WO2018049506A1 - Marqueur du cancer de la prostate miarn - Google Patents

Marqueur du cancer de la prostate miarn Download PDF

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WO2018049506A1
WO2018049506A1 PCT/CA2017/000205 CA2017000205W WO2018049506A1 WO 2018049506 A1 WO2018049506 A1 WO 2018049506A1 CA 2017000205 W CA2017000205 W CA 2017000205W WO 2018049506 A1 WO2018049506 A1 WO 2018049506A1
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hsa
mir
mirna
abundance
disease
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PCT/CA2017/000205
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Paul Christopher BOUTROS
Robert A. BRISTOW
Jouhyun JEON
Bharati Bapat
Stanley Liu
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Ontario Institute For Cancer Research (Oicr)
University Health Network (Uhn)
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Publication of WO2018049506A1 publication Critical patent/WO2018049506A1/fr

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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
    • 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
    • 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
    • C12Q2600/158—Expression markers
    • 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
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present disclosure relates generally to a prostate cancer biomarker signature. More particularly, the present disclosure relates to an miRNA signature for the prognosis of prostate cancer outcomes, which can inform treatment decisions and guide therapy.
  • PCa Prostate cancer
  • PSA prostate-specific antigen
  • DRE digital rectal examination
  • Gleason Score multiple prostate biopsies to assess tumour grade
  • a method of determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising: a) providing a biological fluid sample, preferably urine, from the patient containing miRNA; b) determining or measuring the abundance of at least one miRNA biomarker selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR- 512-5p, and hsa-miR-431 -5p; c) comparing the abundance of said at least one miRNA biomarker in the sample with a reference or control abundance of at least one miRNA biomarker; and d) determining the likelihood of disease or disease progression; wherein a likelihood of disease or disease progression is higher when there is statistically significant higher abundance in the group consisting of: hsa-miR-3195,
  • a computer-implemented method of determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising: a) receiving, at at least one processor, data reflecting the abundance of at least one miRNA biomarker in a subject bodily fluid, preferably urine, selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a- 3p, hsa-miR-512-5p, and hsa-miR-431 -5p; b) constructing, at the at least one processor, an expression profile corresponding to the abundance; c) comparing, at the at least one processor, said subject abundance to corresponding reference or control abundance; d) determining, at the at least one processor, the likelihood of disease progression; wherein a likelihood of disease or disease progression is higher when there is
  • a computer program product for use in conjunction with a general-purpose 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 device for determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising: at least one processor; and electronic memory in communication with the at one processor, the electronic memory storing processor-executable code that, when executed at the at least one processor, causes the at least one processor to: a) receive data reflecting abundance of at least one miRNA biomarker in a patient bodily fluid, preferably urine, selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR- 512-5p, and hsa-miR-431 -5p; b) compare said patient biomarkers to corresponding reference or control
  • Figure 1 shows DRE-urine miRNA transcriptome profile, (a) Overview of analysis of miRNA abundance variance in PCa patients, (b) Parameter selection to optimize miRNA abundance. Similarity (p) represents similarity of miRNA profile between two control samples. Misinterpreted samples indicate the fraction of samples with failed normalization. The similarity between control samples is likely to be increased when there are more misinterpreted samples. Since we considered samples with less than 10% of expressed miRNAs after normalization as misinterpreted samples, this correlation could be an inevitable effect of small size of expressed miRNAs to calculate a similarity. To mitigate this effect, we only considered parameters that show high similarity between controls and zero misinterpreted samples (red arrow), (c) Normalized miRNA transcriptome profile. Green and grey bars (top) represent the number of detected miRNAs in patient urine samples and control samples, respectively. Yellow ( ⁇ 5) and grey ( ⁇ 5) bars (right) represent the number of samples that a given miRNA is detected.
  • Figure 2 shows correlation of miRNA abundance intra- and inter-individual patients,
  • FIG. 3 shows biological properties of miRNAs and their target genes, (a) Chromosomal positions of studied miRNAs. miRNAs are divided into four variable groups depending on ICC (Q25, Q50, Q75 and Q100). Dashed red boxes indicate enriched chromosome of a given group (q ⁇ 0.1 ).
