WO2017190218A1 - Signatures de biopsie liquide pour le cancer de la prostate - Google Patents

Signatures de biopsie liquide pour le cancer de la prostate Download PDF

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WO2017190218A1
WO2017190218A1 PCT/CA2017/000114 CA2017000114W WO2017190218A1 WO 2017190218 A1 WO2017190218 A1 WO 2017190218A1 CA 2017000114 W CA2017000114 W CA 2017000114W WO 2017190218 A1 WO2017190218 A1 WO 2017190218A1
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gene
peptide
peptides
subject
expression
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PCT/CA2017/000114
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English (en)
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Paul Christopher BOUTROS
Richard Ray DRAKE
Oliver John Semmes
Yunee KIM
Jouhyun JEON
Raymond Scott LANCE
Thomas Robert Dieter KISLINGER
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University Health Network
Ontario Institute For Cancer Research (Oicr)
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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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • 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
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57434—Specifically defined cancers of prostate
    • 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
    • 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
    • C12Q2600/158—Expression markers

Definitions

  • This application relates to methods, compositions and systems for the diagnosis or classification of prostate cancer.
  • prostate cancer The worldwide incidence of prostate cancer has been steadily increasing, but many patients harbor tumors of an indolent nature. These indolent tumors grow slowly and pose minimal threat to the life of the patient, in the absence of treatment (i.e. are clinically insignificant). However, once prostate cancer begins to grow aggressively, it metastasizes quickly with lethal consequences. The management of prostate cancer has become an urgent clinical dilemma with significant over-diagnosis and challenges in predicting patient survival Prostate cancers are uniquely heterogeneous with major spatial 3 ⁇ 4 3 and temporal 4 variability in their genomes. Therefore, once cancer has been confirmed, the optimal course of action is tailored to spare patients with indolent disease from unnecessary procedures, while identifying and treating those who would benefit from treatment intensification.
  • a fluid-based biomarker would be ideal.
  • Liquid biopsies such as circulating tumor cells and cell-free DNA 16 have been proposed as promising non-invasive prostate cancer biomarkers, but their detection and enrichment remains technically challenging.
  • Cataloguing the secreted and soluble factors released into the interstitial fluid that bathes the organ of interest may provide a novel inventory of putative disease biomarkers.
  • EPS prostatic secretions
  • Reproducible detection and Quantificatioii of multiple proteins in complex biological matrices is an essential requirement for any potential disease biomarker, but verification of these candidates is a major bottleneck in the pipeline from discovery to clinical implementation.
  • immunoaffmily based assays namely enzyme-linked immunosorbent assays (ELISAs) are used to validate protein biomarkers, but this approach is time-coiisuming, costly, and relies on the existence of validated antibody pairs for every target protein.
  • ELISAs enzyme-linked immunosorbent assays
  • MS Targeted mass spectrometry
  • SRM-MS Selected reaction momtcrirtg mass spectrometry
  • SRM-MS Selected reaction momtcrirtg mass spectrometry
  • a method of diagnosing a subject with prostate cancer comprising: (a) deteennining an expression level of at least 1 gene in a teat sample from the subject selected from the group consisting of the genes identified in Fig.4b; and (b) comparing the expression level of the at least 1 gene in the test sample with a reference expression level of the at least 1 gene from control samples of healthy subjects; wherein a statistically significant difference in the expression of the at least 1 gene in the test sample compared to the reference expression level is an indication that the subject has prostate cancer.
  • a method of classifying a subject with prostate cancer between having a pT2 stage (organ confined) tumor and having a pT3 stage (extracapsular) tumor comprising: (a) determining an expression level of at least 1 gene in a test sample from the subject selected from the group consisting of the genes identified in Fig.
  • a composition comprising a plurality of antibodies capable of specifically binding to a plurality of peptides corresponding to a plurality of the genes in Fig. 4b and Fig. 4d.
  • the plurality of peptides are a plurality of the peptides identified in Fig. 4b and Fig. 4d.
  • the plurality of peptides are the 2, 3, 4, 5, 6, or 7 of the top-ranked peptides in Fig.4b and Fig.4d.
