Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
  • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
  • G16B40/20—Supervised data analysis
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/154—Methylation markers

Definitions

• the disclosure is generally directed to systems and methods for cancer screening using cell free DNA.
• a method is for performing a diagnostic scan for cancer.
• the method comprises obtaining a cell-free DNA sample of an individual.
• the cell-free DNA sample comprises a plurality of originating cell-free DNA molecule fragments.
• the method comprises sequencing, using a single molecule sequencing platform, the cell-free DNA sample to yield a sequencing result.
• the method comprises identifying, using a computational processing system, one or more cell-free DNA features within the sequencing result.
• the method comprises entering, using the computational processing system, the one or more cell-free DNA features within one or more machinelearning models trained to detect the presence of cell-free DNA molecules derived from a cancer within the cell-free DNA sample.
• the method comprises determining, using the computational processing system, whether the cell-free DNA sample contains cell-free DNA molecules derived from a cancer based entering of the one or more cell-free DNA features within the one or more machine-learning models.
• the single molecule sequencing platform is selected from Oxford Nanopore Technologies PromethlON sequencing platform, Oxford Nanopore Technologies MinlON sequencing platform, Oxford Nanopore Technologies GridlON sequencing platform, or Pacific Bioscience’s Single Molecule, Real-Time sequencing platform.
• the one or more cell-free DNA features comprises at least one of: a base modification data feature or a cfDNA molecule fragment length data feature.
• the one or more cell-free DNA features comprises both the base modification data feature and the cfDNA molecule fragment length data feature.
• the base modification data feature comprises at least one of: presence of base modifications, fraction of base modifications, base modifications associated with cancer, or patterns of base modifications associated with cancer.
• base modification features comprise at least one of: cytosine methylation status at various loci, fraction of cytosines methylated, and patterns of cytosines methylated.
• the one or more machine learning models is trained to further determine whether the cell-free sample has a cancer-related characteristic.
• Figure 1 A provides a schematic of tumor cells releasing DNA into circulation.
• Figure 2A provides an example of a computational method to train a machine learning model, select features, and assess the machine learning model.
• Figure 4 provides an example of a computational network of a distributed computational system.
• a cfDNA sample is extracted from an individual and utilized to identify a number features.
• Features can include cfDNA methylation patterns and/or cfDNA fragment size.
• the identified features are utilized within a trained machine-learning model to determine whether the cfDNA originated from a cancer. Detection of cancer via the cfDNA can be utilized to perform various clinical assessments and/or treatments.
• various systems and methods of the disclosure utilize single molecule sequencing to identify a cfDNA methylation pattern and/or cfDNA fragmentation of a sample. Such methylation patterns and fragmentation can infer epigenetic information, which can be utilized to detect cancer-derived cfDNA.
• a biological sample is collected prior to any indication of cancer.
• a biological sample is collected to provide an early screen in order to detect a cancer (e.g., before symptoms of cancer are present or are recognized).
• a biological sample is collected to detect if residual cancer (e.g., MRD) exists after a treatment.
• a biological sample is collected during treatment to determine whether the treatment is providing the desired response.
• Cancers that can be screened include (but are not limited to) acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), anal cancer, astrocytomas, basal cell carcinoma, bile duct cancer, bladder cancer, breast cancer, Burkitt’s lymphoma, cervical cancer, chronic lymphocytic leukemia (CLL) chronic myelogenous leukemia (CML), chronic myeloproliferative neoplasms, colorectal cancer, diffuse large B-cell lymphoma, endometrial cancer, ependymoma, esophageal cancer, esthesioneuroblastoma, Ewing sarcoma, fallopian tube cancer, follicular lymphoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, hairy cell leukemia, hepatocellular cancer, Hodgkin lymphoma, hypopharyngeal cancer,
• ALL acute lymphoblastic leukemia
• a cfDNA sample of originating nucleic acid molecule fragments for a sequencing reaction can have greater than 10,000 originating nucleic acid molecule fragments, greater than 100,000 originating nucleic acid molecule fragments, greater than 1 ,000,000 originating nucleic acid molecule fragments, greater than 10,000,000 originating nucleic acid molecule fragments, greater than 100,000,000 originating nucleic acid molecule fragments, or greater than 1 ,000,000,000 originating nucleic acid molecule fragments.
• Method 100 further identifies (103) cfDNA features from the single molecule sequencing result.
• a cfDNA feature is any data feature that can identified within and/or extracted from the single molecule sequencing result.
• Cell-free DNA features include (but are not limited to) sequence variant data features, base modification data features, and cfDNA molecule fragment length data features.
• Sequence variant data features include (but are not limited to) presence of nucleic acid variants, fraction of nucleic acid variants, and nucleic acid variants associated with cancer (or particular cancer types or subtypes).
• Nucleic acid variants include (but are not limited to) single nucleotide variants (SNVs), insertions, deletions, and transversions.
• SNVs single nucleotide variants
