WO2017185165A1 - Signature génique pour le pronostic du cancer de la prostate - Google Patents

Signature génique pour le pronostic du cancer de la prostate Download PDF

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WO2017185165A1
WO2017185165A1 PCT/CA2017/000095 CA2017000095W WO2017185165A1 WO 2017185165 A1 WO2017185165 A1 WO 2017185165A1 CA 2017000095 W CA2017000095 W CA 2017000095W WO 2017185165 A1 WO2017185165 A1 WO 2017185165A1
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cancer
cna
tumour
risk
patients
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Paul Boutros
Robert G. Bristow
Emilie LALONDE
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University Health Network
Ontario Institute for Cancer Research
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Ontario Institute for Cancer Research
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    • 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
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/575—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57555—Immunoassay; Biospecific binding assay; Materials therefor for cancer of the prostate
    • 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
    • C12Q2600/118—Prognosis of disease development
    • 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
    • C12Q2600/156—Polymorphic or mutational markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/54—Determining the risk of relapse

Definitions

  • the present invention relates to methods for prostate cancer patient prognosis. Specifically, certain embodiments of the present invention relate to a method for determining a risk of recurrence of cancer following a cancer therapy of a patient, comprising determining genomic instability of a tumour of the patient.
  • CNA DNA-based 100-locus copy number alteration
  • This genomic classifier comprises 276 genes and was developed using a ⁇ 27,000 probe array comparative genomic hybridization (aCGH) platform. To more effectively translate this genomic classifier to the clinic, we needed to compress the prognostic signature into a smaller feature size that could be processed from routine diagnostic biopsies and be assayed using a more precise technology.
  • aCGH probe array comparative genomic hybridization
  • the NanoString mRNA platform is CLIA certified and is used to implement Prosigna, an FDA-approved assay measuring thee breast cancer PAM50 intrinsic subtypes 13,14 .
  • Their copy number platform http://www.nanostring.com/products/CNV) is used in the OmniSeq TargetTM assay to use amplifications in ERBB2, FGFR1 and MET to help guide treatment decisions in lung cancer and melanoma patients.
  • a method for determining a risk of recurrence of cancer following a cancer therapy of a patient comprising determining genomic instability of a tumour of the patient by: (a) obtaining a biopsy of the tumour; (b) identifying genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31-loci in Table A; (c) determining a plurality of copy number calls in the genome regions; (d) intersecting the plurality of copy number calls with a reference gene list, to obtain a plurality of Copy Number Alterations (CNA) calls for each gene; (e) generating a CNA tumour profile based on the plurality of CNA calls; (f) comparing the CNA tumour profile to a reference profile of recurring cancer patients and a reference profile of nonrecurring cancer patients; (g) calculating a plurality of statistical distances between the CNA tumour profile and the reference profile of recurring cancer patients and the reference profile of nonrecurring cancer patients; wherein the statistical distance between the CNA tumour profile and the reference
  • a method for determining the risk of recurrence of cancer following a cancer therapy of a patient, comprising determining genomic instability of a tumour of the patient based on: (a) determining, at a processor, a genome of the tumour; (b) determining, by the processor, genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31-loci in Table A; (c) determining, by the processor, a plurality of copy number calls in the genome regions; (d) determining, by the processor, a plurality of Copy Number Alternations (CNA) calls for each gene by intersecting the plurality of copy number calls with a reference gene list; (e) determining, by the processor, a CNA tumour profile based on the plurality of CNA calls; (f) determining, by the processor, a plurality of statistical distances between the CNA tumour profile and a reference profile of recurring cancer patients and a reference profile of nonrecurring
  • a system for determining the risk of recurrence of cancer following a cancer therapy of a patient comprising determining genomic instability
  • the system comprising: a non-transitory computer readable storage medium that stores computer-readable code; a processor operatively coupled to the non-transitory computer readable storage medium, the processor configured to implement the computer-readable code, the computer-readable code configured to: determine a genome of the tumour; determine genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31-loci in Table A; determine a plurality of Copy Number Alterations (CNA) calls for each gene based on intersecting the copy number calls with a reference gene and storing the plurality of CNA calls in the non-transitory computer readable storage medium; determine a CNA tumour profile based on the plurality of CNA calls and storing the CNA tumour profile in a non- transitory computer readable storage medium; determine a plurality of statistical distances between the CNA tumour profile and a reference profile of
  • CNA Copy Number Alter
  • Figure 1 shows genomic classifier reduction and signature-estimated percent genome alteration.