  • a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
  • a computer implemented product for diagnosing or classifying a subject with prostate cancer comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; (b) a database comprising a reference expression profile representing a control, wherein the subject expression profile and the reference profile each have at least one value representing the level of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4&, wherein the computer implemented product compares the reference expression profile to the subject biomarker expression profile, wherein a statistically significant difference or similarity in the expression profiles is used to diagnose or classify the subject with prostate cancer.
  • the computer implemented product carries out the method described herein.
  • a computer readable medium having stored thereon a data structure for storing the computer implemented product described herein.
  • a computer system comprising (a) a database including records comprising a reference expression profile of the level of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4d; (b) a user interface capable of receiving a selection of expression levels of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4d, for use in comparing to the reference expression profile in the database; (c) an output that displays a prediction of diagnosis or classification wherein a statistically significant difference or similarity in the expression levels is used to diagnose or classify the subject with prostate cancer.
  • kits comprising reagents for detecting the level of at least 1 gene identified in Fig, 4b and Fig. 4d, preferably at least 1 peptide in Fig.4b and Fig.4d, in a sample.
  • Figure 1 shows systematic development of targeted proteomics assays in EPS-urines
  • Discovery proteomics data from direct-EPS derived from patients with extracapsular (EC) or organ-confined (OC) prostatic tumors was used to select putative candidates. Proteotypic peptides from these candidates were carefully selected and evaluated by SRM-MS in an EPS- urine background
  • All peptides that passed the above selection criteria were analyzed in clinically stratified EPS-urines (Cohort A). Peptide quantification by SRM-MS was performed and 34 candidates with diagnostic and prognostic potential were identified based on relative abundance changes
  • Figure 2 shows absolute peptide quantification in an independent patient cohort
  • Right panel correlation plot
  • Figure 3 shows univariate analyses to distinguish patient risk groups, (a) Heafmap representation of absolute peptide expression levels for all candidate peptides within Cohort B samples (represented as fmol EPS-urine protein), Peptide expression heatmap is clustered using
  • SPSA Serum PSA
  • Figure 4 shows maohinc-lcaming model to identify biomarker signatures
  • (a) Schematic overview of the macbinc-lcaining approach used to develop multi-feature biomarker signatures
  • (d) The predictive importance of individual peptides to distinguish pathological stage pT3 from stage pT2.
  • Figure 5 shows Pearson correlation for replicate analyses (all sample types analyzed in duplicate), Representation of the reproducibility (Pearson's correlation) for replicate analyses of all peptides analyzed in Cohort B (n-207). Dotted red line represents samples with high correlation (RX),7).
  • Figure 6 shows Pearson correlation for replicate analyses (individual sample types; risk groups). Representation of the reproducibility (Pearson's correlation) for replicate analyses of all peptides analyzed in Cohort B stratified by patient risk group (normal, BPH, pT2, pT3). Dolled red line represents samples with high correlation (RX).7).
  • Figure 7 shows Pearson correlation compared by peptide abundance. Comparison of peptide abundance plotted as a function of Pearson's correlation. Peptides with a high concordance between replicate analyses (RXJ.7) are significantly mote abundant based on SRM quantification.
  • Figure 8 shows chromatographic retention time of individual peptides, (a) Representative chromatogram of the 34 peptides quantified in cohort B. (b) The 34 peptides quantified by SRM- MS in all cohort B samples (207 samples analyzed in duplicates; n-414 SRM-MS analyses) demonstrate highly reproducible retention times.
  • Figure 9 shows peptide abundance in the two separate patient cohorts, (a) Average fold change correlation of cancer vs. normal samples for the 34 peptides quantified in cohorts A and B, left side: avenge fold change (log2) in both cohorts-, rights side: correlation blot (b) Average fold change correlation of pT2 vs. pT3 samples for the 34 peptides quantified in cohorts A and B. left side: average fold change (l°g2) in both cohorts; rights side: correlation blot.
  • Figure 10 shows patient characteristics for all cohort B samples (n-207).
  • Age distribution (a) Age distribution; (b) Ethnicity; (c) Serum PSA distribution (SPSA).
  • Figure 11 shows ROC curves for test set analysis, (a) Diagnostic signature: the performance for the selected peptide signature (pink), serum PSA alone (blue) and randomly selected peptides (grey) are compared, (b) Prognostic signature: the performance for the selected peptide signature (pink), serum PSA alone (blue) and randomly selected peptides (grey) are compered.
