239427 24 CASZ1 3.789958 0.199471 19 SATB2 4.43271 0.184696 24 SMG1P2 8.900718 0.468459 19 RPTOR 11.98578 0.521121 23 BOLA2 8.900718 0.468459 19 NCOR2 6.236985 0.271173 23 LOC613038 8.900718 0.468459 19 RIMBP2 5.323145 0.231441 23 MAD1L1 7.943356 0.418071 19 NXN 4.909253 0.213446 23 ZNF423 6.41927 0.337856 19 INPP5A 4.781912 0.207909 23 CASZ1 4.631459 0.243761 19 RPTOR 11.83216 0.514442 23 KCNQ1 4.348355 0.228861 19 INPP5A 7.120684 0.309595 23 SMG1P2 7.982796 0.420147 19 NCOR2 6.749041 0.293437 23 BOLA2 7.982796 0.420147 19 RIMBP2 6.364259 0.276707 23 LOC613038 7.982796 0.420147 19 MAD1L1 5.93073 0.312144 19 LOC606724 4.431234 0.295416 15

ZNF423 5.242571 0.275925 19 NFIX 4.404545 0.293636 15

CASZ1 4.876325 0.256649 19 ZBTB20 3.903113 0.260208 15

CFAP46 3.280592 0.172663 19 GLI2 6.532372 0.435491 15

MAD1L1 9.665857 0.508729 19 ZBTB20 3.220659 0.214711 15

SMG1P2 8.480735 0.446354 19 BAIAP2 3.082184 0.205479 15

BOLA2 8.480735 0.446354 19 GLI2 3.915004 0.261 15

LOC613038 8.480735 0.446354 19 DLX6-AS1 3.904856 0.260324 15

ZNF423 7.52162 0.395875 19 PRKAG2 5.408447 0.386318 14

CASZ1 5.039069 0.265214 19 IQSEC1 5.405558 0.386111 14

FOXK1 5.681706 0.31565 18 CUX1 5.352775 0.382341 14

TBC1D16 4.757733 0.264319 18 RPS6KA2 5.138169 0.367012 14

MCF2L 3.771779 0.209543 18 C7orf50 4.980817 0.355773 14

ANKRD11 6.6446 0.369144 18 RPS6KA2 5.705567 0.40754 14

FOXK1 5.729092 0.318283 18 PRKAG2 5.346347 0.381882 14

TBC1D16 5.363412 0.297967 18 C7orf50 4.971 0.355071 14

MCF2L 4.485611 0.249201 18 IQSEC1 4.690514 0.335037 14

FOXK1 3.633137 0.201841 18 CUX1 4.036515 0.288323 14

SEPTIN9 3.042159 0.169009 18 ARHGEF10 3.838387 0.274171 14

ANKRD11 4.928495 0.273805 18 RPS6KA2 4.231365 0.30224 14

TBC1D16 4.497394 0.249855 18 CUX1 4.051786 0.289413 14

FOXK1 3.924434 0.218024 18 PPP2R2A 4.002789 0.285914 14

OPCML 7.221906 0.424818 17 MIR548F5 3.630739 0.259338 14

TBX15 3.633948 0.213762 17 PRKAG2 3.54061 0.252901 14

OPCML 6.749132 0.397008 17 ARHGEF10 3.300672 0.235762 14

SIM1 4.667199 0.274541 17 CACNA1H 3.191626 0.227973 14

TBX15 5.971427 0.35126 17 C7orf50 2.963868 0.211705 14

OPCML 5.473333 0.321961 17 GNG7 2.94343 0.210245 14

TBX15 5.575745 0.327985 17 CUX1 5.722125 0.408723 14

OPCML 5.455402 0.320906 17 RPS6KA2 4.713006 0.336643 14

SIM1 3.704432 0.217908 17 TBX5 3.899536 0.278538 14

EBF3 5.790181 0.361886 16 C7orf50 3.651625 0.26083 14

FOXP1 4.298654 0.268666 16 MSI2 6.083076 0.467929 13

NAV2 3.50322 0.218951 16 CLYBL 4.973501 0.382577 13

EBF3 5.106376 0.319148 16 MYT1L 4.916223 0.378171 13

NAV2 4.965713 0.310357 16 MSI2 6.966227 0.535864 13

FOXP1 4.89795 0.306122 16 CLYBL 5.077306 0.390562 13

NAV2 3.925816 0.245363 16 GSE1 4.599659 0.35382 13

EBF3 4.010135 0.250633 16 MSI2 4.272438 0.328649 13

GLI2 7.440531 0.496035 15 MYT1L 3.438404 0.264493 13

ZBTB20 4.040587 0.269372 15 GSE1 3.425092 0.263469 13

SLX1B-SULT1A4 3.79633 0.253089 15 RFX4 3.082134 0.237087 13

SLX1A 3.79633 0.253089 15 MSI2 5.990683 0.460822 13

LOC606724 3.79633 0.253089 15 CLYBL 4.587194 0.352861 13

GLI2 7.602239 0.506816 15 MYT1L 4.512123 0.347086 13

BAIAP2 4.846091 0.323073 15 MEIS2 4.900008 0.408334 12

SLX1B-SULT1A4 4.431234 0.295416 15 ADGRD1 4.681714 0.390143 12

SLX1A 4.431234 0.295416 15 CMIP 4.430408 0.369201 12 TNS3 4.377581 0.364798 12 RGS12 3.022987 0.302299 10 ZC3H3 4.046952 0.337246 12 SKOR1 4.887111 0.488711 10 MAML3 3.846496 0.320541 12 ACOT7 4.533972 0.453397 10 LRBA 3.637413 0.303118 12 LBX1-AS1 4.171722 0.417172 10 FBRSL1 3.636163 0.303014 12 SH3RF3 3.5777 0.35777 10 FBRSL1 5.48669 0.457224 12 SND1 5.70304 0.633671 9 ZC3H3 4.948896 0.412408 12 ATP11A 5.622162 0.624685 9

MAML3 4.671648 0.389304 12 TRAPPCI 2 4.436276 0.49292 9 CMIP 4.629361 0.38578 12 TSPAN9 4.162421 0.462491 9 RASA3 4.196058 0.349671 12 ADAMTS2 3.952429 0.439159 9 ADGRD1 4.121606 0.343467 12 SND1 6.390561 0.710062 9 MIRLET7BHG 3.85566 0.321305 12 ATP11A 5.643567 0.627063 9 LRBA 5.184367 0.432031 12 ADAMTS2 4.303716 0.478191 9 ADGRD1 3.957863 0.329822 12 ASAP1 4.066582 0.451842 9 TBX4 3.821608 0.318467 12 TRAPPCI 2 3.825479 0.425053 9

MIRLET7BHG 3.206211 0.267184 12 TSPAN9 4.932572 0.548064 9 MEIS2 2.957049 0.246421 12 SND1 4.695963 0.521774 9 ZC3H3 4.634894 0.386241 12 ATP11A 4.336107 0.48179 9 FBRSL1 4.454571 0.371214 12 ADAMTS2 3.89938 0.433264 9 RASA3 4.441187 0.370099 12 PAX3 3.546297 0.394033 9 ADGRD1 4.350455 0.362538 12 PACS2 3.341665 0.371296 9 CMIP 3.988099 0.332342 12 RUNX1 3.203476 0.355942 9 TBX4 3.801473 0.316789 12 AXIN2 3.123528 0.347059 9 LRBA 3.722755 0.31023 12 ADAMTS2 5.603878 0.622653 9

MAML3 3.668841 0.305737 12 ATP11A 5.593948 0.62155 9 TNS3 3.624613 0.302051 12 SND1 5.04309 0.560343 9 VGLL4 4.623824 0.420348 11 SLC22A18 3.945131 0.438348 9 CCDC140 4.304976 0.391361 11 TXNRD1 3.837119 0.426347 9 RAD51B 3.648926 0.331721 11 KCNH2 3.78563 0.420626 9 TBCD 3.622455 0.329314 11 TSPAN9 3.637798 0.4042 9 VGLL4 5.236927 0.476084 11 APBA2 3.61274 0.401416 9

CCDC140 4.820168 0.438197 11 SYNJ2 4.734921 0.591865 8 ZC3H12D 3.196704 0.290609 11 LINC00311 4.473012 0.559127 8 CCDC140 4.61614 0.419649 11 PPP2R2B 4.083048 0.510381 8 RAD51B 3.946997 0.358818 11 MCC 3.52537 0.440671 8 ZC3H12D 3.675656 0.334151 11 DLEU1 3.520617 0.440077 8 TSPAN4 4.461114 0.446111 10 MSRA 4.278226 0.534778 8 KLHL29 4.385021 0.438502 10 DNMT3A 4.024289 0.503036 8

AKAP13 4.079004 0.4079 10 NR2E1 4.112219 0.514027 8 TSPAN4 4.902196 0.49022 10 SYNJ2 3.515851 0.439481 8 ACOT7 4.816818 0.481682 10 DLEU1 3.334536 0.416817 8 NR2F1-AS1 4.147053 0.414705 10 LINC00311 3.12969 0.391211 8 SKOR1 4.068187 0.406819 10 MSRA 3.019324 0.377415 8 ACOT7 4.47878 0.447878 10 SYNJ2 4.360336 0.545042 8 NR2F1-AS1 3.503822 0.350382 10 TENM2 3.872294 0.484037 8

TSPAN4 3.394916 0.339492 10 PPP2R2B 3.630106 0.453763 8 GRID1 3.282706 0.328271 10 NAVI 3.829699 0.5471 7 AKAP13 3.113864 0.311386 10 GAK 3.817429 0.545347 7 GAK 4.006282 0.572326 7 CYTH1 3.061654 1.530827 2 NAVI 3.778695 0.539814 7 ANKLE2 6.031201 3.0156 2 EBF2 3.898516 0.556931 7 SLC25A10 3.800057 1.900028 2 NAVI 3.151697 0.450242 7

NAVI 4.116692 0.588099 ⁷ TABLE 115: Cancer Type MB_SHH_IDH

FBXL18 3.818437 0.636406 6

Gene site imp sum imp mean n

CRADD 3.713185 0.618864 6

4.186386 0.051053 82

COQ8A 4.501953 0.750325

2.531088 0.035649 71

COQ8A 4.638227 0.773038

6.521723 0.110538 59

CRADD 3.676599 0.612767

6.521723 0.114416 57

FBXL18 3.413187 0.568865

6.521723 0.120773 54

IRF6 3.056409 0.509402

6.521723 0.123051 53

COQ8A 4.801492 0.800249

6.521723 0.127877 51

FBXL18 3.744823 0.624137

6.205337 0.12664 49

TK1 4.81045 0.96209

5.572565 0.118565 47

ARHGEF7 4.225031 0.845006

5.256179 0.122237 43 TSN AX-DISCI 3.942396 0.788479

5.256179 0.131404 40 TK1 5.723311 1.144662

4.623407 0.124957 37 AP2A2 5.165172 1.033034

3.15463 0.08526 37 ARHGEF7 5.051048 1.01021

4.307021 0.123058 35

TSN AX-DISCI 4.917045 0.983409

4.307021 0.123058 35

PRR5L 3.826285 0.765257

3.185196 0.091006 35

TK1 5.058368 1.011674

4.089401 0.127794 32

TSN AX-DISCI 4.717782 0.943556

3.990635 0.124707 32

RUNDC3A 4.234243 0.846849

3.990635 0.12873 31

AP2A2 3.591004 0.718201

8.014506 0.276362 29

MRC2 3.551757 0.710351

3.674249 0.126698 29

ARHGAP27P1 3.505253 0.701051

3.357863 0.119924 28

PLEKHM1P1 3.505253 0.701051

3.776256 0.15105 25

ARHGEF7 3.352543 0.670509

3.045261 0.12181 25

BTBD9 3.303687 0.660737

3.041477 0.126728 24

TK1 5.499776 1.099955

3.827594 0.166417 23

TSN AX-DISCI 4.941693 0.988339

3.041477 0.132238 23

AP2A2 4.107508 0.821502

2.477374 0.107712 23

RUNDC3A 3.708473 0.741695

2.170949 0.098679 22

TUBA1C 3.897376 0.974344

2.502695 0.119176 21

DAGLB 3.951956 1.317319

1.927917 0.091806 21 SLC25A22 3.541157 1.180386

1.811715 0.090586 20

CHID1 3.035271 1.011757

4.648104 0.244637 19 SLC25A22 3.689941 1.22998

3.141926 0.165365 19 ANKLE2 5.964983 2.982492

3.141926 0.165365 19

ANKLE2 6.397189 3.198595

3.141926 0.165365 19

DISCI 3.796866 1.898433

1.844257 0.097066 19 SLC25A10 3.781775 1.890887

2.800405 0.155578 18

ANKLE2 6.209738 3.104869

1.770802 0.098378 18

DDX31 3.308125 1.654063

4.870262 0.286486 17

DISCI 3.234475 1.617238

2.993498 0.176088 17 SLC25A10 3.131187 1.565593

OPCML 1.755075 0.10324 17 DICER1 2.188384 0.729461 3

TBX15 1.66968 0.098216 17 SRRM3 1.94638 0.648793 3

FOXP1 1.670958 0.104435 16 IGFBPL1 1.873898 0.624633 3

EBF3 1.572011 0.098251 16 LIN28A 1.680143 0.560048 3

GLI2 3.276761 0.218451 15 DERL3 1.619892 0.539964 3

ZBTB20 1.562454 0.104164 15 ANKLE2 5.371886 2.685943 2

PRKAG2 3.090339 0.220739 14 REXO1 1.950061 0.97503 2 PCDHGA12 2.725091 0.194649 14 DDX31 1.909347 0.954674 2 CUX1 2.351194 0.167942 14 SLC25A10 1.732353 0.866176 2 IQSEC1 1.946442 0.139032 14 TBC1D9 1.858811 1.858811 1 SYCP2L 1.889476 0.134963 14 TNRC18P1 1.858811 1.858811 1

SPTBN4 1.707594 0.131353 13

CTNNA2 2.642407 0.220201 12 TABLE 116: Cancer Type MB_WNT

TBX4 2.423594 0.201966 12 Gene site imp sum imp mean n

ISLR2 2.327659 0.193972 12 PTPRN2 10.8371 0.13216 82

FBRSL1 1.630753 0.135896 12 PRDM16 7.265933 0.102337 71

PCDHGC3 2.408705 0.218973 11 HDAC4 17.01119 0.459762 37 NR2F1-AS1 2.429421 0.242942 10 RBFOX3 9.054346 0.258696 35 SLC22A18 3.285046 0.365005 9 PAX6 7.817762 0.223365 35

TXNRD1 2.762601 0.306956 9 DIP2C 6.105956 0.190811 32

ADAMTS2 2.498552 0.277617 9 SOX2-OT 3.383323 0.116666 29 SND1 2.072379 0.230264 9 GALNT9 4.666088 0.172818 27

TRAPPCI 2 1.727633 0.191959 9 SHANK2 4.975523 0.191366 26

RUNX1 1.620398 0.180044 9 ADARB2 3.712105 0.142773 26

LHX4 3.362207 0.420276 8 CAMTAI 10.199 0.40796 25

DLX5 3.307152 0.413394 8 AGAP1 7.697982 0.307919 25

NR2E1 2.426585 0.303323 8 PDGFRA 3.015656 0.120626 25

SYNJ2 1.978768 0.247346 8 NCOR2 6.786393 0.295061 23 DNMT3A 1.844033 0.230504 8 NXN 6.120921 0.266127 23 NXPH1 1.677876 0.209734 8 RPTOR 5.335037 0.231958 23

AFF3 1.665786 0.208223 8 RIMBP2 4.418345 0.192102 23

TRIM6-TRIM34 2.409658 0.344237 7 INPP5A 4.096703 0.178118 23

DUSP6 2.367342 0.338192 7 PRKCZ 6.825763 0.310262 22

EBF2 1.834614 0.262088 7 SKI 6.771276 0.322442 21

TRIM34 2.409658 0.40161 6 ABR 4.717846 0.235892 20

FBXL18 1.900496 0.316749 6 FRMD4A 3.249455 0.162473 20

EPHA10 1.866265 0.311044 6 MAD1L1 14.32428 0.75391 19

SRCIN1 1.838764 0.306461 6 SMG1P2 6.724541 0.353923 19 RUNDC3A 2.635708 0.527142 5 BOLA2 6.724541 0.353923 19 ARHGEF7 2.408727 0.481745 5 LOC613038 6.724541 0.353923 19

GNAO1 1.918317 0.383663 5 CASZ1 4.353618 0.229138 19

ATP2B4 1.669945 0.333989 5 ZNF423 4.227003 0.222474 19

TSN AX-DISCI 1.66602 0.333204 5 KCNQ1 3.560444 0.187392 19

TK1 1.580119 0.316024 5 ANKRD11 5.201042 0.288947 18

TUBA1C 2.900214 0.725053 4 FOXK1 5.148867 0.286048 18

MLC1 1.992379 0.498095 4 TBC1D16 4.107056 0.22817 18

PPM1H 1.623709 0.405927 4 SEPTIN9 3.389096 0.188283 18 SLC25A22 2.547072 0.849024 3 OPCML 6.012083 0.353652 17 PAX6-AS1 3.072803 0.180753 17 DNMT3A 3.10049 0.387561 8

RCN1 3.072803 0.180753 17 MSRA 3.05823 0.382279 8

NAV2 5.489458 0.343091 16 GAK 4.58643 0.655204 7

BAIAP2 5.041965 0.336131 15 AGO2 3.723265 0.531895 7

GLI2 4.504027 0.300268 15 PLEC 3.578589 0.511227 7

NHX 3.66554 0.244369 15 PCCA 3.024493 0.43207 7

KNDC1 3.550985 0.236732 15 PITPNC1 3.007431 0.429633 7

ZBTB20 3.330934 0.222062 15 CRADD 4.274558 0.712426 6

KIRREL3 2.97936 0.198624 15 ROR1 3.09436 0.515727 6

SLX1B-SULT1A4 2.966254 0.19775 15 FBXL18 3.034703 0.505784 6

SLX1A 2.966254 0.19775 15 COLECI 1 3.00026 0.500043 6

CUX1 6.497046 0.464075 14 TSNAX-DISC1 3.3902 0.67804 5

IQSEC1 5.309558 0.379254 14 NPHP4 2.99828 0.599656 5

PRKAG2 4.103524 0.293109 14 EXT1 2.976673 0.744168 4

RPS6KA2 3.719359 0.265668 14 SLC25A22 3.201603 1.067201 3

CACNA1H 3.524163 0.251726 14 ANKLE2 4.340864 2.170432 2

C7orf50 3.282155 0.23444 14 CHTF18 4.334141 2.16707 2

MOB2 3.113394 0.222385 14 KIF21B 3.020143 1.510072 2

MIR548F5 3.068117 0.219151 14

GNG7 2.981215 0.212944 14 TABLE 117: Cancer Type MELN

MYT1L 5.53196 0.425535 13 Gene site imp sum imp mean n

MSI2 5.384966 0.414228 13 PTPRN2 20.07972 0.244875 82

CLYBL 3.963235 0.304864 13 PRDM16 11.87507 0.167254 71

RFX4 3.398564 0.261428 13 PCDHGA1 3.96148 0.067144 59

FBRSL1 4.406138 0.367178 12 PCDHGA2 3.96148 0.0695 57

ADGRD1 3.655516 0.304626 12 PCDHGA3 3.960003 0.073333 54

MEGF6 3.627383 0.302282 12 PCDHGB1 3.960003 0.074717 53

ZC3H3 3.613021 0.301085 12 PCDHGA4 3.960003 0.077647 51

CMIP 3.606883 0.300574 12 PCDHGB2 3.960003 0.080816 49

CTNNA2 3.234056 0.269505 12 HDAC4 18.18092 0.491376 37

CTBP2 3.962087 0.36019 11 RBFOX3 5.033948 0.143827 35

COL4A1 3.499449 0.318132 11 PAX6 4.40364 0.125818 35

VGLL4 3.209159 0.291742 11 DIP2C 10.80042 0.337513 32

FMN1 4.202709 0.420271 10 SHANK2 5.242768 0.201645 26

AKAP13 3.692004 0.3692 10 AGAP1 12.02566 0.481026 25

AXIN2 6.344218 0.704913 9 CAMTAI 5.565815 0.222633 25

TSPAN9 6.202349 0.68915 9 PDGFRA 4.404613 0.176185 25

ATP11A 5.78398 0.642664 9 MEIS1 4.675042 0.194793 24

ADAMTS2 5.710575 0.634508 9 RPTOR 10.88275 0.473163 23

SND1 5.235502 0.581722 9 NCOR2 6.702184 0.291399 23

GPC6 3.336133 0.370681 9 INPP5A 5.909184 0.256921 23

SLC22A18 3.32686 0.369651 9 PRKCZ 4.100656 0.186393 22

VRK2 6.942704 0.867838 8 SKI 10.42127 0.496251 21

PPP2R2B 4.533826 0.566728 8 FRMD4A 6.386336 0.319317 20

RORA 4.217902 0.527238 8 SDK1 5.100932 0.255047 20

ASPSCR1 3.717924 0.46474 8 ABR 4.102454 0.205123 20

DLEU1 3.459904 0.432488 8 MAD1L1 11.59755 0.610397 19

LINC00311 3.311936 0.413992 8 CASZ1 5.539392 0.291547 19 KCNQ1 5.170629 0.272138 19 MSRA 4.226066 0.528258 8 SMG1P2 4.905835 0.258202 19 SMAD3 4.156583 0.519573 8 BOLA2 4.905835 0.258202 19 AFF3 4.068188 0.508523 8 LOC613038 4.905835 0.258202 19 LHX4 3.799541 0.474943 8 TBC1D16 8.824067 0.490226 18 MACROD1 3.773525 0.471691 8 ANKRD11 5.752647 0.319591 18 C19orf25 5.557351 0.793907 7 SEPTIN9 5.209593 0.289422 18 ITPK1 4.294166 0.613452 7 FOXK1 5.117212 0.28429 18 VPS13D 3.978662 0.56838 7 OPCML 4.359006 0.256412 17 GAK 3.899819 0.557117 7 FOXP1 5.82112 0.36382 16 MIR548H4 3.870347 0.552907 7 EBF3 5.292018 0.330751 16 NAVI 3.868259 0.552608 7 GLI2 7.458051 0.497203 15 RXRA 3.867873 0.552553 7 ZBTB20 4.374324 0.291622 15 FBXL18 5.400368 0.900061 6 KIRREL3 4.342944 0.28953 15 FMNL2 4.109786 0.684964 6 RPS6KA2 6.474401 0.462457 14 SLC22A18AS 4.041922 0.673654 6 CUX1 6.042582 0.431613 14 RADIL 3.89196 0.64866 6 IQSEC1 5.9396 0.424257 14 KDM4B 3.77629 0.629382 6 C7orf50 5.879431 0.419959 14 TSNAX-DISC1 5.541611 1.108322 5 PRKAG2 5.215217 0.372516 14 RUNDC3A 5.352348 1.07047 5 ARHGEF10 4.90525 0.350375 14 ARHGEF7 4.019536 0.803907 5 GNG7 4.302019 0.307287 14 BCAR1 3.752209 0.750442 5 MSI2 6.601134 0.50778 13 DAGLB 3.877602 1.292534 3 MYT1L 4.80067 0.369282 13 TBC1D7 3.795514 1.265171 3 GSE1 4.797274 0.369021 13 SOXIO 4.35304 2.17652 2 RFX4 4.172827 0.320987 13 SLC25A10 3.769522 1.884761 2 CMIP 7.044486 0.58704 12 FBRSL1 5.539667 0.461639 12 TABLE 118: Cancer Type MET_MEL TNS3 5.362109 0.446842 12 Gene site imp sum imp mean n GNA12 3.84182 0.320152 12 PTPRN2 23.28815 0.284002 82 MAML3 3.836794 0.319733 12 PRDM16 14.10422 0.198651 71 ZC3H3 3.806348 0.317196 12 PCDHGA1 5.198538 0.088111 59 COL4A1 3.977189 0.361563 11 PCDHGA2 4.775169 0.083775 57 RAD51B 3.939323 0.35812 11 PCDHGA3 4.775169 0.088429 54 TSPAN4 5.621388 0.562139 10 PCDHGB1 4.775169 0.090098 53 RGS12 4.168971 0.416897 10 PCDHGA4 4.458783 0.087427 51 ANKS1B 3.869659 0.386966 10 PCDHGB2 4.458783 0.090996 49 ACOT7 3.84659 0.384659 10 PCDHGA5 4.775169 0.101599 47 FMN1 3.754881 0.375488 10 PCDHGB3 4.799988 0.111628 43 ATP11A 7.987864 0.88754 9 PCDHGA6 4.483602 0.11209 40 SND1 7.076724 0.786303 9 HDAC4 15.6732 0.4236 37 ADAMTS2 5.238921 0.582102 9 PCDHGA7 4.799988 0.129729 37 TSPAN9 4.794222 0.532691 9 PAX6 7.001312 0.200037 35 AXIN2 4.740056 0.526673 9 RBFOX3 6.523178 0.186377 35 TRAPPCI 2 4.546979 0.50522 9 PCDHGB4 4.799988 0.137143 35 PAX3 3.868641 0.429849 9 PCDHGA8 4.799988 0.137143 35 NOTCH 1 3.794656 0.421628 9 DIP2C 8.736887 0.273028 32 SYNJ2 5.876355 0.734544 8 PCDHGB5 4.799988 0.15 32 DLEU1 4.325624 0.540703 8 PCDHGA9 4.799988 0.154838 31 SOX2-OT 6.186026 0.213311 29 TNS3 3.906238 0.32552 12

PCDHGB6 4.799988 0.165517 29 TBX4 3.896038 0.32467 12

PCDHGA10 4.799988 0.171428 28 GNA12 3.760847 0.313404 12

GALNT9 4.480326 0.165938 27 ADGRD1 3.737897 0.311491 12

AGAP1 11.55531 0.462212 25 CCDC140 4.946652 0.449696 11

PDGFRA 5.857961 0.234318 25 COL4A1 4.646674 0.422425 11

MEIS1 4.887093 0.203629 24 RAD51B 3.97844 0.361676 11

PCDHGB7 4.483602 0.186817 24 SPON2 3.963238 0.360294 11

RPTOR 10.90653 0.474197 23 AKAP13 4.293365 0.429336 10

NCOR2 7.582035 0.329654 23 IGF1R 3.950148 0.395015 10

INPP5A 5.808428 0.25254 23 ATP11A 6.829316 0.758813 9

NXN 5.799721 0.252162 23 NOTCH1 4.594711 0.510523 9

RIMBP2 5.085832 0.221123 23 TRAPPCI 2 4.392991 0.48811 9

PCDHGA11 4.483602 0.194939 23 SND1 4.228188 0.469799 9

SKI 8.010844 0.381469 21 ASAP1 4.089509 0.45439 9

FRMD4A 4.993953 0.249698 20 AXIN2 4.088872 0.454319 9

ABR 4.685585 0.234279 20 SMAD3 4.429079 0.553635 8

MAD1L1 10.65961 0.561032 19 RGS20 4.200273 0.525034 8

ZNF423 5.576102 0.293479 19 VRK2 4.133411 0.516676 8

CASZ1 4.063253 0.213855 19 DLEU1 4.086511 0.510814 8

FOXK1 7.808541 0.433808 18 MSRA 3.881242 0.485155 8

TBC1D16 5.840426 0.324468 18 ASPSCR1 3.834464 0.479308 8

ANKRD11 5.655526 0.314196 18 NAVI 5.274058 0.753437 7

HOXA3 5.018438 0.278802 18 MIR548H4 4.091968 0.584567 7

RBFOX1 3.797883 0.210994 18 GAK 4.045485 0.577926 7

TBX15 4.119351 0.242315 17 ITPK1 3.91298 0.558997 7

OPCML 3.844648 0.226156 17 ANK2 4.362606 0.727101 6

SORBS2 4.419792 0.276237 16 SLC22A18AS 4.105502 0.68425 6 NHX 4.959722 0.330648 15 RUNDC3A 5.207004 1.041401 5 GLI2 4.953296 0.33022 15 TSN AX-DISCI 4.928417 0.985683 5 BAIAP2 4.818579 0.321239 15 TBC1D7 5.407551 1.802517 3

LRMDA 4.401163 0.293411 15 SOXIO 3.859623 1.929812 2

ZBTB20 4.342012 0.289467 15

KIRREL3 3.914596 0.260973 15

ARHGEF10 6.140654 0.438618 14 TABLE 119: Cancer Type MMNST

CUX1 5.918096 0.422721 14 Gene site imp sum imp mean n

MIR548F5 5.793953 0.413854 14 PRDM16 6.47284 0.091167 71

PRKAG2 5.082395 0.363028 14 PCDHGA1 4.946677 0.083842 59

IQSEC1 4.546697 0.324764 14 PCDHGA2 4.512407 0.079165 57

C7orf50 3.84999 0.274999 14 PCDHGA3 4.424657 0.081938 54

MSI2 5.84468 0.449591 13 PCDHGB1 4.424657 0.083484 53

RFX4 4.729256 0.363789 13 PCDHGA4 4.424657 0.086758 51

MYT1L 4.557003 0.350539 13 PCDHGB2 4.424657 0.090299 49

CMIP 6.193215 0.516101 12 PCDHGA5 4.424657 0.094142 47

FBRSL1 4.591671 0.382639 12 PCDHGB3 4.108271 0.095541 43

ZC3H3 4.200662 0.350055 12 PCDHGA6 4.424657 0.110616 40

LRBA 4.18372 0.348643 12 HDAC4 8.717707 0.235614 37

MIRLET7BHG 4.086926 0.340577 12 PCDHGA7 4.741043 0.128136 37 PAX6 8.339197 0.238263 3.335163 0.238226 14

PCDHGB4 4.741043 0.135458 3.07831 0.219879 14

PCDHGA8 4.741043 0.135458 2.78253 0.198752 14

RBFOX3 3.738019 0.106801 2.585121 0.184652 14

DIP2C 5.333114 0.16666 2.36413 0.168866 14

PCDHGB5 4.108271 0.128383 2.46895 0.189919 13

PCDHGA9 4.108271 0.132525 5.48478 0.457065 12

PCDHGB6 4.108271 0.141665 3.462573 0.288548 12

SOX2-OT 4.06689 0.140238 2.827938 0.235661 12

PCDHGA10 3.665331 0.130905 2.730173 0.227514 12

AGAP1 6.318014 0.252721 2.419795 0.20165 12

CAMTAI 5.243006 0.20972 3.190738 0.290067 11

PDGFRA 3.660136 0.146405 2.80278 0.254798 11

PCDHGB7 3.665331 0.152722 3.619502 0.36195 10

RPTOR 6.879654 0.299115 3.153595 0.31536 10

NCOR2 3.752568 0.163155 2.635382 0.263538 10

PCDHGA11 3.348945 0.145606 2.47046 0.247046 10

INPP5A 3.273184 0.142312 2.381283 0.238128 10

NXN 3.203231 0.139271 3.842903 0.426989 9

PRKCZ 3.058926 0.139042 3.621175 0.402353 9

SKI 5.061311 0.241015 2.303007 0.25589 9

SIM2 2.404562 0.114503 4.347245 0.543406 8

SDK1 3.56718 0.178359 3.593892 0.449237 8

ABR 2.758593 0.13793 2.484178 0.310522 8

FRMD4A 2.733413 0.136671 2.316054 0.289507 8

MAD1L1 5.8859 0.309784 2.876351 0.410907 7

ZNF423 3.740023 0.196843 2.545239 0.363606 7

SMG1P2 3.218867 0.169414 3.109625 0.518271 6

BOLA2 3.218867 0.169414 2.471809 0.411968 6

LOC613038 3.218867 0.169414 2.463133 0.410522 6

CASZ1 3.080883 0.162152 2.347513 0.391252 6

FOXK1 6.262441 0.347913 2.345463 0.39091 6

TBC1D16 4.226576 0.23481 4.46828 0.893656 5

ANKRD11 3.09493 0.171941 3.126123 0.625225 5

SEPTIN9 2.591572 0.143976 3.086616 0.617323 5

PAX6-AS1 4.46816 0.262833 2.601482 0.520296 5

RCN1 4.46816 0.262833 2.550573 0.850191 3

FOXP1 3.819536 0.238721

2.916835 1.458418 2

SORBS2 2.963446 0.185215

2.916835 1.458418 2

KIRREL3 3.418558 0.227904

GLI2 2.992747 0.199516 15 TABLE 120: Cancer Type MNG_ben-l

NHX 2.916915 0.194461 15

| , Gene si imp sum imp mean n

SLX1B-SULT1A4 2.708597 te

0.180573

13.82051 0.168543 82

SLX1A 2.708597 0.180573

11.89894 0.167591 71

LOC606724 2.708597 0.180573

6.849261 0.116089 59

CUX1 4.698023 0.335573

6.849261 0.120162 57

CACNA1H 3.842483 0.274463

6.532875 0.120979 54

PRKAG2 3.418302 0.244164

PCDHGB1 6.532875 0.123262 53 GLI2 5.663911 0.377594 15 PCDHGA4 6.849261 0.134299 51 KIRREL3 5.414844 0.36099 15 PCDHGB2 6.849261 0.139781 49 BAIAP2 5.169657 0.344644 15 PCDHGA5 6.849261 0.145729 47 NFIX 4.969003 0.331267 15 PCDHGB3 6.574277 0.15289 43 SLX1B-SULT1A4 4.72077 0.314718 15 PCDHGA6 6.574277 0.164357 40 SLX1A 4.72077 0.314718 15 HDAC4 18.90875 0.511047 37 LOC606724 4.72077 0.314718 15 PCDHGA7 6.257891 0.169132 37 KNDC1 4.688957 0.312597 15 RBFOX3 7.710426 0.220298 35 LRMDA 4.292951 0.286197 15 PCDHGB4 6.257891 0.178797 35 RPS6KA2 8.370342 0.597882 14 PCDHGA8 6.257891 0.178797 35 CUX1 6.066803 0.433343 14 PAX6 5.857744 0.167364 35 IQSEC1 5.85587 0.418276 14 DIP2C 10.83243 0.338514 32 MIR548F5 5.695434 0.406817 14 PCDHGB5 5.625119 0.175785 32 C7orf50 5.470546 0.390753 14 PCDHGA9 5.625119 0.181455 31 PRKAG2 4.775703 0.341122 14 PCDHGB6 4.808758 0.165819 29 ARHGEF10 4.659786 0.332842 14 SOX2-OT 4.578854 0.157892 29 MSI2 6.755835 0.51968 13 PCDHGA10 4.808758 0.171741 28 MYT1L 5.168853 0.397604 13 GALNT9 4.679084 0.173299 27 CMIP 7.54371 0.628643 12 SHANK2 6.49194 0.24969 26 ZC3H3 5.654797 0.471233 12 AGAP1 13.00458 0.520183 25 FBRSL1 5.433467 0.452789 12 CAMTAI 7.461963 0.298479 25 MIRLET7BHG 5.316198 0.443017 12 PDGFRA 5.74881 0.229952 25 TNS3 4.843598 0.403633 12 PCDHGB7 4.422271 0.184261 24 GNA12 4.74899 0.395749 12 RPTOR 12.87515 0.559789 23 CTBP2 4.902419 0.445674 11 NXN 8.33912 0.36257 23 TSPAN4 5.826729 0.582673 10 NCOR2 7.104845 0.308906 23 ACOT7 4.817172 0.481717 10 RIMBP2 6.980383 0.303495 23 AKAP13 4.554964 0.455496 10 INPP5A 6.449253 0.280402 23 ATP11A 8.56874 0.952082 9 SKI 11.38974 0.542368 21 SND1 8.150821 0.905647 9 FRMD4A 6.895792 0.34479 20 ADAMTS2 6.501277 0.722364 9 ABR 5.14037 0.257019 20 NOTCH1 4.88438 0.542709 9 SDK1 4.958414 0.247921 20 AXIN2 4.332555 0.481395 9 MAD1L1 13.22295 0.695945 19 DNMT3A 6.086909 0.760864 8 CASZ1 6.12576 0.322408 19 LINC00311 5.472052 0.684007 8 SMG1P2 5.844299 0.307595 19 MSRA 4.427701 0.553463 8 BOLA2 5.844299 0.307595 19 C19orf25 5.998215 0.856888 7 LOC613038 5.844299 0.307595 19 MIR548H4 5.819089 0.831298 7 KCNQ1 5.469755 0.287882 19 VPS13D 5.39522 0.770746 7 ZNF423 5.039602 0.265242 19 NAVI 5.274197 0.753457 7 FOXK1 10.05902 0.558835 18 STRA6 5.817815 0.969636 6 TBC1D16 7.904142 0.439119 18 FMNL2 5.378446 0.896408 6 MCF2L 6.214789 0.345266 18 FBXL18 4.926141 0.821024 6 SEPTIN9 6.02498 0.334721 18 RUNDC3A 4.808672 0.961734 5 ANKRD11 4.350671 0.241704 18 ARHGEF7 4.74985 0.94997 5 FOXP1 7.817105 0.488569 16 TSN AX-DISCI 4.324226 0.864845 5 NAV2 6.435163 0.402198 16 USP20 4.814654 1.604885 3 ZBTB20 6.143446 0.409563 15 SEPTIN9 6.654283 0.369682 18