  • B The correlation of global PGA to signature-estimated PGA using the reduced 31-locus genomic classifier.
  • Signature-estimated PGA is calculated by the fraction of genomic base pairs involved in a region of CNA when considering the 109 genes in the 31- locus genomic classifier only.
  • C The correlation of global PGA to signature-estimated PGA using the reduced 31-locus genomic classifier plus an additional 30 genes which were previously selected to maximize PGA estimation 12 .
  • Figure 2 shows genomic classifier performance in the Combined-Arrays cohort. Cox models are adjusted for clinical variables as in Table 5 RFR: relapse-free rate.
  • A The reduced 100-locus and the 31-locus genomic classifiers effectively stratify patients from the Taylor, Ross-Adams, Hieronymus, and Swiss cohorts, including patients diagnosed with low, intermediate, and high risk disease.
  • B The reduced 31-locus genomic classifier effectively stratifies patients from the Taylor, Ross-Adams, Hieronymus, and Swiss cohorts, including only patients diagnosed with low and intermediate risk disease.
  • C The reduced 31-locus genomic classifier effectively stratifies patients from the Taylor, Hieronymus, and Swiss cohorts, including only patients diagnosed with high risk disease.
  • Figure 3 shows clinical utility of the reduced 31-loci genomic classifier.
  • the "31 loci + risk.” model includes the continuous 31 -locus risk score and the NCCN risk groups whereas the "31 loci + GS/PSA/T" model includes the continuous 31 -locus risk score, Gleason score, T-category, and PSA.
  • B The net reclassification index (NRI) based on using the full clinico-genomic model (31-loci + GS/PSA/T) in comparison to the clinical model with GS/PSA T only for predicting BCR in the Combined-Arrays cohort.
  • (C) Receiver operator curve analysis for predicting metastasis at 10 years with the 31- locus genomic classifier, clinical models, and clinico-genomic models in the Taylor cohort.
  • the "31 loci + risk.” model includes the continuous 31 -locus risk score and the NCCN risk groups whereas the "31 loci + GS/PSA/T” model includes the continuous 31 -locus risk score, Gleason score, T-category, and PSA.
  • GS Gleason Score
  • T T-category from TNM
  • PSA Prostatic Specific Antigen.
  • D The net reclassification index (NRI) based on using the full clinico-genomic model (31 loci + GS/PSA/T) in comparison to the clinical model with GS/PSA/T only for predicting metastasis in the Taylor cohort. The overall NRI is indicated in the legend.
  • Figure 4 shows validation of reduced 31-locus genomic classifier in the CPC-GENE cohort using the NanoString platform.
  • A The Kaplan-Meir curves for the CPC-GENE cohort stratified by the reduced 31-locus genomic signature. The Cox model is adjusted for clinical variables as shown in Table 5.
  • B Receiver operator curve analysis for predicting BCR at 5 years with the 31-locus genomic classifier, clinical models, and clinico-genomic models.
  • the "31 loci + Risk.” model includes the continuous 31-locus risk score and the NCCN risk groups whereas the "31 loci + GS/PSA/T" model includes the continuous 31-locus risk score, Gleason score, T- category, and PSA.
  • AUC Area under the curve
  • GS Gleason Score
  • T T-category from TNM
  • PSA Prostatic Specific Antigen.
  • Figure 5 shows signature reduction strategy. A flowchart of the workflow used in this study. The signature was refined by examining RNA ⁇ DNA associations in the Taylor dataset using logistic regression (see Methods for full details). Of the 276 genes, 36 genes had significant associations using a lenient threshold (false-discovery rate adjusted p-values ⁇ 0.1). We selected the 31 loci containing one of these 36 genes to build the 31 -locus genomic classifier. This 31 -locus (or 109 gene) classifier was trained on the Toronto aCGH cohort, which is the same cohort used to train the original classifier.