  • Q25 and Q100 represent miRNAs that are most and least variable within individuals, respectively, (b) Number of target genes in variable groups, (c) Overlapped target genes among variable groups, (d) Enriched biological functions of target genes in variable groups. In total, 1 ,215 GO terms showing q ⁇ 0.25 in at least one variable group are coloured.
  • Q25, Q50, Q75 and Q100 represent targets that are regulated by specific variable group. Common indicates targets that are regulated by all four variable groups. GO terms with q ⁇ 0.05 in each variable group are shown. Full GO terms and their enriched scores available but not shown.
  • Figure 4 shows predictive model to distinguish different risk groups of PCa.
  • Bold green line indicates median AUC of 10-times repeated 5-fold cross validation.
  • Grey shadow indicates all cross validated AUCs.
  • ROC curves of intra- stable (purple), intra-variable (yellow) and randomly selected (yellow) miRNA signatures are compared,
  • Figure 5 shows optimization of miRNA transcriptome.
  • Figure 6 shows miRNA abundances in tissues and urine samples of PCa patients, (a) Fraction of tissues and urine samples in which miRNA is detected. Each hexbin represents the number of miRNAs that are detected in a given fraction of urine samples and tumour tissues of PCa patients, (b) Presence and absence of miRNA in urine samples and tissues are compared (blue). Absence of miRNA is defined when a given miRNA has 0 of mean normalized count across samples.
  • Figure 7 shows the effect of expression difference on the similarity of miRNA expression between samples. Grey bars represent the miRNA abundance similarity between DRE-urine samples from an identical patient. Green shows the difference of the number of undetected miRNAs in two samples. Purple represents the fraction of undetected miRNAs of each sample.
  • Figure 8 shows clustering of miRNA expressions
  • Figure 9 shows miRNA abundance variability in intra- and inter-individual PCa patient, (a) Coefficient of variation (CV) of intra- (purple) and inter-individual (yellow).
  • CV Coefficient of variation
  • ICC Variable estimates is compared with (b) mean miRNA abundance in samples and (c) number of urine samples that a given miRNA is detected.
  • Figure 10 shows biological properties of miRNAs and their target genes
  • Tested miRNAs are divided into 4 variable groups depending on ICC.
  • Q25, Q50, Q75 and Q100 represent 1 st , 2 nd , 3 rd and 4 th quartile, respectively
  • Figure 11 shows relationship between the miRNA abundances in urine and PCa tissues, (a) Variable group-specific correlation of miRNA abundance between urine samples and PCa tissues. Spearman's p and its statistical significance are tabulated, (b) Correlation coefficients of different variable groups.
  • Figure 14 shows performance evaluation of predictive model, (a) Importance of miRNAs to discriminate two risk groups.
  • Importance of each miRNA represents average mean decrease in accuracy from resampling, (b) Median area under the ROC curve (AUC) depending on the number of selected miRNAs. miRNAs that are ranked from top 2 to top 15 are selected and used to generate a predictive model.
  • Figure 15 shows univariate analysis of miRNAs in a validation cohort, (a) miRNA abundance profile of a validation cohort, (b) Relationship of miRNA abundances between discovery cohort and validation cohort, (c) Fraction of variable groups depending on statistical significance to discriminate two risk groups.
  • Figure 16 shows relationship between miRNA profile similarity and other clinical information. miRNA profile similarity between two samples of a same patient is compared with PSA, Age and time difference between sample preparation (delta of sample preparation). Discovery cohort was used to examine the relationship.
  • Figure 17 shows 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.
  • miRNAs are promising noninvasive biomarkers. They play an essential role in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. Specifically, these small RNAs are involved in prostate cancer development and progression 12 , influence treatment response 13 , are stable under harsh conditions 14 and have been detected in urine 15 .
  • This biomarker comprised of seven miRNAs, was validated in an independent prostate cancer cohort to non-invasively predict high-risk disease at a similar accuracy to the best tissue-based prognostic markers (AUC: 0.71 ).