TABLE 121: Cancer Type MNG_ben-2 ANKRD11 4.890269 0.271682 18

MCF2L 4.862958 0.270164 18 Gene site imp sum imp mean n

FOXP1 6.013375 0.375836 16 PTPRN2 13.45409 0.164074 82

NAV2 5.478894 0.342431 16 PRDM16 12.3714 0.174245 71

EBF3 4.702232 0.29389 16 PCDHGA1 5.613835 0.09515 59

NFIX 5.97872 0.398581 15 PCDHGA2 5.613835 0.098488 ⁵⁷ SLX1B- PCDHGA3 5.575012 0.103241 54 SULT1A4 5.381892 0.358793 15 PCDHGB1 5.575012 0.105189 53 SLX1A 5.381892 0.358793 15 PCDHGA4 5.575012 0.109314 51 LOC606724 5.381892 0.358793 15 PCDHGB2 5.575012 0.113776 49 KIRREL3 4.943827 0.329588 15 PCDHGA5 5.575012 0.118617 47 ZBTB20 4.615239 0.307683 15 PCDHGB3 4.900676 0.113969 43 BAIAP2 4.598236 0.306549 15 PCDHGA6 4.267904 0.106698 40 RPS6KA2 9.649029 0.689216 14 HDAC4 20.05845 0.54212 37 IQSEC1 6.291758 0.449411 14 PCDHGA7 4.267904 0.115349 37 C7orf50 6.014972 0.429641 14 PAX6 7.562683 0.216077 35 PRKAG2 5.576157 0.398297 14 RBFOX3 7.013312 0.20038 35 MIR548F5 4.577077 0.326934 14 PCDHGB4 4.267904 0.12194 35 CUX1 4.429173 0.316369 14 PCDHGA8 4.267904 0.12194 35 ARHGEF10 4.400306 0.314308 14 DIP2C 11.17988 0.349371 32 GSE1 7.665565 0.589659 13 PCDHGB5 4.267904 0.133372 32 MSI2 5.419438 0.41688 13 PCDHGA9 4.267904 0.137674 31 MYT1L 4.303207 0.331016 13 SHANK2 6.742333 0.259321 26 CMIP 7.426731 0.618894 12 ADARB2 4.171369 0.160437 26 ZC3H3 6.74163 0.561802 12 AGAP1 13.829 0.55316 25 GNA12 5.848317 0.48736 12 CAMTAI 6.527083 0.261083 25 RASA3 5.406658 0.450555 12 PDGFRA 5.479023 0.219161 25 FBRSL1 5.397095 0.449758 12 RPTOR 12.86316 0.559268 23 TBX4 5.026797 0.4189 12 NXN 8.208397 0.356887 23 ADGRD1 4.534801 0.3779 12 NCOR2 7.86704 0.342045 23 ACOT7 5.775309 0.577531 10 INPP5A 6.254527 0.271936 23 TSPAN4 5.19165 0.519165 10 RIMBP2 5.449726 0.236945 23 SH3RF3 4.652618 0.465262 10 PRKCZ 6.528564 0.296753 22 AKAP13 4.486244 0.448624 10 SKI 11.00161 0.523886 21 ATP11A 8.379338 0.931038 9 FRMD4A 6.074896 0.303745 20 SND1 8.193473 0.910386 9 ABR 4.863719 0.243186 20 TSPAN9 6.169591 0.68551 9 SDK1 4.582411 0.229121 20 ADAMTS2 4.257765 0.473085 9 MAD1L1 12.37047 0.651078 19 AXIN2 4.167995 0.463111 9 CASZ1 6.339035 0.333633 19 DNMT3A 5.873956 0.734245 8 ZNF423 5.561251 0.292697 19 MSRA 4.879587 0.609948 8 KCNQ1 5.297414 0.278811 19 LINC00311 4.791788 0.598973 8 SMG1P2 5.2427 0.275932 19 AFF3 4.572348 0.571543 8 BOLA2 5.2427 0.275932 19 C19orf25 5.705311 0.815044 7 LOC613038 5.2427 0.275932 19 NAVI 5.457155 0.779594 7 FOXK1 7.706177 0.428121 18 MIR548H4 5.03525 0.719321 7 TBC1D16 6.940259 0.38557 18 VPS13D 4.517048 0.645293 7 CXXC5 4.440766 0.634395 7 SDK1 4.606377 0.230319 20 STRA6 5.02413 0.837355 6 MAD1L1 13.63544 0.717655 19 RADIL 4.864294 0.810716 6 CASZ1 6.490032 0.341581 19 FBXL18 4.811372 0.801895 6 SMG1P2 6.468926 0.34047 19 FMNL2 4.58933 0.764888 6 BOLA2 6.468926 0.34047 19 CRADD 4.280388 0.713398 6 LOC613038 6.468926 0.34047 19 TSN AX-DISCI 5.346518 1.069304 5 KCNQ1 6.287567 0.330925 19 RUNDC3A 4.676827 0.935365 5 ZNF423 5.828266 0.306751 19 ARHGEF7 4.428799 0.88576 5 TBC1D16 8.725799 0.484767 18

FOXK1 8.629545 0.479419 18

TABLE 122: Cancer Type MNG_ben-3 MCF2L 6.375049 0.354169 18 Gene site imp sum imp mean n SEPTIN9 5.866236 0.325902 18 PTPRN2 20.68387 0.252242 82 FOXP1 7.901613 0.493851 16 PRDM16 15.67589 0.220787 71 EBF3 5.143972 0.321498 16 PCDHGA1 6.053782 0.102606 59 NAV2 4.154127 0.259633 16 PCDHGA2 6.053782 0.106207 57 GLI2 6.306125 0.420408 15 PCDHGA3 6.370168 0.117966 54 KIRREL3 6.113129 0.407542 15 PCDHGB1 6.370168 0.120192 53 ZBTB20 5.936453 0.395764 15 PCDHGA4 6.370168 0.124905 51 BAIAP2 4.920905 0.32806 15 PCDHGB2 5.853512 0.119459 49 NFIX 4.920802 0.328053 15 PCDHGA5 5.853512 0.124543 47 SLX1B-

₄₃ SULT1A4 4.25033 0.283355 15 PCDHGB3 5.537126 0.12877

4.25033 0.283355 15 PCDHGA6 5.22074 0.130519 40 ^SLX1A

₃₇ LOC606724 4.25033 0.283355 15 HDAC4 20.47131 0.553279

₃₇ RPS6KA2 9.58383 0.684559 14 PCDHGA7 5.22074 0.141101

5.548519 0.396323 14 RBFOX3 7.450315 0.212866 35 ^GNG7

₃₅ IQSEC1 5.331153 0.380797 14 PAX6 6.806875 0.194482

₃₅ C7orf50 5.028188 0.359156 14 PCDHGB4 4.904354 0.140124

₃₅ PRKAG2 5.008809 0.357772 14 PCDHGA8 4.904354 0.140124

₃₂ MIR548F5 4.352643 0.310903 14 DIP2C 10.59089 0.330965

7.327424 0.563648 13 PCDHGB5 4.587968 0.143374 32 ^MSI2

7.256201 0.558169 13 PCDHGA9 4.587968 0.147999 31 ^GSE1 z MYT1L 4.3416 0.333969 13 SHANK2 4.876093 0.187542 26

7.483342 0.623612 12 AGAP1 13.06947 0.522779 25 ^CMIP

₂₅ ZC3H3 6.132744 0.511062 12 CAMTAI 7.897581 0.315903

₂₅ GNA12 5.702648 0.475221 12 PDGFRA 6.972754 0.27891

FBRSL1 5.368661 0.447388 12 MEIS1 6.104129 0.254339 24

MAML3 5.175135 0.431261 12 SATB2 4.776486 0.19902 24 RPTOR 557206 ₂₃ MIRLET7BHG 4.929492 0.410791 12

12.81574 0.

₂₃ ADGRD1 4.570833 0.380903 12 NXN 8.906232 0.387227

₂₃ GLUD1P2 4.151601 0.377418 11 NCOR2 8.168088 0.355134

4.149873 0.377261 11 INPP5A 7.309307 0.317796 23 ^TBCD

₂₃ ACOT7 5.285428 0.528543 10 HOXB3 5.36647 0.233325

₂₃ TSPAN4 5.22317 0.522317 10 RIMBP2 4.571507 0.198761

₂₂ FMN1 4.161922 0.416192 10 PRKCZ 6.534716 0.297033

8.359196 0.9288 9 SKI 10.2754 0.489305 21 ^SND1

_2Q ATP11A 7.898134 0.87757 9

FRMD4A 8.19508 0.409754 ADAMTS2 6.192204 0.688023 9 ABR 4.632685 0.231634 AXIN2 4.836435 0.537382 9 FRMD4A 6.034431 0.301722 20 CACNA2D4 4.640282 0.515587 9 ABR 5.825177 0.291259 20 LINC00311 4.776345 0.597043 8 MAD1L1 11.00779 0.579357 19 SYNJ2 4.629386 0.578673 8 SMG1P2 6.451549 0.339555 19 DNMT3A 4.550418 0.568802 8 BOLA2 6.451549 0.339555 19 VRK2 4.391817 0.548977 8 LOC613038 6.451549 0.339555 19 MSRA 4.391373 0.548922 8 KCNQ1 6.01296 0.316472 19 C19orf25 6.125387 0.875055 7 CASZ1 5.531977 0.291157 19 NAVI 5.887576 0.841082 7 ZNF423 5.186152 0.272955 19 CXXC5 4.602583 0.657512 7 FOXK1 8.350727 0.463929 18 GAK 4.365581 0.623654 7 SEPTIN9 6.99831 0.388795 18 STRA6 4.980778 0.83013 6 MCF2L 6.022257 0.33457 18 FBXL18 4.916689 0.819448 6 TBC1D16 5.64135 0.313408 18 CRADD 4.235191 0.705865 6 FOXP1 6.280391 0.392524 16

TSN AX-DISCI 5.959609 1.191922 5 NAV2 5.462 0.341375 16 RUNDC3A 4.568501 0.9137 5 EBF3 3.907012 0.244188 16 ARHGEF7 4.229516 0.845903 5 NFIX 6.747804 0.449854 15

GLI2 6.071155 0.404744 15

TABLE 123: Cancer Type MNG_int-A ZBTB20 5.817437 0.387829 15 Gene site imp sum imp mean n KIRREL3 5.119068 0.341271 15 PTPRN2 12.70906 0.154989

KNDC1 4.725142 0.315009 15 PRDM16 11.63816 0.163918 71 SLX1B- SULT1A4 4.604287 0.306952 15 PCDHGA1 5.103482 0.0865 59 SLX1A 4.604287 0.306952 15 PCDHGA2 5.103482 0.089535 57 LOC606724 4.604287 0.306952 15 PCDHGA3 4.787096 0.08865 54

BAIAP2 4.299045 0.286603 15 PCDHGB1 4.787096 0.090323 53 RPS6KA2 8.388311 0.599165 14 PCDHGA4 4.47071 0.087661 51 PRKAG2 5.969594 0.4264 14 PCDHGB2 4.47071 0.091239 49 ARHGEF10 5.511199 0.393657 14 PCDHGA5 4.154324 0.08839 47 C7orf50 5.16093 0.368638 14 PCDHGB3 3.837938 0.089254 43

IQSEC1 4.830224 0.345016 14 HDAC4 18.83304 0.509001 37 CUX1 4.348677 0.31062 14 RBFOX3 7.4952 0.214149 35 GNG7 3.996251 0.285446 14 PAX6 6.917716 0.197649 35 MSI2 5.972012 0.459386 13 DIP2C 9.266203 0.289569 32 GSE1 5.837919 0.449071 13 PCDHGB5 3.837938 0.119936 32 CMIP 7.304481 0.608707 12 PCDHGA9 3.837938 0.123804 31 FBRSL1 5.400748 0.450062 12 GALNT9 6.076149 0.225043 27 ISLR2 4.337054 0.361421 12 SHANK2 7.525851 0.289456 26 ZC3H3 4.310919 0.359243 12 AGAP1 12.5019 0.500076 25

GNA12 4.165423 0.347119 12 CAMTAI 5.854312 0.234172 25 MIRLET7BHG 3.974034 0.331169 12 PDGFRA 5.588541 0.223542 25 TNS3 3.872449 0.322704 12 MEIS1 3.891259 0.162136 24

TBCD 3.872749 0.352068 11 RPTOR 14.11158 0.613547 23 VGLL4 3.823593 0.347599 11 NXN 9.413313 0.409274 23 CTBP2 3.812363 0.346578 11 INPP5A 7.284435 0.316715 23 ACOT7 4.898796 0.48988 10 NCOR2 6.839873 0.297386 23 TSPAN4 4.506346 0.450635 10 PRKCZ 5.423639 0.246529 22

SH3RF3 4.276021 0.427602 10 SKI 10.53312 0.501577 21 AKAP13 4.268271 0.426827 10 SOX2-OT 3.6476 0.125779 29 CHST11 4.217964 0.421796 10 PCDHGA10 3.863568 0.137985 28 SND1 7.821647 0.869072 9 GALNT9 4.52022 0.167416 27 ATP11A 6.425911 0.71399 9 SHANK2 4.557056 0.175271 26 ADAMTS2 5.261391 0.584599 9 AGAP1 11.67976 0.46719 25 NOTCH 1 5.148668 0.572074 9 PDGFRA 6.689215 0.267569 25 TSPAN9 4.710569 0.523397 9 CAMTAI 4.055465 0.162219 25 LINC00311 4.665631 0.583204 8 MEIS1 3.916639 0.163193 24 MACROD1 3.916449 0.489556 8 RPTOR 11.11178 0.483121 23 MSRA 3.844343 0.480543 8 NCOR2 6.268918 0.272562 23 NAVI 5.309092 0.758442 7 RIMBP2 6.09139 0.264843 23 C19orf25 4.837513 0.691073 7 NXN 5.381697 0.233987 23

MIR548H4 4.474154 0.639165 7 INPP5A 4.981406 0.216583 23 VPS 13D 4.415997 0.630857 7 HOXB3 4.55696 0.198129 23 CXXC5 3.911578 0.558797 7 PRKCZ 4.330653 0.196848 22 PCCA 3.903166 0.557595 7 SKI 9.446678 0.449842 21 STRA6 5.093259 0.848877 6 SIM2 4.017456 0.191307 21 RADIL 4.962304 0.827051 6 HOXA-AS3 3.580392 0.170495 21 FBXL18 4.351839 0.725306 6 FRMD4A 5.566009 0.2783 20 SLC22A18AS 3.926613 0.654436 6 ABR 3.899798 0.19499 20 GRK5 3.770511 0.628419 6 MAD1L1 11.63535 0.612387 19

TSN AX-DISCI 5.623399 1.12468 5 SMG1P2 6.677326 0.351438 19 ARHGEF7 4.661456 0.932291 5 BOLA2 6.677326 0.351438 19 RUNDC3A 4.656096 0.931219 5 LOC613038 6.677326 0.351438 19 NDST1 3.786943 0.946736 4 CASZ1 5.756259 0.302961 19

KCNQ1 4.439789 0.233673 19

TABLE 124: Cancer Type MNG_int-B ZNF423 4.205795 0.221358 19 Gene site imp sum imp mean n FOXK1 7.534776 0.418599 18 PTPRN2 13.88466 0.169325 82 MCF2L 6.120838 0.340047 18 PRDM16 8.173372 0.115118 71 TBC1D16 5.90075 0.327819 18 PCDHGA1 5.065148 0.08585 59 SEPTIN9 4.89401 0.271889 18 PCDHGA2 5.065148 0.088862 57 HOXA3 4.728806 0.262711 18 PCDHGA3 5.511897 0.102072 54 FOXP1 6.315857 0.394741 16 PCDHGB1 5.828283 0.109968 53 NAV2 4.609604 0.2881 16 PCDHGA4 5.511897 0.108076 51 GLI2 4.915242 0.327683 15 PCDHGB2 5.511897 0.112488 49 ZBTB20 4.755149 0.31701 15

PCDHGA5 5.511897 0.117274 47 KIRREL3 4.620703 0.308047 15 PCDHGB3 5.195511 0.120826 ₄₃ SLX1B-

SULT1A4 4.161771 0.277451 15 PCDHGA6 4.562739 0.114068 40

„„ SLX1A 4.161771 0.277451 15 HDAC4 15.7932 0.426843 37

₃₇ LOC606724 4.161771 0.277451 15 PCDHGA7 4.562739 0.123317

BAIAP2 3.744433 0.249629 15 PAX6 6.438263 0.18395 35 3₅ RPS6KA2 6.906835 0.493345 14 PCDHGB4 4.562739 0.130364

PRKAG2 5.427565 0.387683 14 PCDHGA8 4.562739 0.130364 35 67118 ₃₂ C7orf50 5.391732 0.385124 14 DIP2C 7.8 0.245847

₃₂ IQSEC1 4.600055 0.328575 14 PCDHGB5 4.562739 0.142586 _ GNG7 3.6835 0.263107 14

PCDHGA9 4.562739 0.147185

5.549748 0.426904 13 PCDHGB6 3.863568 0.133226 MSI2 5.344116 0.411086 13 PAX6 6.216716 0.17762 35 SPTBN4 4.546944 0.349765 13 PCDHGB4 3.757451 0.107356 35 MYT1L 3.70103 0.284695 13 PCDHGA8 3.757451 0.107356 35 CMIP 5.926017 0.493835 12 DIP2C 8.325757 0.26018 32 ZC3H3 4.415395 0.36795 12 PCDHGB5 3.441065 0.107533 32 FBRSL1 4.348837 0.362403 12 PCDHGA9 3.441065 0.111002 31 ADGRD1 3.851014 0.320918 12 SOX2-OT 3.584088 0.123589 29 TBX4 3.806107 0.317176 12 PCDHGA10 3.64925 0.13033 28 GNA12 3.627593 0.302299 12 GALNT9 5.189127 0.19219 27 ACOT7 4.668989 0.466899 10 ADARB2 3.988528 0.153405 26 SPPL2B 4.349542 0.434954 10 AGAP1 11.17486 0.446994 25 LBX1-AS1 4.073318 0.407332 10 CAMTAI 6.000659 0.240026 25 TSPAN4 3.996852 0.399685 10 RPTOR 11.76013 0.51131 23 SND1 7.161256 0.795695 9 NXN 7.659148 0.333006 23 ATP11A 6.51973 0.724414 9 INPP5A 4.761183 0.207008 23 ADAMTS2 4.695024 0.521669 9 NCOR2 4.40035 0.19132 23

NOTCH 1 3.932607 0.436956 9 PRKCZ 4.106408 0.186655 22 MGMT 3.570064 0.396674 9 SKI 8.490324 0.404301 21 LINC00311 5.225881 0.653235 8 FRMD4A 6.787964 0.339398 20 MSRA 4.596947 0.574618 8 ABR 3.669629 0.183481 20 PPP2R2B 3.803844 0.475481 8 MAD1L1 10.7065 0.5635 19 VRK2 3.745483 0.468185 8 SMG1P2 5.009227 0.263644 19 DLEU1 3.627682 0.45346 8 BOLA2 5.009227 0.263644 19 C19orf25 5.584434 0.797776 7 LOC613038 5.009227 0.263644 19 NAVI 4.6096 0.658514 7 CASZ1 4.902776 0.258041 19 VPS 13D 4.118734 0.588391 7 KCNQ1 4.812443 0.253286 19 MIR548H4 3.68959 0.527084 7 FOXK1 6.452948 0.358497 18 RADIL 4.735793 0.789299 6 TBC1D16 5.84581 0.324767 18 STRA6 4.373833 0.728972 6 MCF2L 5.590638 0.310591 18 FBXL18 4.238959 0.706493 6 RBFOX1 4.056975 0.225388 18

TSN AX-DISCI 5.437458 1.087492 5 ANKRD11 3.383844 0.187991 18 RUNDC3A 4.211814 0.842363 5 NAV2 4.440685 0.277543 16 ARHGEF7 4.068087 0.813617 5 FOXP1 3.916603 0.244788 16

EBF3 3.90489 0.244056 16

TABLE 125: Cancer Type MNG_mal ZBTB20 5.465001 0.364333 15 Gene site imp sum imp mean n GLI2 4.969352 0.33129 15 PTPRN2 12.94805 0.157903 82 NFIX 4.533438 0.302229 15 PRDM16 8.949995 0.126056 ₇₁ SLX1B-

SULT1A4 4.473477 0.298232 15 PCDHGA1 3.742834 0.063438 59

SLX1A 4.473477 0.298232 15 PCDHGA2 3.426448 0.060113 57

LOC606724 4.473477 0.298232 15 PCDHGA3 3.441065 0.063723 54

KIRREL3 4.106736 0.273782 15 PCDHGB1 3.441065 0.064926 53

RPS6KA2 8.928392 0.637742 14 PCDHGA4 3.441065 0.067472

₄₃ C7orf50 6.681647 0.477261 14 PCDHGB3 4.073837 0.09474

ARHGEF10 5.338596 0.381328 14 PCDHGA6 3.757451 0.093936 40 3₇ IQSEC1 4.800841 0.342917 14 HDAC4 18.19781 0.491833 PRKAG2 4.714051 0.336718 14 PCDHGA7 3.757451 0.101553 37

CUX1 4.632663 0.330905 14 RBFOX3 8.921133 0.25489 MIR548F5 4.155173 0.296798 14 PCDHGA3 3.210092 0.059446 54 GNG7 3.580244 0.255732 14 PCDHGB1 3.210092 0.060568 53 MSI2 5.133305 0.39487 13 PCDHGB2 2.893706 0.059055 49 MYT1L 3.815554 0.293504 13 PCDHGA5 2.893706 0.061568 47 CMIP 6.809888 0.567491 12 HDAC4 16.10253 0.435203 37 ZC3H3 4.398193 0.366516 12 RBFOX3 4.992208 0.142635 35 GNA12 4.310731 0.359228 12 DIP2C 7.020543 0.219392 32 FBRSL1 4.034154 0.33618 12 GALNT9 3.837148 0.142117 27 CTNNA2 3.687655 0.307305 12 SHANK2 4.458318 0.171474 26 ADGRD1 3.647518 0.30396 12 AGAP1 9.868271 0.394731 25 FGFR2 4.0546 0.3686 11 PDGFRA 3.890312 0.155612 25 CTBP2 3.601913 0.327447 11 CAMTAI 3.737071 0.149483 25 ACOT7 5.255968 0.525597 10 RPTOR 8.560527 0.372197 23 TSPAN4 4.289444 0.428944 10 NXN 5.377517 0.233805 23 SH3RF3 3.83336 0.383336 10 NCOR2 4.435836 0.192862 23 IGF1R 3.476465 0.347647 10 INPP5A 4.025097 0.175004 23 OTX1 3.449022 0.344902 10 PRKCZ 3.311051 0.150502 22 ATP11A 7.824007 0.869334 9 SKI 8.789183 0.418533 21 SND1 7.442588 0.826954 9 FRMD4A 4.962322 0.248116 20

ADAMTS2 5.027489 0.55861 9 SDK1 3.806423 0.190321 20 CACNA2D4 3.628826 0.403203 9 ABR 3.53343 0.176672 20 SMAD3 4.239672 0.529959 8 MAD1L1 9.944865 0.523414 19 DNMT3A 3.946081 0.49326 8 CASZ1 5.357683 0.281983 19 VEPH1 3.88798 0.485998 8 SMG1P2 5.102141 0.268534 19 VRK2 3.825472 0.478184 8 BOLA2 5.102141 0.268534 19 DLEU1 3.764402 0.47055 8 LOC613038 5.102141 0.268534 19 LINC00311 3.678141 0.459768 8 KCNQ1 3.579382 0.188389 19 MIR548H4 5.581196 0.797314 7 FOXK1 6.658177 0.369899 18 NAVI 5.02652 0.718074 7 MCF2L 4.412204 0.245122 18 VPS 13D 4.911104 0.701586 7 TBC1D16 4.185513 0.232528 18 C19orf25 4.766988 0.680998 7 TBX15 2.980007 0.175295 17 RXRA 3.926521 0.560932 7 FOXP1 6.048305 0.378019 16 FBXL18 3.935586 0.655931 6 EBF3 3.478604 0.217413 16 RADIL 3.842419 0.640403 6 GLI2 5.692075 0.379472 15 STRA6 3.640968 0.606828 6 BAIAP2 4.493922 0.299595 15

TSN AX-DISCI 4.753472 0.950694 5 KIRREL3 4.378257 0.291884 15 RUNDC3A 4.748996 0.949799 5 SLX1B-

₅ SULT1A4 4.125981 0.275065 15 ARHGEF7 3.710766 0.742153

SLX1A

4.125981 0.275065 15 STAP2 3.497874 0.874469

LOC606724 4.125981 0.275065 15 NDST1 3.352395 0.838099

RALGAPA2 3.456247 1.728124 ₂ ZBTB20 3.943137 0.262876 15

NFIX 3.623907 0.241594 15

TABLE 126: Cancer Type MNG_SMARCE1 RPS6KA2 6.025742 0.43041 14

IQSEC1 5.110088 0.365006 14 Gene site imp sum imp mean n

ARHGEF10 4.474738 0.319624 14 PTPRN2 12.6468 0.154229 82

PRKAG2 3.804304 0.271736 14 PRDM16 7.651707 0.107771 71

C7orf50 3.55488 0.25392 14 PCDHGA1 3.526478 0.059771 59

CUX1 3.121578 0.22297 14 PCDHGA2 3.526478 0.061868 57 MSI2 4.770678 0.366975 13 CHTF18 2.949343 1.474671 2 MYT1L 4.076914 0.313609 13 GSE1 4.001949 0.307842 13 TABLE 127: Cancer Type MPNST_Atyp CMIP 6.030674 0.502556 12 Gene site imp sum imp mean n FBRSL1 4.65505 0.387921 12 PTPRN2 10.49688 0.128011 82 ZC3H3 3.719253 0.309938 12 PRDM16 9.331638 0.131432 71 ADGRD1 3.181569 0.265131 12 PCDHGA1 4.610238 0.07814 59 CTBP2 3.630217 0.33002 11 PCDHGA2 4.926624 0.086432 57 COL4A1 2.990737 0.271885 11 PCDHGA3 4.926624 0.091234 54 ACOT7 5.373632 0.537363 10 PCDHGB1 4.926624 0.092955 53 GAS7 3.731548 0.373155 10 PCDHGA4 4.926624 0.0966 51 TSPAN4 3.550146 0.355015 10 PCDHGB2 4.926624 0.100543 49 AKAP13 3.401954 0.340195 10 PCDHGA5 4.926624 0.104822 47 ATP11A 6.644252 0.73825 9 PCDHGB3 4.077744 0.094831 43 SND1 5.700985 0.633443 9 PCDHGA6 3.761358 0.094034 40 ADAMTS2 5.450143 0.605571 9 HDAC4 11.4268 0.308832 37 KCNH2 3.652449 0.405828 9 PCDHGA7 3.761358 0.101658 37 TSPAN9 3.398288 0.377588 9 RBFOX3 3.763689 0.107534 35 CACNA2D4 3.264015 0.362668 9 PCDHGB4 3.761358 0.107467 35 TRAPPCI 2 2.944589 0.327177 9 PCDHGA8 3.761358 0.107467 35 LINC00311 4.435695 0.554462 8 DIP2C 4.329398 0.135294 32 DNMT3A 4.416558 0.55207 8 PCDHGB5 4.077744 0.127429 32 DLEU1 3.361173 0.420147 8 PCDHGA9 4.077744 0.13154 31 MSRA 3.294925 0.411866 8 PCDHGB6 4.077744 0.140612 29 SYNJ2 2.961715 0.370214 8 PCDHGA10 3.761358 0.134334 28 ASPSCR1 2.915682 0.36446 8 ADARB2 5.230539 0.201175 26 CXXC5 5.650038 0.807148 7 AGAP1 10.77193 0.430877 25 RXRA 4.165872 0.595125 7 PDGFRA 4.667772 0.186711 25 VPS 13D 4.089338 0.584191 7 PCDHGB7 3.761358 0.156723 24 MIR548H4 3.961827 0.565975 7 MEIS1 2.611056 0.108794 24 NAVI 3.792954 0.541851 7 RPTOR 8.23786 0.358168 23 GAK 3.696837 0.52812 7 NCOR2 7.734358 0.336276 23 C19orf25 3.252634 0.464662 7 HOXB3 4.665346 0.202841 23 FBXL18 4.26533 0.710888 6 RIMBP2 3.795662 0.165029 23 RADIL 3.421879 0.570313 6 PCDHGA11 3.761358 0.163537 23 COQ8A 3.189345 0.531558 6 NXN 3.212903 0.139691 23 SLC22A18AS 3.068701 0.51145 6 HOXA-AS3 8.000536 0.380978 21 CRADD 2.919457 0.486576 6 SKI 7.268894 0.346138 21 RUNDC3A 4.524519 0.904904 5 ZIC4 2.892044 0.137716 21 ARHGEF7 4.430204 0.886041 5 FRMD4A 5.721777 0.286089 20 TSN AX-DISCI 3.723805 0.744761 5 ABR 3.043403 0.15217 20 SDK2 2.924278 0.584856 5 SDK1 3.002207 0.15011 20 GSG1 3.483081 0.87077 4 MAD1L1 6.504966 0.342367 19 STAP2 3.222442 0.80561 4 SMG1P2 5.137537 0.270397 19 DAGLB 2.962308 0.987436 3 BOLA2 5.137537 0.270397 19 SLC6A9 2.929569 0.976523 3 LOC613038 5.137537 0.270397 19 RALGAPA2 3.938003 1.969001 2 ZNF423 4.51416 0.237587 19 SLC25A10 2.971607 1.485803 2 CASZ1 2.803664 0.147561 19 KCNQ1 2.551606 0.134295 19 ARHGEF7 3.716752 0.74335 5 SEPTIN9 3.752735 0.208485 18 TSN AX-DISCI 3.313869 0.662774 5 TBC1D16 3.662815 0.20349 18 RUNDC3A 2.954544 0.590909 5 RBFOX1 3.58725 0.199292 18 ARHGAP25 2.753627 0.550725 5 FOXK1 3.086634 0.17148 18 VAV2 2.665216 0.533043 5 TBX15 3.045215 0.17913 17 TK1 2.58289 0.516578 5 EBF3 2.79486 0.174679 16 DICER1 2.564388 0.854796 3 NAV2 2.583868 0.161492 16 WDR81 2.558472 1.279236 2 GLI2 4.238032 0.282535 15 ZBTB20 3.214638 0.214309 15 TABLE 128: Cancer Type MPNST_Typ BAIAP2 3.051758 0.203451 15 Gene site imp sum imp mean n PCDHGA12 3.761358 0.268668 14 PTPRN2 11.40859 0.139129 82 C7orf50 3.23462 0.231044 14 PRDM16 16.35938 0.230414 71 PRKAG2 3.066192 0.219014 14 PCDHGA1 7.813541 0.132433 59 CUX1 3.004756 0.214625 14 PCDHGA2 7.497155 0.131529 57 MSI2 3.521666 0.270897 13 PCDHGA3 7.813541 0.144695 54 HOXC4 2.965225 0.228094 13 PCDHGB1 8.129927 0.153395 53 CMIP 4.557839 0.37982 12 PCDHGA4 7.682672 0.150641 51 FBRSL1 4.0272 0.3356 12 PCDHGB2 7.682672 0.156789 49 ADGRD1 2.847644 0.237304 12 PCDHGA5 7.366286 0.156729 47 CSMD1 2.623068 0.218589 12 PCDHGB3 7.006468 0.162941 43 SPON2 3.904574 0.354961 11 PCDHGA6 7.006468 0.175162 40 PCDHGC3 3.761358 0.341942 11 HDAC4 18.57195 0.501945 37 TBCD 3.554525 0.323139 11 PCDHGA7 6.243333 0.168739 37 SLC9A3 2.948527 0.268048 11 RBFOX3 7.251853 0.207196 35 CACNA1C 2.885609 0.262328 11 PCDHGB4 6.243333 0.178381 35 SLC38A10 2.782131 0.252921 11 PCDHGA8 6.243333 0.178381 35 AKAP13 3.296816 0.329682 10 PAX6 5.878477 0.167956 35 TSPAN4 2.826546 0.282655 10 DIP2C 8.558231 0.267445 32 OTX1 2.726245 0.272625 10 PCDHGB5 6.559719 0.204991 32 SND1 6.561738 0.729082 9 PCDHGA9 6.559719 0.211604 31 ATP11A 5.144064 0.571563 9 SOX2-OT 7.031322 0.242459 29 TSPAN9 4.146995 0.460777 9 PCDHGB6 5.793022 0.199759 29 ADAMTS2 3.609916 0.401102 9 PCDHGA10 5.793022 0.206894 28 CACNA2D4 3.200749 0.355639 9 GALNT9 5.65269 0.209359 27 NOTCH 1 2.66549 0.296166 9 SHANK2 5.418389 0.2084 26 MGMT 2.65052 0.294502 9 AGAP1 13.66331 0.546533 25 MSRA 3.799832 0.474979 8 CAMTAI 7.369768 0.294791 25 DLEU1 3.293788 0.411723 8 PDGFRA 5.680583 0.227223 25 LINC00311 3.188449 0.398556 8 SATB2 6.052462 0.252186 24 MACROD1 2.626244 0.32828 8 PCDHGB7 6.005152 0.250215 24 GAK 3.474309 0.49633 7 MEIS1 5.069165 0.211215 24 NAVI 2.733392 0.390485 7 RPTOR 11.28283 0.490558 23 MIR548H4 2.627448 0.37535 7 INPP5A 6.336466 0.275499 23 VPS 13D 2.601164 0.371595 7 PCDHGA11 5.688766 0.247338 23 CCDC177 3.719957 0.619993 6 NCOR2 4.059225 0.176488 23 FBXL18 3.461798 0.576966 6 RIMBP2 4.055906 0.176344 23 FMNL2 2.888892 0.481482 6 PRKCZ 4.788565 0.217662 22 SKI 7.873089 0.374909 21 ATP11A 6.044255 0.671584 9 HOXA-AS3 6.084415 0.289734 21 TRAPPCI 2 4.67812 0.519791 9 SIM2 5.226048 0.248859 21 MGMT 4.059266 0.45103 9 ZIC4 4.034207 0.192105 21 DLEU1 5.18969 0.648711 8 FRMD4A 6.957362 0.347868 20 MSRA 4.894293 0.611787 8 SDK1 4.378271 0.218914 20 GATA4 4.400518 0.550065 8 MAD1L1 12.12049 0.637921 19 DNMT3A 3.983243 0.497905 8 CASZ1 7.28862 0.383612 19 NAVI 4.498508 0.642644 7 ZNF423 6.088723 0.320459 19 GAK 4.268656 0.609808 7 SMG1P2 5.066878 0.266678 19 FBXL18 4.842362 0.80706 6 BOLA2 5.066878 0.266678 19 CRADD 4.395121 0.73252 6 LOC613038 5.066878 0.266678 19 PAX1 4.032237 0.672039 6 KCNQ1 4.118846 0.216781 19 RUNDC3A 5.089872 1.017974 5 FOXK1 6.011675 0.333982 18 TSN AX-DISCI 4.853614 0.970723 5 TBC1D16 5.491063 0.305059 18 ARHGEF7 4.292926 0.858585 5 ANKRD11 4.830669 0.268371 18 MCF2L 3.893409 0.216301 18 TABLE 129: Cancer Type MYXGNT PAX6-AS1 5.287482 0.311028 17 Gene site imp sum imp mean n RCN1 5.287482 0.311028 17 PTPRN2 8.063995 0.098341 82 FOXP1 6.513298 0.407081 16 PRDM16 6.015023 0.084719 71 EBF3 5.517271 0.344829 16 PCDHGA1 2.438596 0.041332 59 GLI2 6.64064 0.442709 15 PCDHGA2 2.438596 0.042782 57 BAIAP2 5.555953 0.370397 15 PCDHGA3 2.438596 0.045159 54 KIRREL3 4.728594 0.31524 15 PCDHGB1 2.438596 0.046011 53 IQSEC1 5.990637 0.427903 14 PCDHGA4 2.438596 0.047816 51 RPS6KA2 5.431633 0.387974 14 PCDHGB2 2.438596 0.049767 49 PCDHGA12 4.608739 0.329196 14 PCDHGA5 2.438596 0.051885 47 TBX5 4.533789 0.323842 14 PCDHGB3 2.12221 0.049354 43 CACNA1H 4.164851 0.297489 14 PCDHGA6 2.12221 0.053055 40 PRKAG2 4.033154 0.288082 14 HDAC4 5.344795 0.144454 37 ARHGEF10 4.013836 0.286703 14 PCDHGA7 2.12221 0.057357 37 MIR548F5 3.912287 0.279449 14 PAX6 5.957941 0.170227 35 GSE1 5.106679 0.392821 13 RBFOX3 2.249688 0.064277 35 MSI2 4.801108 0.369316 13 PCDHGB4 2.12221 0.060635 35 SPTBN4 4.123586 0.317199 13 PCDHGA8 2.12221 0.060635 35 CMIP 5.474613 0.456218 12 DIP2C 4.667771 0.145868 32 ZC3H3 5.183168 0.431931 12 PCDHGB5 2.12221 0.066319 32 FBRSL1 4.300782 0.358398 12 PCDHGA9 2.12221 0.068458 31 TNS3 4.118537 0.343211 12 SOX2-OT 4.654458 0.160499 29 CCDC140 5.431056 0.493732 11 PDGFRA 4.560928 0.182437 25 COL4A1 4.814247 0.437659 11 CAMTAI 2.86826 0.11473 25 PCDHGC3 4.292353 0.390214 11 AGAP1 2.62551 0.10502 25 AKAP13 4.837756 0.483776 10 SATB2 4.23038 0.176266 24 FMN1 4.308091 0.430809 10 RPTOR 5.827061 0.25335 23 KLHL29 4.18457 0.418457 10 INPP5A 2.986581 0.129851 23 ACOT7 3.897802 0.38978 10 NXN 2.618838 0.113863 23 SND1 7.906603 0.878511 9 NCOR2 2.359705 0.102596 23 ADAMTS2 6.07413 0.674903 9 PRKCZ 2.771693 0.125986 22 SKI 4.88071 0.232415 21 KCNH2 2.23091 0.247879 9

