AU2017268510B2 - Method for using gene expression to determine prognosis of prostate cancer - Google Patents

Abstract

Molecular assays that involve measurement of expression levels of prognostic biomarkers, or co expressed biomarkers, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likely prognosis for said patient, and likelihood that said patient will have a recurrence of prostate cancer, or to classify the tumor by likelihood of clinical outcome or TMPRSS2 fusion status, are provided herein.

Description

METHOD FOR USING GENE EXPRESSION TO DETERMINE PROGNOSIS OF PROSTATE CANCER [0001] This is a divisional of Australian Patent Application No.2015227398 which is a divisional of No. 2011282892, which is the Australian phase ofPCT/US2011/045253, which claims priority from United States patent application No. 31/368,217, filed 27 July 2010, 61/414,310, filed 16 November 2010 and 61/485,536, filed 15 May 2011. The contents of each application listed in this paragraph are fully incorporated by reference herein.

TECHNICAL FIELD [0002] The present disclosure relates to molecular diagnostic assays that provide information concerning methods to use gene expression profiles to determine prognostic information for cancer patients. Specifically, the present disclosure provides genes and microRNAs, the expression levels of which may be used to determine the likelihood that a prostate cancer patient will experience a local or distant cancer recurrence.

INTRODUCTION [0003] Prostate cancer is the most common solid malignancy in men and the second most common cause of cancer-related death in men in North America and the European Union (EU). In 2008, over 180,000 patients will be diagnosed with prostate cancer in the United States alone and nearly 30,000 will die of this disease. Age is the single most important risk factor for the development of prostate cancer, and applies across all racial groups that have been studied. With the aging of the U.S. population, it is projected that the annual incidence of prostate cancer will double by 2025 to nearly 400,000 cases per year. [0004] Since the introduction of prostate-specific antigen (PSA) screening in the 1990's, the proportion of patients presenting with clinically evident disease has fallen dramatically such that patients categorized as low risk now constitute half of new diagnoses today. PSA is used as a tumor marker to determine the presence of prostate cancer as high PSA levels are associated with prostate cancer. Despite a growing proportion of localized prostate cancer patients presenting with low-risk features such as low stage (TI) disease, greater than 90% of patients in the US still undergo definitive therapy, including prostatectomy or radiation. Only about 15% of these patients would develop metastatic disease and die from their prostate cancer, even in the absence of definitive therapy. A. Bill-Axelson, et al., J Nat’l Cancer Inst. 100(16): 1144-1154 (2008). Therefore, the majority of prostate cancer patients are being overtreated.

2017268510 28 Nov 2017 [0005] Estimates of recurrence risk and treatment decisions in prostate cancer are currently based primarily on PSA levels and/or tumor stage. Although tumor stage has been demonstrated to have significant association with outcome sufficient to be included in pathology reports, the College of American Pathologists Consensus Statement noted that variations in approach to the acquisition, interpretation, reporting, and analysis of this information exist. C. Compton, et al., Arch Pathol Lab Med 124:979-992 (2000). As a consequence, existing pathologic staging methods have been criticized as lacking reproducibility and therefore may provide imprecise estimates of individual patient risk.

SUMMARY [0006] This application discloses molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of cancer recurrence. For example, the likelihood of cancer recurrence could be described in terms of a score based on clinical or biochemical recurrence-free interval. [0007] In addition, this application discloses molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained to identify a risk classification for a prostate cancer patient. For example, patients may be stratified using expression level(s) of one or more genes or microRNAs associated, positively or negatively, with cancer recurrence or death from cancer, or with a prognostic factor. In an exemplary embodiment, the prognostic factor is Gleason pattern.

[0008] The biological sample may be obtained from standard methods, including surgery, biopsy, or bodily fluids. It may comprise tumor tissue or cancer cells, and, in some cases, histologically normal tissue, e.g., histologically normal tissue adjacent the tumor tissue. In exemplary embodiments, the biological sample is positive or negative for a TMPRSS2 fusion. [0009] In exemplary embodiments, expression level(s) of one or more genes and/or microRNAs that are associated, positively or negatively, with a particular clinical outcome in prostate cancer are used to determine prognosis and appropriate therapy. The genes disclosed herein may be used alone or arranged in functional gene subsets, such as cell adhesion/migration, immediate-early stress response, and extracellular matrix-associated. Each gene subset comprises

2017268510 28 Nov 2017 the genes disclosed herein, as well as genes that are co-expressed with one or more of the disclosed genes. The calculation may be performed on a computer, programmed to execute the gene expression analysis. The microRNAs disclosed herein may also be used alone or in combination with any one or more of the microRNAs and/or genes disclosed.

[0010] In exemplary embodiments, the molecular assay may involve expression levels for at least two genes. The genes, or gene subsets, may be weighted according to strength of association with prognosis or tumor microenvironment. In another exemplary embodiment, the molecular assay may involve expression levels of at least one gene and at least one microRNA. The gene-microRNA combination may be selected based on the likelihood that the genemicroRNA combination functionally interact.

BRIEF DESCRIPTION OF THE DRAWING [0011] Figure 1 shows the distribution of clinical and pathology assessments of biopsy Gleason score, baseline PSA level, and clinical T-stage.

DEFINITIONS [0012] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the ai t to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J, Wiley & Sons (New York, NY 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, NY 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.

[0013] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described herein. For purposes of the invention, the following terms are defined below.

[0014] The terms “tumor and “lesion” as used herein, refer to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. Those skilled in the art will realize that a tumor tissue sample may comprise multiple biological elements, such as one or more cancer cells, partial or fragmented cells, tumors in

2017268510 28 Nov 2017 various stages, surrounding histologically normal-appearing tissue, and/or macro or microdissected tissue.

[0015] The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer in the present disclosure include cancer of the urogenital tract, such as prostate cancer. [0016] The “pathology” of cancer includes all phenomena that compromise the wellbeing of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, etc.

[0017] As used herein, the term “prostate cancer” is used interchangeably and in the broadest sense refers to all stages and all forms of cancer arising from the tissue of the prostate gland.

[0018] According to the tumor, node, metastasis (TNM) staging system of the American Joint Committee on Cancer (AJCC), AJCC Cancer Staging Manual (7th Ed., 2010), the various stages of prostate cancer are defined as follows: Tumor: Tl: clinically inapparent tumor not palpable or visible by imaging, Tla: tumor incidental histological finding in 5% or less of tissue resected, Tib: tumor incidental histological finding in more than 5% of tissue resected, Tic: tumor identified by needle biopsy; T2: tumor confined within prostate, T2a: tumor involves one half of one lobe or less, T2b: tumor involves more than half of one lobe, but not both lobes, T2c: tumor involves both lobes; T3: tumor extends through the prostatic capsule, T3a: extracapsular extension (unilateral or bilateral), T3b: tumor invades seminal vesicle(s); T4: tumor is fixed or invades adjacent structures other than seminal vesicles (bladder neck, external sphincter, rectum, levator muscles, or pelvic wall). Node: NO: no regional lymph node metastasis; Nl: metastasis in regional lymph nodes. Metastasis: M0: no distant metastasis; Ml: distant metastasis present.

[0019] The Gleason Grading system is used to help evaluate the prognosis of men with prostate cancer. Together with other parameters, it is incorporated into a strategy of prostate cancer staging, which predicts prognosis and helps guide therapy. A Gleason “score” or “grade” is given to prostate cancer based upon its microscopic appearance. Tumors with a low Gleason score typically grow slowly enough that they may not pose a significant threat to the patients in

2017268510 28 Nov 2017 their lifetimes. These patients are monitored (“watchful waiting” or “active surveillance”) over time. Cancers with a higher Gleason score are more aggressive and have a worse prognosis, and these patients are generally treated with surgery (e.g., radical prostectomy) and, in some cases, therapy (e.g., radiation, hormone, ultrasound, chemotherapy). Gleason scores (or sums) comprise grades of the two most common tumor patterns. These patterns are referred to as Gleason patterns 1-5, with pattern 1 being the most well-differentiated. Most have a mixture of patterns. To obtain a Gleason score or grade, the dominant pattern is added to the second most prevalent pattern to obtain a number between 2 and 10. The Gleason Grades include: Gl: well differentiated (slight anaplasia) (Gleason 2-4); G2: moderately differentiated (moderate anaplasia) (Gleason 5-6); G3-4: poorly differentiated/undifferentiated (marked anaplasia) (Gleason 7-10).

[0020] Stage groupings: Stage I: Tla NO M0 Gl; Stage II: (Tla NO M0 G2-4) or (Tib, c, ΤΙ, T2, NO M0 Any G); Stage III: T3 NO M0 Any G; Stage IV: (T4 NO M0 Any G) or (Any T N1 M0 Any G) or (Any T Any N Ml Any G).