  • ROC curves axe generated from test set
  • Figure 12 shows area under the ROC for randomly generated signatures. Distribution of AUCs for randomly selected peptides to generate predictive models for cancer vs. normal (top panel) or for pT3 vs. pT2 (bottom panel). Pink line indicates AUC for our predictive models based on identified peptides. AUCs are measured from test set.
  • Figure 13 shows power analyses for all analyzed samples to distinguish indicated patient risk groups.
  • Figure 14 shows inter-correlation between peptides, (a) Correlation matrix of peptide-peptide expression. Expression profiles between all 34 peptides quantified in cohort B are compared using Pearson's correlation coefficient (R). Peptide-peptide matrix comparing the quantitative expression profiles of (b) the 6 diagnostic signature peptides and (c) the 7 prognostic signature peptides.
  • Figure 15 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.
  • a method of diagnosing a subject with prostate cancer comprising: (a) determining an expression level of at least 1 gene in a test sample from the subject selected from the group consisting of the genes identified in Fig. 4b; and (b) comparing the expression level of the at least 1 gene in the test sample with a reference expression level of the at least 1 gene from control samples of healthy subjects; wherein a statistically significant difference in the expression of the at least 1 gene in the test sample compared to the reference expression level is an indication that the subject has prostate cancer.
  • classifying means predicting or identifying the particular state of a disease. For example, with respect to prostate cancer, patients may be classified as having a pT2 stage (organ confined) tumor or a pT3 stage (extracapsular) tumor.
  • diagnosis is tine identification of the nature and cause of a certain phenomenon, such as, the identification of disease state in a patient
  • the methods described herein are useful for determining whether a subject has prostate cancer.
  • subject refers to any member of the animal kingdom, preferably a human being and most preferably a human being that has prostate cancer or that is suspected of having prostate cancer.
  • test sample refers to any fluid, cell or tissue sample from a subject which can be assayed for biomarker expression products and/or a reference expression profile, e.g. peptides differentially present in a liquid biopsy.
  • RNA includes mRNA transcripts, and/or specific spliced or other alternative variants of mRNA, including anti-sense products.
  • protein or “peptides”, it refers to proteins expressed by genes are measurable in a sample.
  • level of expression or “expression level” as used herein refers to a measurable level of expression of the products of biomarlcers, such as, without limitation, micro-RNA, or messenger RNA transcript expressed or of a specific exon or other portion of a transcript, the level of proteins, peptides or portions thereof expressed of the biomarkers, the number or presence of DNA polymorphisms of the biomarkers, the enzymatic or other activities of the biomarkers, and the level of specific metabolites.
  • control refers to a specific value or dataset that can be used to prognose or classify the value e.g, expression level or reference expression profile obtained from the test sample associated with an outcome class.
  • a dataset may be obtained from samples from a group of subjects known to have prostate cancer having different tumor states or healthy individuals.
  • the expression data of the biomarkers in the dataset can be used to create a control value that is used in testing samples from new patients.
  • differential expression refers to a difference in the level of expression of the biomarkers that can be assayed by measuring the level of expression of the products of the biomarkers, such as the difference in level of peptide or protein expressed. In a preferred embodiment, the difference is statistically significant.
  • difference in the level of expression refers to an increase or decrease in the measurable expression level of a given biomarker, for example as measured by me amount of peptide as compared with the measurable expression level of a given peptide in a control.
  • expression profile refers to a dataset representing the expression level(s) of one or more biomarkers.
  • An expression profile may represent one subject, or alternatively a consolidated dataset of a cohort of subjects, for example to establish a reference expression profile as a control.
  • the at least 1 gene is at least 2, 3, 4, 5, or 6 genes associated with the top- ranked peptides in Fig.4b.
  • the at least 1 gene comprises 6 genes associated with the 6 top-ranked peptides in Fig.4b.
  • a method of classifying a subject with prostate cancer between having a pT2 stage (organ confined) tumor and having a pT3 stage (extracapsular) tumor comprising: (a) detemnning an expression level of at least 1 gene in a test sample from the subject selected from the group consisting of the genes identified in Fig.