  • FIG. 6 shows prognosis of clinical variables in the CPC-GENE cohort.
  • Cox proportional hazard regression models were fit to each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group). When there were more than two levels (i.e. NCCN risk group) a logrank test was used instead.
  • Figure 7 shows prognosis of clinical variables in the Taylor cohort. Logrank tests were used to quantify the prognosis of each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group).
  • Figure 8 shows prognosis of clinical variables in the Ross-Adams cohort.
  • Logrank tests were used to quantify the prognosis of each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group). Since there were only two patients with PSA ⁇ 20, only two levels were used to evaluate the prognosis of PSA, and a Cox proportional hazard regression model was used to quantify this effect.
  • Figure 9 shows prognosis of clinical variables in the Hieronymus cohort.
  • Cox proportional hazard regression models were fit to each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group). When there were more than two levels (i.e. NCCN risk group) a logrank test was used instead.
  • Figure 0 shows prognosis of clinical variables in the Sweden cohort. Logrank tests were used to quantify the prognosis of each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group).
  • Figure 1 1 shows prognosis of clinical variables in the Combined-Arrays cohort. Logrank tests were used to quantify the prognosis of each individual clinical covariate (A: Gleason score, B: T-category, C: PSA, D: NCCN risk-group).
  • Figure 12 shows association of mRNA abundance with copy number status.
  • the signature was refined by examining RNA ⁇ DNA associations in the Taylor dataset using logistic regression (see Methods for full details). Genes that had both deletions and gains were fit as a three-level factor. The mRNA abundance of the gene was used as a continuous variable and modeled as a function of the CNA state of the same gene. Of the 276 genes from the original full genomic classifier, 36 genes had significant associations for deletions and/or gains using a lenient threshold (false- discovery rate adjusted p-values ⁇ 0.1 ).
  • Figure 13 shows Gini scores for full and 31 -locus genomic classifiers.
  • Gini score (represents the relative importance of each feature in the genomic classifier) of the 100-locusi and reduced 31-locus genomic classifiers.
  • the 31-locus genomic classifier illustrates the loci which were selected due to associations between gene copy number and mRNA abundance.
  • Figure 14 shows correlation of genomic classifier scores in Taylor and Ross-Adams cohorts using the 31-locus genomic classifier compared to the full genomic classifier. A comparison of the genomic classifier scores produced by the full and reduced classifiers. "Yes votes" represents the genomic classifier score produced by the random forest models.
  • Figure 15 shows validation of the 31-locus genomic classifier per cohort. Cox proportional hazard regression models are adjusted for clinical variables as defined in Table 5. The HR and p-value shown are for the genomic classifier term.
  • Figure 16 shows CNA rate per cohort. A- The proportion of CNAs per gene per cohort. B- The proportion of CNAs per patient per cohort.
  • Figure 17 shows validation of the 31-locus genomic classifier in low-risk (A) and intermediate-risk (B) patients from the Combined-arrays cohort.
  • Cox proportional hazard regression models are adjusted for clinical variables as defined in Table 5. The HR and p-value shown are for the genomic classifier term.
  • Figure 18 shows AUC comparison for the 31-locus genomic classifier in each cohort. Receiver operator characteristic (ROC) comparison of various clinico-genomic models. Each model was fit with a Cox proportional hazard regression model and the predicted risk scores were evaluated using in a ROC analysis for each cohort separately: A- Taylor. B- Ross-Adams. C- Hieronymus. D- Sweden.
  • Figure 19 shows AUC comparison for 31 -locus genomic classifier in the Combined- Arrays cohort. A- All patients. B- Low-risk patients. C- Intermediate-risk patients. D- High-risk patients.
  • Figure 20 shows clinical utility of the reduced 31-loci genomic classifier.
  • the probability of BCR increases as the full clinico-genomic risk score increases.
  • the full clinico- genomic risk score is the predicted risk from the multivariate Cox model predicting 5- year BCR.