  • AUC tissue-based prognostic markers
  • a method of determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising: a) providing a biological fluid sample, preferably urine, from the patient containing miRNA; b) determining or measuring the abundance of at least one miRNA biomarker selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR- 512-5p, and hsa-miR-431 -5p; c) comparing the abundance of said at least one miRNA biomarker in the sample with a reference or control abundance of at least one miRNA biomarker; and d) determining the likelihood of disease or disease progression; wherein a likelihood of disease or disease progression is higher when there is statistically significant higher abundance in the sample in comparison with the reference or control
  • the methods described herein are useful for prognosing the outcome of a subject that has, or has had, a cancer associated with the prostate.
  • the cancer may be prostate cancer or a cancer that has metastasized from a cancer of the prostate.
  • subject refers to any member of the animal kingdom, preferably a human being and most preferably a human being that has, has had, or is suspected of having prostate cancer.
  • biological fluid sample refers to any fluid , from a subject which can be assayed for the biomarkers described herein.
  • Biological fluids generally include amniotic fluid, aqueous humour and vitreous humour, bile, blood, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph and perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), serous fluid, semen, smegma, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, and vomit.
  • prognosis refers to the prediction of a clinical outcome associated with a disease subtype which is reflected by a reference profile such as a biomarker reference profile.
  • the prognosis provides an indication of disease progression and includes an indication of likelihood of death due to cancer.
  • the prognosis may be a prediction of metastasis, or alternatively disease recurrence.
  • the clinical outcome class includes a better survival group and a worse survival group.
  • prognosing or classifying means predicting or identifying the clinical outcome of a subject according to the subject's similarity to a reference profile or biomarker associated with the prognosis.
  • prognosing or classifying comprises a method or process of determining whether an individual has a better or worse survival outcome, or grouping individuals into a better survival group or a worse survival group, or predicting whether or not an individual will respond to therapy.
  • control refers to a specific value or dataset that can be used to prognose or classify the value e.g the measured biomarker or reference biomarker profile obtained from the test sample associated with an outcome.
  • a dataset may be obtained from samples from a group of subjects known to have cancer having different tumor states and/or healthy individuals. The state or 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.
  • a cohort of subjects is used to obtain a control dataset.
  • a control cohort patients may be a group of individuals with or without cancer.
  • the at least one miRNA biomarker is at least 2, 3, 4, 5, 6 or 7 patient biomarkers.
  • the at least one miRNA biomarker is all of hsa-miR-3195, hsa-let-7b- 5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR-512-5p, and hsa-miR-431-5p.
  • the method further comprises building a subject biomarker profile from the determined or measured patient biomarkers.
  • the method further comprises building a patient biomarker profile from the determined or measured patient biomarkers.
  • biomarker profile refers to a dataset representing the state or expression level(s) of one or more biomarkers.
  • a biomarker profile may represent one subject, or alternatively a consolidated dataset of a cohort of subjects, for example to establish a reference biomarker profile as a control.
  • the prediction of disease progression is following at least one of active surveillance, surgery, endocrine therapy, chemotherapy, radiotherapy, hormone therapy, gene therapy, thermal therapy, and ultrasound therapy.
  • the method further comprises classifying the patient into a high risk group if the likelihood of disease progression is relatively high or a low risk group if the likelihood of disease progression is relatively low.
  • the method further comprises treating the patient with more aggressive therapy if the patient is in the high risk group.
  • the more aggressive therapy comprises adjuvant therapy, preferably hormone therapy, chemotherapy or radiotherapy.
  • all survival refers to the percentage of or length of time that people in a study or treatment group are still alive following from either the date of diagnosis or the start of treatment for a disease, such as cancer. In a clinical trial, measuring the overall survival is one way to see how well a new treatment works.
  • relapse-free survival refers to, in the case of caner, the percentage of or length of time that people in a study or treatment group survive without any signs or symptoms of that cancer after primary treatment for that cancer. In a clinical trial, measuring the relapse- free survival is one way to see how well a new treatment works. It is defined as any disease recurrence or relapse (local, regional, or distant).
  • good survival or “better survival” as used herein refers to an increased chance of survival as compared to patients in the "poor survival” group.
  • the biomarkers of the application can prognose or classify patients into a "good survival group”. These patients are at a lower risk of death after surgery and can also be categorized into a "low-risk group”.