  • the at least 1 gene is at least 2, 3, 4, 5, 6, or 7 genes associated with the top-ranked peptides in Fig. 4d.
  • the at least 1 gene comprises 7 genes associated with the 7 top-ranked peptides in Fig.4d.
  • the method further comprises producing gene expression profiles comprising a subject gene expression profile and a gene reference expression profile, each having values representing the expression level of the at least 1 gene corresponding the test and control samples respectively.
  • the test sample comprises at least one of prostate-proximal fluid and/or expressed prostatic secretions.
  • test sample is collected directly from the prostate, preferably prior to radical prostatectomy, and/or from urine, preferably post-digital rectal examination urine.
  • determining the expression level of the at least one gene in the test sample comprises measuring in the test sample, the level of at least one peptide corresponding to the protein product of the at least one genc.
  • the method further comprises producing peptide presence profiles comprising t subject peptide expression profile and a reference peptide expression profile, each having values representing the peptide levels of the at least 1 peptide corresponding the test and control samples respectively.
  • the at least one peptide comprises 2, 3, 4, 5, 6, or 7 of the top-ranked peptides in Fig.4b and Fig. 4d.
  • the level of the at least 1 peptide is measured using mass spectrometry, Preferably, the mass spectrometry is targeted mass spectrometry using selected reaction monitoring mass spectrometry.
  • composition comprising a plurality of antibodies capable of specifically binding to a plurality of peptides corresponding to a plurality of the genes in Fig. 4b and Fig. 4d
  • the plurality of peptides are a plurality of the peptides identified in Fig. 4b and Fig. 4d.
  • the plurality of peptides are the 2, 3, 4, 5, 6, or 7 of the top-ranked peptides in Fig.4b and Fig.4d
  • Figure IS 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 IS, mouse 112, and disk drive or solid state drive 114 connected by an I/O interface 109.
  • the mouse 112 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 116.
  • the computer device 100 may form part of a network via a network interface 111, 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
  • 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 cor ⁇ uter-raadableAiseable 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.
  • me ccniputer-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 computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein roe computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
  • a computer implemented product for diagnosing or classifying a subject with prostate cancer comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; (b) a database comprising a reference expression profile representing a control, wherein the subject expression profile and the reference profile each have at least one value representing the level of at least 1 gene identified in Fig. 4b and Fig.
  • the computer implemented product compares the reference expression profile to the subject biomarker expression profile, wherein a statistically significant difference or similarity in the expression profiles is used to diagnose or classify the subject with prostate cancer.
  • the computer implemented product carries out the method described herein.
  • a computer readable medium having stored thereon a data structure for storing the computer implemented product described herein.
  • the data structure is capable of configuring a computer to respond to queries based on records belonging to the data structure, each of the records comprising: (a) a value that identifies a reference expression profile of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4d; (b) a value that identifies the probability of a diagnosis or classification associated with the reference expression profile.
  • a computer system comprising (a) a database including records comprising a reference expression profile of the level of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4d; (b) a user interface capable of receiving a selection of expression levels of at least 1 gene identified in Fig. 4b and Fig. 4d, preferably at least 1 peptide in Fig. 4b and Fig. 4d, for use m comparing to the reference expression profile in the database; (c) an output that displays a prediction of diagnosis or classification wherein a statistically significant difference or similarity in the expression levels is used to diagnose or classify the subject with prostate cancer.
  • kits comprising reagents for detecting the level of at least 1 gene identified in Fig.4b and Fig.4d, preferably at least 1 peptide in Fig.4b and Fig.4d, in a sample.
  • reagents for detecting the level of at least 1 gene identified in Fig.4b and Fig.4d, preferably at least 1 peptide in Fig.4b and Fig.4d in a sample.
  • Prostate cancer patients were selected on the basis of organ confinement or pathological stage. Non-cancer individuals had biopsy confirmed BPH or were considered as individuals with no indication of prostatic disease based on biopsy results. Sample preparation for mass spectrometry
  • Ultrapurc-grade 2,2,2-trifluoroethanoI (TFB), trifluoroacetic add (TFA), iodoacetamide (IAA), and dithiotreitol (DTT) were from Sigma-AldricL HPLC-grade solvents (methanol, acctonitrilc, and water) and formic acid were fiom Fisher Scientific. Mass spcctrometry-grade trypshVLys-C was fiom Promega (Madison, WI). Amicon spin filters, O.S ml, 3 kDa MWCO, were fiom Millipore.