  • Figure 21 shows concordance of NanoString replicates. Concordance of the six sets of replicate samples processed on the NanoString platform.
  • A- CNAs identified in each replicate sample, with genes and samples clustered. All replicates pair with each other, except for one of the three CPCG0462 replicate samples. Only endogenous and housekeeping probes were used.
  • Figure 22 shows suitable configured computer device, and associated communications networks, devices, software and firmware to provide a platform for enabling one or more embodiments as described herein.
  • Prostate cancer is a clinically heterogeneous disease, despite tightly-defined, clinical risk-groups that represent relative prostate cancer-specific mortality. Patients with localized disease are burdened with high rates of both over-treatment and under- treatment, suggesting that current patient stratification schemes are inefficient to triage patients to less or more intensive treatment protocols.
  • the genomic classifier contained 276 genes which were enriched for lipid metabolism genes, and associated with global genomic instability.
  • the 100-locus DNA genomic classifier is also described in WO 2015/106341 , the disclosure of which is hereby incorporated by reference.
  • HR Hazard ratio
  • CI 95% confidence interval
  • a method for determining a risk of recurrence of cancer following a cancer therapy of a patient comprising determining genomic instability of a tumour of the patient by: (a) obtaining a biopsy of the tumour; (b) identifying genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31-loci in Table A; (c) determining a plurality of copy number calls in the genome regions; (d) intersecting the plurality of copy number calls with a reference gene list, to obtain a plurality of Copy Number Alterations (CNA) calls for each gene; (e) generating a CNA tumour profile based on the plurality of CNA calls; (f) comparing the CNA tumour profile to a reference profile of recurring cancer patients and a reference profile of nonrecurring cancer patients; (g) calculating a plurality of statistical distances between the CNA tumour profile and the reference profile of recurring cancer patients and the reference profile of nonrecurring cancer patients; wherein the statistical distance between the CNA tumour profile and the reference
  • genomic instability is the degree of genetic differences that exist between a reference genetic baseline and a genetic sample.
  • the genetic differences that exist may be expressed by proxy with specific reference to the number of copy number calls made between the reference genetic baseline and the genetic sample.
  • locus is a specific genetic region of variable length and identity. A ranking of a selection of relevant loci is found in Table A.
  • copy number call is the quantity of a genetic unit obtained from a genetic sample subjected to a genetic assay. Copy number calls may be assessed thorough the use of an amplified fragment pool assay, as described more fully below.
  • copy number alteration is the value representing a comparison of the copy number call of a given genetic unit to that of a reference genome that may give rise to a determination as to whether there is a loss or gain of genetic material for that given genetic unit.
  • CNA tumour profile is the plurality of CNAs associated with a given genetic tumour sample.
  • reference profile of recurring cancer patients is the plurality of CNAs associated with a given set of genetic tumour samples of a population of patients wherein it is known that cancer reoccurred after a given cancer treatment.
  • reference profile of nonrecurring cancer patients is the plurality of CNAs associated with a given set of genetic tumour samples of a population of patients wherein it is known that cancer did not reoccur after a given cancer treatment.
  • statistical distance is a value representing the comparison of sets of data that gives rise to a determination of the degree of association, or lack thereof, between said sets of data.
  • a specific embodiment of a statistical distance may be the use of a Jaccard distance (Jaccard, 1901 ), as described more fully below.
  • the genome regions are at least loci rankings 1-20, 1-25, or 1-31 in Table A. In an embodiment, the genome regions are a whole tumour genome.
  • the patient has been diagnosed with prostate cancer. In some instances, the patient has been diagnosed with localized prostate cancer. Preferably, the patient has one of a low or intermediate risk for prostate cancer. For example, the patient has one of a low or intermediate risk for prostate cancer as determined by at least one of T-category, Gleason score or pre-treatment prostate-specific antigen blood concentration.