  • poor survival or “worse survival” as used herein refers to an increased risk of disease progression or death as compared to patients in the "good survival” group.
  • biomarkers or genes of the application can prognose or classify patients into a "poor survival group”.
  • FIG. 17 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 1 12, and disk drive or solid state drive 1 14 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 1 14 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 1 1 , 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
  • 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 computer-implemented method of determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising:a) receiving, at at least one processor, data reflecting the abundance of at least one miRNA biomarker in a subject bodily fluid, preferably urine, selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a- 3p, hsa-miR-512-5p, and hsa-miR-431 -5p; b) constructing, at the at least one processor, an expression profile corresponding to the abundance; c) comparing, at the at least one processor, said subject abundance to corresponding reference or control abundance; d) determining, at the at least one processor, the likelihood of disease progression; wherein a likelihood of disease or disease progression is higher when there is statistical
  • a computer program product for use in conjunction with a general-purpose 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 readable medium having stored thereon a data structure for storing the computer program product described herein.
  • a device for determining the likelihood of disease or disease progression in a patient with respect to prostate cancer comprising: at least one processor; and electronic memory in communication with the at one processor, the electronic memory storing processor-executable code that, when executed at the at least one processor, causes the at least one processor to: a) receive data reflecting abundance of at least one miRNA biomarker in a patient bodily fluid, preferably urine, selected from the group consisting of: hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR- 512-5p, and hsa-miR-431 -5p; b) compare said patient biomarkers to corresponding reference or control biomarkers; and c) determining, at the at least one processor, the likelihood of disease progression; wherein a likelihood of disease or
  • processor may be any type of processor, such as, for example, any type of general-purpose microprocessor or microcontroller (e.g., an IntelTM x86, PowerPCTM, ARMTM processor, or the like), a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), or any combination thereof.
  • general-purpose microprocessor or microcontroller e.g., an IntelTM x86, PowerPCTM, ARMTM processor, or the like
  • DSP digital signal processing
  • FPGA field programmable gate array
  • memory may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), or the like.
  • RAM random-access memory
  • ROM read-only memory
  • CDROM compact disc read-only memory
  • electro-optical memory magneto-optical memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically-erasable programmable read-only memory
  • computer readable storage medium (also referred to as a machine-readable medium, a processor-readable medium, or a computer usable medium having a computer- readable program code embodied therein) is a medium capable of storing data in a format readable by a computer or machine.
  • the machine-readable medium can be any suitable tangible, non-transitory medium, including magnetic, optical, or electrical storage medium including a diskette, compact disk read only memory (CD-ROM), memory device (volatile or non-volatile), or similar storage mechanism.
  • the computer readable storage medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure.
  • data structure a particular way of organizing data in a computer so that it can be used efficiently.
  • Data structures can implement one or more particular abstract data types (ADT), which specify the operations that can be performed on a data structure and the computational complexity of those operations.
  • ADT abstract data types
  • a data structure is a concrete implementation of the specification provided by an ADT.
  • Urine sample procurement - Discovery cohort To measure intra- and inter-variability of miRNA abundance, 10 prostate cancer (PCa) patients with Gleason score (GS) 6 were recruited prospectively from the Active Surveillance program of the Odette Cancer Centre at Sunnybrook Health Sciences Centre (Toronto, Canada). Urine samples were obtained during scheduled surveillance check-ups (mean time interval between check-ups is 291 days). Following digital rectal exam (DRE) performed by the attending oncologist, first-catch urine samples (20-70 mL) were collected in vials containing 25 mM EDTA. Within 2-5 hours of collection, samples were centrifuged at 1800g for 10 minutes followed by washing with 5 mL of cold 1x PBS to obtain the urinary cell sediments.
  • DRE digital rectal exam
  • RNA molecules 200 nucleotides or less, including miRNAs were isolated from urinary cell sediments using the Urine miRNA Purification kit (Norgen Biotek Corp., Thorold, Ontario, Canada, Catalogue # 29000) according to the manufacturer's protocol. Following isolation, RNA was purified using ammonium acetate-ethanol precipitation. 25 ⁇ . of 7.5 mM ammonium acetate and 125 ⁇ . of cold 100 % ethanol was added to isolated RNA samples (50 ⁇ ) and left at -80 °C overnight. 1 mL of cold 80 % ethanol was added and samples were centrifuged at 18,000 x g for 30 minutes at 4 °C.