[0021] As used herein, the term “tumor tissue” refers to a biological sample containing one or more cancer cells, or a fraction of one or more cancer cells. Those skilled in the art will recognize that such biological sample may additionally comprise other biological components, such as histologically appearing normal cells (e.g., adjacent the tumor), depending upon the method used to obtain the tumor tissue, such as surgical resection, biopsy, or bodily fluids. [0022] As used herein, the term “AUA risk group” refers to the 2007 updated American Urological Association (AUA) guidelines for management of clinically localized prostate cancer, which clinicians use to determine whether a patient is at low, intermediate, or high risk of biochemical (PSA) relapse after local therapy.

[0023] As used herein, the term “adjacent tissue (AT)” refers to histologically “normal” cells that are adjacent a tumor. For example, the AT expression profile may be associated with disease recurrence and survival.

[0024] As used herein “non-tumor prostate tissue” refers to histologically normalappearing tissue adjacent a prostate tumor.

[0025] Prognostic factors are those variables related to the natural history of cancer, which influence the recurrence rates and outcome of patients once they have developed cancer. Clinical parameters that have been associated with a worse prognosis include, for example,

2017268510 28 Nov 2017 increased tumor stage, PSA level at presentation, and Gleason grade or pattern. Prognostic factors are frequently used to categorize patients into subgroups with different baseline relapse risks.

[0026] The term “prognosis” is used herein to refer to the likelihood that a cancer patient will have a cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as prostate cancer. For example, a “good prognosis” would include long term survival without recurrence and a “bad prognosis” would include cancer recurrence.

[0027] As used herein, the term “expression level” as applied to a gene refers to the normalized level of a gene product, e.g. the normalized value determined for the RNA expression level of a gene or for the polypeptide expression level of a gene.

[0028] The term “gene product” or “expression product” are used herein to refer to the RNA (ribonucleic acid) transcription products (transcripts) of the gene, including mRNA, and the polypeptide translation products of such RNA transcripts. A gene product can be, for example, an unspliced RNA, an mRNA, a splice variant mRNA, a microRNA, a fragmented RNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc.

[0029] The term “RNA transcript” as used herein refers to the RNA transcription products of a gene, including, for example, mRNA, an unspliced RNA, a splice variant mRNA, a microRNA, and a fragmented RNA.

[0030] The term “microRNA” is used herein to refer to a small, non-coding, singlestranded RNA of -18 - 25 nucleotides that may regulate gene expression. For example, when associated with the RNA-induced silencing complex (RISC), the complex binds to specific mRNA targets and causes translation repression or cleavage of these mRNA sequences.

[0031] Unless indicated otherwise, each gene name used herein corresponds to the Official Symbol assigned to the gene and provided by Entrez Gene (URL: www.ncbi.nlm.nih.gov/sites/entrez) as of the filing date of this application.

[0032] The terms “correlated” and “associated” are used interchangeably herein to refer to the association between two measurements (or measured entities). The disclosure provides genes,gene subsets, microRNAs, or microRNAs in combination with genes or gene subsets, the expression levels of which are associated with tumor stage. For example, the increased

2017268510 28 Nov 2017 expression level of a gene or microRNA may be positively correlated (positively associated) with a good or positive prognosis. Such a positive correlation may be demonstrated statistically in various ways, e.g. by a cancer recurrence hazard ratio less than one. In another example, the increased expression level of a gene or microRNA may be negatively correlated (negatively associated) with a good or positive prognosis. In that case, for example, the patient may experience a cancer recurrence.

[0033] The terms “good prognosis” or “positive prognosis” as used herein refer to a beneficial clinical outcome, such as long-term survival without recurrence. The terms “bad prognosis” or “negative prognosis” as used herein refer to a negative clinical outcome, such as cancer recurrence.

[0034] The term “risk classification” means a grouping of subjects by the level of risk (or likelihood) that the subject will experience a particular clinical outcome. A subject may be classified into a risk group or classified at a level of risk based on the methods of the present disclosure, e.g. high, medium, or low risk. A “risk group” is a group of subjects or individuals with a similar level of risk for a particular clinical outcome.

[0035] The term “long-term” survival is used herein to refer to survival for a particular time period, e.g., for at least 5 years, or for at least 10 years.

[0036] The term “recurrence” is used herein to refer to local or distant recurrence (i.e., metastasis) of cancer. For example, prostate cancer can recur locally in the tissue next to the prostate or in the seminal vesicles. The cancer may also affect the surrounding lymph nodes in the pelvis or lymph nodes outside this area. Prostate cancer can also spread to tissues next to the prostate, such as pelvic muscles, bones, or other organs. Recurrence can be determined by clinical recurrence detected by, for example, imaging study or biopsy, or biochemical recurrence detected by, for example, sustained follow-up prostate-specific antigen (PSA) levels > 0.4 ng/mL or the initiation of salvage therapy as a result of a rising PSA level.

[0037] The term “clinical recurrence-free interval (cRFI)” is used herein as time (in months) from surgery to first clinical recurrence or death due to clinical recurrence of prostate cancer. Losses due to incomplete follow-up, other primary cancers or death prior to clinical recurrence are considered censoring events; when these occur, the only information known is that up through the censoring time, clinical recurrence has not occurred in this subject. Biochemical recurrences are ignored for the purposes of calculating cRFI.

2017268510 28 Nov 2017 [0038] The term “biochemical recuirence-free interval (bRFI)” is used herein to mean the time (in months) from surgery to first biochemical recurrence of prostate cancer. Clinical recurrences, losses due to incomplete follow-up, other primary cancers, or death prior to biochemical recurrence are considered censoring events.

[0039] The term “Overall Survival (OS)” is used herein to refer to the time (in months) from surgery to death from any cause. Losses due to incomplete follow-up are considered censoring events. Biochemical recurrence and clinical recurrence are ignored for the purposes of calculating OS.

[0040] The term “Prostate Cancer-Specific Survival (PCSS)” is used herein to describe the time (in years) from surgery to death from prostate cancer. Losses due to incomplete followup or deaths from other causes are considered censoring events. Clinical recurrence and biochemical recurrence are ignored for the purposes of calculating PCSS.

[0041] The term “upgrading” or “upstaging” as used herein refers to a change in Gleason grade from 3+3 at the time of biopsy to 3+4 or greater at the time of radical prostatectomy (RP), or Gleason grade 3+4 at the time of biopsy to 4+3 or greater at the time of RP, or seminal vessical involvement (SVI), or extracapsular involvement (ECE) at the time of RP.

[0042] In practice, the calculation of the measures listed above may vary from study to study depending on the definition of events to be considered censored.

[0043] The term “microarray” refers to an ordered arrangement of hybridizable array elements, e.g. oligonucleotide or polynucleotide probes, on a substrate.

[0044] The term “polynucleotide” generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and doublestranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an

2017268510 28 Nov 2017 oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons, are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells. [0045] The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNArDNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms. [0046] The term “Ct” as used herein refers to threshold cycle, the cycle number in quantitative polymerase chain reaction (qPCR) at which the fluorescence generated within a reaction well exceeds the defined threshold, i.e. the point during the reaction at which a sufficient number of amplicons have accumulated to meet the defined threshold.

[0047] The term “Cp” as used herein refers to “crossing point.” The Cp value is calculated by determining the second derivatives of entire qPCR amplification curves and their maximum value. The Cp value represents the cycle at which the increase of fluorescence is highest and where the logarithmic phase of a PCR begins.

[0048] The terms “threshold” or “thresholding” refer to a procedure used to account for non-linear relationships between gene expression measurements and clinical response as well as to further reduce variation in reported patient scores. When thresholding is applied, all measurements below or above a threshold are set to that threshold value. Non-linear relationship between gene expression and outcome could be examined using smoothers or cubic splines to model gene expression in Cox PH regression on recurrence free interval or logistic regression on recurrence status. D. Cox, Journal of the Royal Statistical Society, Series B 34:187-220 (1972).

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Variation in reported patient scores could be examined as a function of variability in gene expression at the limit of quantitation and/or detection for a particular gene.

[0049] As used herein, the term “amplicon,” refers to pieces of DNA that have been synthesized using amplification techniques, such as polymerase chain reactions (PCR) and ligase chain reactions.

[0050] “Stringency” of hybridization reactions is readily determinable by one of ordinary skill in the ait, and generally is an empirical calculation dependent upon probe length, washing temperature, and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes need lower temperatures. Hybridization generally depends on the ability of denatured DNA to re-anneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and hybridizable sequence, the higher the relative temperature which can be used. As a result, it follows that higher relative temperatures would tend to make the reaction conditions more stringent, while lower temperatures less so. For additional details and explanation of stringency of hybridization reactions, see Ausubel et al., Current Protocols in Molecular Biology (Wiley Interscience Publishers, 1995).