  • the low risk for prostate cancer is determined by at least one of the following: (a) a T-category of T1-T2a, a Gleason score less than or equal to 6, and a pre-treatment prostate-specific antigen blood concentration less than or equal to 10 ng/mL; (b) a T-category of T1-T2a, a Gleason score greater than or equal to 2 and less than or equal to 6, and a pre-treatment prostate-specific antigen blood concentration less than or equal to 10 ng/mL; and (c) a T-category of T1 c, a Gleason score less than or equal to 6, a pre-treatment prostate-specific antigen blood concentration less than or equal to 10 ng/mL, and fewer than 3 biopsy cores of a tumour that are positive for cancer and having less than or equal to 50% cancer in each.
  • the intermediate risk for prostate cancer is determined by at least one of the following: (a) at least one of a T-category of T2b, a Gleason score equal to 7, and a pre-treatment prostate-specific antigen blood concentration greater than 10 ng/mL; (b) at least one of a T-category of T1-T2, a Gleason score equal to or less than 7, and a pre-treatment prostate-specific antigen blood concentration less than or equal to 20 ng/mL;(c) at least one of a T-category of T2b, a Gleason score equal to 7 and a pre-treatment prostate-specific antigen blood concentration greater than 0 ng/ml and equal to or less than 20 ng/mL; and (d) at least one of a T-category of T2b, a T-category of T2c, a Gleason score equal to 7 and a pre-treatment prostate-specific antigen blood concentration greater than 10 ng/
  • FIG. 22 shows a generic computer device 100 that may include a central processing unit (“CPU") 102 connected to a storage unit 104 and to a random access memory 106.
  • the CPU 102 may process an operating system 101 , application program 103, and data 123.
  • the operating system 101 , application program 103, and data 123 may be stored in storage unit 104 and loaded into memory 106, as may be required.
  • Computer device 100 may further include a graphics processing unit (GPU) 122 which is operatively connected to CPU 102 and to memory 106 to offload intensive image processing calculations from CPU 102 and run these calculations in parallel with CPU 102.
  • An operator 107 may interact with the computer device 100 using a video display 108 connected by a video interface 105, and various input/output devices such as a keyboard 1 15, mouse 112, and disk drive or solid state drive 114 connected by an I/O interface 109.
  • the mouse 1 12 may be configured to control movement of a cursor in the video display 108, and to operate various graphical user interface (GUI) controls appearing in the video display 108 with a mouse button.
  • GUI graphical user interface
  • the disk drive or solid state drive 114 may be configured to accept computer readable media 1 16.
  • the computer device 100 may form part of a network via a network interface 1 11 , allowing the computer device 100 to communicate with other suitably configured data processing systems (not shown).
  • One or more different types of sensors 135 may be used
  • the present system and method may be practiced on virtually any manner of computer device including a desktop computer, laptop computer, tablet computer or wireless handheld.
  • the present system and method may also be implemented as a computer- readable/useable medium that includes computer program code to enable one or more computer devices to implement each of the various process steps in a method in accordance with the present invention.
  • the computer devices are networked to distribute the various steps of the operation.
  • the terms computer-readable medium or computer useable medium comprises one or more of any type of physical embodiment of the program code.
  • the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g. an optical disc, a magnetic disk, a tape, etc.), on one or more data storage portioned of a computing device, such as memory associated with a computer and/or a storage system.
  • a method for determining the risk of recurrence of cancer following a cancer therapy of a patient, comprising determining genomic instability of a tumour of the patient based on: (a) determining, at a processor, a genome of the tumour; (b) determining, by the processor, genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31-loci in Table A; (c) determining, by the processor, a plurality of copy number calls in the genome regions; (d) determining, by the processor, a plurality of Copy Number Alternations (CNA) calls for each gene by intersecting the plurality of copy number calls with a reference gene list; (e) determining, by the processor, a CNA tumour profile based on the plurality of CNA calls; (f) determining, by the processor, a plurality of statistical distances between the CNA tumour profile and a reference profile of recurring cancer patients and a reference profile of nonrecurring
  • a system for determining the risk of recurrence of cancer following a cancer therapy of a patient comprising determining genomic instability
  • the system comprising: a non-transitory computer readable storage medium that stores computer-readable code; a processor operatively coupled to the non-transitory computer readable storage medium, the processor configured to implement the computer-readable code, the computer-readable code configured to: determine a genome of the tumour; determine genome regions of the biopsy wherein the regions are at least loci rankings 1-15 of the 31 -loci in Table A; determine a plurality of Copy Number Alterations (CNA) calls for each gene based on intersecting the copy number calls with a reference gene and storing the plurality of CNA calls in the non-transitory computer readable storage medium; determine a CNA tumour profile based on the plurality of CNA calls and storing the CNA tumour profile in a non- transitory computer readable storage medium; determine a plurality of statistical distances between the CNA tumour profile and a reference profile
  • CNAs were called for Ross-Adams cohort with OncoSNP which ranks CNA calls from 1-5.