• Cell-free DNA features can be identified utilizing the sequencing result by any appropriate means. Sequencing results can be processed, filtered, and/or aligned with a reference genome to call out cfDNA data features. For example, in some implementations, cfDNA fragment molecules reads with greater than 800bp are removed from analysis, which is indicate higher molecular weight DNA that is likely contamination derived from lysed cells within the original biological sample. In various implementations, cfDNA fragment molecules reads are removed from analysis if greater than 400 bp, if greater than 500 bp, if greater than 600 bp, if greater than 700 bp, if greater than 800 bp, if greater than 900 bp, or if greater than 1000 bp.
• Cell-free DNA features can be identified by assessing the sequencing reads within the sequencing result. In some implementations, at least 50% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 60% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 70% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 80% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 90% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 95% of the sequencing reads are assessed to identify cell-free DNA features. In some implementations, at least 99% of the sequencing reads are assessed to identify cell-free DNA features.
• Base modification features can include cytosine methylation status at various loci, fraction of cytosines methylated, and patterns of cytosines methylated.
• a methylation status feature is the frequency of a particular cytosine residue modified within a sample (i.e., number of cfDNA molecules that comprise a modification at particular residue).
• fraction of cytosines methylated is number of cytosines methylated within a particular region, per number of bases, and/or on an originating cfDNA molecule.
• a region can be defined by any useful way.
• Regions can be defined by exonic regions, intronic regions, a sequence of one or more genes, one or more regulatory regions, one or more CpG islands, and/or as determined by a user.
• a region can be defined by CpG frequency (e.g. sequences with a density CpG over or under a threshold).
• Various examples of patterns of cytosines methylated can include a frequency that two or more particular cytosines are simultaneously methylated and a frequency that two or more particular CpG islands are simultaneously methylated. Methylation frequency and patterns can be assessed on originating cfDNA molecule level and/or at a sample level. Although cytosine methylation is used as an example, it should be understood that any base modification can be utilized for identifying base modification features.
• Cell-free DNA molecule fragment length data features can be assessed at a sample level.
• the frequency of a particular molecule fragment length or range of lengths can be utilized a feature.
• the variability of molecule fragment lengths within a sample can be utilized as a feature. It has been discovered that the cancer-derived cfDNA fragments exhibit greater variability than those derived from non-cancer cells.
• the utility of cfDNA features can be determined empirically from clinical data comparing sequencing results between individuals having a cancer and controls (i.e., individuals lacking cancer).
• the empirical data can be derived from cfDNA sources and/or cellular sources (e.g., tumor DNA).
• the empirical data can further be derived from any form of methylation sequencing (e.g., inclusive of bisulfite sequencing).
• the empirical data is derived in a manner that is similar to that of method 100 (i.e., cfDNA source and single molecule sequencing).
• the significance can further be determined using a machine learning model, which can be trained to delineate individuals having a cancer and controls and identify which data features provide the best classification and/or regression score.
• Method 100 also determines (105) presence of cancer in an individual based on the identified cfDNA features, which can be determined using one or more trained ML models.
• one or more cfDNA features are utilized within a trained computational that has been trained to classify and/or score a likelihood that a biological sample includes cfDNA molecules derived from a cancer.
• the one or more ML models can include models that further determine various cancer-related characteristics, including (but not limited to) cell of origin, tissue of origin, morphology, cancer subtype, and cancer stage.
• ML models that can be implemented include (but are not limited to) regression-based and/or classification-based models.
• regressionbased models provide a score that indicates a likelihood of the cancer whereas a classification-based model classifies a sample as likely to include or to not include cancer.
• Regression-based models include (but are not limited to) LASSO regression, ridge regression, k-nearest neighbors, elastic net, least angle regression (LAR), and random forest regression.
• Classification-based models include (but are not limited to) support vector machines (SVMs), decision trees, random forests, and naive Bayes.
• SVMs support vector machines
• a regression-based model or a classification-based model is regularized, while in various embodiments, a regression-based model or a classification-based model is gradient boosted.
• Method 100 can optionally perform (107) a clinical intervention when the ML model indicates that the cfDNA sample contains cfDNA molecules derived from a cancer.
• Clinical interventions can include further clinical evaluation of or administration of a treatment to an individual.
• a clinical evaluation is performed, such as (for example) a blood test, medical imaging, physical exam, a tumor biopsy, or any combination thereof.
• a clinical evaluation comprises performing a diagnostic to determine a stage of cancer.
• a treatment is administered, such as (for example) surgery, chemotherapy, radiotherapy, immunotherapy, hormone therapy, targeted drug therapy, medical surveillance, or any combination thereof.
• an individual is assessed and/or treated by medical professional, such as a doctor, nurse, dietician, or similar.