SIM2 2.403596 0.114457 21 ADGRB1 2.222378 0.246931 9

FRMD4A 4.809888 0.240494 20 LINC00311 2.238754 0.279844 8

ZNF423 5.127221 0.269854 19 MSRA 2.182144 0.272768 8

MAD1L1 5.063347 0.266492 19 NXPH1 2.151276 0.268909 8

SMG1P2 3.051539 0.160607 19 DUSP6 4.271224 0.610175 7

BOLA2 3.051539 0.160607 19 FHIT 2.833737 0.40482 7

LOC613038 3.051539 0.160607 19 NAVI 2.786186 0.398027 7

MCF2L 3.682952 0.204608 18 LINC00461 2.366798 0.338114 7

SEPTIN9 3.483625 0.193535 18 LHPP 2.10422 0.300603 7

TBC1D16 3.38681 0.188156 18 FBXL18 2.190535 0.365089 6

FOXK1 3.374784 0.187488 18 CRADD 2.179627 0.363271 6

RBFOX1 2.248114 0.124895 18 CACNA2D3 2.129801 0.354967 6

OPCML 4.650709 0.273571 17 FAM181A 2.11702 0.352837 6

TBX15 3.241245 0.190661 17 RUNDC3A 3.425236 0.685047 5

SORBS2 2.949142 0.184321 16 LIPE-AS1 2.290428 0.572607 4

FOXP1 2.733394 0.170837 16 RBMS3 2.287928 0.571982 4

GLI2 7.012457 0.467497 15 GRIN2B 3.005057 1.001686 3

EMX2OS 2.535671 0.169045 15 BFSP2 2.545119 0.848373 3

CUX1 2.623715 0.187408 14 DAGLB 2.260342 0.753447 3

C7orf50 2.108388 0.150599 14 LOXL3 2.084867 0.694956 3

MSI2 3.427545 0.263657 13 SOXIO 4.027447 2.013724 2

MYT1L 2.912141 0.224011 13

SPTBN4 2.214702 0.170362 13 TABLE 130: Cancer Type NB_MYCN

CMIP 3.717364 0.30978 12 Gene site imp sum imp mean n

FBRSL1 2.420882 0.20174 12 PTPRN2 13.77349 0.167969 82

MEIS2 2.311873 0.192656 12 PRDM16 9.137531 0.128698 71

MIRLET7BHG 2.110346 0.175862 12 HDAC4 14.90914 0.40295 37

CCDC140 2.680388 0.243672 11 PAX6 10.20883 0.291681 35

RAD51B 2.510789 0.228254 11 RBFOX3 5.604467 0.160128 35

VGLL4 2.298538 0.208958 11 DIP2C 12.68854 0.396517 32

FGFR2 2.241963 0.203815 11 SHANK2 4.94307 0.190118 26

GLUD1P2 2.212583 0.201144 11 ADARB2 3.879706 0.149219 26

ZC3H12D 2.073195 0.188472 11 AGAP1 8.455554 0.338222 25

LBX1-AS1 3.409043 0.340904 10 CAMTAI 4.880188 0.195208 25

ACOT7 2.570845 0.257085 10 RPTOR 8.818478 0.383412 23

RGS12 2.401681 0.240168 10 NXN 7.999339 0.347797 23

TP73 2.363542 0.236354 10 NCOR2 5.498296 0.239056 23

GRID1 2.214702 0.22147 10 INPP5A 4.389074 0.190829 23

MAML2 2.119074 0.211907 10 RIMBP2 3.857062 0.167698 23

ATP11A 3.768101 0.418678 9 HOXB3 3.516834 0.152906 23

TRAPPCI 2 3.431857 0.381317 9 PRKCZ 4.116492 0.187113 22

SND1 3.356011 0.37289 9 SKI 7.34881 0.349943 21

NOTCH 1 3.323558 0.369284 9 SIM2 4.031072 0.191956 21

ASAP1 3.018533 0.335393 9 FRMD4A 5.693082 0.284654 20

KAZN 2.509768 0.278863 9 SDK1 3.987692 0.199385 20

KCNMA1 2.356243 0.261805 9 ABR 3.501392 0.17507 20

AXIN2 2.329512 0.258835 9 MAD1L1 11.2606 0.592663 19 ZNF423 6.710876 0.353204 19 CACNA2D4 4.211872 0.467986 9 SMG1P2 6.20605 0.326634 19 KAZN 3.950567 0.438952 9 BOLA2 6.20605 0.326634 19 AXIN2 3.512069 0.39023 9 LOC613038 6.20605 0.326634 19 GPC6 3.439569 0.382174 9 CASZ1 5.513095 0.290163 19 KCNH2 3.396482 0.377387 9 KCNQ1 3.348152 0.176219 19 EGFR 3.281196 0.364577 9 SEPTIN9 4.875206 0.270845 18 MSRA 4.251822 0.531478 8 FOXK1 4.738861 0.26327 18 SYNJ2 3.507904 0.438488 8 ANKRD11 4.444749 0.246931 18 TENM2 3.360203 0.420025 8 MCF2L 3.973541 0.220752 18 NXPH1 3.272993 0.409124 8 TBC1D16 3.477985 0.193221 18 LINC00311 3.247338 0.405917 8 PAX6-AS1 6.095823 0.358578 17 GAK 4.507366 0.643909 7 RCN1 6.095823 0.358578 17 C19orf25 4.332101 0.618872 7 HBG2 4.729845 0.278226 17 NAVI 3.751485 0.535926 7 EBF3 3.910934 0.244433 16 HOXD3 3.302864 0.471838 7 LRMDA 5.250944 0.350063 15 VPS13D 3.270809 0.467258 7 BAIAP2 4.496185 0.299746 15 PACRG 3.188281 0.455469 7 NFATC1 4.454533 0.296969 15 CRADD 4.283501 0.713917 6 KIRREL3 4.373066 0.291538 15 FBXL18 3.71622 0.61937 6 GLI2 4.301867 0.286791 15 FMNL2 3.462802 0.577134 6 ZBTB20 3.974717 0.264981 15 WHKKN2 3.183348 0.530558 6 DLX6-AS1 3.564548 0.237637 15 TSNAX-DISC1 4.991709 0.998342 5 PRKAG2 5.022579 0.358756 14 RUNDC3A 4.645029 0.929006 5 CUX1 4.798593 0.342757 14 ARHGEF7 3.825064 0.765013 5 MIR548F5 3.780039 0.270003 14 MPP7 3.296489 0.659298 5 C7orf50 3.778709 0.269908 14 FYN 4.38385 1.095962 4 MOB2 3.306584 0.236185 14 CHTF18 3.828653 1.914327 2 MSI2 8.759201 0.673785 13 SLC25A10 3.373693 1.686847 2 RFX4 4.748792 0.365292 13 ANKLE2 3.350613 1.675307 2 GSE1 3.671473 0.282421 13 MYT1L 3.629239 0.279172 13 TABLE 131: Cancer Type NB_TMMneg CMIP 6.157676 0.51314 12 Gene site imp sum imp mean n ZC3H3 5.181392 0.431783 12 PTPRN2 18.55652 0.226299 82 FBRSL1 4.383222 0.365268 12 PRDM16 10.22762 0.144051 71 TBX4 4.04326 0.336938 12 PCDHGA1 7.152279 0.121225 59 CSMD1 3.909171 0.325764 12 PCDHGA2 6.835893 0.119928 57 MAML3 3.776111 0.314676 12 PCDHGA3 6.474104 0.119891 54 TNS3 3.647926 0.303994 12 PCDHGB1 6.474104 0.122153 53 RASA3 3.439803 0.28665 12 PCDHGA4 6.046453 0.118558 51 RAD51B 4.731405 0.430128 11 PCDHGB2 5.625262 0.114801 49 CTBP2 3.3705 0.306409 11 PCDHGA5 5.941648 0.126418 47 TSPAN4 5.159493 0.515949 10 PCDHGB3 5.5052 0.128028 43 AKAP13 4.027337 0.402734 10 PCDHGA6 5.5052 0.13763 40 SH3RF3 3.721306 0.372131 10 HDAC4 17.16275 0.463858 37 SND1 5.288539 0.587615 9 PCDHGA7 4.872428 0.131687 37 ADAMTS2 4.883445 0.542605 9 PAX6 11.01388 0.314682 35 ATP11A 4.40828 0.489809 9 RBFOX3 5.676042 0.162173 35 TSPAN9 4.22834 0.469816 9 PCDHGB4 4.556042 0.130173 35 PCDHGA8 4.556042 0.130173 35 ARHGEF10 4.243537 0.30311 14 DIP2C 11.64339 0.363856 32 C7orf50 4.068381 0.290599 14 PCDHGB5 4.239656 0.132489 32 MOB2 3.985323 0.284666 14 GALNT9 5.149882 0.190736 27 MSI2 8.580374 0.660029 13 SHANK2 6.159903 0.236919 26 GSE1 5.724454 0.440343 13 AGAP1 12.75037 0.510015 25 MYT1L 5.487224 0.422094 13 PDGFRA 7.113649 0.284546 25 RFX4 4.115234 0.316556 13 CAMTAI 6.491436 0.259657 25 ZC3H3 6.797012 0.566418 12 SATB2 5.74169 0.239237 24 FBRSL1 5.057732 0.421478 12 MEIS1 5.664513 0.236021 24 ADGRD1 4.916863 0.409739 12 NXN 11.92044 0.51828 23 RASA3 4.568026 0.380669 12 RPTOR 10.02959 0.436069 23 MIRLET7BHG 4.045357 0.337113 12 NCOR2 6.754804 0.293687 23 CTBP2 4.887996 0.444363 11 INPP5A 6.090449 0.264802 23 RAD51B 4.828445 0.43895 11 PRKCZ 7.080414 0.321837 22 TSPAN4 5.385332 0.538533 10 SKI 10.28144 0.489592 21 ADAMTS2 5.853042 0.650338 9 HOXA-AS3 5.049535 0.240454 21 ATP11A 5.785182 0.642798 9 ZIC4 4.250293 0.202395 21 SND1 5.762779 0.640309 9 FRMD4A 7.176558 0.358828 20 TSPAN9 4.99043 0.554492 9 ABR 6.081806 0.30409 20 AXIN2 4.640668 0.51563 9 SDK1 5.981355 0.299068 20 TRAPPCI 2 4.439527 0.493281 9 MAD1L1 12.41341 0.653337 19 CACNA2D4 4.422186 0.491354 9 ZNF423 6.238529 0.328344 19 KCNH2 4.06122 0.451247 9 KCNQ1 5.876033 0.309265 19 MSRA 4.849491 0.606186 8 SMG1P2 5.859726 0.308407 19 TRAPPC9 4.147563 0.518445 8 BOLA2 5.859726 0.308407 19 SYNJ2 4.056103 0.507013 8 LOC613038 5.859726 0.308407 19 RXRA 4.522346 0.646049 7 CASZ1 4.319617 0.227348 19 NAVI 4.433724 0.633389 7 TBC1D16 8.686104 0.482561 18 VPS13D 4.250029 0.607147 7 FOXK1 7.477284 0.415405 18 C19orf25 4.135319 0.59076 7 MCF2L 4.662193 0.259011 18 GAK 4.046351 0.57805 7 ANKRD11 4.195478 0.233082 18 FBXL18 4.988075 0.831346 6 SEPTIN9 4.031914 0.223995 18 CRADD 4.433395 0.738899 6 PAX6-AS1 7.150191 0.420599 17 RUNDC3A 5.152499 1.0305 5 RCN1 7.150191 0.420599 17 TSN AX-DISCI 5.141396 1.028279 5 SIM1 4.51478 0.265575 17 ARHGEF7 4.761732 0.952346 5 FOXP1 6.436364 0.402273 16 DNAAF5 4.360708 0.872142 5 NAV2 4.623006 0.288938 16 GLI2 6.19981 0.413321 15 TABLE 132: Cancer Type NB_TMMpos NHX 4.786288 0.319086 15 Gene site imp sum imp mean n BAIAP2 4.435057 0.29567 15 PTPRN2 16.11115 0.196477 82 SLX1B- PRDM16 6.965467 0.098105 71 SULT1A4 4.290474 0.286032 15

HDAC4 13.48037 0.364334 37 SLX1A 4.290474 0.286032 15

PAX6 10.13358 0.289531 35 LOC606724 4.290474 0.286032 15

RBFOX3 4.920339 0.140581 35 KIRREL3 4.274786 0.284986 15

DIP2C 9.275777 0.289868 32 RPS6KA2 6.530167 0.466441 14

SHANK2 6.227641 0.239525 26 PRKAG2 4.493534 0.320967 14

AGAP1 9.214808 0.368592 25 CAMTAI 7.199671 0.287987 25 ADGRD1 3.856339 0.321362 12 SATB2 3.007551 0.125315 24 CTBP2 4.804597 0.436782 11 NXN 10.45491 0.454561 23 RAD51B 3.351723 0.304702 11 RPTOR 10.04757 0.436851 23 TBCD 3.264218 0.296747 11 NCOR2 4.712774 0.204903 23 TSPAN4 4.852241 0.485224 10 INPP5A 4.166842 0.181167 23 CHST11 4.233357 0.423336 10 PRKCZ 4.993783 0.22699 22 RGS12 4.115475 0.411548 10 SKI 8.623933 0.410663 21 ACOT7 3.890732 0.389073 10 HOXA-AS3 3.366629 0.160316 21 CBFA2T3 3.433004 0.3433 10 ZIC4 3.156767 0.150322 21 AKAP13 3.410488 0.341049 10 FRMD4A 4.815185 0.240759 20 ADAMTS2 5.109962 0.567774 9 SDK1 4.46552 0.223276 20 TSPAN9 4.490392 0.498932 9 MAD1L1 11.1922 0.589063 19 CACNA2D4 3.80421 0.42269 9 SMG1P2 4.049626 0.213138 19 AXIN2 3.526607 0.391845 9 BOLA2 4.049626 0.213138 19 MGMT 3.378644 0.375405 9 LOC613038 4.049626 0.213138 19 SMAD3 5.640512 0.705064 8 ZNF423 3.8885 0.204658 19 MSRA 4.373575 0.546697 8 CASZ1 3.476123 0.182954 19 VRK2 3.327552 0.415944 8 FOXK1 6.894914 0.383051 18 LINC00311 3.275629 0.409454 8

TBC1D16 6.826437 0.379246 18 PPP2R2B 3.03431 0.379289 8 SEPTIN9 3.450705 0.191706 18 MCIDAS 2.978913 0.372364 8 ANKRD11 3.271443 0.181747 18 NAVI 3.844465 0.549209 7 PAX6-AS1 6.216273 0.365663 17 C19orf25 3.733425 0.533346 7 RCN1 6.216273 0.365663 17 VPS13D 3.641552 0.520222 7 HBG2 3.132325 0.184254 17 GAK 3.314179 0.473454 7 FOXP1 4.6681 0.291756 16 RXRA 3.115199 0.445028 7 NAV2 4.575986 0.285999 16 HOXD3 3.091484 0.441641 7 KIRREL3 4.406834 0.293789 15 MIR548H4 3.088767 0.441252 7

BAIAP2 4.20718 0.280479 15 FBXL18 4.014514 0.669086 6 SLX1B- CRADD 3.515875 0.585979 6 SULT1A4 3.998125 0.266542

¹⁵ WHKKN2 3.429234 0.571539 6 SLX1A 3.998125 0.266542

¹⁵ COQ8A 3.36352 0.560587 6 LOC606724 3.998125 0.266542

¹⁵ MYO16 3.136848 0.522808 6

NFATC1 3.722419 0.248161

¹⁵ RUNDC3A 4.982688 0.996538 5 GLI2 3.137561 0.209171

¹⁵ ARHGEF7 3.83097 0.766194 5 RPS6KA2 5.499889 0.392849 14

TSNAX-DISC1 3.685278 0.737056 5 ARHGEF10 4.319529 0.308538 14

BCAR1 3.486815 0.697363 5 PRKAG2 3.795162 0.271083 14

DNAAF5 3.473447 0.694689 5 C7orf50 3.035132 0.216795 14

BACH2 3.340635 0.668127 5 MSI2 8.215674 0.631975 13

NPHP4 3.112474 0.622495 5

MYT1L 5.287032 0.406695 13

DTNA 3.232674 0.808169 4 RFX4 3.737829 0.287525 13

GSG1 3.128085 0.782021 4 GSE1 3.008545 0.231427 13

DAGLB 3.09399 1.03133 3 ZC3H3 6.773834 0.564486 12

DICER1 2.99365 0.997883 3 CMIP 4.753958 0.396163 ¹² SLC25A10 3.024731 1.512366 2 GNA12 4.582202 0.38185 12

MIRLET7BHG 4.044978 0.337082 12 TABLE 133: Cancer Type NFIB_PLEX FBRSL1 3.964516 0.330376 12

Gene site imp sum imp mean n PTPRN2 13.6038 0.1659 82 FOXK1 4.820062 0.267781 18 PRDM16 12.74187 0.179463 71 SEPTIN9 4.378197 0.243233 18 PCDHGA1 7.609956 0.128982 59 ANKRD11 4.17047 0.231693 18 PCDHGA2 7.609956 0.133508 57 PAX6-AS1 4.700585 0.276505 17 PCDHGA3 7.29357 0.135066 54 RCN1 4.700585 0.276505 17 PCDHGB1 7.29357 0.137615 53 FOXP1 4.200912 0.262557 16 PCDHGA4 6.977184 0.136808 51 SORBS2 4.032652 0.252041 16 PCDHGB2 6.977184 0.142392 49 SLX1B- SULT1A4 5.361231 0.357415 15 PCDHGA5 6.532484 0.138989 47 SLX1A 5.361231 0.357415 15 PCDHGB3 5.899712 0.137203 43 LOC606724 5.361231 0.357415 15 PCDHGA6 5.583326 0.139583 40 GLI2 5.193071 0.346205 15 HDAC4 12.87329 0.347927 37 ZBTB20 3.750908 0.250061 15 PCDHGA7 5.26694 0.14235 37 KIRREL3 3.40391 0.226927 15 PAX6 10.60945 0.303127 35 CUX1 5.001913 0.357279 14 RBFOX3 4.967367 0.141925 35 IQSEC1 4.782704 0.341622 14 PCDHGB4 4.950554 0.141444 35 RPS6KA2 4.680709 0.334336 14 PCDHGA8 4.950554 0.141444 35 ARHGEF10 3.493473 0.249534 14 DIP2C 7.630068 0.23844 32 PRKAG2 3.428263 0.244876 14 PCDHGB5 5.26694 0.164592 32 C7orf50 3.396421 0.242602 14 PCDHGA9 5.26694 0.169901 31 MSI2 6.004289 0.461868 13 SOX2-OT 5.19331 0.17908 29 GSE1 5.190069 0.399236 13 PCDHGB6 4.950554 0.170709 29 MYT1L 3.452289 0.265561 13 PCDHGA10 4.950554 0.176805 28 RFX4 3.397458 0.261343 13 GALNT9 3.903223 0.144564 27 FBRSL1 5.176679 0.43139 12 SHANK2 3.595971 0.138307 26 CMIP 5.04849 0.420708 12 AGAP1 11.34453 0.453781 25 ADGRD1 4.376901 0.364742 12 PDGFRA 4.704498 0.18818 25 GNA12 4.328821 0.360735 12 CAMTAI 4.597499 0.1839 25 ZC3H3 3.725687 0.310474 12 PCDHGB7 4.317782 0.179908 24 SPON2 6.316248 0.574204 11 MEIS1 3.490617 0.145442 24 CTBP2 4.24005 0.385459 11 RPTOR 8.659899 0.376517 23 ANAPC16 4.089777 0.371798 11 NCOR2 7.426108 0.322874 23 TBCD 4.036366 0.366942 11 NXN 6.34216 0.275746 23 RAD51B 3.70189 0.336535 11 INPP5A 5.801975 0.25226 23 ACOT7 5.140454 0.514045 10 PCDHGA11 4.317782 0.18773 23 AKAP13 4.18378 0.418378 10 RIMBP2 3.42345 0.148846 23 TSPAN4 3.740082 0.374008 10 PRKCZ 4.501056 0.204593 22 KLHL29 3.563838 0.356384 10 SKI 9.150488 0.435738 21 SND1 4.427409 0.491934 9 FRMD4A 5.618711 0.280936 20 AXIN2 3.728727 0.414303 9 SDK1 4.48406 0.224203 20 ASAP1 3.678608 0.408734 9 ABR 3.444359 0.172218 20 CACNA2D4 3.512472 0.390275 9 MAD1L1 8.973465 0.472288 19 LINC00311 4.314256 0.539282 8 SMG1P2 6.52842 0.343601 19 SMAD3 4.240593 0.530074 8 BOLA2 6.52842 0.343601 19 MSRA 3.942701 0.492838 8 LOC613038 6.52842 0.343601 19 C19orf25 3.692036 0.527434 7 KCNQ1 4.698431 0.247286 19 FBXL18 4.951015 0.825169 6 ZNF423 3.51037 0.184756 19 CCDC177 4.125511 0.687585 6 TBC1D16 5.027192 0.279288 18 RUNDC3A 4.905148 0.98103 5 GLI2 8.548733 0.569916 15 LOC100130872 4.289063 0.857813 KIRREL3 3.966147 0.26441 15 TSN AX-DISCI 3.660081 0.732016 5 BAIAP2 3.944493 0.262966 15 BCAR1 3.402498 0.6805 5 ZBTB20 3.690344 0.246023 15 GSG1 3.580673 0.895168 4 LRMDA 3.364631 0.224309 15

RPS6KA2 6.139807 0.438558 14

TABLE 134: Cancer Type O_IDH IQSEC1 4.328154 0.309154 14 Gene site imp sum imp mean n CUX1 3.333429 0.238102 14 PTPRN2 17.77707 0.216793 82 MSI2 6.180821 0.475448 13 PRDM16 10.35636 0.145864 71 TNS3 5.496717 0.45806 12 HDAC4 11.77959 0.318367 37 ADGRD1 4.486901 0.373908 12 PAX6 9.742512 0.278357 35 MIRLET7BHG 4.423029 0.368586 12 RBFOX3 9.319109 0.26626 35 CMIP 4.400102 0.366675 12 DIP2C 5.326051 0.166439 32 FBRSL1 4.210507 0.350876 12 SOX2-OT 7.391918 0.254894 29 MEGF6 3.597623 0.299802 12 GALNT9 3.939244 0.145898 27 RASA3 3.442699 0.286892 12 SHANK2 3.989276 0.153434 26 ZC3H3 3.320047 0.276671 12 AGAP1 5.848567 0.233943 25 RAD51B 4.015803 0.365073 11 CAMTAI 5.732686 0.229307 25 FGFR2 3.234357 0.294032 11 PDGFRA 4.617729 0.184709 25 TSPAN4 4.43102 0.443102 10 SATB2 4.763835 0.198493 24 ACOT7 4.035124 0.403512 10 MEIS1 4.006012 0.166917 24 NR2F1-AS1 3.742456 0.374246 10 RPTOR 8.845044 0.384567 23 SND1 6.183886 0.687098 9 NXN 4.586101 0.199396 23 ATP11A 6.073319 0.674813 9 NCOR2 3.685137 0.160223 23 ADAMTS2 5.297555 0.588617 9 INPP5A 3.656301 0.15897 23 TSPAN9 4.699971 0.522219 9 RIMBP2 3.332114 0.144875 23 AXIN2 4.010917 0.445657 9 PRKCZ 4.32746 0.196703 22 NEAT1 3.679876 0.408875 9 SKI 9.339571 0.444741 21 ASAP1 3.604679 0.40052 9 FRMD4A 6.884633 0.344232 20 RUNX1 3.36976 0.374418 9 ABR 5.443179 0.272159 20 MSRA 4.663132 0.582891 8 SDK1 4.502169 0.225108 20 DNMT3A 4.382435 0.547804 8 MAD1L1 12.02304 0.632791 19 LINC00311 4.286512 0.535814 8 ZNF423 6.304621 0.331822 19 PPP2R2B 4.000987 0.500123 8 SMG1P2 5.86104 0.308476 19 ESRRG 3.56145 0.445181 8 BOLA2 5.86104 0.308476 19 NAVI 4.088238 0.584034 7 LOC613038 5.86104 0.308476 19 DUSP6 4.062795 0.580399 7 CASZ1 5.032192 0.264852 19 VPS13D 3.777117 0.539588 7 KCNQ1 3.876356 0.204019 19 LINC00461 3.447133 0.492448 7 CFAP46 3.865955 0.203471 19 C19orf25 3.425021 0.489289 7 FOXK1 5.340903 0.296717 18 FBXL18 4.856634 0.809439 6 ANKRD11 5.290515 0.293918 18 SLC22A18AS 3.61879 0.603132 6 TBC1D16 4.520863 0.251159 18 FAM181A 3.292217 0.548703 6 SEPTIN9 4.430265 0.246126 18 CRADD 3.249844 0.541641 6 MCF2L 3.959938 0.219997 18 RUNDC3A 4.771459 0.954292 5 OPCML 4.990801 0.293577 17 PRR5L 4.24277 0.848554 5 NAV2 5.052861 0.315804 16 MRC2 4.151333 0.830267 5 FOXP1 4.679177 0.292449 16 ARHGEF7 3.679727 0.735945 5 TSN AX-DISCI 3.665763 0.733153 5 ABR 4.684248 0.234212 20 AP2A2 3.421652 0.68433 5 FRMD4A 4.50663 0.225332 20

TK1 3.408928 0.681786 5 SDK1 4.455217 0.222761 20 STAP2 5.527156 1.381789 4 MAD1L1 11.15551 0.587132 19 RBMS3 4.067302 1.016826 4 CASZ1 5.790791 0.304778 19 DTNA 3.836485 0.959121 4 KCNQ1 3.722935 0.195944 19 DAGLB 3.993437 1.331146 3 ZNF423 3.597687 0.189352 19 SRRM3 3.919526 1.306509 3 SMG1P2 3.572242 0.188013 19 ANKLE2 4.01036 2.00518 2 BOLA2 3.572242 0.188013 19 SLC25A10 3.973706 1.986853 2 LOC613038 3.572242 0.188013 19 SOX10 3.856613 1.928306 2 TBC1D16 6.813484 0.378527 18 CHTF18 3.272229 1.636114 2 ANKRD11 6.188601 0.343811 18

FOXK1 5.396017 0.299779 18

TABLE 135: Cancer Type SEPTIN9 4.022652 0.223481 18

OLIGOSARC_IDH

MCF2L 3.929171 0.218287 18

Gene site imp sum imp mean n TBX15 5.634993 0.33147 17 PTPRN2 21.63143 0.263798 S2

OPCML 5.175403 0.304435 17 PRDM16 14.17415 0.199636 71

PAX6-AS1 4.060002 0.238824 17 PCDHGA1 6.739654 0.114231 59

RCN1 4.060002 0.238824 17 PCDHGA2 6.423268 0.112689 57

FOXP1 5.074186 0.317137 16 PCDHGA3 6.106882 0.11309 54

SORBS2 4.261092 0.266318 16 PCDHGB1 6.106882 0.115224 53

NAV2 4.257525 0.266095 16 PCDHGA4 6.106882 0.119743

⁵¹ GLI2 7.121 0.474733 15 PCDHGB2 6.106882 0.12463 ⁴⁹ SLX1B- PCDHGA5 5.790496 0.123202 47 SULT1A4 4.871386 0.324759 15 PCDHGB3 4.710492 0.109546 43 SLX1A 4.871386 0.324759 15 HDAC4 12.47842 0.337255 37 LOC606724 4.871386 0.324759 15 PCDHGA7 3.868612 0.104557 37 NFIX 4.779389 0.318626 15 PAX6 10.51905 0.300544 35 BAIAP2 4.73258 0.315505 15 RBFOX3 8.446715 0.241335 35 ZBTB20 4.376098 0.29174 15 PCDHGB4 3.868612 0.110532 35 IQSEC1 4.898046 0.34986 14 PCDHGA8 3.868612 0.110532 35 RPS6KA2 4.826184 0.344727 14 DIP2C 8.32832 0.26026 32 PRKAG2 4.522356 0.323025 14 SOX2-OT 5.793454 0.199774 29 C7orf50 4.165446 0.297532 14 GALNT9 4.635391 0.171681 27 MIR548F5 3.918394 0.279885 14 PDGFRA 6.113805 0.244552 25 ARHGEF10 3.677818 0.262701 14 AGAP1 5.365893 0.214636 25 MSI2 4.478717 0.344517 13 CAMTAI 4.25973 0.170389 25 SPTBN4 4.149056 0.319158 13