[0051] “Stringent conditions” or “high stringency conditions”, as defined herein, typically: (1) employ low ionic strength and high temperature for washing, for example 0.015 M sodium chloride/0.0015 M sodium citrate/0.1% sodium dodecyl sulfate at 50°C; (2) employ during hybridization a denaturing agent, such as formamide, for example, 50% (v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1% polyvinylpyrrolidone/50mM sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM sodium citrate at 42°C; or (3) employ 50% formamide, 5 x SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5 x Denhardt's solution, sonicated salmon sperm DNA (50 pg/ml), 0.1% SDS, and 10% dextran sulfate at 42°C, with washes at 42°C in 0.2 x SSC (sodium chloride/sodium citrate) and 50% formamide, followed by a high-stringency wash consisting of 0.1 x SSC containing EDTA at 55°C.

[0052] “Moderately stringent conditions” may be identified as described by Sambrook et al., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and include the use of washing solution and hybridization conditions (e.g., temperature, ionic strength and %SDS) less stringent that those described above. An example of moderately

2017268510 28 Nov 2017 stringent conditions is overnight incubation at 37°C in a solution comprising: 20% formamide, 5 x SSC (150 mM NaO, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 x Denhardt’s solution, 10% dextran sulfate, and 20 mg/ml denatured sheared salmon sperm DNA, followed by washing the filters in 1 x SSC at about 37-500C. The skilled artisan will recognize how to adjust the temperature, ionic strength, etc. as necessary to accommodate factors such as probe length and the like.

[0053] The terms “splicing” and “RNA splicing” are used interchangeably and refer to RNA processing that removes introns and joins exons to produce mature mRNA with continuous coding sequence that moves into the cytoplasm of an eukaryotic cell.

[0054] The terms “co-express” and “co-expressed”, as used herein, refer to a statistical correlation between the amounts of different transcript sequences across a population of different patients. Pairwise co-expression may be calculated by various methods known in the art, e.g., by calculating Pearson correlation coefficients or Spearman correlation coefficients. Co-expressed gene cliques may also be identified using graph theory. An analysis of co-expression may be calculated using normalized expression data. A gene is said to be co-expressed with a particular disclosed gene when the expression level of the gene exhibits a Pearson correlation coefficient greater than or equal to 0.6.

[0055] A “computer-based system” refers to a system of hardware, software, and data storage medium used to analyze information. The minimum hardware of a patient computerbased system comprises a central processing unit (CPU), and hardware for data input, data output (e.g., display), and data storage. An ordinarily skilled artisan can readily appreciate that any currently available computer-based systems and/or components thereof are suitable for use in connection with the methods of the present disclosure. The data storage medium may comprise any manufacture comprising a recording of the present information as described above, or a memory access device that can access such a manufacture.

[0056] To “record” data, programming or other information on a computer readable medium refers to a process for storing information, using any such methods as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc.

2017268510 28 Nov 2017 [0057] A “processor” or “computing means” references any hardware and/or software combination that will perform the functions required of it. For example, a suitable processor may be a programmable digital microprocessor such as available in the form of an electronic controller, mainframe, server or personal computer (desktop or portable). Where the processor is programmable, suitable programming can be communicated from a remote location to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid state device based). For example, a magnetic medium or optical disk may carry the programming, and can be read by a suitable reader communicating with each processor at its corresponding station.

[0058] As used herein, the terms “active surveillance” and “watchful waiting” mean closely monitoring a patient’s condition without giving any treatment until symptoms appear or change. For example, in prostate cancer, watchful waiting is usually used in older men with other medical problems and early-stage disease.

[0059] As used herein, the term “surgery” applies to surgical methods undertaken for removal of cancerous tissue, including pelvic lymphadenectomy, radical prostatectomy, transurethral resection of the prostate (TURP), excision, dissection, and tumor biopsy/removal. The tumor tissue or sections used for gene expression analysis may have been obtained from any of these methods.

[0060] As used herein, the term “therapy” includes radiation, hormonal therapy, cryosurgery, chemotherapy, biologic therapy, and high-intensity focused ultrasound, [0061] As used herein, the term “TMPRSS fusion” and “TMPRSS2 fusion” are used interchangeably and refer to a fusion of the androgen-driven TMPRSS2 gene with the ERG oncogene, which has been demonstrated to have a significant association with prostate cancer. S. Perner, et al., Urologe A. 46(7):754-760 (2007); S.A. Narod, et al., Br J Cancer 99(6):847-851 (2008). As used herein, positive TMPRSS fusion status indicates that the TMPRSS fusion is present in a tissue sample, whereas negative TMPRSS fusion status indicates that the TMPRSS fusion is not present in a tissue sample. Experts skilled in the art will recognize that there are numerous ways to determine TMPRSS fusion status, such as real-time, quantitative PCR or highthroughput sequencing. See, e.g., K. Mertz, et al., Neoplasis 9(3):200-206 (2007); C. Maher, Nature 458(7234):97-101 (2009).

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Gene Expression Methods Using Genes, Gene Subsets, and microRNAs [0062] The present disclosure provides molecular assays that involve measurement of expression level(s) of one or more genes, gene subsets, microRNAs, or one or more microRNAs in combination with one or more genes or gene subsets, from a biological sample obtained from a prostate cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of cancer recurrence.

[0063] The present disclosure further provides methods to classify a prostate tumor based on expression level(s) of one or more genes and/or microRNAs. The disclosure further provides genes and/or microRNAs that are associated, positively or negatively, with a particular prognostic outcome. In exemplary embodiments, the clinical outcomes include cRFI and bRFI. In another embodiment, patients may be classified in risk groups based on the expression level(s) of one or more genes and/or microRNAs that are associated, positively or negatively, with a prognostic factor. In an exemplary embodiment, that prognostic factor is Gleason pattern.

[0064] Various technological approaches for determination of expression levels of the disclosed genes and microRNAs are set forth in this specification, including, without limitation, RT-PCR, microarrays, high-throughput sequencing, serial analysis of gene expression (SAGE) and Digital Gene Expression (DGE), which will be discussed in detail below. In particular aspects, the expression level of each gene or microRNA may be determined in relation to various features of the expression products of the gene including exons, introns, protein epitopes and protein activity.

[0065] The expression level(s) of one or more genes and/or microRNAs may be measured in tumor tissue. For example, the tumor tissue may obtained upon surgical removal or resection of the tumor, or by tumor biopsy. The tumor tissue may be or include histologically “normal” tissue, for example histologically “normal” tissue adjacent to a tumor. The expression level of genes and/or microRNAs may also be measured in tumor cells recovered from sites distant from the tumor, for example circulating tumor cells, body fluid (e.g., urine, blood, blood fraction, etc.).

[0066] The expression product that is assayed can be, for example, RNA or a polypeptide. The expression product may be fragmented. For example, the assay may use primers that are complementary to target sequences of an expression product and could thus

2017268510 28 Nov 2017 measure full transcripts as well as those fragmented expression products containing the target sequence. Further information is provided in Table A (inserted in specification prior to claims). [0067] The RNA expression product may be assayed directly or by detection of a cDNA product resulting from a PCR-based amplification method, e.g., quantitative reverse transcription polymerase chain reaction (qRT-PCR). (See e.g., U.S. Patent No. 7,587,279). Polypeptide expression product may be assayed using immunohistochemistry (IHC). Further, both RNA and polypeptide expression products may also be is assayed using microarrays.

Clinical Utility [0068] Prostate cancer is currently diagnosed using a digital rectal exam (DRE) and Prostate-specific antigen (PSA) test. If PSA results are high, patients will generally undergo a prostate tissue biopsy. The pathologist will review the biopsy samples to check for cancer cells and determine a Gleason score. Based on the Gleason score, PSA, clinical stage, and other factors, the physician must make a decision whether to monitor the patient, or treat the patient with surgery and therapy.

[0069] At present, clinical decision-making in early stage prostate cancer is governed by certain histopathologic and clinical factors. These include: (1) tumor factors, such as clinical stage (e.g. ΤΙ, T2), PSA level at presentation, and Gleason grade, that are very strong prognostic factors in determining outcome; and (2) host factors, such as age at diagnosis and co-morbidity. Because of these factors, the most clinically useful means of stratifying patients with localized disease according to prognosis has been through multifactorial staging, using the clinical stage, the serum PSA level, and tumor grade (Gleason grade) together. In the 2007 updated American Urological Association (AU A) guidelines for management of clinically localized prostate cancer, these parameters have been grouped to determine whether a patient is at low, intermediate, or high risk of biochemical (PSA) relapse after local therapy. I. Thompson, et al., Guideline for the management of clinically localized prostate cancer, J Urol. 177(6):2106-31 (2007).

[0070] Although such classifications have proven to be helpful in distinguishing patients with localized disease who may need adjuvant therapy after surgery/radiation, they have less ability to discriminate between indolent cancers, which do not need to be treated with local therapy, and aggressive tumors, which require local therapy. In fact, these algorithms are of increasingly limited use for deciding between conservative management and definitive therapy

2017268510 28 Nov 2017 because the bulk of prostate cancers diagnosed in the PSA screening era now present with clinical stage Tic and PSA <10 ng/mL.