  • RNA pellet was washed twice with 0.5 mL of cold 80% ethanol and centrifuged at 18,000 x g for 10 minutes at 4 °C. Ethanol was removed and pellets were allowed to dry at room temperature. Dried pellets were re-suspended in 22 ⁇ _ of nuclease-free water.
  • RNA samples were evaluated using NanoDrop 8000 Spectrophotometer (Thermo Scientific, Wilmington, BE, USA). miRNA profiling was performed using nCounter® Human v.2 miRNA Expression Assay (NanoString Technologies, Seattle, WA, USA). Up to 100 ng per sample was used for profiling. Two batches were loaded with 1 1 samples and a mixture of four PCa cell lines (DU-145, PC-3, C42, and LNCaP). Cell line RNA served as an internal control for normalization of sample miRNA expression between batches. Raw data are deposited at GEO Omnibus (GSE86474, http://www.ncbi.nlm.nih.gov/geo/).
  • Measuring miRNA Abundance and Normalization miRNAs abundance was normalized using the R package NanoStringNorm (v1 .1.20) 1 . It examines the signal intensities of housekeeping genes, positive genes and negative genes by changing optimization options (such as SampleContent, CodeCount, and Background) and normalizes abundance values to show high abundance of housekeeping genes and positive genes but low abundance for negative controls.
  • ⁇ is mean abundance for any miRNA.
  • the individual (A) and replicates (e) effects are assumed to be random with variance, respectively.
  • ICC intra-class correlation coefficient
  • the ICC represents the proportion of inter-individual variance relative to total intra- and inter- individual variance explained by a model.
  • a high ICC indicates a high level of inter-individual variability relative to intra-individual variability.
  • miRBase (v21 ) 3 is used.
  • the database compiles all miRNA sequences and their annotation.
  • miRTarBase (v6.0) 4 is used for the identification of target genes of miRNAs.
  • the database collected experimentally validated miRNA-target interactions. In total, 5,506 miRNA - target gene interactions, which have strong experimental evidence (e.g. reporter assay and western blot), are deposited in the database. These interactions are composed of 515 human miRNAs and 2,180 target genes. We identified 3,462 interactions between 199 studied miRNAs and 1 ,669 potential target genes (data not shown).
  • g:Profiler v0.6.1
  • GO gene ontology
  • PCa can be grouped based on the risk of recurrence.
  • High- risk PCa commonly refers to the most aggressive of tumours.
  • low-risk PCa are unlikely to grow or spread for a few years 6 .
  • Urinary miRNAs are prepared as previously described. Nine batches were loaded with 99 samples and a mixture of four PCa cell lines (DU-145, PC-3, C42, and LNCaP). miRNA abundance is normalized as previously described.
  • Urinary miRNAs are prepared as previously described and loaded to four batches without control sample.
  • Random forest was used to discriminate PCa patient risk groups. Random forest is a widely used machine learning algorithm with excellent performance on many applications in cancer biomarker identification 7 .
  • First, we used an independent training cohort (n 99) to identify miRNA biomarker signatures and generate a predictive model. Relevant intra-stable miRNAs, taken from the Q100 quartile, are selected as features for the predictive model.
  • top-ranked miRNAs were selected as features to build a predictive model.
  • the resulting area under the receiver operating characteristic curve (AUC) was calculated and used as a performance measure.
  • AUC receiver operating characteristic curve
  • a set of top-ranked miRNAs that showed the highest AUC was chosen as the most relevant features (Fig. 14b).
  • Parameters for random forest (mtry and ntree) were optimized by grid search using 5-fold cross validation with a 10 repeat.
  • miRNAs hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451 a, hsa-miR-148a-3p, hsa-miR-512-5p, hsa-miR-431 -5p
  • All machine-learning approaches were performed using randomForestSRC (v2.4.2) for the R statistical environment 8 .