  • Solid phase extraction C18 tips were fiom Agilent Four ml of EPS-urine were concentrated to approximately 500 ⁇ by using a spin filter with a molecular weight cutoff of 3 kDa, and proteins were precipitated overnight by the addition of ice-cold 100% methanol. Protein pellets were washed twice with 100% methanol and air-dried. Protein resolubilization was performed by the addition of 50% TFE at 60 °C for 2 hours. Following reduction with DTT and alkylation with IAA, proteins were digested overnight at 37 °C using mass 2 ⁇ g trypsin/Lys- C. The reaction was quenched by the addition of TFA. Desalting was performed by solid phase extraction using CI 8 tips.
  • Phase 1 peptides were selected and purchased as bulk heavy-isotope labeled peptide standards (JPT Peptide Technologies), also containing 8 peptides that were deemed potentially interesting from our additional EPS proteomics studies
  • 250 finol of each heavy peptide standard was spiked into 1 ug of EPS- urine-digest, with 4-6 transitions monitored over a 40-mmute chromatographic gradient Of the 232 Phase J peptides, 147 (Phase 2 peptides) were reproducibly detectable with a minimum of three transitions in the complex EPS-urine background.
  • a cohort of individual EPS-urines (n - 74) from a heterogeneous population of patients with EC, OC and control (BPH, normal) (Cohort A) was used to analyze all Phase 2 peptides.
  • a total of 1 ⁇ g of peptide from each sample was spiked with 200 fmol of heavy peptide standards that were combined into six batches (batch A-F), consisting of -20 peptides per batch. Visualization and inspection of peaks was performed in Skyline.
  • Each peptide was quantified in a sample by integrating the quantifier ion (most intense ion) of the light peptide with its co-eluting heavy peptide ion, in order to derive a light-to-heavy peptide ratio.
  • the Student's t-test was used to compare the ratios between cancer and controls, as well as EC and OC prostate cancers.
  • a K- fold cross-validation was performed to investigate the diagnostic and prognostic power of the peptides at different p-vahie cutoffs.
  • p- value cut-offs were used, respectively. Further refinements to this list were made by including additional peptides that did not meet the p-value cut-offs, but were potentially promising.
  • peptides SSEDPNEDIVER from protein IGJ and TPAQFDADELR from protein ANXA1 were added to the list of putative prognostic candidates because they were the only candidates that were elevated in the EC tumor group (p-value - 0,25), Two KLK3 peptides (HSQPWQVLVASR and LSEPAELTDAVK) were also added in order to monitor PSA levels in EPS-urine.
  • a multiplexed SRM-MS assay was developed by scheduling all 34 candidates in a single 40-mimrte chromatographic gradient A total of 3 transitions were monitored for the light and heavy versions of each peptide for a total of 204 transitions per analysis. A 2-mimitc acquisition time window was scheduled around the expected peptide elutiontime.
  • MStem approach A recently published approach termed MStern (Berger et al. Mol Cell Proteomics. 2015 Oct;14(10):2814-23) that is based on the high protein binding capabilities of porous PVDF membranes (similar to a Western Blot). This approach can be performed in a 96-well format and is rapidly automatable.
  • the Generalized Linear Model was used to classify samples into two classes: cancer vs. normal (diagnosis) and pT3 vs. pT2 (prognosis).
  • OLM is a widely used machine-learning algorithm that has been applied in various types of biomarker identification ( «.£.
  • OLM outperformed eight other machine learning algorithms, which look for different types of patterns and data properties; random forest (rf), stochastic gradient boosting (gbm), Naive Bayes (nb), boosted generalized linear model (glmboost), lasso and elastic-net-regularized generalized linear model (ghnnet), support vector machine with linear kernel (svrnLinear) and radial basis function (RBF) kernel (svmRadial).
  • rf random forest
  • gbm stochastic gradient boosting
  • nb Naive Bayes
  • glmboost boosted generalized linear model
  • lasso and elastic-net-regularized generalized linear model ghnnet
  • support vector machine with linear kernel svrnLinear
  • RBF radial basis function kernel
  • Z is the normalized peptide expression.