[0071] Patients with Tl prostate cancer have disease that is not clinically apparent but is discovered either at transurethral resection of the prostate (TURP, Tla, Tib) or at biopsy performed because of an elevated PSA (> 4 ng/mL, Tic). Approximately 80% of the cases presenting in 2007 are clinical Tl at diagnosis. In a Scandinavian trial, OS at 10 years was 85% for patients with early stage prostate cancer (T1/T2) and Gleason score < 7, after radical prostatectomy.

[0072] Patients with T2 prostate cancer have disease that is clinically evident and is organ confined; patients with T3 tumors have disease that has penetrated the prostatic capsule and/or has invaded the seminal vesicles. It is known from surgical series that clinical staging underestimates pathological stage, so that about 20% of patients who are clinically T2 will be pT3 after prostatectomy. Most of patients with T2 or T3 prostate cancer are treated with local therapy, either prostatectomy or radiation. The data from the Scandinavian trial suggest that for T2 patients with Gleason grade <7, the effect of prostatectomy on survival is at most 5% at 10 years; the majority of patients do not benefit from surgical treatment at the time of diagnosis. For T2 patients with Gleason > 7 or for T3 patients, the treatment effect of prostatectomy is assumed to be significant but has not been determined in randomized trials. It is known that these patients have a significant risk (10-30%) of recurrence at 10 years after local treatment, however, there are no prospective randomized trials that define the optimal local treatment (radical prostatectomy, radiation) at diagnosis, which patients are likely to benefit from neoadjuvant/adjuvant androgen deprivation therapy, and whether treatment (androgen deprivation, chemotherapy) at the time of biochemical failure (elevated PSA) has any clinical benefit.

[0073] Accurately determining Gleason scores from needle biopsies presents several technical challenges. First, interpreting histology that is borderline between Gleason pattern is highly subjective, even for urologic pathologists. Second, incomplete biopsy sampling is yet another reason why the “predicted” Gleason score on biopsy does not always correlate with the actual “observed” Gleason score of the prostate cancer in the gland itself. Hence, the accuracy of Gleason scoring is dependent upon not only the expertise of the pathologist reading the slides, but also on the completeness and adequacy of the prostate biopsy sampling strategy. T. Stamey, Urology 45:2-12 (1995). The gene/microRNA expression assay and associated information

2017268510 28 Nov 2017 provided by the practice of the methods disclosed herein provide a molecular assay method to facilitate optimal treatment decision-making in early stage prostate cancer. An exemplary embodiment provides genes and microRNAs, the expression levels of which are associated (positively or negatively) with prostate cancer recurrence. For example, such a clinical tool would enable physicians to identify T2/T3 patients who are likely to recur following definitive therapy and need adjuvant treatment.

[0074] In addition, the methods disclosed herein may allow physicians to classify tumors, at a molecular level, based on expression level(s) of one or more genes and/or microRNAs that are significantly associated with prognostic factors, such as Gleason pattern and TMPRSS fusion status. These methods would not be impacted by the technical difficulties of intra-patient variability, histologically determining Gleason pattern in biopsy samples, or inclusion of histologically normal appearing tissue adjacent to tumor tissue. Multi-analyte gene/microRNA expression tests can be used to measure the expression level of one or more genes and/or microRNAs involved in each of several relevant physiologic processes or component cellular characteristics. The methods disclosed herein may group the genes and/or microRNAs. The grouping of genes and microRNAs may be performed at least in part based on knowledge of the contribution of those genes and/or microRNAs according to physiologic functions or component cellular characteristics, such as in the groups discussed above. Furthermore, one or more microRNAs may be combined with one or rnoregenes. The gene-microRNA combination may be selected based on the likelihood that the gene-microRNA combination functionally interact. The formation of groups (or gene subsets), in addition, can facilitate the mathematical weighting of the contribution of various expression levels to cancer recurrence. The weighting of a gene/microRNA group representing a physiological process or component cellular characteristic can reflect the contribution of that process or characteristic to the pathology of the cancer and clinical outcome.

[0075] Optionally, the methods disclosed may be used to classify patients by risk, for example risk of recurrence. Patients can be partitioned into subgroups (e.g., tertiles or quartiles) and the values chosen will define subgroups of patients with respectively greater or lesser risk. [0076] The utility of a disclosed gene marker in predicting prognosis may not be unique to that marker. An alternative marker having an expression pattern that is parallel to that of a disclosed gene may be substituted for, or used in addition to, that co-expressed gene or

2017268510 28 Nov 2017 microRNA. Due to the co-expression of such genes or microRNAs, substitution of expression level values should have little impact on the overall utility of the test. The closely similar expression patterns of two genes or microRNAs may result from involvement of both genes or microRNAs in the same process and/or being under common regulatory control in prostate tumor cells. The present disclosure thus contemplates the use of such co-expressed genes,gene subsets, or microRNAs as substitutes for, or in addition to, genes of the present disclosure.

Methods of Assaying Expression Levels oi·' a Gene Prodi ct [0077] The methods and compositions of the present disclosure will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry, which are within the skill of the ait. Exemplary techniques are explained fully in the literature, such as, “Molecular Cloning: A Laboratory Manual”, 2nd edition (Sambrook etal., 1989); “Oligonucleotide Synthesis” (M.J. Gait, ed., 1984); “Animal Cell Culture” (R.I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4th edition (D.M. Weir & C.C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J.M. Miller & M.P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F.M. Ausubel et al,, eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994).

[0078] Methods of gene expression profiling include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, and proteomicsbased methods. Exemplary methods known in the art for the quantification of RNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852854 (1992)); and PCR-based methods, such as reverse transcription PCT (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Antibodies may be employed that can recognize sequence-specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).

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Reverse Transcriptase PCR (RT-PCR) [0079] Typically, mRNA or microRNA is isolated from a test sample. The stalling material is typically total RNA isolated from a human tumor, usually from a primary tumor. Optionally, normal tissues from the same patient can be used as an internal control. Such normal tissue can be histological ly-appearing normal tissue adjacent a tumor. mRNA or microRNA can be extracted from a tissue sample, e.g., from a sample that is fresh, frozen (e.g. fresh frozen), or paraffin-embedded and fixed (e.g. formalin-fixed).

[0080] General methods for mRNA and microRNA extraction are well known in the ail and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer’s instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, WI), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

[0081] The sample containing the RNA is then subjected to reverse transcription to produce cDNA from the RNA template, followed by exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RN A can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA), following the manufacturer’s instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

[0082] PCR-based methods use a thermostable DNA-dependent DNA polymerase, such as a Taq DNA polymerase. For example, TaqMan® PCR typically utilizes the 5’-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target

2017268510 28 Nov 2017 amplicon, but any enzyme with equivalent 5’ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction product. A third oligonucleotide, or probe, can be designed to facilitate detection of a nucleotide sequence of the amplicon located between the hybridization sites the two PCR primers. The probe can be detectably labeled, e.g., with a reporter dye, and can further be provided with both a fluorescent dye, and a quencher fluorescent dye, as in a Taqman® probe configuration. Where a Taqman® probe is used, during the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

[0083] TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, high-throughput platforms such as the ABI PRISM 7700 Sequence Detection System® (Perkin-Elmer-Applied Biosystems, Foster City, CA, USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the procedure is run on a LightCycler® 480 (Roche Diagnostics) real-time PCR system, which is a microwell plate-based cycler platform.

[0084] 5' -Nuclease assay data are commonly initially expressed as a threshold cycle (“Ct”). Fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The threshold cycle (Ct) is generally described as the point when the fluorescent signal is first recorded as statistically significant. Alternatively, data may be expressed as a crossing point ( “Cp”). The Cp value is calculated by determining the second derivatives of entire qPCR amplification curves and their maximum value. The Cp value represents the cycle at which the increase of fluorescence is highest and where the logarithmic phase of a PCR begins.

[0085] To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard gene (also referred to as a reference gene) is expressed at a quite constant level among cancerous and non-cancerous tissue of the same origin (i.e., a level that is not significantly different among normal and cancerous tissues), and is not significantly affected by the experimental treatment (i.e., does not

2017268510 28 Nov 2017 exhibit a significant difference in expression level in the relevant tissue as a result of exposure to chemotherapy), and expressed at a quite constant level among the same tissue taken from different patients. For example, reference genes useful in the methods disclosed herein should not exhibit significantly different expression levels in cancerous prostate as compared to normal prostate tissue. RNAs frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin. Exemplary reference genes used for normalization comprise one or more of the following genes: AAMP, ARF1, ATP5E, CLTC, GPS1, and PGK1. Gene expression measurements can be normalized relative to the mean of one or more (e.g., 2, 3,4, 5, or more) reference genes. Reference-normalized expression measurements can range from 2 to 15, where a one unit increase generally reflects a 2-fold increase in RNA quantity.