• ML model to determine the presence of cancer based on cfDNA features.
• the model is trained such that it can be utilized in a cancer diagnostic screen utilizing a cfDNA sample and single molecule sequencing platform.
• a ML model can utilize one or more cfDNA features. Accordingly, various embodiments are directed to the selection of one or more cfDNA features to be utilized within a ML model trained to determine the presence of cancer.
• a cfDNA feature is any data feature that can identified within and/or extracted from the single molecule sequencing result.
• Cell-free DNA features include (but are not limited to) sequence variant data features, base modification data features, cfDNA molecule fragment length data features.
• Sequence variant data features include (but are not limited to) presence of nucleic acid variants, fraction of nucleic acid variants, and nucleic acid variants associated with cancer (or particular cancer types or subtypes).
• Nucleic acid variants include (but are not limited to) single nucleotide variants (SNVs), insertions, deletions, and transversions.
• SNVs single nucleotide variants
• Base modification features include (but are not limited to) presence of base modifications (both generally and/or at particular residues), fraction of base modifications (e.g., fraction of methylated cytosines), base modifications (and patterns thereof) associated with cancer (or particular cancer types or subtypes).
• Modified nucleobases include (but are not limited to) 5-methylcytosine (5mC), 5- hydroxymethylcytosine (5hmC), [3-glucosyl-5-hydroxymethylcytosine (5gmC), 5- formylcytosine (5fC), and N6-methyladenine (6mA).
• Cell-free DNA molecule fragment length data features include (but are not limited to) fraction of particular (or range of) cfDNA molecule fragment lengths and variability of particular (or range of) cfDNA molecule fragment lengths.
• Base modification features can include cytosine methylation status at various loci, fraction of cytosines methylated, and patterns of cytosines methylated.
• a methylation status feature is the frequency of a particular cytosine residue modified within a sample (i.e., number of cfDNA molecules that comprise a modification at particular residue).
• fraction of cytosines methylated is number of cytosines methylated within a particular region, per number of bases, and/or on an originating cfDNA molecule.
• a region can be defined by any useful way.
• Regions can be defined by exonic regions, a sequence of one or more genes, one or more regulatory regions, one or more CpG islands, and/or as determined by a user.
• a region can be defined by CpG frequency (e.g. sequences with a density CpG over or under a threshold).
• Various examples of patterns of cytosines methylated can include a frequency that two or more particular cytosines are simultaneously methylated and a frequency that two or more particular CpG islands are simultaneously methylated. Methylation frequency and patterns can be assessed on originating cfDNA molecule level and/or at a sample level. Although cytosine methylation is used as an example, it should be understood that any base modification can be utilized for identifying base modification features.
• Cell-free DNA molecule fragment length data features can be assessed at a sample level. In some implementations, the frequency of a particular molecule fragment length or range of lengths can be utilized a feature. In some implementations, the variability of molecule fragment lengths within a sample can be utilized as a feature. It has been discovered that the cancer-derived cfDNA fragments exhibit greater variability than those derived from non-cancer cells. [0064] Method 200 can begin by generating (201 ) a set of candidate features. Cell- free DNA features can be identified utilizing a cfDNA sequencing result by any appropriate means. Sequencing results can be processed, filtered, and/or aligned with a reference genome to call out cfDNA data features.
• cfDNA fragment molecules reads with greater than 800bp are removed from analysis, which is indicate higher molecular weight DNA that is likely contamination derived from lysed cells within the original biological sample.
• cfDNA fragment molecules reads are removed from analysis if greater than 400 bp, if greater than 500 bp, if greater than 600 bp, if greater than 700 bp, if greater than 800 bp, if greater than 900 bp, or if greater than 1000 bp.
• Method 200 also obtains (203) population sequencing data and identifies cfDNA features therein.
• the obtained sequencing data can comprise sequencing data from two or more different cohorts, each individual within cohort sharing a particular medical trait.
• the sequencing data comprises data of a cohort afflicted with a cancer and a control cohort (i.e. , free of cancer).
• the two or more cohorts can be delineated by a cancer-related characteristic. Examples of cancer-related characteristics include (but are not limited to) cell of origin, tissue of origin, morphology, cancer subtype, and cancer stage.
• a cohort of breast cancer individuals can be delineated by cancer subtypes, such as (for example) luminal A, luminal B, HER2-positive, and triple negative breast cancer (TNBC).
• TNBC triple negative breast cancer
• Method 200 also builds and trains (205) a machine learning model to differentiate cfDNA samples among the two or more cohorts (e.g., cancer vs. control). Any method to train a ML model can be utilized. Generally, cfDNA features are identified from the population sequencing data and assigned according to the cohort the feature data was derived from. The ML model can utilize the population data for one or more cfDNA features to learn to differentiate the two or more cohorts. In some implementations, a leave-one-out cross validation (LOOCV) machine-learning model is used to build and train a model. In each LOOCV round, the model is iteratively trained on all samples except for one sample that left out. Model performance can be evaluated on the left-out sample.