SATB2 5.00547 0.208561 24 MYT1L 3.576019 0.275078 13 MEIS1 4.846214 0.201926 24 ISLR2 5.440996 0.453416 12 PCDHGB7 3.885005 0.161875 24 FBRSL1 4.808985 0.400749 12 RPTOR 10.2269 0.444648 23 MIRLET7BHG 4.647168 0.387264 12 INPP5A 6.950543 0.302198 23 TNS3 3.685582 0.307132 12 NCOR2 6.548584 0.284721 23 GNA12 3.615064 0.301255 12 PRKCZ 6.525097 0.296595 22 CMIP 3.567877 0.297323 12 SKI 7.803382 0.37159 21 CTBP2 4.14712 0.377011 11 SIM2 4.1068 0.195562 21 SPON2 3.576957 0.325178 11 ZIC4 3.965825 0.188849 21 SKOR1 4.506284 0.450628 10 MAML2 3.792377 0.379238 10 ADARB2 5.29415 0.203621 26

TSPAN4 3 .691292 0.369129 10 AGAP1 11.35434 0.454173 25

ADGRB1 4 .933429 0.548159 9 CAMTAI 10.98461 0.439384 25

AXIN2 4 .733289 0.525921 9 PDGFRA 6.909238 0.27637 25

ASAP1 4 .082458 0.453606 9 SATB2 8.571627 0.357151 24

KCNH2 3 .947404 0.4386 9 MEIS1 7.849198 0.32705 24

KAZN 3 .921414 0.435713 9 PCDHGB7 5.309981 0.221249 24

ADAMTS2 3 .82162 0.424624 9 RPTOR 13.02038 0.566103 23

APBA2 3 .721262 0.413474 9 INPP5A 9.175249 0.398924 23

VRK2 4 .851925 0.606491 8 NCOR2 8.533146 0.371006 23

LINC00311 4 .619994 0.577499 8 NXN 6.064108 0.263657 23

DLEU1 4 .410582 0.551323 8 HOXB3 5.491228 0.238749 23

MCC 4 .231691 0.528961 8 PCDHGA11 5.309981 0.230869 23

DNMT3A 3 .769963 0.471245 8 PRKCZ 6.851413 0.311428 22

C19orf25 4 .000033 0.571433 7 SKI 12.70136 0.604827 21

SLC22A18AS 3 .7963 0.632717 6 SIM2 6.757476 0.321785 21

CRADD 3 .744372 0.624062 6 FRMD4A 10.16063 0.508031 20

TSN AX-DISCI 4 .748255 0.949651 5 SDK1 6.123697 0.306185 20

RUNDC3A 4 .609288 0.921858 5 ABR 5.984562 0.299228 20

STAP2 5 .128221 1.282055 4 MAD1L1 12.05065 0.634245 19

RBMS3 3 .735742 0.933936 4 ZNF423 12.0421 0.633795 19

SMG1P2 6.543669 0.344404 19

TABLE 136: Cancer Type PA_CORT BOLA2 6.543669 0.344404 19 Gene site imp sum imp mean n LOC613038 6.543669 0.344404 19 PTPRN2 26.60108 0.324403 82 CASZ1 5.71895 0.300997 19 PRDM16 21.33245 0.300457 71 FOXK1 9.183113 0.510173 18 PCDHGA1 9.343276 0.158361 59 MCF2L 7.131597 0.3962 18 PCDHGA2 9.659662 0.169468 57 RBFOX1 5.787189 0.321511 18 PCDHGA3 9.184591 0.170085 54 ANKRD11 5.647336 0.313741 18 PCDHGB1 8.868205 0.167325 53 TBC1D16 5.201895 0.288994 18 PCDHGA4 8.868205 0.173886 51 OPCML 9.879184 0.581128 17 PCDHGB2 8.726713 0.178096 49 FOXP1 7.81498 0.488436 16 PCDHGA5 8.040168 0.171067 47 SORBS2 6.551892 0.409493 16 PCDHGB3 7.407396 0.172265 43 NAV2 6.508386 0.406774 16 PCDHGA6 6.682433 0.167061 40 GLI2 11.30732 0.753821 15 HDAC4 14.9952 0.405276 37 ZBTB20 7.170015 0.478001 15 PCDHGA7 6.366047 0.172055 37 KIRREL3 5.782804 0.38552 15 PAX6 14.82851 0.423672 35 LRMDA 5.330559 0.355371 15 RBFOX3 10.41433 0.297552 35 BAIAP2 5.257505 0.3505 15 PCDHGB4 6.321425 0.180612 35 RPS6KA2 6.803775 0.485984 14 PCDHGA8 6.321425 0.180612 35 IQSEC1 6.731631 0.480831 14 DIP2C 11.97159 0.374112 32 PRKAG2 6.726839 0.480489 14 PCDHGB5 6.005039 0.187657 32 CUX1 6.109245 0.436375 14 PCDHGA9 6.005039 0.193711 31 ARHGEF10 5.664014 0.404572 14 SOX2-OT 12.47069 0.430024 29 C7orf50 5.596942 0.399782 14 PCDHGB6 5.441033 0.187622 29 MSI2 6.596031 0.507387 13 GALNT9 5.83543 0.216127 27 MIR9-3HG 5.784035 0.444926 13 SHANK2 9.288615 0.357254 26 MYT1L 5.43139 0.417799 13 KIF26B 5.38755 0.414427 13 DIP2C 12.43841 0.3887 32

RFX4 5.191773 0.399367 13 PCDHGB5 5.188257 0.162133 32

CMIP 6.630744 0.552562 12 PCDHGA9 5.188257 0.167363 31

MIRLET7BHG 6.544033 0.545336 12 SOX2-OT 13.43776 0.463371 29

MEIS2 5.905022 0.492085 12 SHANK2 5.102298 0.196242 26

ADGRD1 5.134363 0.427864 12 CAMTAI 10.52437 0.420975 25

RAD51B 6.287653 0.571605 11 AGAP1 10.44052 0.417621 25

FGFR2 6.131599 0.557418 11 PDGFRA 7.810578 0.312423 25

VGLL4 6.041934 0.549267 11 MEIS1 8.770815 0.365451 24

CCDC140 5.647124 0.513375 11 SATB2 6.73535 0.28064 24

SPON2 5.191216 0.471929 11 RPTOR 13.2166 0.574635 23

LBX1-AS1 6.618751 0.661875 10 INPP5A 7.865212 0.341966 23

AKAP13 5.601161 0.560116 10 HOXB3 6.650084 0.289134 23

CHST11 5.46443 0.546443 10 NXN 5.752376 0.250103 23

NTM 5.110489 0.511049 10 NCOR2 5.49232 0.238797 23 SND1 6.666831 0.740759 9 PRKCZ 6.759222 0.307237 22

ADGRB1 6.605145 0.733905 9 SKI 12.17179 0.579609 21 TSPAN9 6.101004 0.677889 9 SIM2 5.437087 0.258909 21 ATP11A 5.788809 0.643201 9 FRMD4A 8.140409 0.40702 20 NOTCH 1 5.409682 0.601076 9 ABR 6.71436 0.335718 20 AXIN2 5.36063 0.595626 9 SDK1 5.668025 0.283401 20 TRAPPCI 2 5.300167 0.588907 9 MAD1L1 13.69774 0.720933 19 LINC00311 5.447325 0.680916 8 ZNF423 10.42024 0.548433 19

MSRA 5.326954 0.665869 8 SMG1P2 8.785156 0.462377 19 DLEU1 5.270307 0.658788 8 BOLA2 8.785156 0.462377 19 DUSP6 7.401435 1.057348 7 LOC613038 8.785156 0.462377 19 LINC00461 6.722238 0.96032 7 CASZ1 5.862427 0.308549 19 RUNDC3A 5.392471 1.078494 5 FOXK1 9.295546 0.516419 18

MCF2L 7.672036 0.426224 18

TABLE 137: Cancer Type PAJNF SEPTIN9 6.396588 0.355366 18

Gene site imp sum imp mean n TBC1D16 5.890404 0.327245 18

PTPRN2 26.36911 0.321575 82 ANKRD11 5.202671 0.289037 18

PRDM16 21.97585 0.309519 71 OPCML 10.85961 0.6388 17

PCDHGA1 8.267047 0.140119 59 TBX15 5.389743 0.317044 17

PCDHGA2 7.950661 0.139485 57 NAV2 7.235721 0.452233 16

PCDHGA3 7.950661 0.147234 54 FOXP1 6.534835 0.408427 16

PCDHGB1 7.950661 0.150012 53 SORBS2 5.818589 0.363662 16

PCDHGA4 7.950661 0.155895 51 GLI2 11.79622 0.786415 15

PCDHGB2 7.634275 0.155802 49 ZBTB20 7.39887 0.493258 15

PCDHGA5 7.108589 0.151247 47 EMX2OS 5.889967 0.392664 15

PCDHGB3 7.108589 0.165316 43 KIRREL3 5.272782 0.351519 15

PCDHGA6 6.708268 0.167707 40 BAIAP2 5.193254 0.346217 15

HDAC4 13.94982 0.377022 37 IQSEC1 6.665892 0.476135 14

PCDHGA7 5.947182 0.160735 37 RPS6KA2 6.482534 0.463038 14

PAX6 14.94418 0.426977 35 CUX1 6.332662 0.452333 14

RBFOX3 8.664253 0.24755 35 TBX5 5.998658 0.428476 14

PCDHGB4 5.947182 0.169919 35 PRKAG2 5.424972 0.387498 14

PCDHGA8 5.947182 0.169919 35 MSI2 8.809547 0.677657 13 MIR9-3HG 7.266107 0.558931 13 PCDHGB3 3.113131 0.072398 43 MYT1L 6.182617 0.475586 13 PCDHGA6 2.796745 0.069919 40 TNS3 6.483199 0.540267 12 HDAC4 7.246665 0.195856 37 CMIP 6.235741 0.519645 12 RBFOX3 4.115757 0.117593 35 ADGRD1 6.01181 0.500984 12 PAX6 2.950393 0.084297 35 ZC3H3 5.848371 0.487364 12 DIP2C 3.94419 0.123256 32 MIRLET7BHG 5.809331 0.484111 12 SOX2-OT 6.305877 0.217444 29 TBX4 5.309311 0.442443 12 AGAP1 5.710979 0.228439 25 RAD51B 6.756107 0.614192 11 CAMTAI 4.469934 0.178797 25 FGFR2 5.887634 0.535239 11 PDGFRA 4.121216 0.164849 25 VGLL4 5.848507 0.531682 11 MEIS1 4.003889 0.166829 24 CCDC140 5.387338 0.489758 11 SATB2 2.608137 0.108672 24 LBX1-AS1 6.170454 0.617045 10 RPTOR 5.723025 0.248827 23 SH3RF3 5.498813 0.549881 10 INPP5A 3.546366 0.15419 23 ACOT7 5.262586 0.526259 10 NCOR2 2.912079 0.126612 23 AKAP13 5.213203 0.52132 10 PRKCZ 4.349281 0.197695 22 CHST11 5.049935 0.504993 10 SKI 6.671857 0.317707 21 SND1 6.644614 0.73829 9 FRMD4A 5.899552 0.294978 20 ATP11A 6.482206 0.720245 9 ABR 2.703796 0.13519 20 AXIN2 5.892237 0.654693 9 SDK1 2.608231 0.130412 20 ADGRB1 5.660024 0.628892

ZNF423 7.885591 0.415031 19 NOTCH 1 5.494313 0.610479 9 MAD1L1 7.709643 0.405771 19 TSPAN9 5.424342 0.602705 9 SMG1P2 4.091947 0.215366 19 LINC00311 5.644053 0.705507 8 BOLA2 4.091947 0.215366 19 DLEU1 5.378806 0.672351 8 LOC613038 4.091947 0.215366 19 GRIK2 5.373596 0.671699 8 FOXK1 5.212468 0.289582 18 DUSP6 7.573024 1.081861 7 MCF2L 5.179781 0.287766 18 LINC00461 5.998306 0.856901 7 OPCML 5.864633 0.344978 17 ITPKB 5.242199 0.748886 7 TBX15 2.668844 0.156991 17 SOX6 5.231915 0.747416 7 NAV2 5.230587 0.326912 16 FBXL18 5.169746 0.861624 6 FOXP1 4.08692 0.255432 16 CRADD 5.025567 0.837595 6 GLI2 7.167995 0.477866 15 RUNDC3A 5.624716 1.124943 5 LRMDA 4.316013 0.287734 15 TSN AX-DISCI 5.453342 1.090668 5 EMX2OS 3.598047 0.23987 15 SOX10 5.45089 2.725445 2 ZBTB20 2.822678 0.188179 15

CUX1 3.947981 0.281999 14

TABLE 138: Cancer Type TBX5 3.517706 0.251265 14 PA_INF_FGFR PRKAG2 2.751418 0.19653 14

Gene site imp sum imp mean n RPS6KA2 2.59765 0.185546 14 PTPRN2 11.26445 0.137371 82

IQSEC1 2.530759 0.180769 14 PRDM16 8.496392 0.119667 71

MSI2 4.465475 0.343498 13 PCDHGA1 3.049854 0.051692 59

RFX4 2.444632 0.188049 13 PCDHGA2 2.733468 0.047956 57

CMIP 4.639566 0.38663 12 PCDHGA3 3.049854 0.056479 54

MIRLET7BHG 2.910187 0.242516 12 PCDHGB1 3.049854 0.057544 53

RAD51B 3.984986 0.362271 11 PCDHGA4 3.049854 0.059801 51

VGLL4 3.739061 0.339915 11 PCDHGB2 3.049854 0.062242 49

SLC38A10 2.5T1T16 0.234343 11 PCDHGA5 3.049854 0.064891 47

BCL11B 3.701392 0.370139 10 LBX1-AS1 3.091577 0.309158 10 PCDHGA1 9.007676 0.152672 59 NTM 2.980801 0.29808 10 PCDHGA2 9.007676 0.158029 57 GAS7 2.846296 0.28463 10 PCDHGA3 8.374904 0.155091 54 SH3RF3 2.667166 0.266717 10 PCDHGB1 8.374904 0.158017 53 SPPL2B 2.645354 0.264535 10 PCDHGA4 8.374904 0.164214 51 ACOT7 2.448463 0.244846 10 PCDHGB2 7.995381 0.163171 49 CHST11 2.421619 0.242162 10 PCDHGA5 7.177763 0.152718 47 NOTCH 1 4.133478 0.459275 9 PCDHGB3 6.848598 0.15927 43 SND1 3.736434 0.415159 9 PCDHGA6 6.891039 0.172276 40 ATP11A 3.493815 0.388202 9 HDAC4 13.57788 0.36697 37 AXIN2 3.352521 0.372502 9 PCDHGA7 6.574653 0.177693 37 RUNX1 3.281493 0.36461 9 PAX6 11.37816 0.32509 35 ASAP1 3.213416 0.357046 9 RBFOX3 7.228479 0.206528 35 TSPAN9 3.02109 0.335677 9 PCDHGB4 5.610741 0.160307 35 TRAPPCI 2 2.816738 0.312971 9 PCDHGA8 5.610741 0.160307 35 PACS2 2.584394 0.287155 9 DIP2C 12.45531 0.389228 32 SLC22A18 2.443995 0.271555 9 PCDHGB5 5.335334 0.166729 32 MSRA 3.595183 0.449398 8 PCDHGA9 5.65172 0.182314 31 SMAD3 2.708286 0.338536 8 SOX2-OT 11.54569 0.398127 29 LINC00311 2.682674 0.335334 8 PCDHGB6 5.428568 0.187192 29 DNMT3A 2.550167 0.318771 8 PCDHGA10 5.744954 0.205177 28 MCC 2.440322 0.30504 8 GALNT9 4.869047 0.180335 27 DUSP6 4.996075 0.713725 7 SHANK2 7.031771 0.270453 26 SOX6 3.850643 0.550092 7 AGAP1 10.51541 0.420616 25 LINC00461 3.688793 0.52697 7 CAMTAI 10.12603 0.405041 25 NAVI 3.635722 0.519389 7 PDGFRA 8.291798 0.331672 25 LOC145845 3.302787 0.550464 6 SATB2 7.24036 0.301682 24 FBXL18 3.030425 0.505071 6 MEIS1 6.286198 0.261925 24 LRRFIP1 2.755256 0.459209 6 PCDHGB7 5.112182 0.213008 24 COQ8A 2.530892 0.421815 6 RPTOR 12.23551 0.531979 23 RUNDC3A 4.458772 0.891754 5 INPP5A 8.05987 0.350429 23 TEAD1 3.100721 0.620144 5 NCOR2 6.856589 0.298113 23 TSN AX-DISCI 2.784059 0.556812 5 NXN 5.412195 0.235313 23 STARD13 2.516872 0.503374 5 HOXB3 5.195531 0.225893 23 RBMS3 2.565293 0.641323 4 PRKCZ 6.410068 0.291367 22 DTNA 2.55449 0.638623 4 SKI 11.88106 0.565765 21 MYT1 2.492204 0.623051 4 ZIC4 5.131981 0.24438 21 LINC00856 2.470881 0.61772 4 SIM2 5.037634 0.239887 21 VOPP1 2.447651 0.611913 4 FRMD4A 8.255047 0.412752 20 GRIN2B 3.976503 1.325501 3 ABR 7.36424 0.368212 20 BFSP2 2.8358 0.945267 3 SDK1 4.85756 0.242878 20 SOX10 4.418234 2.209117 2 MAD1L1 10.80834 0.56886 19 SLC25A10 2.584089 1.292045 2 SMG1P2 8.670162 0.456324 19

BOLA2 8.670162 0.456324 19

TABLE 139: Cancer Type PA_MID LOC613038 8.670162 0.456324 19 Gene site imp sum imp mean n ZNF423 6.865541 0.361344 19 PTPRN2 24.92907 0.304013 82 FOXK1 9.060215 0.503345 18 PRDM16 22.38057 0.315219 71 TBC1D16 8.611254 0.478403 18 MCF2L 6.904808 0.3836 18 ARHGEF7 4.927637 0.985527 5 RBFOX1 6.215358 0.345298 18 SOX10 5.52976 2.76488 2 ANKRD11 6.088915 0.338273 18 SEPTIN9 4.9001 0.272228 18 TABLE 140: Cancer Type PB_FOXR2 OPCML 9.261312 0.544783 17 Gene site imp sum imp mean n PAX6-AS1 5.151432 0.303025 17 PTPRN2 7.990592 0.097446 82 RCN1 5.151432 0.303025 17 PRDM16 5.878697 0.082799 71 NAV2 6.342771 0.396423 16 HDAC4 9.817625 0.265341 37 FOXP1 5.687228 0.355452 16 PAX6 4.582936 0.130941 35 SORBS2 5.485831 0.342864 16 RBFOX3 4.257452 0.121641 35 GLI2 10.1483 0.676553 15 DIP2C 4.849074 0.151534 32 ZBTB20 6.74146 0.449431 15 GALNT9 4.664578 0.172762 27 EMX2OS 6.561478 0.437432 15 ADARB2 2.531088 0.09735 26 KIRREL3 5.503255 0.366884 15 CAMTAI 7.388806 0.295552 25 BAIAP2 4.921008 0.328067 15 AGAP1 5.664287 0.226571 25 RPS6KA2 7.793022 0.556644 14 PDGFRA 2.983932 0.119357 25 CUX1 6.182666 0.441619 14 MEIS1 2.457591 0.1024 24 IQSEC1 6.070246 0.433589 14 RPTOR 6.022756 0.261859 23 TBX5 5.372219 0.38373 14 NXN 4.43426 0.192794 23 PRKAG2 5.282265 0.377305 14 NCOR2 4.165875 0.181125 23 MSI2 6.939435 0.533803 13 RIMBP2 3.892405 0.169235 23 RFX4 5.583069 0.429467 13 INPP5A 3.431163 0.149181 23 MYT1L 5.004928 0.384994 13 SKI 4.913274 0.233965 21 SPTBN4 4.915305 0.3781 13 SDK1 2.733652 0.136683 20 CMIP 5.591839 0.465987 12 MAD1L1 10.74638 0.565599 19 FBRSL1 5.362243 0.446854 12 CASZ1 3.359893 0.176836 19 MEIS2 5.221624 0.435135 12 ZNF423 3.082744 0.16225 19 TNS3 4.892318 0.407693 12 SMG1P2 2.643986 0.139157 19 FGFR2 6.145005 0.558637 11 BOLA2 2.643986 0.139157 19 VGLL4 6.111036 0.555549 11 LOC613038 2.643986 0.139157 19 RAD51B 5.814694 0.528609 11 FOXK1 3.833278 0.21296 18 CCDC140 5.413668 0.492152 11 TBC1D16 2.536714 0.140929 18 LBX1-AS1 6.627648 0.662765 10 ANKRD11 2.507306 0.139295 18 SH3RF3 6.510324 0.651032 10 HBG2 5.796202 0.340953 17 NTM 4.970905 0.497091 10 TBX15 5.622326 0.330725 17 ATP11A 6.420082 0.713342 9 FOXP1 3.682609 0.230163 16 SND1 6.282092 0.69801 9 NAV2 3.015244 0.188453 16 NOTCH 1 5.579218 0.619913 9 KNDC1 3.337509 0.222501 15 ADGRB1 5.235298 0.5817 ₉ SLX1B-

2 PAN9 4.850493 ₉ SULT1A4 .538607 0.16924 15 TS 0.538944 SLX1A 2.538607 0.16924 15 LINC00311 6.110924 0.763866 LOC606724 2.538607 0.16924 15 GRIK2 5.325754 0.665719

NFATC1 2.416337 0.161089 15 MSRA 5.235986 0.654498

CUX1 2.668292 0.190592 14 DLEU1 5.195263 0.649408

C7orf50 2.4789 0.177064 14 DUSP6 7.486675 1.069525 TBX5 2.46133 0.175809 14 LINC00461 5.641189 0.805884 MOB2 2.370305 0.169307 14 NAVI 5.002949 0.714707

, MSI2 4.002983 0.307922 13

RUNDC3A 5.714321 1.142864 MYT1L 3.035138 0.233472 13 CHTF18 4.125188 2.062594 2

GSE1 2.260512 0.173886 13 UTRN 2.665067 1.332534 2

TNS3 4.970195 0.414183 12 KIF21B 2.591026 1.295513 2

FBRSL1 3.684879 0.307073 12 UHRF1 2.588786 1.294393 2

ZC3H3 3.247704 0.270642 12 TRIM65 2.373132 1.186566 2

GNA12 2.330387 0.194199 12 DDX31 2.254488 1.127244 2

CTBP2 2.994621 0.272238 11 ARL6IP6 2.833138 2.833138 1

RAD51B 2.761373 0.251034 11 KCNV2 2.68683 2.68683 1

ETS1 2.687183 0.268718 10 DDT 2.658782 2.658782 1

AKAP13 2.401156 0.240116 10 DNAJC27 2.281123 2.281123 1

AUTS2 2.378175 0.237818 10

SND1 4.92843 0.547603 9

TABLE 141: Cancer Type PB GrplA

CACNA2D4 4.16434 0.462704 9

Gene site imp sum imp mean n

ATP11A 3.792183 0.421354 9

PTPRN2 13.14354 0.160287 82

ADAMTS2 3.289882 0.365542 9

PRDM16 12.4281 0.175044 71

TSPAN9 3.275225 0.363914 9

PCDHGA1 4.717988 0.079966 59

GPC6 2.691387 0.299043 9

PCDHGA2 4.717988 0.082772 57

PDE6B 2.400909 0.266768 9

PCDHGA3 4.290336 0.079451 54

RUNX1 2.349199 0.261022 9

PCDHGB1 4.290336 0.08095 53

MGMT 2.259619 0.251069 9

PCDHGA4 4.606722 0.090328 51

SSBP3 2.23661 0.248512 9

PCDHGB2 4.606722 0.094015 49

VRK2 5.188252 0.648532 8

PCDHGA5 4.606722 0.098015 47

PPP2R2B 3.874229 0.484279 8

PCDHGB3 4.290336 0.099775 43

DNMT3A 3.389235 0.423654 8

PCDHGA6 4.290336 0.107258 40

DLEU1 2.512305 0.314038 8

HDAC4 15.02807 0.406164 37

TRAPPC9 2.408322 0.30104 8

PCDHGA7 3.97395 0.107404 37

MIR124-2HG 2.862366 0.408909 7

RBFOX3 8.054898 0.23014 35

FUR 2.743506 0.391929 7

PAX6 6.607636 0.18879 35

TRIM6-TRIM34 2.397273 0.342468 7

PCDHGB4 3.97395 0.113541 35

PITPNC1 2.387203 0.341029 7

PCDHGA8 3.97395 0.113541 35

MIR548H4 2.325792 0.332256 7

DIP2C 7.234737 0.226086 32

HOTAIR 2.259323 0.32276 7

PCDHGB5 3.97395 0.124186 32

LDLRAD4 2.259092 0.322727 7

PCDHGA9 3.657564 0.117986 31

TRAK1 2.697156 0.449526 6

GALNT9 5.728502 0.212167 27

DNAJB6 2.595136 0.432523 6

SHANK2 4.017317 0.154512 26

MYO 16 2.504651 0.417442 6

CAMTAI 9.204105 0.368164 25

TRIM34 2.397273 0.399546 6

AGAP1 7.880278 0.315211 25

TSN AX-DISCI 4.231114 0.846223 5

PDGFRA 3.962118 0.158485 25

ARHGEF7 2.573357 0.514671 5

PCDHGB7 4.265517 0.17773 24

TSTD1 2.266536 0.453307 5

MEIS1 4.019856 0.167494 24

GSG1 2.662472 0.665618 4

RPTOR 10.82454 0.470632 23

EXT1 2.49718 0.624295 4

NCOR2 8.674575 0.377155 23

RASGRP3 3.037567 1.012522 3

NXN 8.626693 0.375074 23

SLC25A22 2.963633 0.987878 3

INPP5A 7.050943 0.306563 23

SLC12A9 2.824573 0.941524 3

RIMBP2 6.203164 0.269703 23

ANKRD33B 2.399937 0.799979 3

PCDHGA11 4.265517 0.185457 23

CCDC167 2.397717 0.799239

³ PRKCZ 4.345374 0.197517 22

DAGLB 2.310307 0.770102 SKI 7.982678 0.380128 21 AXIN2 5.396535 0.599615 9

MAD1L1 14.00072 0.73688 19 TSPAN9 4.939725 0.548858 9

SMG1P2 6.150135 0.323691 19 ADAMTS2 4.653586 0.517065 9

BOLA2 6.150135 0.323691 19 ATP11A 4.587726 0.509747 9

LOC613038 6.150135 0.323691 19 CACNA2D4 4.375753 0.486195 9

CASZ1 6.003889 0.315994 19 PDE6B 3.91789 0.435321 9

KCNQ1 5.093913 0.268101 19 VRK2 8.419695 1.052462 8

CFAP46 3.876613 0.204032 19 PPP2R2B 4.94812 0.618515 8

ZNF423 3.739536 0.196818 19 TRAPPC9 3.803547 0.475443 8

FOXK1 5.324759 0.29582 18 ASPSCR1 3.785069 0.473134 8

SEPTIN9 3.795298 0.21085 18 DLEU1 3.769648 0.471206 8

ANKRD11 3.789854 0.210547 18 MIR548H4 4.051045 0.578721 7

HBG2 5.257775 0.309281 17 MIR124-2HG 3.881805 0.554544 7

TBX15 4.572216 0.268954 17 CALD1 4.047304 0.674551 6

OPCML 3.915604 0.23033 17 TRAK1 3.896317 0.649386 6

NAV2 5.577801 0.348613 16 TSNAX-DISC1 4.975083 0.995017 5

FOXP1 3.802156 0.237635 16 ARHGEF7 3.716847 0.743369 5

BAIAP2 4.821444 0.32143 15 CHTF18 4.686664 2.343332 2

KNDC1 4.609228 0.307282 15

ZBTB20 4.515262 0.301017 15 TABLE 142: Cancer Type PB. GrplB

CUX1 6.211835 0.443703 14 Gene site imp sum imp mean n

C7orf50 5.385842 0.384703 14 PTPRN2 4.33509 0.052867 82

IQSEC1 5.384054 0.384575 14 PRDM16 3.020796 0.042546 71

MIR548F5 4.897931 0.349852 14 PCDHGA1 2.763024 0.046831 59

GNG7 3.984604 0.284615 14 PCDHGA2 2.763024 0.048474 57

MOB2 3.924169 0.280298 14 PCDHGA3 2.763024 0.051167 54

ARHGEF10 3.835325 0.273952 14 PCDHGB1 2.763024 0.052133 53

PCDHGA12 3.632745 0.259482 14 PCDHGA4 2.763024 0.054177 51

MSI2 8.145857 0.626604 13 PCDHGB2 2.763024 0.056388 49

MYT1L 5.718421 0.439879 13 PCDHGA5 2.763024 0.058788 47

GSE1 4.969846 0.382296 13 PCDHGB3 2.763024 0.064256 43

RFX4 4.509334 0.346872 13 HDAC4 8.343297 0.225495 37

FBRSL1 5.839394 0.486616 12 RBFOX3 4.992446 0.142641 35

MIRLET7BHG 5.794207 0.482851 12 AGAP1 2.587405 0.103496 25

ZC3H3 5.362076 0.44684 12 RPTOR 5.172582 0.224895 23

CMIP 4.219709 0.351642 12 INPP5A 4.131043 0.179611 23

MAML3 3.660775 0.305065 12 NXN 3.094062 0.134524 23

CTBP2 4.396751 0.399705 11 HOXB3 2.730562 0.11872 23

SLC38A10 4.038955 0.367178 11 SKI 3.986412 0.189829 21

RAD51B 3.924346 0.356759 11 FRMD4A 2.158766 0.107938 20

LBX1-AS1 5.08053 0.508053 10 MAD1L1 9.902993 0.52121 19

AKAP13 4.666204 0.46662 10 CASZ1 4.723625 0.248612 19

FMN1 4.42103 0.442103 10 SMG1P2 2.819412 0.14839 19

AUTS2 4.372976 0.437298 10 BOLA2 2.819412 0.14839 19

ETS1 4.309239 0.430924 10 LOC613038 2.819412 0.14839 19

NR5A2 4.294828 0.429483 10 FOXK1 3.799291 0.211072 18

NBEA 3.794825 0.379483 10 TBX15 4.455878 0.26211 17

SND1 6.776733 0.75297 9 FOXP1 4.913795 0.307112 16 NAV2 3.62173 0.226358 16 TSN AX-DISCI 3.917575 0.783515 5

DLX6-AS1 2.999526 0.199968 15 LOC100132215 2.575006 0.515001 5

GLI2 2.899444 0.193296 15 CACNA2D2 2.279359 0.455872 5

KIRREL3 2.675446 0.178363 15 CASP8 2.221321 0.444264 5

BAIAP2 2.320908 0.154727 15 OTP 2.208145 0.441629 5

CUX1 2.991178 0.213656 14 ARHGEF7 2.133698 0.42674 5

MIR548F5 2.280434 0.162888 14 ITGA5 2.638831 0.659708 4

PPP2R2A 2.269914 0.162137 14 EXT1 2.523982 0.630995 4

MSI2 4.460783 0.343137 13 GSG1 2.312788 0.578197 4

MYT1L 3.527057 0.271312 13 MSC-AS1 2.179854 0.544963 4

ZC3H3 3.987479 0.33229 12 SLC12A9 2.456232 0.818744 3

MIRLET7BHG 3.400021 0.283335 12 SLC1A7 2.330836 0.776945 3

CMIP 3.032205 0.252684 12 EPAS1 2.278496 0.759499 3

RAD51B 2.502474 0.227498 11 ANKRD33B 2.113427 0.704476 3

WNT5A 2.212228 0.201112 11 DAGLB 2.10671 0.702237 3

LBX1-AS1 4.354166 0.435417 10 CHTF18 3.997221 1.99861 2

AUTS2 3.360625 0.336062 10 TRIM65 2.555016 1.277508 2

ETS1 2.697473 0.269747 10 KIF21B 2.54759 1.273795 2

ANKS1B 2.618779 0.261878 10 UHRF1 2.546712 1.273356 2

RGS12 2.286667 0.228667 10 DDX31 2.195924 1.097962 2

SKOR1 2.18314 0.218314 10 ERI3 2.141521 1.07076 2

NR5A2 2.165993 0.216599 10 KCNV2 2.80926 2.80926 1

NBEA 2.145445 0.214545 10 DDT 2.680828 2.680828 1

BCL11B 2.102114 0.210211 10 ARL6IP6 2.602609 2.602609 1

SND1 4.161108 0.462345 9 DNAJC27 2.364602 2.364602 1

CACNA2D4 3.857369 0.428597 9

ATP11A 3.444519 0.382724 9 TABLE 143: Cancer Type PB Grp2

TSPAN9 3.284664 0.364963 9 Gene site imp sum imp mean n

AXIN2 3.194411 0.354935 9 PTPRN2 5.879729 0.071704 82

MGMT 2.792104 0.310234 9 PRDM16 6.245735 0.087968 71

GPC6 2.725476 0.302831 9 PCDHGA1 2.531088 0.0429 59

RUNX1 2.635013 0.292779 9 PCDHGA2 2.531088 0.044405 57

VRK2 4.104786 0.513098 8 PCDHGA3 2.531088 0.046872 54

VEPH1 3.963503 0.495438 8 PCDHGB1 2.531088 0.047756 53

PPP2R2B 3.139021 0.392378 8 PCDHGA4 2.214702 0.043426 51

MACROD1 2.846503 0.355813 8 PCDHGB2 2.214702 0.045198 49

MSRA 2.51368 0.31421 8 PCDHGA5 2.214702 0.047121 47

TRAPPC9 2.181993 0.272749 8 HDAC4 6.957028 0.188028 37

DLEU1 2.165119 0.27064 8 PAX6 3.41803 0.097658 35

GDNF 3.616465 0.516638 7 DIP2C 3.646075 0.11394 32

MIR124-2HG 3.448424 0.492632 7 AGAP1 6.205598 0.248224 25

MIR548H4 3.005505 0.429358 7 CAMTAI 5.497657 0.219906 25

DUSP6 2.645882 0.377983 7 MEIS1 6.099519 0.254147 24

PITPNC1 2.205611 0.315087 7 INPP5A 4.568314 0.198622 23

NAVI 2.160334 0.308619 7 RPTOR 4.296497 0.186804 23

TRAK1 2.474917 0.412486 6 RIMBP2 3.736288 0.162447 23

COLECI 1 2.403608 0.400601 6 NCOR2 3.441629 0.149636 23

ARHGAP18 2.123442 0.353907 6 HOXB3 2.803838 0.121906 23 NXN 2.478876 0.107777 23 TRAPPC9 3.05946 0.382433 8 PRKCZ 3.156096 0.143459 22 POU6F2 2.759699 0.344962 8 SKI 4.063587 0.193504 21 TENM2 2.439959 0.304995 8 FRMD4A 2.151691 0.107585 20 MIR124-2HG 2.84696 0.406709 7 MAD1L1 10.73492 0.564996 19 MIR548H4 2.247968 0.321138 7 CASZ1 4.948578 0.260451 19 PBX1 2.68946 0.448243 6 SMG1P2 4.05105 0.213213 19 ARHGAP18 2.545821 0.424303 6