[0086] Real time PCR is compatible both with quantitative competitive PCR, where internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986-994 (1996). [0087] The steps of a representative protocol for use in the methods of the present disclosure use fixed, paraffin-embedded tissues as the RNA source. For example, mRNA isolation, purification, primer extension and amplification can be performed according to methods available in the art. (see, e.g., Godfrey et al. J. Molec. Diagnostics 2: 84-91 (2000); Specht et al„ Am. J. Pathol, 158: 419-29 (2001)). Briefly, a representative process stalls with cutting about 10 μπι thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA depleted from the RNA-containing sample. After analysis of the RNA concentration, RNA is reverse transcribed using gene specific primers followed by RTPCR to provide for cDNA amplification products.

Design of Intron-Based PCR Primers and Probes [0088] PCR primers and probes can be designed based upon exon or intron sequences present in the mRNA transcript of the gene of interest. Primer/probe design can be performed using publicly available software, such as the DNA BLAT software developed by Kent, W.J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations.

[0089] Where necessary or desired, repetitive sequences of the target sequence can be masked to mitigate non-specific signals. Exemplary tools to accomplish this include the Repeat

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Masker program available on-line through the Baylor College of Medicine, which screens DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked intron sequences can then be used to design primer and probe sequences using any commercially or otherwise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. See S. Rrawetz, S. Misener, Bioinformatics Methods and Protocols: Methods in Molecular Biology, pp. 365-386 (Humana Press).

[0090] Other factors that can influence PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementar y primer sequences, and 3 '-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases, and exhibit Tm's between 50 and 80 0C, e.g. about 50 to 70 0C.

[0091] For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, CW. et al, “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press,. New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T.N. Primerselect: Primer and probe design. Methods Mol. Biol, 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.

[0092] Table A provides further information concerning the primer, probe, and amplicon sequences associated with the Examples disclosed herein.

MassARRAY® System [0093] In MassARRAY-based methods, such as the exemplary method developed by Sequenom, Inc. (San Diego, CA) following the isolation of RNA and reverse transcription, the obtained cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal standard. The cDNA/competitor mixture is PCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivarion of the alkaline phosphatase, the PCR products from the competitor and cDNA are subjected to primer extension, which generates distinct mass signals

2017268510 28 Nov 2017 for the competitor- and cDNA-derives PCR products. After purification, these products are dispensed on a chip array, which is pre-loaded with components needed for analysis with matrixassisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated. For further details see, e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064 (2003).

Other PCR-based Methods [0094] Further PCR-based techniques that can find use in the methods disclosed herein include, for example, BeadArray® technology (Illumina, San Diego, CA; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); BeadsArray for Detection of Gene Expression® (BADGE), using the commercially available LuminexlOO LabMAP® system and multiple color-coded microspheres (Luminex Corp., Austin, TX) in a rapid assay for gene expression (Yang et al., Genome Res. 11:1888-1898 (2001)); and high coverage expression profiling (HiCEP) analysis (Fukumuraet al., Nucl. Acids. Res. 31(16) e94 (2003).

Microarrays [0095] Expression levels of a gene or microArray of interest can also be assessed using the microarray technique. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are arrayed on a substrate. The arrayed sequences are then contacted under conditions suitable for specific hybridization with detectably labeled cDNA generated from RNA of a test sample. As in the RT-PCR method, the source of RNA typically is total RNA isolated from a tumor sample, and optionally from normal tissue of the same patient as an internal control or cell lines. RNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

[0096] For example, PCR amplified inserts of cDNA clones of a gene to be assayed are applied to a substrate in a dense array. Usually at least 10,000 nucleotide sequences are applied to the substrate. For example, the microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After washing under stringent

2017268510 28 Nov 2017 conditions to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding RNA abundance. [0097] With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et at, Proc. Natl. Acad. ScL USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip® technology, or Incyte's microarray technology.

Serial Analysis of Gene Expression (SAGE) [0098] Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51 (1997).

Gene Expression Analysis by Nucleic Acid Sequencing [0099] Nucleic acid sequencing technologies are suitable methods for analysis of gene expression. The principle underlying these methods is that the number of times a cDNA sequence is detected in a sample is directly related to the relative expression of the RNA corresponding to that sequence. These methods are sometimes referred to by the term Digital Gene Expression (DGE) to reflect the discrete numeric property of the resulting data. Early methods applying this principle were Serial Analysis of Gene Expression (SAGE) and Massively

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Parallel Signature Sequencing (MPSS). See, e.g,, S. Brenner, et al,, Nature Biotechnology 18(6):630-634 (2000). More recently, the advent of “next-generation” sequencing technologies has made DGE simpler, higher throughput, and more affordable. As a result, more laboratories are able to utilize DGE to screen the expression of more genes in more individual patient samples than previously possible. See, e.g., J. Marioni, Genome Research 18(9):1509-1517 (2008); R. Morin, Genome Research 18(4):610-621 (2008); A. Mortazavi, Nature Methods 5(7):621-628 (2008); N. Cloonan, Nature Methods 5(7):613-619 (2008).

Isolating RNA from Body Fluids [00100] Methods of isolating RNA for expression analysis from blood, plasma and serum (see, e.g., K. Enders, et al., Clin Chem 48,1647-53 (2002) (and references cited therein) and from urine (see, e.g., R. Boom, et al., J Clin Microbiol. 28, 495-503 (1990) and references cited therein) have been described.

Immunohistochemistry [00101] Immunohistochemistry methods are also suitable for detecting the expression levels of genes and applied to the method disclosed herein. Antibodies (e.g., monoclonal antibodies) that specifically bind a gene product of a gene of interest can be used in such methods. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten' labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody can be used in conjunction with a labeled secondary antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

Proteomics [00102] The term “proteome” is defined as the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as “expression proteomics”). Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N- terminal sequencing, and (3) analysis of the data using bioinformatics.

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General Description of the mRNA/microRNA Isolation, Purification and Amplification [00103] The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA or microRNA isolation, purification, primer extension and amplification are provided in various published journal articles. (See, e.g., T.E. Godfrey, et al,. J. Molec. Diagnostics 2: 84-91 (2000); K. Specht et al., Am. J. Pathol. 158: 419-29 (2001), M. Cronin, et al., Am J Pathol 164:35-42 (2004)). Briefly, a representative process starts with cutting a tissue sample section (e.g.about 10 pm thick sections of a paraffin-embedded tumor tissue sample). The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair is performed if desired. The sample can then be subjected to analysis, e.g., by reverse transcribed using gene specific promoters followed by RT-PCR.

Statistical Analysis Of Expression Levels in Identification of Genes and MicroRNAs [00104] One skilled in the art will recognize that there are many statistical methods that may be used to determine whether there is a significant relationship between a parameter of interest (e.g., recurrence) and expression levels of a marker gene/microRNA as described here. In an exemplary embodiment, the present invention provides a stratified cohort sampling design (a form of case-control sampling) using tissue and data from prostate cancer patients. Selection of specimens was stratified by T stage (ΤΙ, T2), year cohort (<1993, >1993), and prostatectomy Gleason Score (low/intermediate, high). All patients with clinical recurrence were selected and a sample of patients who did not experience a clinical recurrence was selected. For each patient, up to two enriched tumor specimens and one normal-appearing tissue sample was assayed.

[00105] All hypothesis tests were reported using two-sided p-values. To investigate if there is a significant relationship of outcomes (clinical recurrence-free interval (cRFI), biochemical recurrence-free interval (bRFI), prostate cancer-specific survival (PCSS), and overall survival (OS)) with individual genes and/or microRNAs, demographic or clinical covariates Cox Proportional Hazards (PH) models using maximum weighted pseudo partiallikelihood estimators were used and p-values from Wald tests of the null hypothesis that the hazard ratio (HR) is one are reported. To investigate if there is a significant relationship between individual genes and/or microRNAs and Gleason pattern of a particular sample, ordinal logistic

2017268510 28 Nov 2017 regression models using maximum weighted likelihood methods were used and p-values from Wald tests of the null hypothesis that the odds ratio (OR) is one are reported.

Coexpression Analysis [00106] The present disclosure provides a method to determine tumor stage based on the expression of staging genes, or genes that co-express with particular staging genes. To perform particular biological processes, genes often work together in a concerted way, i.e. they are coexpressed. Co-expressed gene groups identified for a disease process like cancer can serve as biomarkers for tumor status and disease progression. Such co-expressed genes can be assayed in lieu of, or in addition to, assaying of the staging gene with which they are co-expressed.

[00107] In an exemplary embodiment, the joint correlation of gene expression levels among prostate cancer specimens under study may be assessed. For this purpose, the correlation structures among genes and specimens may be examined through hierarchical cluster methods. This information may be used to confirm that genes that are known to be highly correlated in prostate cancer specimens cluster together as expected. Only genes exhibiting a nominally significant (unadjusted p < 0.05) relationship with cRFI in the univariate Cox PH regression analysis will be included in these analyses.