  • X is the peptide expression in each sample, ⁇ is the mean of the normal controls, ⁇ is the standard deviation of the normal controls.
  • ⁇ is the mean of the normal controls.
  • ⁇ is the standard deviation of the normal controls.
  • top-ranked peptides from top 3 to top IS
  • AUQ receiver operating characteristic curve
  • Phase 1 peptides were first evaluated for reproducible detection by SRM-MS in EPS-urine samples.
  • Each Phase 1 peptide was purchased as a crude heavy isotope labeled synthetic peptide and spiked into pooled EPS-urines to evaluate their suitability for targeted proteomics assays, directly within the biomarker matrix.
  • Light (endogenous) and heavy (synthetic) peptides were monitored in SRM mode, and data were manually inspected to select peptides that had at least 3 fragments ions aligned at the expected peptide elution time, had co-eluting light and heavy peptides, had minimal interference and were reproducible.
  • Phase 2 peptides we performed relative quantification in a medium-sized cohort of EPS-urines (n*74; Cohort A; Table 1), using the crude heavy isotope labeled synthetic peptides as internal standards (see Methods). The goal of this initial quantification was to evaluate peptide performance in relevant clinical samples, while reducing the rwmber of peptides to be moved forward to the next development steps. Briefly, a Student's t-test was performed to compare the ' ratios of peptide abundance between cancer and non-cancer groups (termed: diagnostic), as well as EC and OC prostate cancers (termed: prognostic).
  • the first criteria used to select candidates as potential diagnostic and prognostic biomarkers were p- value cutoffs of 0.05 and 0.1, respectively.
  • a higher p- value cut-off was used to select prognostic candidates in order to avoid removing putative candidates at mis early stage for distinguishing cancer comparisons (EC vs. OC cancer groups), Refinements to the candidate list were made by adding peptides representing the proteins IGJ and ANXAJ, because these peptides were the only candidates even trend up- regulated in the EC tumor group (p ⁇ 0.25).
  • two KLK3 peptides were added to monitor PSA levels.
  • each peptide that met the above criteria was manually inspected for SRM-MS trace quality.
  • Fig. 4a To identify subsets of peptides that can serve as liquid-biopsy signatures and integrate them into unified predictors mat accurately discriminate among our distinct patient risk groups, we employed a machine-learning analysis (Fig. 4a). Briefly, we evaluated quantified peptides as input features for machine learning and identified the most relevant ones using Generalized Linear Models (GLMs) 22 . The selection of reliable features reduces the dimensionality of the feature space and leads to better performance in machine learning To select relevant peptides, we measured the importance of each peptide to discriminate two classes of patient risk groups (cancer vs. normal controls or pT2 vs. pT3 prostate cancers) and subsequently systematically selected the top-ranked peptides as features to build predictive models.
  • GLMs Generalized Linear Models
  • Nelder JA Wedderburn RWM. Generalized Linear Models. Journal of the Royal Statistical Society 135, 370-384 (1972).
  • Prostate-specific antigen (PSA) isoform p2PSA in combination with total PSA and free PSA improves diagnostic accuracy in prostate cancer detection. Eur Urol 57, 921-927 (2010).
  • Ju H Brasier AR. Variable selection methods for developing a biomarker panel for prediction of dengue hemorrhagic fever. BMC research notes 6, 365 (2013).

Abstract

La présente invention concerne un procédé de diagnostic d'un sujet atteint de cancer de la prostate, comprenant : (a) la détermination d'un taux d'expression d'au moins 1 gène dans un échantillon d'essai provenant du sujet choisi dans le groupe constitué des gènes identifiés sur la figure 4b ; et (b) la comparaison du taux d'expression de l'au moins 1 gène dans l'échantillon d'essai à un taux d'expression de référence de l'au moins 1 gène d'échantillons témoins de sujets sains ; dans lequel une différence statistiquement significative de l'expression de l'au moins 1 gène dans l'échantillon d'essai par rapport au taux d'expression de référence est une indication que le sujet présente un cancer de la prostate.
PCT/CA2017/000114 2016-05-06 2017-05-05 Signatures de biopsie liquide pour le cancer de la prostate WO2017190218A1 (fr)

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