  • a rank of 1 indicates high confidence calls, and a rank of 5 represents the least confident calls.
  • the normalized genomic data was downloaded from GEO (accession GSE73076).
  • ASCAT version 2.1
  • One of three copy number states was assigned to each segment relative to the average genome ploidy (AGP) per tumour sample as calculated by ASCAT.
  • the following thresholds were tested to assign copy number states: 0.6, 0.7, 0.8, 0.9 and 1.0.
  • a threshold of 0.9 was selected, such that the median percent genome alteration of the cohort is between 2-4%, which is consistent with the median PGA of other published prostate cancer cohorts 12 .
  • the three cohorts processed using CNA microarrays were combined to assess patient prognosis in a larger cohort of 461 patients, allowing for examination of effect within NCCN risk groups.
  • the CPC-GENE cohort consists of 102 prostate cancer patients treated by RadP, a subset of the Canadian Prostate Cancer Genome Network (CPC-GENE).
  • Fresh frozen RadP specimens were obtained mostly (69/100) from the University Health Network Pathology BioBank, and the remaining 33 from the Genito-Urinary BioBank of the Centre Hospitalier Universitaire de Quebec (CHUQ). Whole blood and informed consent were collected during follow-up clinical appointments.
  • Tumour tissues had been collected according to protocols approved by the University Health Network Research Ethics Board (UHN 06-0822-CE, UHN 11-0024-CE, CHUQ 2012-913:H12- 03-192). GS and tumour cellularity were independently evaluated by two genitourinary pathologists (TvdK, BT). Importantly, none of the patients included in this study were used in the initial genomic classifier discovery 12 . Samples were cut into 60 x 10 pm sections, and one in every 10 cuts 4 pm section was H&E-stained.
  • NanoString platform we processed 102 RadP specimens and blood samples from 30 patients were used to generate a pooled-normal reference.
  • the NanoString RCC files were loaded into the R statistical language with the NanoStringNorm package (v1 .1 .20) All 132 samples passed quality control as evaluated by coverage and variance of control genes, and no observable batch effect (Adjusted Rand Index 0.209).
  • the sample:region term estimates the log copy number and NanoString defined thresholds are used to convert these continuous results to ternary copy number calls.
  • NanoStringNorm 24 was updated to perform additional quality control and normalization techniques specific to CNA analysis (Lalonde ef a/. , in preparation).
  • 31 loci (containing 109 genes) were used to re-train a random forest 25 in the original aCGH data, consisting of 126 low- and intermediate-risk patients treated with IGRT 12 .
  • the randomForest package (v4.6-10) with 100,000 trees and otherwise default parameters was used to train the 31 -locus genomic classifier, which was then applied to the remaining cohorts alone ("31 -locus genomic classifier") or in combination with the clinical variables NCCN risk group ("31 -loci + risk clinico-genomic classifier"), or GS, PSA and T-category ("31 -loci + GS/PSA/T clinico-genomic classifier”).
  • NCCN risk group 31 -loci + risk clinico-genomic classifier
  • GS, PSA and T-category (3 -loci + GS/PSA/T clinico-genomic classifier”
  • Biochemical recurrence is defined as two consecutive PSA readings above 0.2 ng/mL, or salvage treatment.
  • 48 CPC-GENE patients (48%), 46 Taylor patients (30%), 24 Hieronymus patients (23%), and 42 Swiss patients (49%) have had a biochemical failure.
  • Median time to event was estimated using the Kaplan-Meier approach 26 .