[00108] One skilled in the art will recognize that many co-expression analysis methods now known or later developed will fall within the scope and spirit of the present invention. These methods may incorporate, for example, correlation coefficients, co-expression network analysis, clique analysis, etc., and may be based on expression data from RT-PCR, microarrays, sequencing, and other similar technologies. For example, gene expression clusters can be identified using pair-wise analysis of correlation based on Pearson or Spearman correlation coefficients. (See, e.g., Pearson K. and Lee A., Biometrika 2, 357 (1902); C. Spearman, Amer. J. Psychol 15:72-101 (1904); J. Myers, A. Well, Research Design and Statistical Analysis, p. 508 (2nd Ed., 2003).)

Normalization of Expression Levels [00109] The expression data used in the methods disclosed herein can be normalized. Normalization refers to a process to correct for (normalize away), for example, differences in the amount of RNA assayed and variability in the quality of the RNA used, to remove unwanted sources of systematic variation in Ct or Cp measurements, and the like. With respect to RT-PCR experiments involving archived fixed paraffin embedded tissue samples, sources of systematic

2017268510 28 Nov 2017 variation are known to include the degree of RNA degradation relative to the age of the patient sample and the type of fixative used to store the sample. Other sources of systematic variation are attributable to laboratory processing conditions.

[00110] Assays can provide for normalization by incorporating the expression of certain normalizing genes, which do not significantly differ in expression levels under the relevant conditions. Exemplary normalization genes disclosed herein include housekeeping genes. (See, e.g., E. Eisenberg, et al., Trends in Genetics 19(7):362-365 (2003).) Normalization can be based on the mean or median signal (Ct or Cp) of all of the assayed genes or a large subset thereof (global normalization approach). In general, the normalizing genes, also referred to as reference genes should be genes that are known not to exhibit significantly different expression in prostate cancer as compared to non-cancerous prostate tissue, and are not significantly affected by various sample and process conditions, thus provide for normalizing away extraneous effects. [00111] In exemplary embodiments, one or more of the following genes are used as references by which the mRNA or microRNA expression data is normalized: A AMP, ARF1, ATP5E, CLTC, GPS1, and PGK1. In another exemplary embodiment, one or more of the following microRNAs are used as references by which the expression data of microRNAs are normalized: hsa-miR-106a; hsa-miR-146b-5p; hsa-miR-191; hsa-miR-19b; and hsa-miR-92a. The calibrated weighted average Ct or Cp measurements for each of the prognostic and predictive genes or microRNAs may be normalized relative to the mean of five or more reference genes or microRNAs.

[00112] Those skilled in the art will recognize that normalization may be achieved in numerous ways, and the techniques described above are intended only to be exemplary, not exhaustive.

Standardization of Expression Leveks [00113] The expression data used in the methods disclosed herein can be standardized. Standardization refers to a process to effectively put all the genes or microRNAs on a comparable scale. This is performed because some genes or microRNAs will exhibit more variation (a broader range of expression) than others. Standardization is performed by dividing each expression value by its standard deviation across all samples for that gene or microRNA. Hazard ratios are then interpreted as the relative risk of recurrence per 1 standard deviation increase in expression.

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Kits of the Invention [00114] The materials for use in the methods of the present invention are suited for preparation of kits produced in accordance with well-known procedures. The present disclosure thus provides kits comprising agents, which may include gene (or microRNAj-specific or gene (or microRNA)-selective probes and/or primers, for quantifying the expression of the disclosed genes or microRNAs for predicting prognostic outcome or response to treatment. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular fixed paraffin-embedded tissue samples and/or reagents for RNA amplification. In addition, the kits may optionally comprise the reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present invention. The kits may comprise containers (including microliter plates suitable for use in an automated implementation of the method), each with one or more of the various materials or reagents (typically in concentrated form) utilized in the methods, including, for example, chromatographic columns, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers of the present invention (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase). Mathematical algorithms used to estimate or quantify prognostic or predictive information are also properly potential components of kits.

Reports [00115] The methods of this invention, when practiced for commercial diagnostic puiposes, generally produce a report or summary of information obtained from the hereindescribed methods. For example, a report may include information concerning expression levels of one or more genes and /or microRNAs, classification of the tumor or the patient’s risk of recurrence, the patient’s likely prognosis or risk classification, clinical and pathologic factors, and/or other information. The methods and reports of this invention can further include storing the report in a database. The method can create a record in a database for the subject and populate the record with data. The report may be a paper report, an auditory report, or an electronic record. The report may be displayed and/or stored on a computing device (e.g., handheld device, desktop computer, smart device, website, etc.). It is contemplated that the report is provided to a physician and/or the patient. The receiving of the report can further

2017268510 28 Nov 2017 include establishing a network connection to a server computer that includes the data and report and requesting the data and report from the server computer.

Computer program [00116] The values from the assays described above, such as expression data, can be calculated and stored manually. Alternatively, the above-described steps can be completely or partially performed by a computer program product. The present invention thus provides a computer program product including a computer readable storage medium having a computer program stored on it. The program can, when read by a computer, execute relevant calculations based on values obtained from analysis of one or more biological sample from an individual (e.g., gene expression levels, normalization, standardization, thresholding, and conversion of values from assays to a score and/or text or graphical depiction of tumor stage and related information). The computer program product has stored therein a computer program for performing the calculation.

[00117] The present disclosure provides systems for executing the program described above, which system generally includes: a) a central computing environment; b) an input device, operatively connected to the computing environment, to receive patient data, wherein the patient data can include, for example, expression level or other value obtained from an assay using a biological sample from the patient, or microarray data, as described in detail above; c) an output device, connected to the computing environment, to provide information to a user (e.g., medical personnel); and d) an algorithm executed by the central computing environment (e.g., a processor), where the algorithm is executed based on the data received by the input device, and wherein the algorithm calculates an expression score, thresholding, or other functions described herein. The methods provided by the present invention may also be automated in whole or in part.

[00118] All aspects of the present invention may also be practiced such that a limited number of additional genes and/or microRNAs that are co-expressed or functionally related with the disclosed genes, for example as evidenced by statistically meaningful Pearson and/or Spearman correlation coefficients, are included in a test in addition to and/or in place of disclosed genes.

2017268510 28 Nov 2017 [00119] Having described the invention, the same will be more readily understood through reference to the following Examples, which are provided by way of illustration, and are not intended to limit the invention in any way.

Examples

Example 1: RNA Yield and Gene Expression Profiles in Prostate Cancer Biopsy Cores [00120] Clinical tools based on prostate needle core biopsies are needed to guide treatment planning at diagnosis for men with localized prostate cancer. Limiting tissue in needle core biopsy specimens poses significant challenges to the development of molecular diagnostic tests. This study examined RNA extraction yields and gene expression profiles using an RT-PCR assay to characterize RNA from manually micro-dissected fixed paraffin embedded (FPE) prostate cancer needle biopsy cores. It also investigated the association of RNA yields and gene expression profiles with Gleason score in these specimens.

Patients and Samples [00121] This study determined the feasibility of gene expression profile analysis in prostate cancer needle core biopsies by evaluating the quantity and quality of RNA extracted from fixed paraffin-embedded (FPE) prostate cancer needle core biopsy specimens. Forty-eight (48) formalin-fixed blocks from prostate needle core biopsy specimens were used for this study. Classification of specimens was based on interpretation of the Gleason score (2005 Infl Society of Urological Pathology Consensus Conference) and percentage tumor (<33%, 33-66%, >66%) involvement as assessed by pathologists.

Table 1: Distribution of cases

Gleason score Category	-<33% Tumor	-33-66% Tumor	->66% Tumor
Low (<6)	5	5	6
Intermediate (7)	5	5	6
High (8, 9, 10)	5	5	6
Total	15	15	18

Assay Methods [00122] Fourteen (14) serial 5 pm unstained sections from each FPE tissue block were included in the study. The first and last sections for each case were H&E stained and histologically reviewed to confirm the presence of tumor and for tumor enrichment by manual micro-dissection.

2017268510 28 Nov 2017 [00123] RNA from enriched tumor samples was extracted using a manual RNA extraction process. RNA was quantitated using the RiboGreen® assay and tested for the presence of genomic DNA contamination. Samples with sufficient RNA yield and free of genomic DNA tested for gene expression levels of a 24-gene panel of reference and cancer-related genes using quantitative RT-PCR. The expression was normalized to the average of 6 reference genes (AAMP, ARF1, ATP5E, CLTC, EEF1 Al, and GPX1).

Statistical Methods [00124] Descriptive statistics and graphical displays were used to summarize standard pathology metrics and gene expression, with stratification for Gleason Score category and percentage tumor involvement category. Ordinal logistic regression was used to evaluate the relationship between gene expression and Gleason Score category.