• LOOCV leave-one-out cross validation
• LOOCV training is attractive because it reduces overfitting and provides a more accurate assessment of the overall stability. See Fig. 2B for a schematic on LOOCV training.
• Any appropriate machine learning model and architecture can be utilized.
• multiple trained machine models are utilized and/or combined (e.g., an ensemble model).
• ML models that can be implemented include (but are not limited to) regression-based and/or classification-based models.
• regressionbased models provide a score that indicates a likelihood of the cancer whereas a classification-based model classifies a sample as likely to include or to not include cancer.
• Regression-based models include (but are not limited to) LASSO regression, ridge regression, k-nearest neighbors, elastic net, least angle regression (LAR), and random forest regression.
• Classification-based models include (but are not limited to) support vector machines (SVMs), decision trees, random forests, and naive Bayes.
• SVMs support vector machines
• a regression-based model or a classification-based model is regularized, while in various embodiments, a regression-based model or a classification-based model is gradient boosted.
• Method 200 also selects (207) cfDNA features to yield a robust model for classifying and/or predicting the likelihood that a cfDNA sample includes cfDNA derived from a cancer.
• Features can also be selected for classifying and/or predicting the likelihood that a cancer has a cancer-related characteristic.
• the first type was statistically significant CpG sites between case and control: we applied FDR-corrected statistical testing to identify the differentially methylated CpG sites across whole genome between case and control groups.
• the second type was case-specific CpG sites; these were sites that were found to have read coverage in only case samples and no control samples.
• the third type of informative site was control-specific CpG sites. These were sites that were found to have read coverage in only control samples and no case samples.
• the final score considers all CpG sites. For each informative CpG site, we also record information about the extent of methylation for the case and control group.
• cfDNA cytosine methylation features can be evaluated and selected.
• a statistical test can be performed for each CpG site in order to determine those with statistically significant methylation for differentiating among the two or more cohorts.
• Each sample’s methylation bedgraph file can be merged using the bedtools unionbedg command. This created an aggregated matrix where the rows are the union of all observed CpG sites, columns are each sequenced sample, and the values of the matrix correspond to the methylation value.
• physical features of sequenced cfDNA can be automatically extracted as the statistics of the estimated size distribution of cfDNA molecules for each sample. To do so, the aggregate distribution of the aligned sequences for each sample was fitted to a two-component normal distribution. This yields six fitted features associated with each sample.
• a random forest model (or similar model) can be trained based on those cfDNA molecule fragment length features, evaluating all samples using LOOCV.
• the initial mean parameters of mono- and bi-nucleosome can be set as 167bp and 330 bp, respectively.
• the cfDNA molecule fragment length features of mononucleosome and di-nucleosome were performed by the estimated means, variations, and the probabilities from the fitted mixture normal distribution.
• a matrix where the rows consist of each sample, and columns are the statistics of the cfDNA nucleosome fragments can be generated.
• a ML model can be assessed and scored by mapping the prioritized CpG sites to a given LOOCV validation sample’s CpG sites through an intersection on genomic positions, which may be useful when utilizing a sparse sequenced dataset. This intersection procedure accommodates for missing data in contrast to other classification-based methods that rely on imputation. The score can then be calculated by determining the likelihood ratio of a given CpG site’s methylation status to match the case cohort and normalized by averaging across all intersected CpG sites.
• the output of this step is then multiple scores: a score based on intersection with statistically significant CpG sites, a score based on case-specific CpG sites, a score based on control-specific sites, and one based on all CpG sites. Models and features that provide the best scores can be utilized for a diagnostic cancer screening assay.
• a computational processing system for determining the presence of cancer in a cfDNA sample in accordance with the various methods of the disclosure typically utilizes a processing system including one or more of a CPU, GPU and/or neural processing engine.
• sequencing results are processed and assessed to detect cfDNA molecules derived from cancer within a sample using a computational processing system.
• the computational processing system is housed within a computing device associated with sequencer.
• the computational processing system is housed separately from the sequencer and receives the sequencing results.
• the computational processing system is implemented using a software application on a computing device such as (but not limited to) mobile phone, a tablet computer, and/or portable computer.
• the computational processing system 300 includes a processor system 302, an I/O interface 304, and a memory system 306.
• the processor system 302, I/O interface 304, and memory system 306 can be implemented using any of a variety of components appropriate to the requirements of specific applications including (but not limited to) CPUs, GPUs, ISPs, DSPs, wireless modems (e.g., WiFi, Bluetooth modems), serial interfaces, depth sensors, IMUs, pressure sensors, ultrasonic sensors, volatile memory (e.g., DRAM) and/or nonvolatile memory (e.g., SRAM, and/or NAND Flash).
• volatile memory e.g., DRAM
• nonvolatile memory e.g., SRAM, and/or NAND Flash