  • the univariate prognosis of GS, T-category and PSA were assessed with Cox proportional hazard regression models when the variable had only two levels, and otherwise with a logrank test. ( Figures 6-1 1 ).
  • Cox proportional hazard regression models were used for creating both continuous and discretized risk scores.
  • the survMisc package (v0.4.6) was used to determine the optimal threshold for dichotomizing patient risk scores (0.01207 for the microarrays cohorts and 0.44101 for the NanoString cohort).
  • the risk score was fit in both the univariate setting and in the multivariate setting where appropriate clinical covariates were included, depending on the cohort (Table 5).
  • Proportional hazard assumptions were tested with the R function cox.zph which assesses the correlation of survival time with the scaled Schoenfeld residuals of each variable; a p-value threshold of 0.05 was used to identify variables which failed the assumption.
  • Table 6 Signature loci per chromosome in full and reduced genomic classifiers.
  • Table 7 Cox proportional hazard models for the 31-locus genomic classifier using 8- month and 5-year BCR endpoints.
  • a model was fit for the dichotomized genomic classifier risk score for each individual cohort and for the Combined-Arrays cohort. Models were also fit for each NCCN risk group using the patients from the Combined- Arrays cohort only. Each model was fit twice, one predicting for 18-month relapse and another for 5-year relapse.
  • Table 8 Cox proportional hazard models for the reduced genomic classifier using a continuous risk-score and 18-month and 5-year BCR endpoints.
  • a model was fit for the continuous genomic classifier risk score for each individual cohort and for the Combined-Arrays cohort. Models were also fit for each NCCN risk group using the patients from the Combined-Arrays cohort only. Each model was fit twice, one predicting for 18-month relapse and another for 5-year relapse.
  • Patient prognosis is evaluated by 10-year bRFR in the Combined-arrays cohort.
  • Table 9 Multivariate models for 10-year metastasis-free survival in the Taylor cohort.
  • A- A Cox proportional hazard regression for patients predicted to develop metastasis by the reduced 31 -loci genomic classifier ("Signature +").
  • CI confidence interval
  • HR hazard ratio
  • AUC area under the survival receiver operator curve
  • These increases in AUC also hold within each NCCN risk group, illustrating that even within tight clinical cohorts, the reduced genomic classifier is useful in addition to standard clinical covariates (Figure 19).
  • AUC has limited utility within homogeneous clinical risk groups and it is difficult to assess whether a statistically significant increase in AUC is clinically meaningful 28 .
  • the clinical utility of the clinico-genomic classifiers is even more pronounced.
  • the AUC for the 31-locus clinico-genomic classifiers are 0.89 and 0.85 compared to 0.79 and 0.75 for the NCCN risk group and the GS/PSA T classifiers, respectively, indicating increased clinical utility in guiding patient management compared to clinical standards (Figure 3C).
  • Table 10 Multivariate Cox proportional hazard regression model for the clinical variables in the CPC-GENE cohort for 10-year BCR-free progression.
  • the 31 -locus classifier is the strongest predictor and only significant variable within the model, with an adjusted HR of 6.41 per unit increase of the continuous risk score.
  • the survival AUC for the clinical model is only 0.62 but increases to 0.72 after addition of the 31 loci, indicating a clear benefit to considering the genomic classifier along with standard clinical variables.
  • Our 00-locus genomic classifier was the first DNA-based multi-locus gene signature proposed for stratification of localized prostate cancer patients 12 .
  • this genomic classifier we refine this genomic classifier to 31 loci showing DNA-RNA associations, show its utility in three public datasets comprising 461 men with localized disease, and validate the genomic classifier in a tightly defined clinical risk group using a clinically-relevant technology.
  • the NanoString CNV platform requires only 300 ng DNA, making it achievable for routine use after patient biopsy. To our knowledge, this is the first study using NanoString's CNV platform to measure a multi-locus gene signature.