BOLA2 4.05105 0.213213 19 TRAK1 2.507813 0.417969 6 LOC613038 4.05105 0.213213 19 COLEC11 2.262199 0.377033 6 ZNF423 2.440375 0.128441 19 DNAJB6 2.220931 0.370155 6 ANKRD11 3.160691 0.175594 18 FBXL18 2.177682 0.362947 6 FOXK1 2.457772 0.136543 18 TSN AX-DISCI 4.397586 0.879517 5 TBX15 4.553703 0.267865 17 OTP 3.698275 0.739655 5 FOXP1 6.345018 0.396564 16 ARHGEF7 2.679132 0.535826 5 NAV2 3.64718 0.227949 16 IL17D 2.157772 0.431554 5 EBF3 2.315293 0.144706 16 SDK2 2.15301 0.430602 5

KNDC1 2.920987 0.194732 15 GSG1 2.673792 0.668448 4 BAIAP2 2.468116 0.164541 15 EXT1 2.545343 0.636336 4 SLX1B- PCSK9 2.268441 0.56711 4 SULT1A4 2.427079 0.161805

¹⁵ SLC25A22 2.812923 0.937641 3 SLX1A 2.427079 0.161805

¹⁵ LOC339874 2.524663 0.841554 3 LOC606724 2.427079 0.161805

¹⁵ DAGLB 2.358333 0.786111 3 NHX 2.350296 0.156686

¹⁵ ARMC2 2.267345 0.755782 3

NFATC1 2.289517 0.152634

¹⁵ RASGRP3 2.162284 0.720761 3 GLI2 2.25982 0.150655

¹⁵ CHTF18 4.034748 2.017374 2 ARHGEF10 3.657226 0.26123 14

KIF21B 2.700938 1.350469 2 GNG7 2.38778 0.170556 14

UHRF1 2.699644 1.349822 2 PRKAG2 2.314672 0.165334 14

TRIM65 2.5883 1.29415 2 MYT1L 5.2682 0.405246 13

UTRN 2.432967 1.216483 2 MSI2 2.388799 0.183754 13

KCNV2 2.747887 2.747887 1

MIRLET7BHG 5.382895 0.448575 12

ARL6IP6 2.674833 2.674833 1 ZC3H3 3.236118 0.269677 12

DDT 2.616198 2.616198 1 FBRSL1 2.620636 0.218386 1

¹? Z DNAJC27 2.40222 2.40222 1 CMIP 2.242281 0.186857 12 AKAP13 2.754346 0.275435 ¹⁰ TABLE 144: Cancer Type PGG LBX1-AS1 2.32726 0.232726 10 Gene site imp sum imp mean n NR5A2 2.32255 0.232255 10

PTPRN2 16.42773 0.200338 82 NBEA 2.279871 0.227987 10

PRDM16 14.89853 0.209838 71

KCNIP4 2.153457 0.215346 10

PCDHGA1 5.194941 0.08805 59 SND1 4.69454 0.521616 9

PCDHGA2 4.647119 0.081528 57 ADAMTS2 4.195005 0.466112 9

PCDHGA3 4.647119 0.086058 54 TSPAN9 4.119054 0.457673 9

PCDHGB1 4.647119 0.087681 53 CACNA2D4 3.496569 0.388508 9

PCDHGA4 4.647119 0.09112 51 MGMT 3.047656 0.338628 9

PCDHGB2 4.330733 0.088382 49 ATP11A 2.55549 0.283943 9

PCDHGA5 4.330733 0.092143 47 VRK2 4.823612 0.602952 8

PCDHGB3 4.014347 0.093357 43 DNMT3A 3.277359 0.40967 8

PCDHGA6 3.697961 0.092449 40

PPP2R2B 3.154545 0.394318

HDAC4 14.70154 0.397339 37 PCDHGA7 3.697961 0.099945 37 ARHGEF10 4.04954 0.289253 14 PAX6 9.396488 0.268471 35 PRKAG2 3.917642 0.279832 14 RBFOX3 6.943902 0.198397 35 MIR548F5 3.862537 0.275896 14 DIP2C 12.19685 0.381152 32 MSI2 6.451313 0.496255 13 PCDHGB5 3.697961 0.115561 32 MYT1L 5.457573 0.419813 13 PCDHGA9 3.697961 0.119289 31 GSE1 4.099706 0.315362 13 GALNT9 4.477861 0.165847 27 KIF26B 3.745511 0.288116 13 SHANK2 6.641535 0.255444 26 MIRLET7BHG 5.921882 0.49349 12 ADARB2 4.992732 0.192028 26 ZC3H3 4.467121 0.37226 12 AGAP1 7.522315 0.300893 25 ADGRD1 4.429613 0.369134 12 CAMTAI 6.748919 0.269957 25 MAML3 4.106386 0.342199 12 PDGFRA 6.040879 0.241635 25 FBRSL1 3.995056 0.332921 12 MEIS1 4.902005 0.20425 24 RAD51B 4.875002 0.443182 11 SATB2 4.458027 0.185751 24 CACNA1C 4.625387 0.42049 11 RPTOR 10.34906 0.449959 23 CTBP2 4.50338 0.409398 11 NCOR2 6.429091 0.279526 23 TBCD 4.406564 0.400597 11 NXN 4.656189 0.202443 23 ZC3H12D 3.972821 0.361166 11 INPP5A 4.200631 0.182636 23 TSPAN4 4.339881 0.433988 10 RIMBP2 4.182212 0.181835 23 ACOT7 4.068272 0.406827 10 PRKCZ 6.843107 0.31105 22 GAS7 3.926778 0.392678 10 SKI 6.831861 0.325327 21 SND1 7.366746 0.818527 9 SIM2 4.967137 0.23653 21 ADAMTS2 6.07964 0.675516 9 ZIC4 4.39678 0.20937 21 ATP11A 4.000599 0.444511 9 HOXA-AS3 4.352134 0.207244 21 TSPAN9 3.858385 0.428709 9 ABR 5.548789 0.277439 20 CACNA2D4 3.789858 0.421095 9 SDK1 5.017472 0.250874 20 SYNJ2 5.070239 0.63378 8 FRMD4A 4.471013 0.223551 20 MSRA 4.537357 0.56717 8 MAD1L1 13.69728 0.720909 19 CELF4 4.18234 0.522792 8 SMG1P2 6.309543 0.332081 19 DNMT3A 4.131511 0.516439 8 BOLA2 6.309543 0.332081 19 C19orf25 5.169302 0.738472 7 LOC613038 6.309543 0.332081 19 NAVI 4.871626 0.695947 7 CASZ1 5.556523 0.292449 19 RXRA 4.485045 0.640721 7 KCNQ1 5.062779 0.266462 19 GAK 4.428979 0.632711 7 ZNF423 4.958846 0.260992 19 FBXL18 5.28216 0.88036 6 FOXK1 8.033926 0.446329 18 COQ8A 3.727351 0.621225 6 ANKRD11 6.119522 0.339973 18 TSN AX-DISCI 5.16109 1.032218 5 HOXA3 4.581533 0.25453 18 ARHGEF7 3.877893 0.775579 5 SEPTIN9 3.734312 0.207462 18 DAGLB 3.770107 1.256702 3 OPCML 7.091087 0.417123 17 SLC25A10 3.948105 1.974052 2 FOXP1 8.141302 0.508831 16 GRTP1 3.86273 3.86273 1 EBF3 4.276513 0.267282 16 SORBS2 3.805694 0.237856 16 TABLE 145: Cancer Type PGNT GLI2 5.218606 0.347907 15 Gene site imp sum imp mean n BAIAP2 4.62765 0.30851 15 PTPRN2 9.670636 0.117935 82 ZBTB20 3.998661 0.266577 15 PRDM16 8.537715 0.12025 71 RPS6KA2 6.719789 0.479985 14 PCDHGA1 2.847474 0.048262 59 CUX1 5.035008 0.359643 14 PCDHGA2 2.847474 0.049956 57 C7orf50 4.943359 0.353097 14 PCDHGA3 2.847474 0.052731 54 PCDHGB1 2.847474 0.053726 53 BCL11B 2.919731 0.291973 10 PCDHGA4 2.847474 0.055833 51 CHST11 2.693586 0.269359 10 PCDHGB2 2.531088 0.051655 49 ACOT7 2.588561 0.258856 10 PCDHGA5 2.214702 0.047121 47 KLHL29 2.406422 0.240642 10 HDAC4 7.100621 0.191909 37 TSPAN4 2.280454 0.228045 10 PAX6 4.879341 0.13941 35 ATP11A 3.527614 0.391957 9 RBFOX3 2.850514 0.081443 35 KCNMA1 3.421058 0.380118 9 DIP2C 5.382506 0.168203 32 NOTCH1 3.228355 0.358706 9 SOX2-OT 2.240588 0.077262 29 SND1 2.915387 0.323932 9 SHANK2 3.518303 0.135319 26 AXIN2 2.791748 0.310194 9 AGAP1 5.469833 0.218793 25 TSPAN9 2.749423 0.305491 9 CAMTAI 4.959348 0.198374 25 ASAP1 2.576769 0.286308 9 MEIS1 2.463832 0.10266 24 ADGRB1 2.478578 0.275398 9 RPTOR 6.32875 0.275163 23 KCNH2 2.303838 0.255982 9 PRKCZ 3.144122 0.142915 22 BAHCC1 3.218199 0.402275 8 SKI 5.674442 0.270212 21 MSRA 3.205927 0.400741 8 SIM2 2.861346 0.136255 21 GRIK2 2.896612 0.362077 8 FRMD4A 5.798668 0.289933 20 RORA 2.826827 0.353353 8 MAD1L1 6.815063 0.358688 19 SYNJ2 2.665264 0.333158 8 ZNF423 5.541159 0.29164 19 RGS20 2.654073 0.331759 8 SMG1P2 3.766805 0.198253 19 DNMT3A 2.543642 0.317955 8 BOLA2 3.766805 0.198253 19 LINC00311 2.510965 0.313871 8 LOC613038 3.766805 0.198253 19 DPP6 2.350778 0.293847 8 SEPTIN9 3.855717 0.214207 18 ARHGAP22 2.221081 0.277635 8 FOXK1 3.789053 0.210503 18 DUSP6 3.995818 0.570831 7 MCF2L 2.451037 0.136169 18 NAVI 3.539936 0.505705 7 TBC1D16 2.251131 0.125063 18 LINC00461 2.956634 0.422376 7 OPCML 6.251591 0.367741 17 FBXL18 2.998454 0.499742 6 TBX15 2.563061 0.150768 17 COQ8A 2.471408 0.411901 6 SORBS2 4.230863 0.264429 16 FAM181A 2.180075 0.363346 6 FOXP1 3.326476 0.207905 16 CACNA2D3 2.170913 0.361819 6 NAV2 2.86515 0.179072 16 RUNDC3A 4.022233 0.804447 5 GLI2 8.035971 0.535731 15 TSN AX-DISCI 3.170826 0.634165 5 ZBTB20 2.977912 0.198527 15 ARHGEF7 2.536285 0.507257 5 KIRREL3 2.341256 0.156084 15 RBMS3 2.939999 0.735 4 IQSEC1 3.602837 0.257345 14 LINC00856 2.41614 0.604035 4 CUX1 2.624781 0.187484 14 DINA 2.32145 0.580363 4 MYT1L 3.361744 0.258596 13 CORO2B 2.251054 0.562763 4 MSI2 3.038353 0.233719 13 DAGLB 2.701334 0.900445 3 RFX4 2.731063 0.210082 13 RGL1 2.50912 0.836373 3 MIR9-3HG 2.453844 0.188757 13 LOXL3 2.453125 0.817708 3 CMIP 2.524447 0.210371 12 GRIN2B 2.211003 0.737001 3 ADGRD1 2.355411 0.196284 12 SOXIO 4.516727 2.258363 2 MEIS2 2.345753 0.195479 12 COROIC 2.939766 1.469883 2 MIRLET7BHG 2.28207 0.190173 12 SLC25A10 2.621386 1.310693 2 RAD51B 3.396644 0.308786 11 SMURF1 2.53297 1.266485 2 LBX1-AS1 3.297492 0.329749 10 DUSP7 3.308439 3.308439 1 SH3RF3 3.275721 0.327572 10 TABLE 146: Cancer Type PIN_CYT GPC6 2.079566 0.231063 9

Gene site imp sum imp mean CACNA2D4 1.944875 0.216097 9

PTPRN2 1.741058 0.021232

AXIN2 1.744232 0.193804 9

PRDM16 3.334924 0.046971

ATP11A 1.715972 0.190664 9 HDAC4 5.936564 0.160448

SSBP3 1.468395 0.163155 9 RBFOX3 1.518927 0.043398

VRK2 2.977551 0.372194 8 GALNT9 2.24148 0.083018 27 PPP2R2B 2.61938 0.327422 8

CAMTAI 3.032036 0.121281 25 DNMT3A 2.384503 0.298063 8 AGAP1 2.468052 0.098722 25 NR2E1 1.935458 0.241932 8 PDGFRA 1.916142 0.076646 25 DLEU1 1.926288 0.240786 8 INPP5A 3.35832 0.146014 23 MCIDAS 1.619196 0.202399 8

RIMBP2 2.847474 0.123803 23 AFF3 1.575851 0.196981 8

RPTOR 2.690902 0.116996 23 NAVI 2.3868 0.340971 7

NXN 1.606425 0.069845 23 MIR548H4 1.755887 0.250841 7

PRKCZ 2.295075 0.104322 22 LDLRAD4 1.680758 0.240108 7

SKI 2.26782 0.107991 21 NRG1 1.569307 0.224187 7

SIM2 1.479891 0.070471 21 PACRG 1.542894 0.220413 7

ABR 2.010051 0.100503 20 TRAK1 2.55303 0.425505 6

MAD1L1 7.546451 0.397182 19 CRADD 2.177719 0.362953 6

SMG1P2 2.546606 0.134032 19 CCDC85C 1.924137 0.32069 6 BOLA2 2.546606 0.134032 19 PRKCE 1.78586 0.297643 6 LOC613038 2.546606 0.134032 19 HIVEP3 1.765887 0.294315 6 CASZ1 1.756619 0.092454 19 FBXL18 1.574633 0.262439 6

ANKRD11 1.688539 0.093808 18 STK24 1.44151 0.240252 6

BAIAP2 3.160719 0.210715 15 TSN AX-DISCI 3.510266 0.702053 5

GLI2 1.781405 0.11876 15 VAV2 2.269564 0.453913 5

KNDC1 1.753354 0.11689 15 SNX29 1.787393 0.357479 5

KIRREL3 1.430049 0.095337 15 SCOC 1.782484 0.356497 5 CUX1 2.227876 0.159134 14 ARHGEF7 1.750087 0.350017 5 MIR548F5 1.83105 0.130789 14 SDK2 1.669768 0.333954 5 IQSEC1 1.792469 0.128034 14 BCAR1 1.634295 0.326859 5

ARHGEF10 1.58193 0.112995 14 PARD3 1.515853 0.303171 5

MYT1L 2.873446 0.221034 13 EXT1 2.145258 0.536314 4

MSI2 2.667305 0.205177 13 EML1 1.453677 0.363419 4

RFX4 2.219412 0.170724 13 DAGLB 2.341444 0.780481 3

GSE1 1.898316 0.146024 13 SLC25A22 1.992387 0.664129 3

ZC3H3 2.480448 0.206704 12 LOC339874 1.933398 0.644466 3

CMIP 2.010323 0.167527 12 BFSP2 1.802792 0.600931 3

TBCD 1.497155 0.136105 11 CHTF18 3.607335 1.803668 2 ACOT7 1.987582 0.198758 10 TRIM65 2.240251 1.120126 2 LBX1-AS1 1.898316 0.189832 10 UHRF1 1.879138 0.939569 2 AUTS2 1.756206 0.175621 10 DISCI 1.837896 0.918948 2

CHST11 1.58193 0.158193 10 ERI3 1.784265 0.892132 2

ETS1 1.459477 0.145948 10 KIF21B 1.741481 0.87074 2

SND1 4.04963 0.449959 9 SLC25A10 1.56997 0.784985 2 ADAMTS2 2.74312 0.304791 9 KCNV2 2.351678 2.351678 1 MGMT 2.635724 0.292858 9 ARL6IP6 2.349183 2.349183 1 TSPAN9 2.101132 0.233459 9 DNAJC27 2.020613 2.020613 1 DDT 1.965552 1.965552 1 HBG2 3.055271 0.179722 17 GTF2E2 1.722938 1.722938 1 FOXP1 5.64517 0.352823 16 DLG4 1.600331 1.600331 1 GLI2 3.31874 0.221249 15 SMAGP 1.589762 1.589762 1 ZBTB20 2.717999 0.1812 15 CAMK4 1.487366 1.487366 1 MOB2 3.821702 0.272979 14 CPEB4 1.450927 1.450927 1 C7orf50 3.812754 0.27234 14

CUX1 3.309291 0.236378 14

TABLE 147: Cancer Type PIN_RB MYT1L 5.914172 0.454936 13 Gene site imp sum imp mean MSI2 5.606145 0.431242 13 PTPRN2 6.807633 0.08302

GSE1 3.507548 0.269811 13 PRDM16 8.820872 0.124238

RFX4 3.292323 0.253256 13 PCDHGA1 3.070622 0.052044

HOXC4 3.167416 0.243647 13 PCDHGA2 3.070622 0.053871

FBRSL1 3.437212 0.286434 12 PCDHGA3 3.070622 0.056863

ZC3H3 2.976908 0.248076 12 PCDHGB1 2.754236 0.051967

ADGRD1 2.819879 0.23499 12 PCDHGA4 2.754236 0.054005

VGLL4 3.617583 0.328871 11 PCDHGB2 2.754236 0.056209

CTBP2 3.607351 0.327941 11 HDAC4 10.06115 0.271923

RAD51B 3.084489 0.280408 11 PCDHGA7 2.754236 0.074439

AUTS2 3.876295 0.38763 10 RBFOX3 7.194733 0.205564

AKAP13 3.807734 0.380773 10 PAX6 4.643137 0.132661

LBX1-AS1 3.365908 0.336591 10 PCDHGB4 2.754236 0.078692

ANKS1B 3.045053 0.304505 10 PCDHGA8 2.754236 0.078692

ETS1 2.974553 0.297455 10 DIP2C 5.631461 0.175983

KLHL29 2.870455 0.287046 10 PCDHGB5 2.754236 0.08607

ACOT7 2.7672 0.27672 10 PCDHGA9 2.754236 0.088846

SH3RF3 2.758512 0.275851 10 GALNT9 4.351717 0.161175

SND1 6.161496 0.684611 9 SHANK2 2.77855 0.106867

ADAMTS2 5.206113 0.578457 9 AGAP1 7.896254 0.31585 25 ATP11A 4.405099 0.489455 9 CAMTAI 5.377977 0.215119 25 TSPAN9 4.084057 0.453784 9 RPTOR 7.070887 0.30743 23 CACNA2D4 3.871799 0.4302 9 NXN 6.481997 0.281826 23 AXIN2 3.679095 0.408788 9 INPP5A 6.462468 0.280977 23 GPC6 2.841096 0.315677 9 NCOR2 4.823809 0.209731 23 TRAPPC12 2.810486 0.312276 9 HOXB3 3.182751 0.13838 23 PACS2 2.7717 0.307967 9 PRKCZ 4.531267 0.205967 22 VRK2 5.571942 0.696493 8 SKI 5.140933 0.244806 21 PPP2R2B 4.519998 0.565 8 MAD1L1 11.37283 0.59857 19 MSRA 3.153244 0.394156 8 KCNQ1 4.609785 0.24262 19 LHX4 3.040615 0.380077 8 CASZ1 4.242069 0.223267 19 DNMT3A 2.998912 0.374864 8 CFAP46 3.058498 0.160974 19 ASPSCR1 2.759335 0.344917 8 SEPTIN9 4.156349 0.230908 18 PITPNC1 3.392984 0.484712 7 TBC1D16 4.078198 0.226567 18 VPS13D 3.215465 0.459352 7 FOXK1 3.599246 0.199958 18 MIR548H4 2.873085 0.410441 7 RBFOX1 2.905804 0.161434 18 NAVI 2.784796 0.397828 7 TBX15 3.849284 0.226428 17 TRAK1 2.702981 0.450497 6 PAX6-AS1 3.403635 0.200214 17 TSNAX-DISC1 4.436183 0.887237 5 RCN1 3.403635 0.200214 17 ARHGEF7 3.758143 0.751629 5 SDK2 3.042551 0.60851 5 HOXB3 3.719388 0.161713 23 CPEB1-AS1 2.902565 0.580513 5 PRKCZ 5.345998 0.243 22 GSG1 3.067506 0.766877 4 SKI 7.127028 0.339382 21 EXT1 2.905124 0.726281 4 SDK1 6.179441 0.308972 20 CCDC167 2.885096 0.961699 3 MAD1L1 12.31632 0.648227 19 DAGLB 2.788926 0.929642 3 CASZ1 5.175372 0.272388 19 CHTF18 4.526632 2.263316 2 SMG1P2 4.984239 0.262328 19 UHRF1 2.829708 1.414854 2 BOLA2 4.984239 0.262328 19 TRIM65 2.721111 1.360556 2 LOC613038 4.984239 0.262328 19 ANKLE2 2.696564 1.348282 2 KCNQ1 4.91896 0.258893 19 KCNV2 3.041097 3.041097 1 ZNF423 3.344899 0.176047 19 DDT 2.931778 2.931778 1 FOXK1 7.797997 0.433222 18 ARL6IP6 2.85111 2.85111 1 MCF2L 5.436094 0.302005 18

TBC1D16 5.1288 0.284933 18

Cancer Type

TABLE 148: ANKRD11 4.182539 0.232363 18 PITAD_ACTH

OPCML 6.113981 0.359646 17

Gene site imp sum imp mean n

PAX6-AS1 4.225622 0.248566 17 PTPRN2 19.17421 0.233832 82

RCN1 4.225622 0.248566 17 PRDM16 9.122084 0.12848 71

HBG2 4.047264 0.238074 17 PCDHGA1 5.542064 0.093933 59

FOXP1 7.553211 0.472076 16 PCDHGA2 5.138416 0.090148 57

GLI2 3.75433 0.250289 15 PCDHGA3 4.82203 0.089297 54

BAIAP2 3.658439 0.243896 15 PCDHGB1 4.505644 0.085012 53

CUX1 6.189493 0.442107 14 PCDHGA4 3.741984 0.073372 51

IQSEC1 5.463401 0.390243 14 PCDHGB2 3.741984 0.076367 49

PRKAG2 5.378718 0.384194 14 PCDHGA5 3.425598 0.072885 47

RPS6KA2 4.50413 0.321724 14 PCDHGB3 3.425598 0.079665 43

C7orf50 3.729526 0.266395 14 PCDHGA6 3.741984 0.09355 40

MIR548F5 3.263675 0.23312 14 HDAC4 12.09289 0.326835 37

GSE1 5.168568 0.397582 13 PCDHGA7 3.425598 0.092584 37

MSI2 4.73444 0.364188 13 PAX6 10.45792 0.298798 35

MYT1L 3.517965 0.270613 13 RBFOX3 7.596219 0.217035 35

CMIP 6.7903 0.565858 12 PCDHGB4 3.425598 0.097874 35

FBRSL1 5.656115 0.471343 12 PCDHGA8 3.425598 0.097874 35

GNA12 4.95367 0.412806 12 DIP2C 6.007771 0.187743 32

ZC3H3 4.462214 0.371851 12 PCDHGB5 3.425598 0.10705 32

TNS3 4.14463 0.345386 12 GALNT9 3.861279 0.14301 27

ADGRD1 3.764111 0.313676 12 SHANK2 6.779732 0.260759 26

CTNNA2 3.461448 0.288454 12 ADARB2 3.476029 0.133693 26

CACNA1C 4.810843 0.437349 11 AGAP1 11.98423 0.479369 25

AKAP13 4.99846 0.499846 10 CAMTAI 7.135416 0.285417 25

ACOT7 4.708338 0.470834 10 PDGFRA 4.281752 0.17127 25

TP73 3.68141 0.368141 10 MEIS1 3.580472 0.149186 24

MAML2 3.65158 0.365158 10 RPTOR 12.00629 0.522013 23

CHST11 3.513526 0.351353 10 NCOR2 5.886835 0.255949 23

RGS12 3.455723 0.345572 10 RIMBP2 5.040982 0.219173 23

TSPAN4 3.341946 0.334195 10 INPP5A 4.731128 0.205701 23

STK32C 3.235654 0.323565 10 NXN 4.077344 0.177276 23

SND1 7.617901 0.846433 9 ATP11A 7.281694 0.809077 9 PRKCZ 6.175028 0.280683 22 ADAMTS2 4.790399 0.532267 9 SKI 9.224385 0.439256 21 SSBP3 3.831948 0.425772 9 SDK1 5.625445 0.281272 20 TSPAN9 3.532304 0.392478 9 ABR 4.106215 0.205311 20 CPNE4 3.276085 0.364009 9 FRMD4A 3.90104 0.195052 20 SYNJ2 3.941616 0.492702 8 MAD1L1 12.00363 0.63177 19 VRK2 3.743569 0.467946 8 CASZ1 6.659107 0.350479 19 DNMT3A 3.547929 0.443491 8 SMG1P2 4.54033 0.238965 19 TRAPPC9 3.488493 0.436062 8 BOLA2 4.54033 0.238965 19 MSRA 3.329945 0.416243 8 LOC613038 4.54033 0.238965 19 NAVI 4.903574 0.700511 7 KCNQ1 4.009293 0.211015 19 C19orf25 4.106974 0.586711 7 ZNF423 3.732505 0.196448 19 ITPK1 3.776112 0.539445 7 FOXK1 7.302009 0.405667 18 GAK 3.584731 0.512104 7 ANKRD11 5.736872 0.318715 18 SLC22A18AS 3.579437 0.596573 6 SEPTIN9 4.774176 0.265232 18 COQ8A 3.330149 0.555025 6 TBC1D16 3.603049 0.200169 18 FBXL18 3.249672 0.541612 6 MCF2L 3.522413 0.19569 18 ARHGEF7 3.920434 0.784087 5 FOXP1 6.732947 0.420809 16 AP2A2 3.275345 0.655069 5 NAV2 5.31472 0.33217 16 DAGLB 3.283237 1.094412 3 KIRREL3 5.025011 0.335001 15 CHTF18 3.458887 1.729444 2 GLI2 4.239524 0.282635 15

NFIX 3.86524 0.257683 15

TABLE 149: Cancer Type PITAD_GON ZBTB20 3.610608 0.240707 15 Gene site imp sum imp mean n RPS6KA2 6.927184 0.494799 14 PTPRN2 15.85732 0.193382 82 PRKAG2 5.894677 0.421048 14 PRDM16 12.4923 0.175948 71 CUX1 4.79929 0.342806 14 PCDHGA1 3.525377 0.059752 59 C7orf50 4.465788 0.318985 14 PCDHGA2 3.525377 0.061849 57 MSI2 6.420891 0.493915 13 PCDHGA3 3.841763 0.071144 54 GSE1 4.474357 0.344181 13 PCDHGB1 3.525377 0.066517 53 MYT1L 3.477324 0.267486 13 PCDHGA4 3.525377 0.069125 51 CMIP 6.222363 0.51853 12 PCDHGA5 3.525377 0.075008 47 ZC3H3 4.932071 0.411006 12 PCDHGB3 3.525377 0.081986 43 FBRSL1 4.480177 0.373348 12 PCDHGA6 3.525377 0.088134 40 GNA12 4.310212 0.359184 12 HDAC4 13.45165 0.363558 37 ADGRD1 4.306251 0.358854 12 PAX6 7.585868 0.216739 35 MIRLET7BHG 3.922727 0.326894 12 RBFOX3 6.519025 0.186258 35 TNS3 3.742759 0.311897 12 DIP2C 9.028569 0.282143 32 MAML3 3.663325 0.305277 12 GALNT9 6.832147 0.253042 27 CTBP2 4.493697 0.408518 11 SHANK2 5.430148 0.208852 26 RAD51B 3.947974 0.358907 11 ADARB2 3.809447 0.146517 26 AKAP13 5.092606 0.509261 10 AGAP1 11.92867 0.477147 25 TSPAN4 4.565719 0.456572 10 CAMTAI 6.389659 0.255586 25 ACOT7 4.106723 0.410672 10 RPTOR 12.3548 0.537165 23 CHST11 3.920325 0.392032 10 NCOR2 7.588369 0.329929 23 SND1 6.453557 0.717062 9 NXN 7.13107 0.310047 23 ATP11A 6.421095 0.713455 9 INPP5A 5.005737 0.217641 23 ADAMTS2 5.474662 0.608296 9 RIMBP2 4.987523 0.216849 23 CACNA2D4 4.585301 0.509478 9 TRAPPCI 2 4.327882 0.480876 9 SHANK2 3.848093 0.148004 26 KCNH2 3.714634 0.412737 9 AGAP1 6.120262 0.24481 25 AXIN2 3.553005 0.394778 9 CAMTAI 5.518994 0.22076 25 ASAP1 3.381626 0.375736 9 RPTOR 8.079101 0.351265 23 VRK2 5.230801 0.65385 8 NCOR2 5.196516 0.225935 23 SYNJ2 4.191457 0.523932 8 NXN 4.64885 0.202124 23 MSRA 4.182854 0.522857 8 INPP5A 3.51989 0.153039 23 PPP2R2B 4.044417 0.505552 8 RIMBP2 3.375559 0.146763 23 LINC00311 3.788514 0.473564 8 PCDHGA11 2.531088 0.110047 23 AFF3 3.6804 0.46005 8 PRKCZ 6.614886 0.300677 22 MACROD1 3.556003 0.4445 8 SKI 7.920704 0.377176 21 DNMT3A 3.45295 0.431619 8 SDK1 6.738556 0.336928 20 VPS 13D 4.166295 0.595185 7 FRMD4A 4.832466 0.241623 20 MIR548H4 3.94891 0.56413 7 ABR 3.304098 0.165205 20 GAK 3.462955 0.494708 7 MAD1L1 9.049154 0.476271 19 NAVI 3.447503 0.4925 7 CFAP46 3.751009 0.197422 19 RXRA 3.371265 0.481609 7 KCNQ1 3.581092 0.188479 19 CRADD 5.277152 0.879525 6 SMG1P2 2.951013 0.155316 19 FBXL18 4.201993 0.700332 6 BOLA2 2.951013 0.155316 19 SLC22A18AS 3.40043 0.566738 6 LOC613038 2.951013 0.155316 19 TSN AX-DISCI 5.18505 1.03701 5 FOXK1 6.068214 0.337123 18 ARHGEF7 4.85859 0.971718 5 ANKRD11 3.188781 0.177154 18 AP2A2 4.302334 0.860467 5 OPCML 4.582781 0.269575 17 RUNDC3A 4.192518 0.838504 5 FOXP1 5.549672 0.346855 16 FAM53B 3.451051 0.69021 5 EBF3 3.280538 0.205034 16 DENND2B 3.757544 0.939386 4 NAV2 3.09908 0.193693 16 CHTF18 3.908401 1.954201 2 GLI2 4.199646 0.279976 15

ANKLE2 3.602716 1.801358 2 ZBTB20 3.087348 0.205823 15

KIRREL3 3.044419 0.202961 15

TABLE 150: Cancer Type PITAD_PRL ARHGEF10 4.468218 0.319158 14 Gene site imp sum imp mean n CUX1 3.573829 0.255273 14

PTPRN2 10.92341 0.133212 82 PRKAG2 2.858734 0.204195 14 PRDM16 17.84186 0.251294 71 RPS6KA2 2.641252 0.188661 14 PCDHGA1 2.870937 0.04866 59 RFX4 3.881895 0.298607 13 PCDHGA2 3.480246 0.061057 57 GSE1 2.848839 0.219141 13 PCDHGA3 3.16386 0.05859 54 TNS3 4.855949 0.404662 12 PCDHGB1 3.16386 0.059695 53 CMIP 4.550525 0.37921 12 PCDHGA4 3.16386 0.062036 51 FBRSL1 3.956517 0.32971 12 PCDHGB2 3.16386 0.064569 49 ZC3H3 3.604721 0.300393 12 PCDHGA5 3.16386 0.067316 47 ADGRD1 2.568643 0.214054 12 PCDHGB3 2.531088 0.058863 43 SLC38A10 3.41188 0.310171 11 HDAC4 9.680024 0.261622 37 RAD51B 3.317418 0.301583 11 PCDHGA7 2.531088 0.068408 37 CACNA1C 3.123701 0.283973 11 RBFOX3 9.268066 0.264802 35 TBCD 2.849245 0.259022 11 PAX6 4.731554 0.135187 35 AKAP13 3.419932 0.341993 10 DIP2C 4.837681 0.151178 32 CHST11 3.350029 0.335003 10 GALNT9 3.34937 0.124051 27 KLHL29 3.337935 0.333793 10 ADARB2 4.48861 0.172639 26 ACOT7 2.874701 0.28747 10 TSPAN4 2.770126 0.277013 10 ADARB2 2.548463 0.098018 26 NBEA 2.651581 0.265158 10 AGAP1 6.683091 0.267324 25 KCNH2 5.639847 0.62665 9 CAMTAI 4.304365 0.172175 25 ATP11A 4.264072 0.473786 9 PDGFRA 2.864174 0.114567 25

KAZN 3.524144 0.391572 9 SATB2 2.826366 0.117765 24

SND1 3.523703 0.391523 9 RPTOR 6.468808 0.281253 23

AXIN2 3.172624 0.352514 9 NXN 4.209588 0.183026 23

TSPAN9 2.975484 0.330609 9 NCOR2 4.094608 0.178026 23 CACNA2D4 2.82727 0.314141 9 INPP5A 2.309147 0.100398 23 PRDM8 5.089011 0.636126 8 PRKCZ 3.7406 0.170027 22 TRAPPC9 3.2271 0.403387 8 SKI 5.727075 0.272718 21