• the memory system is capable of storing a sequencing data 308 and an application for detecting cfDNA molecules derived from cancer within a cfDNA sample 310.
• the application can be downloaded and/or stored in non-volatile memory.
• the application for detecting cfDNA molecules derived from cancer within a cfDNA sample is capable of configuring the processing system to implement computational processes including (but not limited to) the computational processes described above and/or combinations and/or modified versions of the computational processes described above.
• the detecting cfDNA molecules derived from cancer within a cfDNA sample application 310 trains ML models, selects cfDNA features 312, and can utilize the sequence data 308 to yield a result 314 that classifies or scores the likelihood that a sample includes cfDNA derived from cancer in a sample.
• the result 314 is temporarily stored in the memory system during processing and/or saved for use in downstream applications.
• computational processes and/or other processes utilized in the provision of assessing modification status in sequencing results in accordance with various embodiments of the disclosure can be implemented on any of a variety of processing devices including combinations of processing devices. Accordingly, computational devices in accordance with the disclosure should be understood as not limited to specific computational processing systems, but can be implemented using any of the combinations of systems described herein and/or modified versions of the systems described herein to perform the processes, combinations of processes, and/or modified versions of the processes described herein.
• FIG. 4 an embodiment with distributed computing devices is illustrated. Such embodiments may be useful where computing power is not possible at a local level, and a central computing device (e.g., server) performs one or more features, functions, methods, and/or steps described herein.
• a computing device 402 e.g., server
• a network 404 wireless and/or wireless
• it can receive inputs from one or more computing devices, including cfDNA sequencing data or other relevant information from one or more other remote devices 410.
• any outputs can be transmitted to one or more computing devices 406, 408, 410 for entering into records, taking medical action —including (but not limited to) clinical assessment and/or therapeutic administration (e.g., immunotherapy, chemotherapy, radiation therapy, etc.) — and/or any other action relevant to a cancer diagnosis or characterization.
• medical action including (but not limited to) clinical assessment and/or therapeutic administration (e.g., immunotherapy, chemotherapy, radiation therapy, etc.) — and/or any other action relevant to a cancer diagnosis or characterization.
• Such actions can be transmitted directly to a medical professional (e.g., via messaging, such as email, SMS, voice/vocal alert) for such action and/or entered into medical records.
• the instructions for the processes can be stored in any of a variety of non-transitory computer readable media appropriate to a specific application.
• Various embodiments are directed towards utilizing detection of cancer to perform clinical interventions.
• an individual has a liquid or waste biopsy screened and processed by methods described herein to indicate that the individual has cancer and thus an intervention is to be performed.
• Clinical interventions include clinical evaluations and treatments.
• Clinical evaluations include (but not limited to) blood tests, medical imaging, physical exams, and tumor biopsies.
• Treatments include (but not limited to) surgery, chemotherapy, radiotherapy, immunotherapy, hormone therapy, targeted drug therapy, and medical surveillance.
• diagnostics are preformed to determine the particular stage of cancer.
• an individual is assessed and/or treated by medical professional, such as a doctor, nurse, dietician, or similar.
• a cancer can be detected utilizing a sequencing result of cell-free nucleic acids derived from a biological sample.
• cancer is detected when the sequencing result includes cfDNA features that when entered into a ML model indicate the presence of cancer. Accordingly, in a number of embodiments, cell-free nucleic acids are extracted, processed, and sequenced, and the sequencing result is analyzed to detect cancer. This process is especially useful in a clinical setting to provide a diagnostic scan.
• the diagnostic scan can be performed as part of routine screening or as part of a cancer surveillance effort. In some embodiments, the diagnostic scan is performed prior to any indication of cancer. In some embodiments, the diagnostic scan is performed to provide an early screen in order to detect a cancer (e.g., before symptoms of cancer are present or are recognized). In some embodiments, the diagnostic scan is performed to detect if residual cancer (e.g., MRD) exists after a treatment. In some embodiments, the diagnostic scan is performed during treatment to determine whether the treatment is providing the desired response.
• residual cancer e.g., MRD
• diagnostic scans can be performed for any neoplasm type, including (but not limited to) acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), anal cancer, astrocytomas, basal cell carcinoma, bile duct cancer, bladder cancer, breast cancer, Burkitt’s lymphoma, cervical cancer, chronic lymphocytic leukemia (CLL) chronic myelogenous leukemia (CML), chronic myeloproliferative neoplasms, colorectal cancer, diffuse large B-cell lymphoma, endometrial cancer, ependymoma, esophageal cancer, esthesioneuroblastoma, Ewing sarcoma, fallopian tube cancer, follicular lymphoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, hairy cell leukemia, hepatocellular cancer, Hodgkin lymphoma, hypopharyngeal
• a number of embodiments are directed towards performing a diagnostic scan on cell-free nucleic acids of an individual and then based on results of the scan indicating cancer, performing further clinical evaluation and/or treating the individual.