  • the 31 -locus genomic classifier is able to sub-stratify patients across all NCCN risk groups. Patient management could therefore integrate both clinical risk group and our genomic classifier score. Compared to using the GS/PSA T clinico-pathological variables, the additional use of our 31 -locus genomic classifier is more accurate. Based on Cox proportional hazard models, the smallest impact for the genomic classifier is in the intermediate-risk group which highlights the difficulty in further stratifying the risk for these patients; to improve predictions, information from other molecular or microenvironmental information may be required 12,16 .
  • the performance of the clinico-genomic classifier is considerably better than the clinical classifier (AUC 0.61 vs. 0.72).
  • the 31 -locus genomic classifier is particularly promising in the low-risk group, where patients identified as good prognosis by the signature have 10-year bRFR of 87% and are candidates for treatment de-escalation trials (Figure 17).
  • the 31 -locus genomic classifier is also highly effective in the high-risk group, where it identifies patients failing rapidly (within 18 months) who might benefit from more aggressive initial treatment targeting occult metastases and prevent disease progression.
  • Some prognostic molecular signatures are currently available to patients in the clinic such as Oncotype DX Genomic Prostate Score 11 , ProMark 31 , Prolans 9 and Decipher 10 .
  • the first two signatures cater to low-risk patients and aim to identify the patients least likely to progress on active surveillance protocols.
  • the Prolans signature is most similar to ours; it is intended for all patients with localized disease and is meant to be used in conjunction with clinical variables to identify patients for increased or decreased treatment compared to standard protocols.
  • the Decipher classifier identifies higher risk patients that are likely to fail therapy and develop metastasis. It is also used to identify patients that would benefit from adjuvant androgen deprivation therapy in conjunction with RadP 32 .
  • NanoStringNorm an extensible R package for the pre- processing of NanoString mRNA and miRNA data.
  • Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. Journal of Clinical Oncology 33, 944-951 (2015).

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Abstract

La présente invention concerne un procédé de détermination d'un risque de récidive d'un cancer après une thérapie du cancer d'un patient, comprenant la détermination de l'instabilité génomique d'une tumeur du patient à partir d'une biopsie par identification de régions génomiques de la biopsie, les régions étant au moins des classements de locus de 1 à 15 de 31 locus spécifiques et utilisation d'appels de nombre de copies et calcul d'une pluralité de distances statistiques entre le profil de tumeur CNA et un profil de référence de patients atteints d'un cancer récidivant pour déterminer le risque de récidive du cancer après la thérapie du cancer du patient.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018170578A1 (fr) * 2017-03-20 2018-09-27 Ontario Institute For Cancer Research (Oicr) Risque de cancer basé sur la clonalité tumorale
EP4042161A4 (fr) * 2019-10-09 2023-12-20 The University of North Carolina at Chapel Hill Altérations du nombre de copies (cna) d'adn pour déterminer des phénotypes de cancer

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WO2015106341A1 (fr) * 2014-01-17 2015-07-23 Ontario Institute For Cancer Research (Oicr) Signature génomique obtenue à partir d'une biopsie pour pronostiquer un cancer de la prostate

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LALONDE, E. ET AL.: "Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study", LANCET ONCOL., vol. 15, no. 13, December 2014 (2014-12-01), pages 1521 - 1532, XP055404748, ISSN: 1474-5488 *
LALONDE, E.: "Translating a prognostic DNA genomic classifier into the clinic: Retrospective validation in 563 localized prostate tumors", EUROPEAN UROLOGY, vol. 30716-3, no. 16, 1 November 2016 (2016-11-01), pages S0302 - 2838, XP085072611, Retrieved from the Internet <URL:https://dx.doi.org/10.2016/j.eururo.2016.10.013> [retrieved on 20170519] *
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018170578A1 (fr) * 2017-03-20 2018-09-27 Ontario Institute For Cancer Research (Oicr) Risque de cancer basé sur la clonalité tumorale
US12286678B2 (en) 2017-03-20 2025-04-29 Ontario Institute For Cancer Research (Oicr) Cancer risk based on tumour clonality
EP4042161A4 (fr) * 2019-10-09 2023-12-20 The University of North Carolina at Chapel Hill Altérations du nombre de copies (cna) d'adn pour déterminer des phénotypes de cancer

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