SYNJ2 3.139206 0.392401 8 SDK1 5.562399 0.27812 20

AFF3 2.851203 0.3564 8 ABR 5.144643 0.257232 20

TENM2 2.744674 0.343084 8 FRMD4A 2.272281 0.113614 20

VRK2 2.73707 0.342134 8 MAD1L1 7.497513 0.394606 19

CDH4 2.54359 0.317949 8 CFAP46 2.668222 0.140433 19

GAK 3.126348 0.446621 7 CASZ1 2.446375 0.128757 19

RXRA 3.028493 0.432642 7 TBC1D16 2.486189 0.138122 18

BTBD11 2.810303 0.401472 7 MCF2L 2.353362 0.130742 18

NAVI 2.770524 0.395789 7 HBG2 2.993198 0.17607 17

MYO 16 3.435703 0.572617 6 OPCML 2.840465 0.167086 17

FBXL18 3.395152 0.565859 6 FOXP1 2.89223 0.180764 16 CRADD 2.952569 0.492095 6 NAV2 2.421048 0.151316 16 C10orf90 2.779358 0.463226 6 BAIAP2 4.257113 0.283808 15 ANKS1A 2.620006 0.436668 6 GLI2 3.74488 0.249659 15

TSN AX-DISCI 3.402578 0.680516 5 LRMDA 3.245548 0.21637 15

ARHGEF7 3.189843 0.637969 5 KNDC1 2.746169 0.183078 15

AP2A2 2.800656 0.560131 5 ARHGEF10 3.637593 0.259828 14

NPHP4 2.718223 0.543645 5 CUX1 3.39947 0.242819 14

BCAR1 2.691854 0.538371 5 IQSEC1 2.79111 0.199365 14 STON1- MIR548F5 2.63285 0.188061 14 GTF2A1L 2.564437 0.512887

⁵ CACNA1H 2.559313 0.182808 14 GSG1 2.88867 0.722167 4 GNG7 2.308896 0.164921 14

DICER1 2.718233 0.906078 3 RPS6KA2 2.262334 0.161595 14

ERI3 2.857462 1.428731 2 MSI2 4.443542 0.341811 13 SLC25A10 2.623878 1.311939

Cancer Type ² RFX4 3.866979 0.29746 13

TABLE 151: PITAD_STH DENSE1 GSE1 3.189499 0.245346 13

Gene site imp sum imp mean _n CMIP 4.92255 0.410213 12

PTPRN2 10.27842 0.125347 82 FBRSL1 3.633172 0.302764 12

PRDM16 11.33441 0.15964 71 TNS3 3.298056 0.274838 12

HDAC4 12.51061 0.338124 37 ZC3H3 2.913897 0.242825 12 RBFOX3 10.33693 0.295341 35 MEGF6 2.726935 0.227245 12 PAX6 7.931791 0.226623 35 ADGRD1 2.691479 0.22429 12 DIP2C 4.742445 0.148201 32 CTNNA2 2.554934 0.212911 12

SOX2-OT 3.740453 0.128981 29 GNA12 2.465474 0.205456 12 GALNT9 2.654267 0.098306 27 CTBP2 3.160477 0.287316 11 SHANK2 6.240972 0.240037 26 TSPAN4 3.575588 0.357559 10 TP73 2.661734 0.266173 10 LMF1 2.552461 0.255246 10 PRDM16 18.99297 0.267507 71

RGS12 2.538724 0.253872 10 PCDHGA1 7.882026 0.133594 59

BCL11B 2.524088 0.252409 10 PCDHGA2 7.437325 0.130479 57

ACOT7 2.448677 0.244868 10 PCDHGA3 6.804553 0.12601 54

TRAPPCI 2 4.772877 0.53032 9 PCDHGB1 6.804553 0.128388 53

ATP11A 4.469811 0.496646 9 PCDHGA4 6.804553 0.133423 51

CACNA2D4 4.395031 0.488337 9 PCDHGB2 6.488167 0.132412 49

KCNH2 4.206333 0.46737 9 PCDHGA5 6.171781 0.131314 47

KAZN 3.652715 0.405857 9 PCDHGB3 5.539009 0.128814 43

ADAMTS2 3.514329 0.390481 9 PCDHGA6 4.458758 0.111469 40

SND1 3.276925 0.364103 9 HDAC4 14.43817 0.390221 37

PRDM8 5.748341 0.718543 8 PCDHGA7 4.458758 0.120507 37

MSRA 3.95826 0.494783 8 RBFOX3 9.902052 0.282916 35

AFF3 3.37929 0.422411 8 PAX6 7.951795 0.227194 35

TRAPPC9 3.052095 0.381512 8 PCDHGB4 4.142372 0.118353 35

DNMT3A 2.812413 0.351552 8 PCDHGA8 4.142372 0.118353 35

MACROD1 2.75926 0.344908 8 DIP2C 5.458819 0.170588 32

PPP2R2B 2.59441 0.324301 8 PCDHGB5 4.775144 0.149223 32

VRK2 2.574109 0.321764 8 PCDHGA9 4.277945 0.137998 31

CELF4 2.360714 0.295089 8 PCDHGB6 4.594331 0.158425 29

RXRA 2.621084 0.374441 7 SOX2-OT 4.381182 0.151075 29

LHPP 2.36559 0.337941 7 PCDHGA10 4.277945 0.152784 28

ITPKB 2.339152 0.334165 7 GALNT9 3.40227 0.12601 27

CRADD 3.538289 0.589715 6 ADARB2 8.255536 0.317521 26

FBXL18 3.002949 0.500492 6 SHANK2 6.470832 0.248878 26

C10orf90 2.578042 0.429674 6 AGAP1 6.24246 0.249698 25

GRK5 2.534788 0.422465 6 CAMTAI 4.805518 0.192221 25

TRAK1 2.488331 0.414722 6 PDGFRA 4.758412 0.190336 25

TSN AX-DISCI 3.761812 0.752362 5 PCDHGB7 3.961559 0.165065 24

TEAD1 2.984378 0.596876 5 RPTOR 9.501283 0.413099 23

AP2A2 2.937556 0.587511 5 NCOR2 8.322187 0.361834 23

VAV2 2.282187 0.456437 5 NXN 6.27367 0.272768 23

GSG1 2.90875 0.727187 4 HOXB3 6.035781 0.262425 23

SCG5 2.765914 0.691479 4 RIMBP2 4.314302 0.187578 23

LINC00856 2.394591 0.598648 4 PCDHGA11 3.645173 0.158486 23

DAGLB 3.118583 1.039528 3 PRKCZ 6.665697 0.302986 22

GNAS 2.364452 0.788151 3 SKI 6.064214 0.288772 21

TRIM65 2.832082 1.416041 2 ZIC4 3.876225 0.184582 21

ERI3 2.578356 1.289178 2 ABR 4.708382 0.235419 20

GALK2 2.572375 1.286187 2 FRMD4A 4.646882 0.232344 20

SLC25A10 2.480457 1.240229 2 SDK1 4.557783 0.227889 20

CACNA1D 2.393238 1.196619 2 SMG1P2 6.376076 0.335583 19 DISCI 2.322679 1.16134 2 BOLA2 6.376076 0.335583 19

LOC613038 6.376076 0.335583 19

TABLE 152: Cancer Type MAD1L1 6.313695 0.3323 19

PITAD_STH_DENSE2 ZNF423 5.971828 0.314307 19

Gene site imp sum imp mean ⁿ KCNQ1 4.104771 0.216041 19

PTPRN2 15.26347 0.18614 ⁸² CASZ1 3.518041 0.18516 19 FOXK1 5.737213 0.318734 18 TRAPPC9 3.415412 0.426927 8 ANKRD11 5.407599 0.300422 18 AP2A2 3.729851 0.74597 5 SEPTIN9 4.293449 0.238525 18 TSN AX-DISCI 3.398771 0.679754 5 HOXA3 3.872172 0.215121 18 DAGLB 3.850281 1.283427 3 RBFOX1 3.745567 0.208087 18 OPCML 6.021126 0.354184 I⁷ TABLE 153: Cancer Type

PIT AD_STH_SP ARSE PAX6-AS1 3.880785 0.228281 17

Gene site imp sum imp mean n RCN1 3.880785 0.228281

_ PTPRN2 13.40369 0.16346 82 FOXP1 6.844759 0.427797 16

_ PRDM16 12.82439 0.180625 71 NAV2 5.02674 0.314171 16

PCDHGB1 3.352538 0.063255 53 EBF3 3.502228 0.218889 16

, , PCDHGB2 3.352538 0.068419 49 SORBS2 3.432584 0.214536 16

PCDHGB3 3.352538 0.077966 43 GLI2 6.503929 0.433595

PCDHGA6 3.352538 0.083813 40 ZBTB20 4.605473 0.307032

SLX1B- HDAC4 16.85468 0.455532 37 SULT1A4 3.622028 0.241469 15 RBFOX3 14.15142 0.404326 35 SLX1A 3.622028 0.241469 15 PAX6 9.704796 0.27728 35 LOC606724 3.622028 0.241469 15 DIP2C 8.826223 0.275819 32 BAIAP2 3.511134 0.234076 15 GALNT9 4.266274 0.15801 27 CACNA1H 4.422444 0.315889 14 SHANK2 7.465451 0.287133 26 CUX1 4.074849 0.291061 14 ADARB2 4.498758 0.173029 26

ARHGEF10 3.826456 0.273318 14 AGAP1 7.299004 0.29196 25 PRKAG2 3.716511 0.265465 14 CAMTAI 5.061254 0.20245 25 GSE1 5.515004 0.424231 13 PDGFRA 3.664179 0.146567 25 MSI2 5.261013 0.404693 13 SATB2 4.242972 0.176791 24 RFX4 5.115566 0.393505 13 RPTOR 11.09559 0.482417 23 SPTBN4 4.003012 0.307924 13 NCOR2 6.299198 0.273878 23 ZC3H3 4.158095 0.346508 12 INPP5A 6.283045 0.273176 23 CMIP 3.573186 0.297766 12 RIMBP2 5.67775 0.246859 23 CSMD1 3.466178 0.288848 12 NXN 5.0646 0.2202 23 CTBP2 4.442545 0.403868 11 HOXB3 3.682036 0.160089 23 RAD51B 3.346196 0.3042 11 PRKCZ 5.625055 0.255684 22 AKAP13 4.173414 0.417341 10 SKI 8.432147 0.401531 21 ACOT7 3.680344 0.368034 10 FRMD4A 5.518485 0.275924 20 IGF1R 3.578494 0.357849 10 SDK1 4.798074 0.239904 20 AUTS2 3.505236 0.350524 10 ABR 3.574142 0.178707 20 ATP11A 6.090584 0.676732 9 MAD1L1 9.960365 0.52423 19 CACNA2D4 5.851531 0.65017 9 ZNF423 5.497898 0.289363 19 TRAPPCI 2 4.298298 0.477589 9 SMG1P2 4.80028 0.252646 19 TSPAN9 4.152279 0.461364 9 BOLA2 4.80028 0.252646 19 KCNH2 4.010049 0.445561 9 LOC613038 4.80028 0.252646 19 ASAP1 3.541546 0.393505 9 KCNQ1 4.395199 0.231326 19 VRK2 5.539611 0.692451 8 CASZ1 3.608576 0.189925 19 PRDM8 5.353148 0.669143 8 FOXK1 8.166915 0.453717 18 PPP2R2B 4.450425 0.556303 8 ANKRD11 6.129595 0.340533 18 AFF3 4.31238 0.539048 8 TBC1D16 3.452645 0.191814 18

LINC00311 3.475399 0.434425 8 OPCML 5.462946 0.32135 17 MSRA 3.461528 0.432691 8 PAX6-AS1 4.304029 0.253178 17 RCN1 4.304029 0.253178 17 TRAPPC9 3.46805 0.433506 8 TBX15 3.435127 0.202066 17 RXRA 3.794134 0.542019 7 FOXP1 5.410888 0.338181 16 MIR548H4 3.691354 0.527336 7 NAV2 4.808997 0.300562 16 GAK 3.459276 0.494182 7 EBF3 4.764121 0.297758 16 TACC2 3.360077 0.480011 7 GLI2 6.997879 0.466525 15 CRADD 3.897166 0.649528 6 BAIAP2 5.692046 0.37947 15 FBXL18 3.662146 0.610358 6 KNDC1 4.077211 0.271814 15 TSN AX-DISCI 4.760379 0.952076 5 EMX2OS 3.885562 0.259037 15 AP2A2 4.038805 0.807761 5 SLX1B- GSG1 3.372484 0.843121 4 SULT1A4 3.659946 0.243996

DAGLB 3.99515 1.331717 3 SLX1A 3.659946 0.243996

CHTF18 3.713627 1.856814 2 LOC606724 3.659946 0.243996

ANKLE2 3.494424 1.747212 2 RPS6KA2 6.638682 0.474192

PRKAG2 6.132531 0.438038 14 TABLE 154: Cancer Type PITAD TSH ARHGEF10 4.643365 0.331669 14 Gene site imp sum imp mean n C7orf50 4.411775 0.315127 14 PTPRN2 9.40886 0.114742 82 IQSEC1 3.399586 0.242828 14 PRDM16 8.15208 0.114818 71 MIR548F5 3.376316 0.241165 14 PCDHGA1 3.588337 0.060819 59 MSI2 6.528839 0.502218 13 PCDHGA2 3.588337 0.062953 57 RFX4 5.217316 0.401332 13 PCDHGA3 3.588337 0.066451 54 MYT1L 4.046081 0.311237 13 PCDHGB1 3.588337 0.067704 53 GSE1 4.011884 0.308606 13 PCDHGA4 3.271951 0.064156 51 SPTBN4 3.601558 0.277043 13 PCDHGB2 2.955565 0.060318 49 CMIP 6.117375 0.509781 12 PCDHGA5 2.639179 0.056153 47 ZC3H3 5.444204 0.453684 12 PCDHGB3 2.758601 0.064154 43 TNS3 5.354996 0.44625 12 PCDHGA6 2.758601 0.068965 40 ADGRD1 5.26842 0.439035 12 HDAC4 10.52559 0.284475 37 FBRSL1 4.25555 0.354629 12 PCDHGA7 2.442215 0.066006 37 GNA12 3.503295 0.291941 12 PAX6 6.486982 0.185342 35 ZC3H12D 4.218637 0.383512 11 RBFOX3 3.830891 0.109454 35 SORCS2 3.698609 0.336237 11 PCDHGB4 2.442215 0.069778 35 AKAP13 4.70569 0.470569 10 PCDHGA8 2.442215 0.069778 35 ACOT7 3.971765 0.397176 10 DIP2C 7.394914 0.231091 32 TSPAN4 3.701377 0.370138 10 SHANK2 3.225038 0.12404 26 GAS7 3.366629 0.336663 10 ADARB2 2.658934 0.102267 26 ATP11A 5.825838 0.647315 9 AGAP1 7.263333 0.290533 25 SND1 5.535797 0.615089 9 CAMTAI 2.501118 0.100045 25 ADAMTS2 5.045606 0.560623 9 SATB2 2.786999 0.116125 24 TSPAN9 4.213473 0.468164 9 NXN 5.136839 0.223341 23 KCNH2 3.969071 0.441008 9 NCOR2 5.129748 0.223033 23 AXIN2 3.460817 0.384535 9 RPTOR 4.39311 0.191005 23 ASAP1 3.383619 0.375958 9 HOXB3 2.82018 0.122617 23 SLC22A18 3.362796 0.373644 9 PRKCZ 3.016776 0.137126 22 PRDM8 8.49027 1.061284 8 SKI 6.751464 0.321498 21 VRK2 4.955177 0.619397 8 SDK1 4.373052 0.218653 20 MSRA 4.39264 0.54908 8 FRMD4A 3.314172 0.165709 20 AFF3 4.035116 0.50439 8 ABR 2.657323 0.132866 20 MAD1L1 6.086807 0.320358 19 DLEU1 2.465536 0.308192 8 CASZ1 3.663955 0.19284 19 RGS20 2.391527 0.298941 8 CFAP46 2.797395 0.147231 19 MIR548H4 3.555513 0.50793 7 MCF2L 4.413719 0.245207 18 GAK 3.04005 0.434293 7 ANKRD11 2.864419 0.159134 18 NAVI 2.746391 0.392342 7 FOXK1 2.683913 0.149106 18 WWOX 2.399897 0.342842 7 TBC1D16 2.389987 0.132777 18 CRADD 3.84197 0.640328 6 OPCML 4.165582 0.245034 17 MYO16 3.041736 0.506956 6 HBG2 2.690066 0.158239 17 C10orf90 2.747828 0.457971 6 FOXP1 4.589209 0.286826 16 CCDC177 2.686978 0.44783 6 NAV2 2.811994 0.17575 16 FBXL18 2.558178 0.426363 6 EBF3 2.623072 0.163942 16 SLC22A18AS 2.501483 0.416914 6 GLI2 3.237879 0.215859 15 FMNL2 2.451875 0.408646 6 KIRREL3 2.622052 0.174803 15 TSN AX-DISCI 3.926508 0.785302 5 ARHGEF10 4.635274 0.331091 14 RUNDC3A 2.731088 0.546218 5 C7orf50 2.702027 0.193002 14 VAV2 2.390701 0.47814 5 TBX5 2.650681 0.189334 14 TRIM65 2.9884 1.4942 2 PRKAG2 2.608078 0.186291 14 ERI3 2.800313 1.400157 2 MOB2 2.462272 0.175877 14 DISCI 2.716734 1.358367 2 MIR548F5 2.406059 0.171861 14 SEC25A10 2.528998 1.264499 2 MSI2 3.551726 0.27321 13 GSE1 3.496661 0.268974 13 TABLE 155: Cancer Type PITUI RFX4 3.356876 0.258221 13 Gene site imp sum imp mean n CMIP 4.937844 0.411487 12 PTPRN2 16.35368 0.199435 82 FBRSL1 4.458035 0.371503 12 PRDM16 17.93226 0.252567 71 ZC3H3 3.146758 0.26223 12 PCDHGA1 5.085676 0.086198 59 ANAPC16 2.795207 0.25411 11 PCDHGA2 4.76929 0.083672 57 CTBP2 2.687582 0.244326 11 PCDHGA3 4.452904 0.082461 54 TSPAN4 3.957528 0.395753 10 PCDHGB1 4.452904 0.084017 53 ACOT7 2.81231 0.281231 10 PCDHGA4 4.452904 0.087312 51 RGS12 2.686067 0.268607 10 HDAC4 14.25155 0.385177 37 CHST11 2.678145 0.267815 10 PAX6 9.270981 0.264885 35 LMF1 2.640024 0.264002 10 RBFOX3 7.115698 0.203306 35 GAS7 2.375665 0.237566 10 DIP2C 13.48333 0.421354 32 ATP11A 3.874103 0.430456 9 SOX2-OT 8.123976 0.280137 29 CACNA2D4 3.353338 0.372593 9 GALNT9 4.640102 0.171856 27 AXIN2 3.323844 0.369316 9 SHANK2 4.865952 0.187152 26 SND1 3.224096 0.358233 9 AGAP1 11.49114 0.459645 25

TRAPPCI 2 3.075952 0.341772 9 CAMTAI 7.897637 0.315905 25 SLC22A18 2.797384 0.31082 9 PDGFRA 7.691037 0.307641 25 TSPAN9 2.676189 0.297354 9 MEIS1 4.320301 0.180013 24 PRDM8 6.328073 0.791009 8 RPTOR 12.62394 0.548867 23 AFF3 3.212441 0.401555 8 INPP5A 5.879629 0.255636 23 SYNJ2 3.208876 0.40111 8 NXN 5.407584 0.235112 23 VRK2 2.968834 0.371104 8 NCOR2 4.989473 0.216934 23 TMEM132D 2.829233 0.353654 8 HOXB3 4.541869 0.197473 23 MSRA 2.729491 0.341186 8 PRKCZ 6.302859 0.286494 22 DNMT3A 2.627419 0.328427 8 SKI 11.24166 0.535317 21 SDK1 6.244901 0.312245 20 SPON2 4.76136 0.432851 11 FRMD4A 5.904146 0.295207 20 CTBP2 4.157937 0.377994 11 ABR 5.17257 0.258628 20 ANAPC16 4.073549 0.370323 11 MAD1L1 11.27934 0.593649 19 TSPAN4 4.648571 0.464857 10

ZNF423 7.31106 0.384793 19 SND1 7.283474 0.809275 9

CASZ1 7.082702 0.372774 19 TSPAN9 6.014893 0.668321 9 SMG1P2 4.913495 0.258605 19 ATP11A 5.609787 0.62331 9 BOLA2 4.913495 0.258605 19 ADAMTS2 5.269587 0.58551 9 LOC613038 4.913495 0.258605 19 AXIN2 5.174351 0.574928 9

CFAP46 4.129305 0.217332 19 TRAPPCI 2 5.121161 0.569018 9 TBC1D16 6.475237 0.359735 18 NOTCH1 4.106032 0.456226 9 FOXK1 6.437992 0.357666 18 APBA2 4.026487 0.447387 9 ANKRD11 6.218053 0.345447 18 MSRA 4.601965 0.575246 8

SEPTIN9 5.672606 0.315145 18 DLEU1 4.408934 0.551117 8

MCF2L 5.129604 0.284978 18 LINC00311 4.229846 0.528731 8

HOXA3 4.503527 0.250196 18 NAVI 4.588824 0.655546 7 OPCML 4.540251 0.267074 17 LHPP 4.53367 0.647667 7 FOXP1 7.982608 0.498913 16 ITPK1 4.415617 0.630802 7 EBF3 4.580607 0.286288 16 GAK 4.379344 0.625621 7 NAV2 4.297127 0.26857 16 MIR548H4 4.350271 0.621467 7 GLI2 7.704142 0.513609 15 CXXC5 4.263329 0.609047 7

KIRREL3 6.256932 0.417129 15 FBXL18 5.388879 0.898147 6 SLX1B- KDM4B 4.607895 0.767983 6 SULT1A4 4.782061 0.318804

¹⁵ CRADD 4.43432 0.739053 6 SLX1A 4.782061 0.318804

¹⁵ SLC22A18AS 4.289809 0.714968 6

LOC606724 4.782061 0.318804

¹⁵ RUNDC3A 5.516918 1.103384 5 NHX 4.600342 0.306689

¹⁵ TSNAX-DISC1 5.126587 1.025317 5 LRMDA 4.532995 0.3022 ¹⁵ ARHGEF7 4.647192 0.929438 5 ZBTB20 4.05497 0.270331 15

RPS6KA2 7.382522 0.527323 14

TABLE Cancer Type CUX1 6.962169 0.497298

156: PLASMACYT IQSEC1 6.392684 0.45662 14 Gene site imp sum imp mean n PRKAG2 5.315705 0.379693 14 PTPRN2 8.246297 0.100565 82

C7orf50 4.96574 0.354696 14 PRDM16 5.021667 0.070728 71 MIR548F5 4.911218 0.350801 14 PCDHGA1 2.531088 0.0429 59 MSI2 6.700335 0.51541 13 PCDHGA2 2.214702 0.038854 57 MYT1L 5.452084 0.419391 13 PCDHGA3 2.214702 0.041013 54

KIF26B 4.344524 0.334194 13 PCDHGB1 2.214702 0.041787 53

RASA3 5.80698 0.483915 12 PCDHGA4 2.214702 0.043426 51

ZC3H3 5.498845 0.458237 12 PCDHGB2 2.214702 0.045198 49

TNS3 5.471934 0.455995 12 PCDHGA5 2.214702 0.047121 47

MIRLET7BHG 5.35895 0.446579 12 PCDHGB3 1.898316 0.044147 43

ADGRD1 4.977938 0.414828 12 HDAC4 6.778115 0.183192 37

MEGF6 4.348627 0.362386 12 RBFOX3 2.794424 0.079841 35

FBRSL1 4.31256 0.35938 12 DIP2C 2.766692 0.086459 32

CMIP 4.105199 0.3421 12 ADARB2 2.563395 0.098592 26 GNA12 4.057769 0.338147 12 SHANK2 2.42321 0.0932 26

TBCD 5.109895 0.464536 11 AGAP1 3.322519 0.132901 25 CAMTAI 1.999995 0.08 25 VRK2 3.179022 0.397378 8 PDGFRA 1.744295 0.069772 25 PPP2R2B 2.486706 0.310838 8 NCOR2 5.251438 0.228323 23 AFF3 2.243318 0.280415 8 RPTOR 4.188742 0.182119 23 LMX1B 2.190566 0.273821 8 RIMBP2 2.680694 0.116552 23 TENM2 1.774757 0.221845 8 NXN 1.627722 0.070771 23 CXXC5 2.699262 0.385609 7 SKI 4.880693 0.232414 21 VPS13D 2.384939 0.340706 7 SDK1 3.198217 0.159911 20 GAK 2.216817 0.316688 7 FRMD4A 1.928019 0.096401 20 PTPN20 2.158241 0.30832 7 MAD1L1 6.103592 0.321242 19 SBNO2 1.747097 0.249585 7 ZNF423 2.655007 0.139737 19 C19orf25 1.722623 0.246089 7 CFAP46 2.078795 0.10941 19 NRG1 1.701523 0.243075 7 FOXK1 3.750882 0.208382 18 RXRA 1.669708 0.23853 7

RBFOX1 3.393021 0.188501 18 GALNT2 1.659583 0.237083 7 ANKRD11 2.943663 0.163537 18 RADIL 3.252262 0.542044 6 OPCML 3.435498 0.202088 17 SLC22A18AS 2.353238 0.392206 6 FOXP1 3.530632 0.220665 16 COQ8A 2.030139 0.338356 6 GLI2 4.521672 0.301445 15 C10orf90 1.902737 0.317123 6 KIRREL3 2.842758 0.189517 15 ANKS1A 1.82408 0.304013 6 COL23A1 1.801013 0.120068 15 LRRFIP1 1.753638 0.292273 6 RPS6KA2 4.061507 0.290108 14 GPR39 1.713 0.2855 6 IQSEC1 2.897213 0.206944 14 RERE 1.686278 0.281046 6 CUX1 2.586123 0.184723 14 GRK5 1.631163 0.27186 6 C7orf50 2.041189 0.145799 14 TSNAX-DISC1 3.625664 0.725133 5 PPP2R2A 1.710245 0.12216 14 RUNDC3A 3.259257 0.651851 5 MOB2 1.619517 0.11568 14 SDK2 2.180742 0.436148 5 GSE1 4.175492 0.321192 13 CADM1 1.836276 0.367255 5 MSI2 3.493614 0.26874 13 AGAP2 1.816382 0.363276 5

KIF26B 1.842195 0.141707 13 EXT1 1.638349 0.409587 4 MYT1L 1.787706 0.137516 13 DICER1 2.158844 0.719615 3 CMIP 3.406765 0.283897 12 TBC1D7 2.089024 0.696341 3 FBRSL1 2.78353 0.231961 12 SLC25A22 1.787343 0.595781 3 ZC3H3 2.696535 0.224711 12 SLC25A10 2.297623 1.148812 2 CTNNA2 1.966395 0.163866 12 SOXIO 1.903551 0.951776 2 RASA3 1.810327 0.150861 12 ANKLE2 1.717654 0.858827 2 ZC3H12D 2.712185 0.246562 11 CHTF18 1.680153 0.840077 2 COL4A1 2.341256 0.212841 11 WNT5A 2.017909 0.183446 11 TABLE 157: Cancer Type PLNTY TSPAN4 2.697755 0.269776 10 Gene site imp sum imp mean n

AKAP13 1.847862 0.184786 10 PTPRN2 4.592259 0.056003 82 ACOT7 1.832347 0.183235 10 PRDM16 10.5896 0.149149 71 SND1 4.433875 0.492653 9 PCDHGA1 2.614902 0.04432 59 TSPAN9 2.937856 0.326428 9 PCDHGA2 2.614902 0.045875 57 TRAPPCI 2 2.580023 0.286669 9 PCDHGA3 2.931288 0.054283 54 ATP11A 2.491575 0.276842 9 PCDHGB1 2.931288 0.055307 53 SLC22A18 2.371564 0.263507 9 PCDHGA4 2.931288 0.057476 51 CACNA2D4 1.97832 0.219813 9 PCDHGB2 2.931288 0.059822 49 NOTCH 1 1.954531 0.21717 9 PCDHGA5 2.614902 0.055636 47 PCDHGB3 2.298516 0.053454 43 ADGRD1 3.000292 0.250024 12 PCDHGA6 2.298516 0.057463 40 TNS3 2.650311 0.220859 12 HDAC4 3.59828 0.097251 37 RAD51B 3.031553 0.275596 11 PCDHGA7 2.298516 0.062122 37 ZC3H12D 2.559618 0.232693 11 PAX6 6.015438 0.17187 35 VGLL4 2.433622 0.221238 11 RBFOX3 3.828772 0.109393 35 IGF1R 3.313095 0.33131 10 PCDHGB4 2.298516 0.065672 35 ACOT7 2.988113 0.298811 10 PCDHGA8 2.298516 0.065672 35 LBX1-AS1 2.149892 0.214989 10 DIP2C 4.939448 0.154358 32 GRID1 2.12101 0.212101 10 PCDHGB5 2.298516 0.071829 32 KCNH2 3.479583 0.38662 9 PCDHGA9 2.298516 0.074146 31 ATP11A 3.173518 0.352613 9 SOX2-OT 3.593428 0.123911 29 SND1 3.141462 0.349051 9 ADARB2 3.330645 0.128102 26 AXIN2 2.811114 0.312346 9 CAMTAI 4.272184 0.170887 25 ASAP1 2.576206 0.286245 9 AGAP1 3.630501 0.14522 25 ADAMTS2 2.44998 0.27222 9 SATB2 3.604525 0.150189 24 ADGRB1 2.384403 0.264934 9 RPTOR 5.262184 0.228791 23 TSPAN9 2.143241 0.238138 9 INPP5A 3.839962 0.166955 23 DLEU1 3.024339 0.378042 8 NXN 2.55193 0.110953 23 ASPSCR1 2.599788 0.324973 8 NCOR2 2.510179 0.109138 23 LHX4 2.444245 0.305531 8 SKI 4.587922 0.218472 21 RORA 2.327736 0.290967 8 FRMD4A 4.040872 0.202044 20 DNMT3A 2.232974 0.279122 8 ABR 3.338525 0.166926 20 LINC00311 2.168563 0.27107 8 SDK1 2.746142 0.137307 20 GDF6 2.15933 0.269916 8 MAD1L1 6.361444 0.334813 19 MSRA 2.123309 0.265414 8 ZNF423 4.483254 0.235961 19 ESRRG 2.106696 0.263337 8 SMG1P2 3.957845 0.208308 19 DUSP6 3.533096 0.504728 7 BOLA2 3.957845 0.208308 19 C19orf25 2.548903 0.364129 7 LOC613038 3.957845 0.208308 19 NAVI 2.374574 0.339225 7 CASZ1 2.704101 0.142321 19 LINC01140 2.203162 0.314737 7 FOXK1 4.424514 0.245806 18 GLI3 2.193183 0.313312 7 MCF2L 2.873774 0.159654 18 CXXC5 2.112419 0.301774 7 TBC1D16 2.564648 0.14248 18 FBXL18 3.23133 0.538555 6 SEPTIN9 2.303752 0.127986 18 KIFC3 2.434479 0.486896 5 OPCML 3.49484 0.205579 17 BACH2 2.375159 0.475032 5 NAV2 2.375735 0.148483 16 PRR5L 2.340852 0.46817 5 GLI2 3.829269 0.255285 15 ARHGEF7 2.295883 0.459177 5 BAIAP2 2.233153 0.148877 15 UNQ6494 2.974765 0.743691 4 ZBTB20 2.214702 0.147647 15 SASH1 2.875664 0.718916 4 IQSEC1 2.635744 0.188267 14 GRIN2B 2.509571 0.836524 3 RPS6KA2 2.547296 0.18195 14 SOXIO 2.661932 1.330966 2 CUX1 2.349358 0.167811 14 SLC25A10 2.5237 1.26185 2 MSI2 4.667992 0.359076 13 MAP3K3 2.450964 1.225482 2 MYT1L 3.166894 0.243607 13 GSE1 3.007342 0.231334 13 TABLE 158: Cancer Type PPTID_A RFX4 2.54279 0.195599 13 Gene site imp sum imp mean n SPTBN4 2.137458 0.16442 13 PTPRN2 8.648116 0.105465 82 CMIP 3.542088 0.295174 12 PRDM16 7.928549 0.11167 71 PCDHGA1 3.106457 0.052652 59 GNG7 3.253313 0.23238 14 PCDHGA2 3.106457 0.054499 57 MYT1L 5.567428 0.428264 13 PCDHGA6 3.106457 0.077661 40 MSI2 4.478131 0.344472 13 HDAC4 11.75065 0.317585 37 ZC3H3 5.773265 0.481105 12 PAX6 5.665158 0.161862 35 CMIP 3.940051 0.328338 12 RBFOX3 4.977408 0.142212 35 FBRSL1 3.777821 0.314818 12 DIP2C 7.142101 0.223191 32 MEGF6 3.331258 0.277605 12 GALNT9 3.925303 0.145382 27 ADGRD1 3.207913 0.267326 12 SHANK2 5.177983 0.199153 26 TNS3 3.131242 0.260937 12 AGAP1 6.640154 0.265606 25 RASA3 3.062385 0.255199 12 CAMTAI 5.470891 0.218836 25 TBX4 3.004938 0.250412 12 MEIS1 3.424578 0.142691 24 WNT5A 4.500214 0.40911 11 PCDHGB7 3.106457 0.129436 24 ZC3H12D 3.281566 0.298324 11 RPTOR 9.444429 0.410627 23 VGLL4 3.164956 0.287723 11 NXN 6.62009 0.28783 23 RAD51B 2.972988 0.270272 11 NCOR2 6.27054 0.272632 23 GRID1 3.841244 0.384124 10 INPP5A 5.072765 0.220555 23 AKAP13 3.603546 0.360355 10 PCDHGA11 3.106457 0.135063 23 ACOT7 3.424298 0.34243 10 RIMBP2 3.094265 0.134533 23 SKOR1 3.26378 0.326378 10 PRKCZ 4.55387 0.206994 22 SPPL2B 2.959423 0.295942 10 SKI 8.101099 0.385767 21 ASIC2 2.928988 0.292899 10 ABR 2.948097 0.147405 20 SND1 6.042522 0.671391 9 MAD1L1 13.62232 0.716964 19 ATP11A 5.278739 0.586527 9 CASZ1 5.498024 0.28937 19 ADAMTS2 4.97865 0.553183 9 ZNF423 4.953023 0.260685 19 CACNA2D4 4.332074 0.481342 9 SMG1P2 4.178841 0.219939 19 PACS2 3.237413 0.359713 9 BOLA2 4.178841 0.219939 19 GPC6 3.168188 0.352021 9 LOC613038 4.178841 0.219939 19 TSPAN9 2.999866 0.333318 9 KCNQ1 4.015099 0.211321 19 SSBP3 2.969011 0.32989 9 FOXK1 5.225656 0.290314 18 VRK2 6.574839 0.821855 8 TBC1D16 4.093026 0.22739 18 TRAPPC9 3.850618 0.481327 8 SEPTIN9 3.318672 0.184371 18 DNMT3A 3.237578 0.404697 8 ANKRD11 3.091681 0.17176 18 PPP2R2B 3.190039 0.398755 8 PAX6-AS1 3.842142 0.226008 17 MIR548H4 3.731732 0.533105 7 RCN1 3.842142 0.226008 17 CXXC5 3.725549 0.532221 7 FOXP1 5.118198 0.319887 16 TENM3 3.387221 0.483889 7 NAV2 3.377477 0.211092 16 GAK 3.332254 0.476036 7 KNDC1 4.38695 0.292463 15 RXRA 2.978021 0.425432 7 ZBTB20 3.861839 0.257456 15 PITPNC1 2.95111 0.421587 7 GLI2 3.446048 0.229737 15 FBXL18 3.449678 0.574946 6 BAIAP2 3.396298 0.22642 15 TRAK1 3.390001 0.565 6 NFATC1 3.386909 0.225794 15 CCDC85C 3.026196 0.504366 6 KIRREL3 2.94463 0.196309 15 TSN AX-DISCI 4.501816 0.900363 5 ARHGEF10 5.205047 0.371789 14 ARHGEF7 3.06617 0.613234 5 MOB2 4.696577 0.33547 14 SDK2 2.942077 0.588415 5 CUX1 4.484666 0.320333 14 PWWP2B 3.23489 0.808722 4 IQSEC1 3.452896 0.246635 14 GSG1 3.157531 0.789383 4 C7orf50 3.327407 0.237672 14 SLC25A22 3.273268 1.091089 3 CHTF18 4.393304 2.196652 2 FBRSL1 3.045172 0.253764 12 KCNV2 3.002687 3.002687 1 ZC3H3 2.82838 0.235698 12