Results [00125] The RNA yield per unit surface area ranged from 16 to 2406 ng/mm2. Higher RNA yield was observed in samples with higher percent tumor involvement (p=0.02) and higher Gleason score (p=0.01). RNA yield was sufficient (> 200ng) in 71% of cases to permit 96-well RT-PCR, with 87% of cases having >100ng RNA yield. The study confirmed that gene expression from prostate biopsies, as measured by qRT-PCR, was comparable to FPET samples used in commercial molecular assays for breast cancer. In addition, it was observed that greater biopsy RNA yields are found with higher Gleason score and higher percent tumor involvement. Nine genes were identified as significantly associated with Gleason score (p < 0.05) and there was a large dynamic range observed for many test genes.

Example 2: Gene Expression Analysis for Genes Associated with Prognosis in Prostate Cancer

Patients and Samples [00126] Approximately 2600 patients with clinical stage T1/T2 prostate cancer treated with radical prostatectomy (RP) at the Cleveland Clinic between 1987 and 2004 were identified. Patients were excluded from the study design if they received neo-adjuvant and/or adjuvant therapy, if pre-surgical PSA levels were missing, or if no tumor block was available from initial diagnosis. 127 patients with clinical recurrence and 374 patients without clinical recurrence after radical prostatectomy were randomly selected using a cohort sampling design. The specimens were stratified by T stage (ΤΙ, T2), year cohort (< 1993, >1993), and prostatectomy Gleason

2017268510 28 Nov 2017 score (low/intermediate, high). Of the 501 sampled patients, 51 were excluded for insufficient tumor; 7 were excluded due to clinical ineligibility; 2 were excluded due to poor quality of gene expression data; and 10 were excluded because primary Gleason pattern was unavailable. Thus, this gene expression study included tissue and data from 111 patients with clinical recurrence and 330 patients without clinical recurrence after radical prostatectomies performed between 1987 and 2004 for treatment of early stage (ΤΙ, T2) prostate cancer.

[00127] Two fixed paraffin embedded (FPE) tissue specimens were obtained from prostate tumor specimens in each patient. The sampling method (sampling method A or B) depended on whether the highest Gleason pattern is also the primary Gleason pattern. For each specimen selected, the invasive cancer cells were at least 5.0 mm in dimension, except in the instances of pattern 5, where 2.2 mm was accepted. Specimens were spatially distinct where possible.

Table 2: Sampling Methods

Sampling Method A	Sampling Method B
For patients whose prostatectomy primary Gleason pattern is also the highest Gleason pattern	For patients whose prostatectomy primary Gleason pattern is not the highest Gleason pattern
Specimen 1 (Al) - primary Gleason pattern Select and mark largest focus (greatest crosssectional area) of primary Gleason pattern tissue. Invasive cancer area > 5.0 mm.	Specimen 1 (Bl) = highest Gleason pattern Select highest Gleason pattern tissue from spatially distinct area from specimen B2, if possible. Invasive cancer area at least 5.0 mm if selecting secondary pattern, at least 2.2 mm if selecting Gleason pattern 5.
Specimen 2 (A2) = secondary Gleason pattern Select and mark secondary Gleason pattern tissue from spatially distinct area from specimen A1. Invasive cancer area > 5.0 mm.	Specimen 2 (B2) = primary Gleason pattern Select largest focus (greatest cross-sectional area) of primary Gleason pattern tissue. Invasive cancer area > 5.0 mm.

[00128] Histologically normal appearing tissue (NAT) adjacent to the tumor specimen (also referred to in these Examples as “non-tumor tissue”) was also evaluated. Adjacent tissue was collected 3 mm from the tumor to 3 mm from the edge of the FPET block. NAT was preferentially sampled adjacent to the primary Gleason pattern. In cases where there was insufficient NAT adjacent to the primary Gleason pattern, then NAT was sampled adjacent to the secondary or highest Gleason pattern (A2 or Bl) per the method set forth in Table 2. Six (6) 10 pm sections with beginning H&E at 5 pm and ending unstained slide at 5 pm were prepared

2017268510 28 Nov 2017 from each fixed paraffin-embedded tumor (FPET) block included in the study. All cases were histologically reviewed and manually micro-dissected to yield two enriched tumor samples and, where possible, one normal tissue sample adjacent to the tumor specimen.

Assay Method [00129] In this study, RT-PCR analysis was used to determine RNA expression levels for 738 genes and chromosomal rearrangements (e.g., TMPRSS2-ERG fusion or other ETS family genes) in prostate cancer tissue and surrounding NAT in patients with early-stage prostate cancer treated with radical prostatectomy.

[00130] The samples were quantified using the RiboGreen assay and a subset tested for presence of genomic DNA contamination. Samples were taken into reverse transcription (RT) and quantitative polymerase chain reaction (qPCR). All analyses were conducted on referencenormalized gene expression levels using the average of the of replicate well crossing point (CP) values for the 6 reference genes (AAMP, ARF1, ATP5E, CLTC, GPS1, PGK1).

Statistical Analysis and Results [00131] Primary statistical analyses involved 111 patients with clinical recurrence and 330 patients without clinical recurrence after radical prostatectomy for early-stage prostate cancer stratified by T-stage (ΤΙ, T2), year cohort (<1993, >1993), and prostatectomy Gleason score (low/intermediate, high). Gleason score categories are defined as follows: low (Gleason score < 6), intermediate (Gleason score = 7), and high (Gleason score > 8). A patient was included in a specified analysis if at least one sample for that patient was evaluable. Unless otherwise stated, all hypothesis tests were reported using two-sided p-values. The method of Storey was applied to the resulting set of p-values to control the false discovery rate (FDR) at 20%. J. Storey, R. Tibshirani, Estimating the Positive False Discovery Rate Under Dependence, with Applications to DNA Microarrays, Dept, of Statistics, Stanford Univ. (2001).

[00132] Analysis of gene expression and recurrence-free interval was based on univariate Cox Proportional Hazards (PH) models using maximum weighted pseudo-partial-likelihood estimators for each evaluable gene in the gene list (727 test genes and 5 reference genes). Pvalues were generated using Wald tests of the null hypothesis that the hazard ratio (HR) is one. Both unadjusted p-values and the q-value (smallest FDR at which the hypothesis test in question is rejected) were reported. Un-adjusted p-values <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the

2017268510 28 Nov 2017 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient (Specimens Al and B2 as described in Table 2); (2) analysis using the highest Gleason pattern specimen from each patient (Specimens Al and Bl as described in Table 2).

[00133] Analysis of gene expression and Gleason pattern (3, 4, 5) was based on univariate ordinal logistic regression models using weighted maximum likelihood estimators for each gene in the gene list (727 test genes and 5 reference genes). P-values were generated using a Wald test of the null hypothesis that the odds ratio (OR) is one. Both unadjusted p-values and the q-value (smallest FDR at which the hypothesis test in question is rejected) were reported. Un-adjusted pvalues <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the 2 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient (Specimens Al and B2 as described in Table 2); (2) analysis using the highest Gleason pattern specimen from each patient (Specimens Al and Bl as described in Table 2).

[00134] It was determined whether there is a significant relationship between cRFI and selected demographic, clinical, and pathology variables, including age, race, clinical tumor stage, pathologic tumor stage, location of selected tumor specimens within the prostate (peripheral versus transitional zone), PSA at the time of surgery, overall Gleason score from the radical prostatectomy, year of surgery, and specimen Gleason pattern. Separately for each demographic or clinical variable, the relationship between the clinical covariate and cRFI was modeled using univariate Cox PH regression using weighted pseudo partial-likelihood estimators and a p-value was generated using Wald’s test of the null hypothesis that the hazard ratio (HR) is one. Covariates with unadjusted p-values <0.2 may have been included in the covariate-adjusted analyses.

[00135] It was determined whether there was a significant relationship between each of the individual cancer-related genes and cRFI after controlling for important demographic and clinical covariates. Separately for each gene, the relationship between gene expression and cRFI was modeled using multivariate Cox PH regression using weighted pseudo partial-likelihood estimators including important demographic and clinical variables as covariates. The independent contribution of gene expression to the prediction of cRFI was tested by generating a p-value from a Wald test using a model that included clinical covariates for each nodule

2017268510 28 Nov 2017 (specimens as defined in Table 2). Un-adjusted p-values <0.05 were considered statistically significant.

[00136] Tables 3A and 3B provide genes significantly associated (p<0.05), positively or negatively, with Gleason pattern in the primary and/or highest Gleason pattern. Increased expression of genes in Table 3Α is positively associated with higher Gleason score, while increased expression of genes in Table 3B are negatively associated with higher Gleason score.