Landscapes

• Life Sciences & Earth Sciences (AREA)
• Health & Medical Sciences (AREA)
• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Chemical & Material Sciences (AREA)
• Proteomics, Peptides & Aminoacids (AREA)
• Bioinformatics & Cheminformatics (AREA)
• General Health & Medical Sciences (AREA)
• Analytical Chemistry (AREA)
• Biophysics (AREA)
• Biotechnology (AREA)
• Medical Informatics (AREA)
• Spectroscopy & Molecular Physics (AREA)
• Evolutionary Biology (AREA)
• Bioinformatics & Computational Biology (AREA)
• Theoretical Computer Science (AREA)
• Organic Chemistry (AREA)
• Genetics & Genomics (AREA)
• Data Mining & Analysis (AREA)
• Zoology (AREA)
• Immunology (AREA)
• Wood Science & Technology (AREA)
• Molecular Biology (AREA)
• Pathology (AREA)
• Bioethics (AREA)
• Hospice & Palliative Care (AREA)
• Software Systems (AREA)
• Oncology (AREA)
• Public Health (AREA)
• Evolutionary Computation (AREA)
• Microbiology (AREA)
• Epidemiology (AREA)
• Databases & Information Systems (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Artificial Intelligence (AREA)
• Biochemistry (AREA)
• General Engineering & Computer Science (AREA)
• Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
• Investigating Or Analysing Biological Materials (AREA)

Applications Claiming Priority (3)

Publications (2)

Family

ID=89849935

Family Applications (1)

Country Status (2)

Families Citing this family (1)

Family Cites Families (2)

• 2023
  • 2023-08-07 WO PCT/US2023/071782 patent/WO2024031097A2/en not_active Ceased
  • 2023-08-07 EP EP23851023.4A patent/EP4566072A4/de active Pending

Also Published As

Similar Documents

Legal Events