TNS3 2.557913 0.213159 12

TABLE 159: Cancer Type PPTID_B CMIP 2.288377 0.190698 12

Gene site imp_sum imp_mean n CTBP2 2.788195 0.253472 11

PTPRN2 8.484208 0.103466 82 ZC3H12D 2.239715 0.20361 11

PRDM16 6.943077 0.09779 71 AKAP13 3.237211 0.323721 10 HDAC4 8.404246 0.227142 37 GRID1 3.106122 0.310612 10 RBFOX3 6.997953 0.199942 35 AUTS2 2.828559 0.282856 10 PAX6 3.67728 0.105065 35 CHST11 2.687753 0.268775 10 DIP2C 3.475297 0.108603 32 ACOT7 2.607843 0.260784 10

GALNT9 3.870262 0.143343 27 RGS12 2.553201 0.25532 10

ADARB2 3.612563 0.138945 26 SH3RF3 2.431616 0.243162 10

SHANK2 3.330041 0.128079 26 BCL11B 2.284903 0.22849 10 CAMTAI 6.690953 0.267638 25 SND1 5.532557 0.614729 9 AGAP1 5.593718 0.223749 25 CACNA2D4 4.192445 0.465827 9 RPTOR 5.735448 0.249367 23 ATP11A 4.069455 0.452162 9 NXN 4.622589 0.200982 23 ADAMTS2 4.05582 0.450647 9 NCOR2 4.485016 0.195001 23 SSBP3 3.069115 0.341013 9

RIMBP2 3.27382 0.14234 23 AXIN2 3.020683 0.335631 9

INPP5A 2.682003 0.116609 23 GPC6 2.724216 0.302691 9

PRKCZ 3.291448 0.149611 22 MGMT 2.711353 0.301261 9 SKI 4.675435 0.22264 21 TSPAN9 2.675168 0.297241 9 FRMD4A 2.973194 0.14866 20 VRK2 5.368419 0.671052 8 MAD1L1 12.50555 0.658187 19 PPP2R2B 4.094538 0.511817 8

CASZ1 5.302585 0.279083 19 DNMT3A 3.275917 0.40949 8

SMG1P2 4.394225 0.231275 19 TRAPPC9 2.865515 0.358189 8 BOLA2 4.394225 0.231275 19 ASPSCR1 2.363132 0.295392 8 LOC613038 4.394225 0.231275 19 RORA 2.300871 0.287609 8 CFAP46 2.656597 0.139821 19 PITPNC1 2.553579 0.364797 7

KCNQ1 2.480621 0.130559 19 MIR548H4 2.484623 0.354946 7 ZNF423 2.454238 0.12917 19 TRAK1 3.108244 0.518041 6 FOXK1 3.026596 0.168144 18 COLEC11 2.68768 0.447947 6 TBC1D16 2.546024 0.141446 18 CRADD 2.675599 0.445933 6

SEPTIN9 2.507428 0.139302 18 ARHGAP18 2.507719 0.417953 6

FOXP1 5.314247 0.33214 16 MYO16 2.22252 0.37042 6

EBF3 3.263849 0.203991 16 TSN AX-DISCI 4.85897 0.971794 5

KNDC1 3.293924 0.219595 15 ARHGEF7 2.83885 0.56777 5 BAIAP2 2.481642 0.165443 15 CPEB1-AS1 2.454983 0.490997 5 CUX1 4.417765 0.315555 14 SDK2 2.210224 0.442045 5 ARHGEF10 3.118517 0.222751 14 GSG1 2.669005 0.667251 4

IQSEC1 2.944971 0.210355 14 EXT1 2.617463 0.654366 4 MIR548F5 2.813738 0.200981 14 RGL3 2.907137 0.969046 3 MSI2 4.724375 0.363413 13 SLC25A22 2.866456 0.955485 3 RFX4 3.458651 0.26605 13 SLC1A7 2.483699 0.8279 3 GSE1 3.364669 0.258821 13 ANKRD33B 2.320262 0.773421 3

MYT1L 2.990422 0.230032 13 DICER1 2.311872 0.770624 3 TBX4 3.882401 0.323533 12 CHTF18 4.241545 2.120773 2 UHRF1 2.699139 1.34957 2 RPS6KA2 3.344509 0.238894 14 UTRN 2.618679 1.309339 2 CUX1 3.268432 0.233459 14 KIF21B 2.540768 1.270384 2 MYT1L 3.692749 0.284058 13 TRIM65 2.452721 1.226361 2 GSE1 2.119264 0.16302 13 DISCI 2.234015 1.117008 2 ZC3H3 2.388201 0.199017 12 KCNV2 2.919489 2.919489 1 MIRLET7BHG 2.292468 0.191039 12 ARL6IP6 2.893929 2.893929 1 FBRSL1 2.218248 0.184854 12 DDT 2.784336 2.784336 1 CMIP 1.53067 0.127556 12 DNAJC27 2.448671 2.448671 1 ZC3H12D 2.136626 0.194239 11

CTBP2 1.69002 0.153638 11

TABLE 160: Cancer Type PTPR_A OTX1 1.953174 0.195317 10 Gene site imp sum imp mean n BCL11B 1.950789 0.195079 10 PTPRN2 6.431058 0.078428 82 NR2F1-AS1 1.892998 0.1893 10 PRDM16

0.090239 71 CHST11 1.58193 0.158193 10 HDAC4 4.815664 0.130153 37 ATP11A 3.986085 0.442898 9 RBFOX3 4.732637 0.135218 35 SND1 2.730913 0.303435 9 PAX6 3.06234 0.087495 35 CACNA2D4 2.522718 0.280302 9 DIP2C 1.560496 0.048765 32 KAZN 2.258768 0.250974 9 AGAP1 3.830952 0.153238 25 ASAP1 1.875856 0.208428 9 PDGFRA 2.657855 0.106314 25 AXIN2 1.840427 0.204492 9 CAMTAI 2.622421 0.104897 25 RUNX1 1.767827 0.196425 9 SATB2 1.957909 0.08158 24 TRAPPCI 2 1.712675 0.190297 9 NXN 4.055782 0.176338 23 KCNH2 1.708484 0.189832 9 INPP5A 3.642302 0.158361 23 VRK2 2.221373 0.277672 8 RIMBP2 2.060349 0.08958 23 DLEU1 1.917547 0.239693 8 RPTOR 1.800764 0.078294 23 AFF3 1.715766 0.214471 8 PRKCZ 2.333896 0.106086 22 PPP2R2B 1.570035 0.196254 8 SKI 2.951222 0.140534 21 LHX2 2.101609 0.30023 7 ZIC4 1.708484 0.081356 21 NAVI 1.77072 0.25296 7 SDK1 2.579709 0.128985 20 PACRG 1.681042 0.240149 7 FRMD4A 2.401239 0.120062 20 PITPNC1 1.670551 0.23865 7 MAD1L1 3.63752 0.191448 19 RXRA 1.564443 0.223492 7 CASZ1 3.147355 0.16565 19 ANKS1A 2.930102 0.48835 6 ZNF423 1.860218 0.097906 19 COLEC11 2.887782 0.481297 6 SEPTIN9 2.497207 0.138734 18 MYO16 1.966721 0.327787 6 FOXK1 1.89308 0.105171 18 LRRFIP1 1.686953 0.281159 6 ANKRD11 1.55778 0.086543 18 FBXL18 1.684368 0.280728 6 OPCML 2.030878 0.119463 17 RUNDC3A 2.696328 0.539266 5 NAV2 1.69002 0.105626 16 VAV2 2.04255 0.40851 5 GLI2 3.175797 0.21172 15 TSN AX-DISCI 1.952279 0.390456 5 NHX 2.470268 0.164685 15 PRR5L 1.570697 0.314139 5 DLX6-AS1 1.898316 0.126554 15 TK1 1.518714 0.303743 5 LRMDA 1.835344 0.122356 15 ZBTB16 1.493677 0.298735 5 COL23A1 1.58193 0.105462 15 RBMS3 2.001273 0.500318 4 SLX1B- VOPP1 1.77671 0.444177 4 SULT1A4 1.58193 0.105462

¹⁵ PPM1H 1.669106 0.417277 4 SLX1A 1.58193 0.105462

¹⁵ MDM4 1.521963 0.380491 4 LOC606724 1.58193 0.105462

¹⁵ CRB2 1.50801 0.377002 4 RREB1 1.501624 0.375406 4 ZNF423 6.394277 0.336541 19 NUDT1 2.223731 0.741244 3 MAD1L1 5.88838 0.309915 19 BFSP2 2.189251 0.72975 3 SMG1P2 5.428921 0.285733 19 SLC6A9 1.727698 0.575899 3 BOLA2 5.428921 0.285733 19 KCNIP1 1.709047 0.569682 3 LOC613038 5.428921 0.285733 19 GRIN2B 1.553661 0.517887 3 KCNQ1 5.30451 0.279185 19 SLC25A22 1.487383 0.495794 3 CASZ1 4.145031 0.21816 19 TRIM65 2.703379 1.351689 2 FOXK1 7.353913 0.408551 18 SLC7A5 2.459291 1.229645 2 SEPTIN9 5.788672 0.321593 18 CYTH1 1.918087 0.959043 2 MCF2L 4.622146 0.256786 18 DENND11 1.903764 0.951882 2 ANKRD11 3.704187 0.205788 18 SLC25A10 1.708904 0.854452 2 TBC1D16 3.524561 0.195809 18 PDE4D 1.688655 0.844328 2 OPCML 6.572972 0.386645 17 EXT2 1.667987 0.833993 2 FOXP1 5.653943 0.353371 16 ANKLE2 1.623952 0.811976 2 NAV2 4.61417 0.288386 16 RNF216 1.498119 0.74906 2 GLI2 6.422342 0.428156 15 GTF2E2 1.912954 1.912954 1 KIRREL3 5.718362 0.381224 15

LRMDA 4.481352 0.298757 15

TABLE 161: Cancer Type PTPR_B BAIAP2 4.371077 0.291405 15 Gene site imp sum imp mean n KNDC1 4.080509 0.272034 15 PTPRN2 18.55999 0.226341 82 ZBTB20 3.991421 0.266095 15 PRDM16 13.61668 0.191784 71 DLX6-AS1 3.544562 0.236304 15 PCDHGA1 4.135023 0.070085 59 RPS6KA2 7.537867 0.538419 14 PCDHGA2 3.688052 0.064703 57 CUX1 6.525536 0.46611 14 HDAC4 16.42095 0.443809 37 IQSEC1 4.926658 0.351904 14 RBFOX3 7.485864 0.213882 35 C7orf50 3.627468 0.259105 14 PAX6 5.608833 0.160252 35 CACNA1H 3.439488 0.245678 14 DIP2C 9.689409 0.302794 32 MYT1L 5.823839 0.447988 13 SOX2-OT 4.770368 0.164495 29 GSE1 5.04051 0.387732 13 GALNT9 5.415977 0.200592 27 MSI2 4.337492 0.333653 13 SHANK2 7.102857 0.273187 26 RFX4 3.999428 0.307648 13 ADARB2 4.593242 0.176663 26 HOXC4 3.809413 0.293032 13 AGAP1 11.12632 0.445053 25 MAML3 5.628857 0.469071 12 CAMTAI 7.763658 0.310546 25 ZC3H3 5.147971 0.428998 12 PDGFRA 5.687196 0.227488 25 CMIP 4.897106 0.408092 12 SATB2 5.259094 0.219129 24 FBRSL1 4.625681 0.385473 12 RPTOR 11.15487 0.484994 23 ADGRD1 3.982114 0.331843 12 NXN 7.122185 0.30966 23 ZC3H12D 4.02877 0.366252 11 NCOR2 5.254821 0.22847 23 SLC38A10 3.986959 0.362451 11 INPP5A 5.161147 0.224398 23 ANAPC16 3.5516 0.322873 11 RIMBP2 4.389689 0.190856 23 CTBP2 3.407661 0.309787 11 PRKCZ 5.665208 0.257509 22 TSPAN4 4.745661 0.474566 10 SKI 6.496584 0.309361 21 AKAP13 3.975967 0.397597 10 SIM2 4.620253 0.220012 21 LBX1-AS1 3.623374 0.362337 10 ZIC4 4.306227 0.205058 21 SND1 5.018913 0.557657 9 ABR 5.388896 0.269445 20 ATP11A 4.871857 0.541317 9 FRMD4A 4.694034 0.234702 20 SSBP3 4.321963 0.480218 9 SDK1 3.941165 0.197058 20 ADAMTS2 4.314706 0.479412 9 ASAP1 4.019661 0.446629 9 SDK1 6.399034 0.319952 20 CACNA2D4 3.893763 0.43264 9 ABR 5.681297 0.284065 20 KCNH2 3.800532 0.422281 9 MAD1L1 11.5667 0.608774 19 AXIN2 3.775791 0.419532 9 ZNF423

0.33344 19 RUNX1 3.624731 0.402748 9 CASZ1 5.397979 0.284104 19 VRK2 6.313109 0.789139 8 SMG1P2 4.917543 0.258818 19 DLEU1 5.319829 0.664979 8 BOLA2 4.917543 0.258818 19 PPP2R2B 4.572386 0.571548 8 LOC613038 4.917543 0.258818 19 DNMT3A 3.907375 0.488422 8 FOXK1 7.718463 0.428803 18 SYNJ2 3.601273 0.450159 8 TBC1D16 5.120114 0.284451 18 CXXC5 4.054631 0.579233 7 ANKRD11 4.25437 0.236354 18 MIR548H4 3.684494 0.526356 7 HOXA3 3.97575 0.220875 18 RXRA 3.553296 0.507614 7 PAX6-AS1 5.228992 0.307588 17 PLEC 3.466331 0.49519 7 RCN1 5.228992 0.307588 17 VPS 13D 3.431918 0.490274 7 TBX15 5.186025 0.30506 17 COLECI 1 3.717555 0.619593 6 FOXP1 6.348768 0.396798 16 FBXL18 3.690005 0.615001 6 NAV2 5.44581 0.340363 16 SLC22A18AS 3.658081 0.60968 6 SORBS2 5.172554 0.323285 16 RUNDC3A 4.428237 0.885647 5 GLI2 8.08092 0.538728 15 VAV2 3.785684 0.757137 5 ZBTB20 6.828758 0.455251 15

TSN AX-DISCI 3.404829 0.680966 5 LRMDA 5.159864 0.343991 15 AP2A2 3.400921 0.680184 5 BAIAP2 4.856771 0.323785 15 ANKLE2 3.509638 1.754819 2 EMX2OS 4.686864 0.312458 15 TRIM65 3.451435 1.725717 2 NFIX 4.573463 0.304898 15

KIRREL3 4.522711 0.301514 15

TABLE 162: Cancer Type PXA KNDC1 3.907792 0.260519 15

Gene site imp sum imp mean n PRKAG2 5.613557 0.400968 14 PTPRN2 20.26125 0.247088 82 RPS6KA2 5.561453 0.397247 14 PRDM16 19.38353 0.273007 71 CUX1 5.005901 0.357564 14 PCDHGA1 4.002326 0.067836 59 C7orf50 4.775226 0.341088 14 HDAC4 14.11703 0.381541 37 CACNA1H 4.104379 0.29317 14 PAX6 9.525175 0.272148 35 IQSEC1 3.912602 0.279472 14 RBFOX3 6.963974 0.198971 35 ARHGEF10 3.803718 0.271694 14 DIP2C 11.88264 0.371333 32 MIR548F5 3.773457 0.269533 14 SOX2-OT 5.857559 0.201985 29 MSI2 6.204519 0.477271 13 GALNT9 4.077147 0.151005 27 SPTBN4 5.489224 0.422248 13 CAMTAI 8.413322 0.336533 25 MYT1L 4.504388 0.346491 13 PDGFRA 7.680209 0.307208 25 RFX4 4.274853 0.328835 13 AGAP1 5.894966 0.235799 25 ZC3H3 6.241606 0.520134 12 SATB2 7.42217 0.309257 24 MIRLET7BHG 6.222207 0.518517 12 MEIS1 4.695101 0.195629 24 CMIP 4.766455 0.397205 12 RPTOR 12.58887 0.547342 23 ISLR2 4.365895 0.363825 12 NXN 7.073047 0.307524 23 FBRSL1 4.273014 0.356084 12 INPP5A 6.55427 0.284968 23 ADGRD1 4.122763 0.343564 12 NCOR2 5.953292 0.258839 23 MAML3 4.109295 0.342441 12 PRKCZ 7.531474 0.34234 22 CTNNA2 3.944939 0.328745 12 SKI 7.788973 0.370903 21 RASA3 3.937396 0.328116 12 FRMD4A 6.41236 0.320618 20 RAD51B 4.84344 0.440313 11 ZC3H12D 4.806616 0.436965 11 INPP5A 4.454718 0.193683 23 CTBP2 4.244572 0.38587 11 RPTOR 3.447995 0.149913 23 TBCD 3.967836 0.360712 11 PRKCZ 4.444889 0.20204 22 VGLL4 3.936592 0.357872 11 SKI 5.546912 0.264139 21 AUTS2 4.110377 0.411038 10 ZIC4 2.592634 0.123459 21 KLHL29 4.033775 0.403377 10 SDK1 3.813111 0.190656 20 SH3RF3 3.825704 0.38257 10 ABR 3.572512 0.178626 20 SND1 5.693729 0.632637 9 MAD1L1 10.37545 0.546076 19 TRAPPCI 2 4.834168 0.53713 9 SMG1P2 4.341857 0.228519 19 ADAMTS2 4.623229 0.513692 9 BOLA2 4.341857 0.228519 19 RUNX1 4.543854 0.504873 9 LOC613038 4.341857 0.228519 19 SSBP3 4.42173 0.491303 9 CASZ1 3.823251 0.201224 19 CACNA2D4 4.382012 0.48689 9 ZNF423 3.358649 0.176771 19 KAZN 4.311313 0.479035 9 KCNQ1 2.699977 0.142104 19 KCNMA1 4.033786 0.448198 9 ANKRD11 3.737851 0.207658 18 NEAT1 3.908167 0.434241 9 TBC1D16 2.700736 0.150041 18 EGFR 3.820696 0.424522 9 FOXK1 2.346755 0.130375 18 TSPAN9 3.772676 0.419186 9 OPCML 5.968518 0.351089 17 SLC22A18 3.769861 0.418873 9 PAX6-AS1 3.503387 0.206082 17 MCC 4.523935 0.565492 8 RCN1 3.503387 0.206082 17 AFF3 4.40779 0.550974 8 FOXP1 5.755484 0.359718 16 LINC00311 4.102662 0.512833 8 EBF3 3.313564 0.207098 16 DLEU1 3.844641 0.48058 8 SLX1B-

SULT1A4 3.1196 0.207973 15 DUSP6 5.719013 0.817002 7 SLX1A 3.1196 0.207973 15 CRADD 4.168197 0.694699 6 LOC606724 3.1196 0.207973 15 SLC22A18AS 4.007575 0.667929 6 LRMDA 3.00295 0.200197 15 FBXL18 3.850436 0.641739 6

KNDC1 2.790788 0.186053 15

TSN AX-DISCI 4.344618 0.868924 5

CUX1 3.751913 0.267994 14 ARHGEF7 3.753659 0.750732 5 PRKAG2 3.03044 0.21646 14 STAP2 3.92153 0.980383 4 25 ARHGE 0.208307 14 SLC25A10 3.8526 1.926312 2 F10 2.916299 MOB2 2.684732 0.191767 14

Cancer Type IQSEC1 2.533911 0.180994 14

TABLE 163: RB MYT1L 4.702899 0.361761 13

Gene site imp sum imp mean n MSI2 4.178197 0.3214 13 PTPRN2 4.901971 0.05978 82 GSE1 2.787183 0.214399 13 PRDM16 8.704295 0.122596 71 MIRLET7BHG 4.585815 0.382151 12 HDAC4 9.898104 0.267516 37 FBRSL1 3.606563 0.300547 12 RBFOX3 4.910006 0.140286 35 ZC3H3 3.079863 0.256655 12 PAX6 2.413042 0.068944 35 CMIP 2.793971 0.232831 12 DIP2C 5.273425 0.164795 32 RAD51B 2.416006 0.219637 11 GALNT9 5.441205 0.201526 27 RGS12 3.158603 0.31586 10 SHANK2 5.317331 0.204513 26 AKAP13 2.677057 0.267706 10 AGAP1 6.973388 0.278936 25 FMN1 2.601988 0.260199 10 CAMTAI 5.24227 0.209691 25 ADGRA1 2.36914 0.236914 10 NCOR2 5.797198 0.252052 23 SH3RF3 2.355094 0.235509 10 NXN 5.140158 0.223485 23 ADAMTS2 4.911167 0.545685 9 RIMBP2 4.585475 0.199368 23 ATP11A 4.712607 0.523623 9 SND1 4.524242 0.502694 9 DIP2C 5.428499 0.169641 32 TSPAN9 3.336084 0.370676 9 GALNT9 3.318927 0.122923 27 CACNA2D4 2.887287 0.32081 9 SHANK2 3.188553 0.122637 26 MGMT 2.880248 0.320028 9 CAMTAI 9.759744 0.39039 25 AXIN2 2.525171 0.280575 9 AGAP1 7.47166 0.298866 25 VRK2 4.160478 0.52006 8 PDGFRA 2.523211 0.100928 25 ABLIM2 3.597908 0.449739 8 RPTOR 5.980657 0.260029 23 PPP2R2B 3.516886 0.439611 8 NCOR2 5.458186 0.237312 23 AFF3 2.643811 0.330476 8 INPP5A 5.290319 0.230014 23 DNMT3A 2.571322 0.321415 8 RIMBP2 3.048244 0.132532 23 ASPSCR1 2.46467 0.308084 8 HOXB3 2.345977 0.101999 23 MSRA 2.456027 0.307003 8 NXN 2.30479 0.100208 23 DLEU1 2.425934 0.303242 8 PRKCZ 4.998182 0.22719 22 HOXB-AS3 2.749782 0.392826 7 SKI 5.721301 0.272443 21 SOX6 2.533076 0.361868 7 MAD1L1 11.286 0.594 19 NAVI 2.501446 0.357349 7 SMG1P2 4.790748 0.252145 19 MIR548H4 2.427279 0.346754 7 BOLA2 4.790748 0.252145 19 ARHGAP45 3.888732 0.648122 6 LOC613038 4.790748 0.252145 19 CRADD 3.279068 0.546511 6 CASZ1 2.627735 0.138302 19 MYO 16 2.502379 0.417063 6 FOXK1 2.847926 0.158218 18 PRKN 2.450354 0.408392 6 TBC1D16 2.641408 0.146745 18 COLECI 1 2.353995 0.392332 6 PAX6-AS1 5.304462 0.312027 17 TSN AX-DISCI 4.482762 0.896552 5 RCN1 5.304462 0.312027 17 ARHGEF7 3.244894 0.648979 5 HBG2 3.274533 0.19262 17 SDK2 2.972038 0.594408 5 TBX15 2.751941 0.161879 17 EXT1 2.383674 0.595919 4 FOXP1 5.574482 0.348405 16

CCND2 3.257018 1.085673 3 EBF3 3.503737 0.218984 16 SLC25A22 3.094645 1.031548 3 NAV2 3.411115 0.213195 16 CCDC167 2.502263 0.834088 3 LRMDA 3.450637 0.230042 15 DICER1 2.371142 0.790381 3 KNDC1 3.081972 0.205465 15 CHTF18 4.258503 2.129251 2 BAIAP2 2.981749 0.198783 15 ANKLE2 3.009641 1.504821 2 IQSEC1 3.484644 0.248903 14 TRIM65 2.683372 1.341686 2 ARHGEF10 3.254416 0.232458 14 KIF21B 2.646695 1.323348 2 MIR548F5 2.976081 0.212577 14 UHRF1 2.60598 1.30299 2 GNG7 2.94744 0.210531 14 GNB5 2.408241 1.204121 2 MOB2 2.765342 0.197524 14 KCNV2 2.913913 2.913913 1 C7orf50 2.647253 0.18909 14 DDT 2.827667 2.827667 1 PPP2R2A 2.380527 0.170038 14 ARL6IP6 2.792841 2.792841 1 MSI2 6.174824 0.474986 13 DNAJC27 2.353962 2.353962 1 MYT1L 4.461313 0.343178 13

FBRSL1 4.266439 0.355537 12

TABLE 164: Cancer Type RB_MYCN MIRLET7BHG 3.926465 0.327205 12 Gene site imp sum imp mean n ZC3H3 3.870611 0.322551 12

PTPRN2 7.195324 0.087748 82 VGLL4 2.825087 0.256826 11 PRDM16 4.382494 0.061725 71 GLUD1P2 2.322585 0.211144 11 HDAC4 8.272875 0.223591 37 LBX1-AS1 3.264628 0.326463 10 PAX6 5.111473 0.146042 35 ETS1 2.722256 0.272226 10 RBFOX3 4.800351 0.137153 35 AKAP13 2.48025 0.248025 10 AUTS2 2.349618 0.234962 10 TABLE 165: Cancer Type RGNT NBEA 2.335896 0.23359 10 Gene site imp sum imp mean n SND1 5.29855 0.588728 9 PTPRN2 28.35935 0.345846 82 TSPAN9 4.693493 0.521499 9 PRDM16 18.3351 0.258241 71

ATP11A 3.860071 0.428897 9 PCDHGA1 4.71592 0.079931 59

ADAMTS2 3.566091 0.396232 9 PCDHGA2 4.71592 0.082735 57 AXIN2 3.420051 0.380006 9 PCDHGA3 4.71592 0.087332 54 MGMT 3.342588 0.371399 9 PCDHGB1 4.71592 0.08898 53 CACNA2D4 3.219986 0.357776 9 PCDHGA4 4.71592 0.092469 51

GPC6 3.118737 0.346526 9 PCDHGB2 5.032306 0.1027 49

PPP2R2B 4.045886 0.505736 8 HDAC4 12.65834 0.342117 37

TRAPPC9 3.484088 0.435511 8 PAX6 11.53394 0.329541 35

VRK2 3.126736 0.390842 8 RBFOX3 9.405788 0.268737 35 DNMT3A 3.056051 0.382006 8 DIP2C 8.505616 0.265801 32 AFF3 2.644018 0.330502 8 SOX2-OT 11.7201 0.404141 29

TRIM6-TRIM34 3.253256 0.464751 7 GALNT9 4.717065 0.174706 27 VPS 13D 2.927376 0.418197 7 SHANK2 7.435439 0.285978 26 MIR548H4 2.505199 0.357886 7 ADARB2 4.672043 0.179694 26 CCDC85C 4.449552 0.741592 6 AGAP1 11.2714 0.450856 25

TRAK1 3.16209 0.527015 6 CAMTAI 9.239245 0.36957 25 CRADD 2.834375 0.472396 6 PDGFRA 7.122884 0.284915 25 PBX1 2.718575 0.453096 6 SATB2 7.173512 0.298896 24 TRIM34 2.707714 0.451286 6 MEIS1 6.522382 0.271766 24

MYO 16 2.67766 0.446277 6 RPTOR 10.93966 0.475637 23

TSN AX-DISCI 4.532751 0.90655 5 NCOR2 8.649125 0.376049 23

SDK2 3.207546 0.641509 5 INPP5A 6.930626 0.301332 23

ARHGEF7 2.946983 0.589397 5 HOXB3 6.642556 0.288807 23 SNX29 2.567895 0.513579 5 NXN 5.837885 0.253821 23 CACNA2D2 2.316446 0.463289 5 PRKCZ 7.999369 0.363608 22 GSG1 2.796905 0.699226 4 SKI 11.84054 0.563835 21

EXT1 2.782114 0.695529 4 SIM2 9.07104 0.431954 21 DGKD 2.413085 0.603271 4 FRMD4A 8.287969 0.414398 20 TULP4 3.428444 1.142815 3 ABR 6.945064 0.347253 20 SLC25A22 2.87053 0.956843 3 SDK1 5.84499 0.292249 20

EPAS1 2.488338 0.829446 3 MAD1L1 13.51159 0.711136 19 DAGLB 2.487952 0.829317 3 SMG1P2 9.561161 0.503219 19 CHID1 2.437806 0.812602 3 BOLA2 9.561161 0.503219 19 ANKRD33B 2.360123 0.786708 3 LOC613038 9.561161 0.503219 19

CHTF18 4.257121 2.128561 2 ZNF423 8.580089 0.451584 19

KIF21B 2.723222 1.361611 2 CASZ1 6.041944 0.317997 19

UHRF1 2.656527 1.328264 2 KCNQ1 4.706689 0.24772 19 TRIM65 2.587965 1.293982 2 MCF2L 9.160776 0.508932 18 ATG4B 2.378033 1.189016 2 FOXK1 7.883264 0.437959 18 KCNV2 2.844112 2.844112 1 ANKRD11 6.391641 0.355091 18

ARL6IP6 2.692015 2.692015 1 TBC1D16 5.996482 0.333138 18 DDT 2.610675 2.610675 1 SEPTIN9 5.99005 0.332781 18 DNAJC27 2.459424 2.459424 1 OPCML 9.744459 0.573203 17 NAV2 6.679508 0.417469 16 FOXP1 5.694375 0.355898 16 RUNDC3A 5.492642 1.098528 5 SORBS2 4.846608 0.302913 16 VAV2 4.711378 0.942276 5 EBF3 4.526261 0.282891 16 TSN AX-DISCI 4.625445 0.925089 5 GLI2 12.80633 0.853756 15 STAP2 5.101563 1.275391 4 ZBTB20 6.33536 0.422357 15 RBMS3 4.78651 1.196628 4

EMX2OS 5.934061 0.395604 15 SOXIO 5.438382 2.719191 2

LRMDA 4.801419 0.320095 15

BAIAP2 4.424395 0.29496 15 TABLE 166: Cancer Type SCHW

IQSEC1 6.656421 0.475459 14 Gene site imp sum imp mean n

RPS6KA2 6.340967 0.452926 14 PTPRN2 18.12891 0.221084 82

CUX1 6.335945 0.452568 14 PRDM16 17.27083 0.243251 71

PRKAG2 5.634796 0.402485 14 PCDHGA1 4.622623 0.07835 59 C7orf50 4.816359 0.344026 14 PCDHGA2 4.622623 0.081099 57 ARHGEF10 4.794289 0.342449 14 PCDHGA3 3.922562 0.07264 54

MSI2 7.173944 0.551842 13 PCDHGB1 3.922562 0.074011 53

MYT1L 6.796579 0.522814 13 PCDHGA4 3.922562 0.076913 51

RFX4 5.610106 0.431547 13 PCDHGB2 4.238948 0.086509 49

MIR9-3HG 4.546077 0.349698 13 PCDHGA5 3.922562 0.083459 47

CMIP 6.03703 0.503086 12 HDAC4 14.71199 0.397621 37

MIRLET7BHG 5.241599 0.4368 12 PAX6 12.27248 0.350642 35

ZC3H3 4.856416 0.404701 12 RBFOX3 6.903027 0.197229 35

TNS3 4.479188 0.373266 12 DIP2C 11.14531 0.348291 32

VGLL4 5.726971 0.520634 11 SOX2-OT 9.275725 0.319853 29

RAD51B 5.633707 0.512155 11 SHANK2 5.840376 0.22463 26

CCDC140 5.44346 0.49486 11 ADARB2 4.148071 0.159541 26

ZC3H12D 5.207577 0.473416 11 AGAP1 9.096533 0.363861 25

FGFR2 4.749396 0.431763 11 CAMTAI 6.895361 0.275814 25 SH3RF3 5.116472 0.511647 10 PDGFRA 6.799302 0.271972 25

KLHL29 4.973237 0.497324 10 SATB2 4.169054 0.173711 24

NTM 4.777075 0.477708 10 RPTOR 11.85899 0.515608 23

MAML2 4.754856 0.475486 10 INPP5A 7.671982 0.333564 23

CHST11 4.634003 0.4634 10 NCOR2 6.603778 0.287121 23

AKAP13 4.481869 0.448187 10 PRKCZ 6.016055 0.273457 22

ATP11A 6.542237 0.726915 9 SKI 13.01664 0.61984 21

SND1 6.464573 0.718286 9 FRMD4A 7.580493 0.379025 20

ASAP1 6.008052 0.667561 9 SDK1 5.778566 0.288928 20 ADAMTS2 5.236207 0.581801 9 ABR 5.296441 0.264822 20 NOTCH 1 5.15284 0.572538 9 MAD1L1 11.97168 0.630088 19