  • miRNA abundance in tissue samples miRNA expression data of prostate adenocarcinoma is downloaded from the TCGA data Portal (https://portal.gdc.cancer.gov). A total of 480 tumour samples with sample code '01 ' and vial code 'A' were found at the time the data were downloaded. Normalized quantification expression levels for these samples were further examined for each investigated miRNA.
  • a subset of miRNAs was detected in urine: 481 were detected in at least one sample, 358 in at least three samples, and 25 in all 22 samples (normalized read count > 0; Fig. 5c). miRNAs detected in urine overlap strongly with those expressed in PCa tissue 16 : 88.98% (428/481 ) of miRNAs detected in at least one urine sample were also detected in at least one PCa tumour tissue (Fig. 6a,b). There are 53 miRNAs (7.88%, 53/673) that are only observed in urine. Meanwhile, 167 miRNAs are only observed in PCa tumour tissue (Fig. 6b).
  • Fig. 9a To understand which miRNA species are most- and least-variable within individuals, we used variance analysis (Fig. 9a). Overall, 89.93% of miRNA species showed more variability between individuals than within individuals (Fig. 2b). To deconvolve the variance of individual miRNA into intra- and inter-individual components, we performed linear mixed-effects modelling (LMM) and measured the intra-class correlation coefficient (ICC). The higher a miRNA's ICC, the more it varies primarily across individuals rather than within them. Overall, 41 ⁇ 28% of total variance occurs between individuals (Fig. 2c). It suggests that there is a subset of miRNAs that show unusual variability within individuals. These particular urinary miRNAs should be excluded from biomarker-discovery studies due to their inherent variability (Table 3).
  • LMM linear mixed-effects modelling
  • ICC intra-class correlation coefficient
  • Targets of intra-variable miRNAs are involved in the initiation or perpetuation of an immune response (q ⁇ 0.05). Meanwhile, targets of miRNAs that are moderately intra-variable (Q50) were involved in nucleotide metabolic process and vesicular transport. Targets of miRNAs that are moderately intra-stable (Q75) were likely to be located at extracellular matrix and regulated cell morphogenesis, homeostatic process and defense response. Finally, significantly intra-stable miRNAs (Q100) targeted genes that were likely to be located at plasma membrane region and adherens junctions and participated in the organization of extracellular structure and actin cytoskeleton.
  • Fig. 4a To generate potential clinical utility of urine miRNAs predictive of patient risk-groups, we used a standard signature generation strategy to create a multi-miRNA risk model (Fig. 4a). To select relevant miRNAs that are stable within individuals, we performed 10-times repeated 5- fold cross validation using a training cohort and measured the importance of miRNA to discriminate two risk groups (Fig. 14a). We then subsequently selected the top-ranked miRNAs as features to build predictive models using random forest. The final model comprised the seven intra-stable miRNAs (Table 1 and Fig. 14b). From the cross-validation, we found that the predictive model strongly distinguished the two groups, with median AUC of 0.74 (95% confidence interval, 0.69 - 0.76, green bold line in Fig. 4b). Table 1
  • Urinary microRNA-based signature improves accuracy of detection of clinically relevant prostate cancer within the prostate-specific antigen grey zone.

Abstract

L'invention concerne une méthode de détermination de la probabilité d'une maladie ou de la progression d'une maladie chez un patient en ce qui concerne le cancer de la prostate, la méthode constitant à : a) fournir un échantillon de fluide biologique, de préférence de l'urine, provenant du patient contenant un miARN; b) déterminer ou mesurer l'abondance d'au moins un biomarqueur de miARN choisi dans le groupe constitué par : hsa-miR-3195, hsa-let-7b-5p, hsa-miR-144-3p, hsa-miR-451a, hsa-miR-148a-3p, hsa-miR-512-5p et hsa-miR-431-5p; c) comparer l'abondance dudit ou desdits biomarqueurs de miARN dans l'échantillon à l'abondance de référence ou témoin d'au moins un biomarqueur de miARN; et d) déterminer la probabilité d'une maladie ou de la progression d'une maladie; la probabilité d'une maladie ou de la progression d'une maladie étant plus élevée lorsqu'il y a une abondance supérieure statistiquement significative dans l'échantillon par comparaison à l'abondance de référence ou témoin.
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