AXIN2 4.595346 0.510594 9 ZNF423 7.148908 0.376258 19

ADGRB1 4.587885 0.509765 9 SMG1P2 6.66403 0.350738 19

TRAPPCI 2 4.519644 0.502183 9 BOLA2 6.66403 0.350738 19

LINC00311 6.528932 0.816116 8 LOC613038 6.66403 0.350738 19

MSRA 4.541659 0.567707 8 CASZ1 5.936643 0.312455 19 BAHCC1 4.471947 0.558993 8 FOXK1 8.515619 0.47309 18 DUSP6 7.783034 1.111862 7 TBC1D16 7.090589 0.393922 18

NAVI 5.24062 0.74866 7 SEPTIN9 6.76469 0.375816 18 LINC00461 4.893859 0.699123 7 ANKRD11 4.752093 0.264005 18 FBXL18 4.979822 0.82997 6 MCF2L 4.090501 0.22725 18 PAX6-AS1 7.6399 0.449406 17 LINC00461 4.541013 0.648716 7 RCN1 7.6399 0.449406 17 DUSP6 3.932552 0.561793 7 FOXP1 6.437553 0.402347 16 C19orf25 3.930343 0.561478 7 NAV2 6.150183 0.384386 16 RXRA 3.880434 0.554348 7 GLI2 7.068317 0.471221 15 FBXL18 5.204025 0.867338 6 ZBTB20 4.663726 0.310915 15 CCDC177 4.889198 0.814866 6 BAIAP2 4.607747 0.307183 15 SLC22A18AS 3.930148 0.655025 6 KIRREL3 4.381034 0.292069 15 RUNDC3A 5.719208 1.143842 5 NHX 3.930406 0.262027 15 TSN AX-DISCI 4.920333 0.984067 5 CUX1 6.776737 0.484053 14 ARHGEF7 4.223877 0.844775 5

RPS6KA2 5.766261 0.411876 14 TBC1D7 3.852973 1.284324 3 C7orf50 4.866972 0.347641 14 SOXIO 4.443244 2.221622 2 CACNA1H 4.858249 0.347018 14 SLC25A10 3.978218 1.989109 2 IQSEC1 4.78237 0.341598 14 ARHGEF10 4.535499 0.323964 14 TABLE 167: Cancer Type SEGA MIR548F5 3.867145 0.276225 14 Gene site imp sum imp mean n MSI2 6.901005 0.530847 13 PTPRN2 29.02048 0.353908 82 MYT1L 4.843067 0.372544 13 PRDM16 26.6926 0.375952 71 CMIP 6.485181 0.540432 12 PCDHGA1 8.996433 0.152482 59 ZC3H3 5.697761 0.474813 12 PCDHGA2 8.680047 0.152282 57 TNS3 5.012632 0.417719 12 PCDHGA3 8.034496 0.148787 54 ADGRD1 4.211266 0.350939 12 PCDHGB1 7.71811 0.145625 53

FBRSL1 4.155268 0.346272 12 PCDHGA4 7.71811 0.151335 51 VGLL4 4.828727 0.438975 11 PCDHGB2 7.401724 0.151056 49 FGFR2 4.826384 0.438762 11 PCDHGA5 6.994791 0.148825 47 RAD51B 4.779676 0.434516 11 PCDHGB3 6.269448 0.145801 43 CTBP2 4.41779 0.401617 11 PCDHGA6 6.269448 0.156736 40 SPON2 4.072423 0.37022 11 HDAC4 19.58775 0.529399 37 ANAPC16 3.97173 0.361066 11 PCDHGA7 5.953062 0.160894 37 ZC3H12D 3.883146 0.353013 11 PAX6 14.09937 0.402839 35 TSPAN4 4.781925 0.478193 10 RBFOX3 9.517654 0.271933 35 ACOT7 4.757242 0.475724 10 PCDHGB4 5.636676 0.161048 35

AKAP13 4.524347 0.452435 10 PCDHGA8 5.636676 0.161048 35 SH3RF3 4.35329 0.435329 10 DIP2C 13.04091 0.407528 32 GAS7 4.327703 0.43277 10 PCDHGB5 5.636676 0.176146 32 NR2F1-AS1 3.88907 0.388907 10 SOX2-OT 10.00762 0.34509 29 SND1 6.696641 0.744071 9 GALNT9 8.426724 0.312101 27 ATP11A 6.204406 0.689378 9 SHANK2 8.662506 0.333173 26 TRAPPCI 2 5.123195 0.569244 9 ADARB2 7.09401 0.272847 26 ADAMTS2 4.999018 0.555446 9 AGAP1 13.3909 0.535636 25 SPECC1 3.979139 0.442127 9 CAMTAI 10.94498 0.437799 25 KCNH2 3.875947 0.430661 9 PDGFRA 7.666178 0.306647 25

LINC00311 5.825952 0.728244 8 SATB2 8.571828 0.357159 24 MSRA 5.302974 0.662872 8 MEIS1 6.573848 0.27391 24 GRIK2 4.49141 0.561426 8 RPTOR 16.42404 0.714089 23 DNMT3A 4.206901 0.525863 8 NCOR2 12.07995 0.525215 23 DLEU1 3.939931 0.492491 8 NXN 7.265405 0.315887 23 MIR548H4 4.554369 0.650624 7 HOXB3 5.758261 0.250359 23 INPP5A 5.558288 0.241665 23 TNS3 5.780302 0.481692 12

PRKCZ 8.292005 0.376909 22 CTNNA2 5.759007 0.479917 12

SKI 11.23111 0.534815 21 TBX4 5.550192 0.462516 12

ZIC4 6.017229 0.286535 21 ZC3H12D 6.890304 0.626391 11

FRMD4A 8.362341 0.418117 20 VGLL4 6.233194 0.566654 11

SDK1 7.144768 0.357238 20 SPON2 6.144651 0.558605 11

ABR 6.229435 0.311472 20 FGFR2 5.607308 0.509755 11

MAD1L1 14.39206 0.757477 19 RAD51B 5.571527 0.506502 11

ZNF423 11.2155 0.59029 19 ACOT7 5.817013 0.581701 10

CASZ1 7.8292 0.412063 19 NR2F1-AS1 5.500147 0.550015 10

SMG1P2 7.050443 0.371076 19 ATP11A 7.600119 0.844458 9

BOLA2 7.050443 0.371076 19 SND1 7.195881 0.799542 9

LOC613038 7.050443 0.371076 19 ADAMTS2 6.730098 0.747789 9

FOXK1 9.55413 0.530785 18 AXIN2 6.121065 0.680118 9

ANKRD11 7.699424 0.427746 18 TRAPPCI 2 5.98463 0.664959 9

MCF2L 7.344672 0.408037 18 MSRA 6.237314 0.779664 8

TBC1D16 6.84073 0.380041 18 LINC00311 5.4446 0.680575 8

SEPTIN9 6.522388 0.362355 18 NAVI 5.966218 0.852317 7

OPCML 7.410812 0.43593 17 VPS13D 5.545241 0.792177 7

TBX15 6.290804 0.370047 17 TSN AX-DISCI 5.601673 1.120335 5

PAX6-AS1 5.799636 0.341155 17 RUNDC3A 5.495706 1.099141 5

RCN1 5.799636 0.341155 17

NAV2 6.981482 0.436343 16 TABLE 168: Cancer Type SFT_HMPC

FOXP1 6.970221 0.435639 16 Gene site imp sum imp mean n

SORBS2 6.046814 0.377926 16 PTPRN2 12.24457 0.149324 82

GLI2 9.618994 0.641266 15 PRDM16 8.735533 0.123036 71

ZBTB20 7.555842 0.503723 15 PCDHGA1 2.896052 0.049086 59

LRMDA 6.710239 0.447349 15 PCDHGA2 3.212438 0.056359 57

SLX1B- HDAC4 15.42218 0.416816 37

SULT1A4 6.386947 0.425796

¹⁵ RBFOX3 5.849642 0.167133 35

SLX1A 6.386947 0.425796

¹⁵ PAX6 4.371199 0.124891 35

LOC606724 6.386947 0.425796

¹⁵ DIP2C 7.825877 0.244559 32

KIRREL3 6.201228 0.413415

¹⁵ SOX2-OT 3.668157 0.126488 29

NHX 6.051682 0.403445

¹⁵ GALNT9 3.100134 0.11482 27

KNDC1 5.465558 0.364371

¹⁵ SHANK2 4.732856 0.182033 26

RPS6KA2 8.600181 0.614299 14

ADARB2 3.034991 0.11673 26

MIR548F5 6.434363 0.459597 14

AGAP1 9.403838 0.376154 25

CUX1 6.083478 0.434534 14

PDGFRA 5.60567 0.224227 25

ARHGEF10 6.002265 0.428733 14

CAMTAI 3.06187 0.122475 25

PRKAG2 5.718673 0.408477 14

RPTOR 8.730063 0.379568 23

MSI2 10.13503 0.779618 13

NXN 4.785711 0.208074 23

MYT1L 6.429776 0.494598

¹³ NCOR2 3.654354 0.158885 23

SPTBN4 5.741059 0.44162

¹³ INPP5A 2.705938 0.117649 23

RFX4 5.600712 0.430824 13

PRKCZ 5.284244 0.240193 22

ZC3H3 7.296484 0.60804

¹² SKI 5.042624 0.240125 21

CMIP 6.604265 0.550355

¹² FRMD4A 4.644678 0.232234 20

MIRLET7BHG 6.154127 0.512844

¹² SDK1 4.376963 0.218848 20

FBRSL1 6.109633 0.509136

¹² MAD1L1 8.270841 0.435307 19 ZNF423 4.948134 0.260428 19 AXIN2 3.578296 0.397588 9

KCNQ1 3.124892 0.164468 19 ADAMTS2 3.089809 0.343312 9 SMG1P2 2.74439 0.144442 19 SSBP3 2.952884 0.328098 9 BOLA2 2.74439 0.144442 19 MGMT 2.887198 0.3208 9

LOC613038 2.74439 0.144442 19 EGFR 2.665465 0.296163 9

FOXK1 5.302466 0.294581 18 DLEU1 3.117279 0.38966 8

TBC1D16 4.312583 0.239588 18 VEPH1 2.998216 0.374777 8

SEPTIN9 3.934468 0.218582 18 C19orf25 4.207244 0.601035 7

MCF2L 3.924385 0.218021 18 VPS13D 3.727292 0.53247 7

ANKRD11 3.412792 0.1896 18 MIR548H4 3.215443 0.459349 7

TBX15 3.145523 0.185031 17 PCCA 3.061603 0.437372 7

OPCML 2.788615 0.164036 17 LINC01140 2.728831 0.389833 7

FOXP1 6.145645 0.384103 16 LINC00461 2.705385 0.386484 7

NAV2 5.405925 0.33787 16 TACC2 2.680219 0.382888 7

EBF3 4.332831 0.270802 16 NAVI 2.666223 0.380889 7

NHX 4.838829 0.322589 15 CRADD 3.201265 0.533544 6

ZBTB20 4.600342 0.306689 15 FBXL18 3.084465 0.514077 6

GLI2 4.120826 0.274722 15 STRA6 3.051614 0.508602 6

SLX1B- SLC22A18AS 2.968643 0.494774 6 SULT1A4 3.41389 0.227593

¹⁵ FMNL2 2.839545 0.473257 6

SLX1A 3.41389 0.227593

¹⁵ TSN AX-DISCI 4.482228 0.896446 5 LOC606724 3.41389 0.227593

¹⁵ RUNDC3A 4.415637 0.883127 5 RPS6KA2 6.101722 0.435837 14

BCAR1 2.89008 0.578016 5

IQSEC1 5.106277 0.364734 14

ARHGEF7 2.633655 0.526731 5

C7orf50 4.751788 0.339413 14

DAGLB 3.082435 1.027478 3

PRKAG2 3.580027 0.255716 14

DICER1 2.741322 0.913774 3

CUX1 3.278226 0.234159 14

SLC25A10 3.081614 1.540807 2

MSI2 4.156053 0.319696 13

CHTF18 2.836534 1.418267 2

MYT1L 3.817407 0.293647 13

' RALGAPA2 2.792279 1.39614 2

RFX4 3.072925 0.236379 13

HOXC4 2.635727 0.202748 ¹³ TABLE 169: Cancer Type SNUC_IDH2

CMIP 5.84176 0.486813 12 Gene site imp sum imp mean n

FBRSL1 4.394747 0.366229 12

PTPRN2 1.943547 0.023702 82

ADGRD1 4.266942 0.355578 12

PCDHGA1 1.997666 0.033859 59

MIRLET7BHG 3.586183 0.298849

¹² PCDHGA2 1.997666 0.035047 57

MAML3 2.631919 0.219327

¹² PCDHGA3 1.997666 0.036994 54

COL4A1 3.672924 0.333902 11

PCDHGB1 1.997666 0.037692 53

PCDHGC3 2.896052 0.263277 11

PCDHGA4 1.997666 0.03917 51

SLC38A10 2.870792 0.260981 11

PCDHGB2 1.997666 0.040769 49

TSPAN4 4.515049 0.451505

¹⁰ PCDHGA5 1.997666 0.042504 47

GAS7 3.967262 0.396726

¹⁰ PCDHGB3 1.997666 0.046457 43

AKAP13 3.756983 0.375698 10

HDAC4 6.857323 0.185333 37

ACOT7 3.560002 0.356 10

PAX6 2.847474 0.081356 35

KLHL29 3.262059 0.326206

¹⁰ DIP2C 4.269002 0.133406 32

BCL11B 2.961893 0.296189

¹⁰ SOX2-OT 2.93842 0.101325 29

SH3RF3 2.812441 0.281244 10

SHANK2 1.912098 0.073542 26

CHST11 2.709323 0.270932 10

AGAP1 5.525905 0.221036 25

SND1 4.516105 0.501789 9

PDGFRA 2.284215 0.091369 25 RPTOR 4.514173 0.196268 23 TRAPPC9 1.898316 0.237289 8 RIMBP2 2.847474 0.123803 23 MIR548H4 2.378158 0.339737 7 NCOR2 2.500333 0.10871 23 ITPK1 2.179421 0.311346 7 INPP5A 2.413317 0.104927 23 NAVI 2.020792 0.288685 7 PRKCZ 2.569685 0.116804 22 VPS 13D 1.986051 0.283722 7 SKI 5.805435 0.276449 21 CXXC5 1.916586 0.273798 7 SIM2 1.898316 0.090396 21 FBXL18 3.390413 0.565069 6 FRMD4A 3.44193 0.172097 20 COQ8A 2.523787 0.420631 6

MAD1L1 4.732794 0.249094 19 CRADD 2.414877 0.40248 6 SMG1P2 3.661984 0.192736 19 ANKS1A 2.243284 0.373881 6 BOLA2 3.661984 0.192736 19 CASP8 3.74288 0.748576 5 LOC613038 3.661984 0.192736 19 RUNDC3A 2.976877 0.595375 5 KCNQ1 3.422943 0.180155 19 ARHGEF7 2.906795 0.581359 5 ZNF423 1.898316 0.099911 19 ATP2B4 2.576039 0.515208 5 FOXK1 6.056056 0.336448 18 TSN AX-DISCI 2.401902 0.48038 5 HOXA3 2.782692 0.154594 18 GAREM2 2.047481 0.409496 5 FOXP1 4.921271 0.307579 16 BCAR1 1.962986 0.392597 5 BAIAP2 2.430132 0.162009 15 GRIP1 1.962096 0.392419 5 ZBTB20 2.120404 0.14136 15 CADM1 1.882871 0.376574 5

RPS6KA2 3.714062 0.26529 14 TUBA1C 3.333471 0.833368 4 CUX1 3.528898 0.252064 14 NHSL1 2.851141 0.712785 4 SYCP2L 2.657751 0.189839 14 STAP2 2.843336 0.710834 4 IQSEC1 2.574887 0.18392 14 GSG1 2.639775 0.659944 4 PRKAG2 2.135232 0.152517 14 RAI1 2.44892 0.61223 4

HOXA10- LINC00856 2.153666 0.538417 4 HOXA9 2.543701 0.195669 13

ZMIZ1 2.070196 0.517549 4 MSI2 1.916386 0.147414 13

DINA 2.010819 0.502705 4

CMIP 3.489611 0.290801 12

DICER1 2.424191 0.808064 3 FBRSL1 2.473605 0.206134 12

SLC6A9 2.405465 0.801822 3 MAML3 2.226349 0.185529 12

TMBIM1 1.95763 0.652543 3 TNS3 2.066662 0.172222 12

DAGLB 1.900425 0.633475 3 GLUD1P2 2.424013 0.220365 11

RALGAPA2 2.676986 1.338493 2

RAD51B 1.840497 0.167318 11

SFXN5 2.155911 1.077956 2 TSPAN4 3.382803 0.33828 10

CHTF18 2.137129 1.068564 2 ACOT7 3.110649 0.311065 10

ERI3 1.891715 0.945857 2 SPPL2B 3.037791 0.303779 10

TRIP6 1.846681 0.923341 2 AKAP13 2.417812 0.241781 10 TOM1L2 1.848306 1.848306 1

BCL11B 2.215527 0.221553 10 ATP11A 5.310308 0.590034 9 TABLE 170: Cancer Type ST EPN_RELA_A SND1 3.85585 0.428428 9

Gene site imp sum imp mean n ADAMTS2 3.107474 0.345275 9

PTPRN2 17.2971 0.21094 82

AXIN2 2.218268 0.246474 9

PRDM16 19.37879 0.272941 71 SLC22A18 2.2033 0.244811 9

PCDHGA1 4.72789 0.080134 59 TSPAN9 2.069869 0.229985 9

PCDHGA2 4.739979 0.083158 57 LHX4 4.005276 0.500659 8

PCDHGA3 5.056365 0.093636 54 LINC00311 3.115602 0.38945 8

PCDHGB1 5.056365 0.095403 53

DLEU1 2.856946 0.357118 8

PCDHGA4 5.056365 0.099144 51 MSRA 2.169484 0.271186 8

PCDHGB2 4.739979 0.096734 49 PCDHGA5 4.423593 0.094119 47 CUX1 6.280038 0.448574 14 HDAC4 14.85662 0.40153 37 MIR548F5 6.087254 0.434804 14 RBFOX3 11.18775 0.31965 35 ARHGEF10 5.1838 0.370271 14 PAX6 10.94274 0.31265 35 PRKAG2 4.945042 0.353217 14 DIP2C 9.369048 0.292783 32 IQSEC1 4.481342 0.320096 14 SOX2-OT 7.508662 0.258919 29 C7orf50 4.389609 0.313543 14 SHANK2 6.785761 0.260991 26 MSI2 5.643963 0.434151 13 ADARB2 5.195607 0.199831 26 KIF26B 4.835693 0.371976 13 AGAP1 10.34313 0.413725 25 MYT1L 4.631923 0.356302 13 CAMTAI 8.581708 0.343268 25 CMIP 6.13285 0.511071 12 PDGFRA 4.889735 0.195589 25 TNS3 5.978262 0.498189 12 SATB2 6.065678 0.252737 24 MIRLET7BHG 5.291088 0.440924 12 MEIS1 4.400045 0.183335 24 ZC3H3 4.69617 0.391347 12 RPTOR 10.77007 0.468264 23 MEGF6 4.450577 0.370881 12 NCOR2 7.261868 0.315733 23 ADGRD1 4.430574 0.369215 12 HOXB3 6.289484 0.273456 23 SPON2 6.044808 0.549528 11 RIMBP2 6.108486 0.265586 23 ZC3H12D 5.88004 0.534549 11 INPP5A 5.45339 0.237104 23 RAD51B 4.696695 0.426972 11 NXN 4.543369 0.197538 23 VGLL4 4.221628 0.383784 11 SKI 13.41231 0.638682 21 ACOT7 4.993015 0.499302 10 FRMD4A 7.713994 0.3857 20 TSPAN4 4.86048 0.486048 10 ABR 6.297255 0.314863 20 AKAP13 4.647912 0.464791 10 SDK1 4.755602 0.23778 20 RGS12 4.436793 0.443679 10 MAD1L1 12.61895 0.664155 19 TP73 4.201124 0.420112 10 CASZ1 9.451384 0.497441 19 ATP11A 6.458921 0.717658 9 ZNF423 9.394324 0.494438 19 SND1 6.24855 0.694283 9 SMG1P2 6.725338 0.353965 19 TSPAN9 5.107321 0.56748 9 BOLA2 6.725338 0.353965 19 TRAPPCI 2 4.820956 0.535662 9 LOC613038 6.725338 0.353965 19 KCNH2 4.469588 0.496621 9 FOXK1 7.081048 0.393392 18 LHX4 6.248439 0.781055 8 ANKRD11 6.395685 0.355316 18 DLEU1 5.49409 0.686761 8 MCF2L 5.828254 0.323792 18 ESRRG 4.802341 0.600293 8 TBC1D16 5.509621 0.30609 18 MCC 4.536967 0.567121 8 SEPTIN9 4.832762 0.268487 18 MSRA 4.388299 0.548537 8 RBFOX1 4.165318 0.231407 18 NAVI 4.705733 0.672248 7 OPCML 8.123436 0.477849 17 FBXL18 4.375528 0.729255 6 TBX15 5.684761 0.334398 17 FAM181A 4.224451 0.704075 6 PAX6-AS1 5.461725 0.321278 17 RAPGEF4 5.454854 1.090971 5 RCN1 5.461725 0.321278 17 RUNDC3A 5.18667 1.037334 5 FOXP1 7.261761 0.45386 16 TSN AX-DISCI 4.485901 0.89718 5 EBF3 5.979732 0.373733 16 CACNA1I 4.452073 0.890415 5 NAV2 5.436301 0.339769 16 RBMS3 4.961287 1.240322 4 SORBS2 4.579467 0.286217 16 AIRE 5.495247 1.831749 3 GLI2 10.33434 0.688956 15 SOX10 4.277515 2.138757 2 BAIAP2 5.463403 0.364227 15 NHX 4.619551 0.30797 15 TABLE 171: Cancer Type ST. _EPN_RELA_B KIRREL3 4.424412 0.294961 15 Gene site imp sum imp mean n RPS6KA2 8.107066 0.579076 14 PTPRN2 6.970855 0.08501 82 PRDM16 11.41428 0.160765 71 MYT1L 2.379207 0.183016 13

HDAC4 7.48412 0.202274 37 ZC3H3 4.9008 0.4084 12

PAX6 5.459408 0.155983 35 CMIP 3.312897 0.276075 12

RBFOX3 3.248363 0.09281 35 MIRLET7BHG 3.241903 0.270159 12

DIP2C 5.933432 0.18542 32 MEGF6 2.898242 0.24152 12

SOX2-OT 2.742155 0.094557 29 VGLL4 5.431527 0.493775 11

GALNT9 2.788617 0.103282 27 ZC3H12D 3.427563 0.311597 11

SHANK2 5.004528 0.192482 26 CTBP2 2.874648 0.261332 11

ADARB2 2.645506 0.10175 26 FGFR2 2.788321 0.253484 11

AGAP1 5.146507 0.20586 25 BCL11B 2.935288 0.293529 10

CAMTAI 3.906327 0.156253 25 ACOT7 2.739333 0.273933 10

PDGFRA 2.738299 0.109532 25 TSPAN4 2.616147 0.261615 10

MEIS1 2.669315 0.111221 24 CBFA2T3 2.411994 0.241199 10

RPTOR 6.480226 0.281749 23 TP73 2.352828 0.235283 10

NXN 3.638481 0.158195 23 ASAP1 5.023262 0.55814 9

PRKCZ 3.711455 0.168703 22 ATP11A 4.541166 0.504574 9

SKI 7.203011 0.343001 21 SND1 3.976947 0.441883 9

FRMD4A 5.16313 0.258157 20 TSPAN9 3.27575 0.363972 9

MAD1L1 6.181267 0.32533 19 MGMT 2.518999 0.279889 9

ZNF423 4.906818 0.258254 19 KCNMA1 2.49215 0.276906 9

SMG1P2 3.801968 0.200104 19 GPC6 2.304978 0.256109 9

BOLA2 3.801968 0.200104 19 SSBP3 2.286095 0.254011 9

LOC613038 3.801968 0.200104 19 DLEU1 3.149543 0.393693 8

CFAP46 3.182928 0.167523 19 MSRA 2.999965 0.374996 8

CASZ1 2.943001 0.154895 19 RGS20 2.536688 0.317086 8

ANKRD11 3.682395 0.204577 18 PPP2R2B 2.440601 0.305075 8

TBC1D16 3.537223 0.196512 18 RORA 2.42611 0.303264 8

FOXK1 2.351994 0.130666 18 CXXC5 3.429377 0.489911 7

OPCML 3.719092 0.21877 17 NAVI 2.688131 0.384019 7

FOXP1 4.956949 0.309809 16 C19orf25 2.569401 0.367057 7

NAV2 2.942074 0.18388 16 RXRA 2.485559 0.35508 7

GLI2 8.384025 0.558935 15 LRRFIP1 2.88193 0.480322 6

EMX2OS 4.11018 0.274012 15 FBXL18 2.636018 0.439336 6

NHX 3.229254 0.215284 15 PTPRG 2.557336 0.426223 6 SLX1B- TSPEAR 2.483097 0.41385 6 SULT1A4 2.887604 0.192507 15 FMNL2 2.451128 0.408521 6

SLX1A 2.887604 0.192507 15 FAM181A 2.388272 0.398045 6

LOC606724 2.887604 0.192507 15 ROR1 2.295268 0.382545 6

LRMDA 2.745954 0.183064 15 RUNDC3A 4.666214 0.933243 5

BAIAP2 2.742197 0.182813 15 ARHGEF7 3.127367 0.625473 5

RPS6KA2 5.374521 0.383894 14 CACNA1I 2.879023 0.575805 5

PPP2R2A 3.042989 0.217356 14 BACH2 2.637348 0.52747 5

C7orf50 2.841244 0.202946 14 KLHL25 2.629848 0.52597 5

MSI2 3.957011 0.304385 13 VAV2 2.287829 0.457566 5

GSE1 3.15414 0.242626 13 CRB2 2.916757 0.729189 4

RFX4 2.746025 0.211233 13 RBMS3 2.754591 0.688648 4

MIR9-3HG 2.662139 0.20478 13 STAP2 2.431634 0.607909 4

KIF26B 2.395133 0.184241 13 VOPP1 2.371361 0.59284 4 NDST1 2.286881 0.57172 4 MCF2L 1.973949 0.109664 18

DAGLB 2.907537 0.969179 3 SORBS2 3.428588 0.214287 16

SOXIO 2.807608 1.403804 2 NAV2 2.842011 0.177626 16

ANKLE2 2.601542 1.300771 2 GT .17 3.321443 0.22143 15

ZBTB20 3.16892 0.211261 15

TABLE 172: Cancer Type VGLL KIRREL3 2.682165 0.178811 15

Gene site imp sum imp mean n RPS6KA2 2.333337 0.166667 14

PTPRN2 8.129568 0.099141 82 CUX1 1.883684 0.134549 14 PRDM16 5.586161 0.078678 71 ZC3H3 3.189378 0.265781 12 PCDHGA1 3.18076 0.053911 59 CMIP 2.961735 0.246811 12 PCDHGA2 2.864374 0.050252 57 GNA12 2.147446 0.178954 12 PCDHGA3 2.864374 0.053044 54 MIRLET7BHG 2.0589 0.171575 12 PCDHGB1 2.864374 0.054045 53 FBRSL1 2.046613 0.170551 12 PCDHGA4 3.18076 0.062368 51 RAD51B 2.403676 0.218516 11 PCDHGB2 3.497146 0.07137 49 CTBP2 2.309302 0.209937 11 PCDHGA5 3.497146 0.074407 47 ZC3H12D 2.282122 0.207466 11 PCDHGB3 2.864374 0.066613 43 AKAP13 3.166309 0.316631 10 PCDHGA6 2.547988 0.0637 40 NR2F1-AS1 2.231467 0.223147 10 HDAC4 5.164737 0.139587 37 TSPAN4 2.168185 0.216819 10 PCDHGA7 2.231602 0.060314 37 SH3RF3 2.088737 0.208874 10 PAX6 4.799113 0.137118 35 RGS12 2.080292 0.208029 10 PCDHGB4 2.547988 0.0728 35 ATP11A 3.240776 0.360086 9 PCDHGA8 2.547988 0.0728 35 NOTCH1 3.133691 0.348188 9 RBFOX3 1.94682 0.055623 35 RUNX1 3.124978 0.34722 9 DIP2C 4.353201 0.136038 32 KCNMA1 3.046948 0.33855 9 PCDHGB5 2.864374 0.089512 32 TRAPPCI 2 2.487117 0.276346 9 PCDHGA9 2.864374 0.092399 31 SND1 2.33234 0.259149 9 SOX2-OT 2.601665 0.089713 29 ASAP1 2.204126 0.244903 9 PCDHGB6 2.337064 0.080588 29 AXIN2 2.039296 0.226588 9 PCDHGA10 2.337064 0.083467 28 LINC00311 3.314817 0.414352 8 CAMTAI 2.968054 0.118722 25 MSRA 2.891438 0.36143 8 AGAP1 2.343429 0.093737 25 NRXN1 2.7291 0.341137 8 SATB2 3.133869 0.130578 24 RORA 2.435753 0.304469 8 PCDHGB7 2.337064 0.097378 24 MCC 2.30493 0.288116 8 RPTOR 6.717009 0.292044 23 BAHCC1 2.268006 0.283501 8 INPP5A 3.589923 0.156084 23 DLEU1 2.029465 0.253683 8 PCDHGA11 2.020678 0.087856 23 LINC00461 3.59814 0.51402 7 PRKCZ 1.896197 0.086191 22 DUSP6 3.172866 0.453267 7 SKI 6.018103 0.286576 21 NAVI 2.787893 0.39827 7

FRMD4A 3.907012 0.195351 20 CXXC5 2.268509 0.324073 7 MAD1L1 4.300474 0.226341 19 PRKCA 2.199418 0.314203 7 SMG1P2 3.091146 0.162692 19 ITPK1 2.124622 0.303517 7 BOLA2 3.091146 0.162692 19 GAK 1.916057 0.273722 7 LOC613038 3.091146 0.162692 19 SLC22A18AS 2.826172 0.471029 6 ZNF423 2.608954 0.137313 19 RADIL 1.977831 0.329638 6 CASZ1 2.143041 0.112792 19 ARHGEF7 2.019588 0.403918 5 SEPTIN9 3.568942 0.198275 18 TSN AX-DISCI 1.943715 0.388743 5 FOXK1 2.279568 0.126643 18 RBMS3 2.803228 0.700807 4 SASH1 1.943826 0.485956 4

PARD3B 1.914289 0.478572 4

GRIN2B 3.556806 1.185602 3

DAGLB 2.499239 0.83308 3

TBC1D7 2.400705 0.800235 3

ANKRD33B 2.062176 0.687392 3

SOXIO 4.385587 2.192794 2

MTHFR 2.260277 1.130139 2

SLC25A10 2.107022 1.053511 2

PLEKHO2 2.755423 2.755423 1

ZNF280D 1.907871 1.907871 1

Claims

-248- CLAIMS

1. A computer- implemented method for the diagnostic classification of cancer, the method comprising: classifying a cancer using a classification algorithm trained using at least data pertaining to biological states of all gene sites in Table 1 (SEQ ID No. 1 to SEQ ID No. 688), wherein the biological states are derived from classified cancer types, wherein classifying the cancer comprises applying the classification algorithm to data pertaining to biological states of a set of gene sites of a cancer sample, wherein the set of gene sites comprises at least 3 gene sites of the cancer sample genome selected from the gene sites in Table 1 (SEQ ID No. 1 to SEQ ID No. 688).

2. The computer- implemented method of claim 1, wherein the classification algorithm is based on at least one of: discriminant analysis, discriminant functional analysis, a kernel method, multidimensional scaling, a nonparametric method, Partial Least Squares, a treebased method, a generalized linear model, a principal components based method, a generalized additive model, a fuzzy logic based method, a neural network, and a genetic algorithm based method.

3. The computer-implemented method of claim 1 or 2, further comprising: determining a biological state pertaining to each of the at least 3 gene sites of the cancer sample genome; and determining a biological state pattern of the set of gene sites based on the determined biological states of the at least 3 gene sites.

4. The computer-implemented method of claims 1 to 3, wherein the biological state is selected from a group consisting of epigenetic state, mutation state, copy number and RNA expression, in particular wherein the epigenetic state is a methylation state.

5. The computer-implemented method of any one of claims 1 to 4, wherein the set of gene sites comprises at least 10, preferably at least 20 or at least 30 or at least 40 or at least 50 or at least 60 or at least 70 or at least 80 or at least 90 or at least 100 gene sites or all gene sites in Table 1 (SEQ ID No. 1 to SEQ ID No. 688).

6. The computer- implemented method of any one of claims 1 to 5, wherein the gene sites are the gene sites with the highest values of variable importance in Tables 3 to 172, respectively.

7. The computer-implemented method of any one of claims 1 to 6, wherein the biological states of the gene sites comprise the biological states of the gene sites as listed in Table 1 (SEQ ID No. 1 to SEQ ID No. 688) and up to 12 kb, preferably up to 10 kb or up to 8 kb or up to 6 kb or up to 4 kb or up to 2 kb, upstream and/or downstream of the genes.

8. The computer-implemented method of any one of claims 1 to 6, wherein the biological states of the gene sites comprise exclusively the biological states of the gene sites as listed in Table 1 (SEQ ID No. 1 to SEQ ID No. 688) without any bases upstream and/or downstream of the gene sites.

9. The computer- implemented method of any one of claims 1 to 8, wherein the biological state is a methylation state and/or the biological state pattern is a methylation state pattern.

10. The computer-implemented method of any one of claims 1 to 9, wherein the cancer is selected from the group consisting of carcinomas, sarcomas, myelomas, neural crest lineage tumors including melanoma, leukemia, lymphoma and mixed types.

11. The computer-implemented method of any one of claims 1 to 10, wherein the cancer is a cancer listed in Table 2.

12. The computer- implemented method of any one of claims 1 to 11, further comprising: determining a further biological state different from the biological states and pertaining to at least one of the gene sites pertaining to the cancer sample genome, wherein the further biological state is selected from the group consisting of epigenetic state, mutation state, RNA expression and copy number; and correlating the further biological state of the at least one gene site pertaining to the cancer sample genome with the classified cancer type.

13. The computer-implemented method of any one of claims 1 to 12, wherein the at least 3 gene sites include one or more of: PTPRN2 (SEQ ID No. 491), PRDM16 (SEQ ID No.477), HDAC4 (SEQ ID No.249), PAX6 (SEQ ID No. 431) and MAD1L1 (SEQ ID No. 349).

14. A computer-readable storage medium having computer-executable instructions stored, that, when executed, cause a computer to perform a method according to claim 1.

15. A system for diagnosing cancer, comprising: one or more processors; and a memory coupled to the one or more processors and comprising instructions executable by the one or more processors to implement the method according to claim 1.