Table 3Α

Gene significantly (p<0.05) associated with Gleason pattern for all specimens in the primary Gleason pattern or highest Gleason pattern odds ratio (OR) > 1.0 (Increased

expre Table 3A	ssion is positively associated with Primary Pattern	ligher Gleason Score) Highest Pattern
Official Symbol	OR	p-value	OR	p-value
ALCAM	1.73	<001	1.36	0.009
ANLN	1.35	0.027
APOCI	1.47	0.005	1.61	<001
APOE	1.87	<001	2.15	<001
ASAP2	1.53	0.005
ASPN	2.62	<001	2.13	<001
ATP5E AURKA	1.35_ 1.1-1	_ 0.035 0.010		— -
AURKB	1.59	<001	1.56	<001
BAX BGN	1.43 2'58	0.006 <ooT	2.82	<001
BIRC5	1.45	0.003	1.79	<001
BMP6	2.37	<001	1.68	<001
BMPRIB BRCA2	L5A	0.002	1.45	0.013
BUB1	1.73	<001	1.57	<001
CACNA1D	1.31	0.045	1.31	0.033
CADPS			1.30	0.023
CCNB1	1.43	0.023
CCNE2	1.52	0.003	1.32	0.035
CD276	2.20	<001	1.83	<001
CD68			1.36	0.022
CDC20	1.69	<001	1.95	<001
CDC6	1.38	0.024	1.46	<001
CDH11			1.30	0.029
CDKN2B	1.55	0.001	1.33	0.023
CDKN2C	1.62	<001	1.52	<001
CDKN3	1.39	0.010	1.50	0.002
CENPF	1.96	<001	1.71	<001
CHRAC1			1.34	0.022
CLDN3			1.37	0.029

2017268510 28 Nov 2017

Table 3A	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
COL1A1	2.23	<001	2.22	<001
COL1A2 COL3AT	1.90	<.□01	1.42 ΖΪ3	0.005 <001
COLS Al	1.88	<.001	2.35	<001
CRISP3	1.33	0.040	1.26	0.050
CTHRC1	2.01	<001	1.61	<001
CTNND2	1.48	0.007	1.37	0.011
DAPK1	1.44	0.014
DIAPH1		L34	0.032	1.79	<001
DIO2		1.56	0.001
DLL4	1.38	0.026	1.53	<001
ECE1	1.54	0.012	1.40	0.012
ENY2	1.35	0.046	1.35	0.012
EZH2	1.39	0.040
F2R	2.37	<001	2.60	<001
FAM49B	1.57	0.002	1.33	0.025
FAP^ FCGR3A	2.36 2.10	<001 <obT	1.89 1.83	<001 <001
GNPTAB	1.78	<001	1.54	<001
GSK3B HRAS	1.62	0.003	1.39	0.018
HSD17B4	2.91	<001	1.57	<001
HSPA8	1.48	0.012	1.34	0.023
IFI30 IGFBP3	L64	<001	1.45 1.29	0.013 0.037
ILli	1.52	0.001	1.31	0.036
INHBA	2.55	<001	2.30	<001
ITGA4 jagi	1.68	<δόί	1.35 L40	0.028 0.005
KCNN2	1.50	0.004
KCTD12			1.38	0.012
KHDRBS3	1.85	<001	1.72	<001
KIF4A	1.50	0.010	1.50	<001
KLK14	1.49	0.001	1.35	<001
KPNA2______ KRT2	1.68	0.004	1.65	0.001
		1.33	0.022
KRT75			1.27	0.028
LAMCI	1.44	0.029
LAPTM5 LTBP2	1.36 1.42	0.025 0.023	1.31 1.66	0.042 <001
MANF			1.34	0.019
MAOA	1.55	0.003	1.50	<001
MAP3K5 MDK	1.55 1.47	0Ό06 0.013	1.44 L29	0.001, 0041

2017268510 28 Nov 2017

Table 3A	Primary Pattern	Highest	Pattern
Official Symbol	OR	p-value	OR	p-value
MDM2			1.31	0.026
MELK ΜΜΡΪΪ	1.64 233	<001 <001	1.64 i.66	<001 <001
MYBL2	1.41	0.007	1.54	<001
MYO6			1.32	0.017
NETO2			1.36	0.018
NOX4	1.84	<001	1.73	<001
NPM1	1.68	0.001
NRIP3			1.36	0.009
NRP1	1.80	0.001	1.36	0.019
OSM	1.33	0.046
PATE1	1.38	0.032
PECAM1	1.38	0.021	1.31	0.035
PGD	1.56	0.010
PLK1	1.51	0.004	1.49	0.002
PLOD2			1.29	0.027
POSTN PPP3CA	1.70 1.38	0.047 0Ό37	1.55_ 1.37	0.006 0.006
PTK6	1.45	0.007	1.53	<001
PTTG1 RAB31	-		1.51 1.31	<001 0.030
RAD21	2.05	<001	1.38	0.020
RAD51	1.46	0.002	1.26	0.035
RAFI RALBP1	1.46 1.37	0.017 0.043		.
RHOC			1.33	0.021
ROBO2	1.52	0.003	1.41	0.006
RRM2 SAT1	J.77 1.67	<001 0Ό02	1.50 Ϊ.61	<001 <.001
SDC1	1.66	0.001	1.46	0.014
SEC14L1	1.53	0.003	1.62	<001
SESN3	1.76	<001	1.45	<001
SFRP4	2.69	<001	2.03	<001
SHMT2	1.69	0.007	1.45	0.003
SKIL			1.46	0.005
SOX4	1.42	0.016	1.27	0.031
SPARC	1.40	0.024	1.55	<001
SPINK 1			1.29	0.002
SPP1 TFDP1	1.51 1.48	0.002 0.014	1.80	<001
THBS2	1.87	<001	1.65	<001
THY1	1.58	0.003	1.64	<001
TK1 TOP2A	1.79 2.30	<001 <001	1.42 2.01	0.00J <Όθί

2017268510 28 Nov 2017

Table 3A	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
TPD52	1.95	<001	1.30	0.037
TPX2	2.12	<001	1.86	<001
TYMP	1.36	0.020
TYMS	1.39	0.012	1.31	0.036
UBE2C	1.66	<001	1.65	<001
UBE2T	1.59	<001	1.33	0.017
UGDH			1.28	0.049
UGT2B15	1.46	0.001	1.25	0.045
UHRF1	1.95	<001	1.62	<001
VDR	1.43	0.010	1.39	0.018
WNT5A	1.54	0.001	1.44	0.013

Table 3B.

Gene significantly (p<0.05) associated with Gleason pattern for all specimens in the primary Gleason pattern or highest Gleason pattern odds ratio (OR) < 1.0 (Increased expression is negatively associated with higher Gleason score)

Table 3B	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
ABCA5	0.78	0.041
ABCG2	0.65	0.001	0.72	0.012
ACOX2	0.44	<001	0.53	<001
ADH5	0.45	<001	0.42	<001
AFAP1			0.79	0.038
AIG1			0.77	0.024
AKAP1	0.63	0.002
AKR1C1	0.66	0.003	0.63	<001
AKT3	0.68	0.006	0.77	0.010
ALDH1A2	0.28	<001	0.33	<001
ALKBH3	0.77	0.040	0.77	0.029
AMPD3	0.67	0.007
ANPEP	0.68	0.008	0.59	<001
ANXA2	0.72	0.018
APC			0.69	0.002
AXIN2	0.46	<001	0.54	<001
AZGP1	0.52	<001	0.53	<001
BIK	0.69	0.006	0.73	0.003
BINI	0.43	<001	0.61	<001
BTG3			0.79	0.030
BTRC	0.48	<001	0.62	<001
C7	0.37	<001	0.55	<001
CADM1	0.56	<001	0.69	0.001
CAV1	0.58	0.002	0.70	0.009
CAV2	0.65	0.029

2017268510 28 Nov 2017

Table 3B	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
CCNH	0.67	0.006	0.77	0.048
CD 164	0.59	0.003	0.57	<001
CDC25B	0.77	0.035
CDHI			0.66	<001
CDK2			0.71	0.003
CDKN1C	0.58	<001	0.57	<001
CDS2			0.69	0.002
CHN1	0.66	0.002
COL6A1	0.44	<001	0.66	<001
COL6A3	0.66	0.006
CSRP1	0.42	0.006
CTGF	0.74	0.043
CTNNA1	0.70	<001	0.83	0.018
CTNNB1	0.70	0.019
CTNND1			0.75	0.028
CUL1			0.74	0.011
CXCL12	0.54	<001	0.74	0.006
CYP3A5	0.52	<001	0.66	0.003
CYR61	0.64	0.004	0.68	0.005
DDR2	0.57	0.002	0.73	0.004
DES	0.34	<001	0.58	<001
DLGAP1	0.54	<001	0.62	<001
DNM3	0.67	0.004
DPP4	0.41	<001	0.53	<001
DPT	0.28	<001	0.48	<001
DUSP1	0.59	<001	0.63	<001
EDNRA	0.64	0.004	0.74	0.008
EGF			0.71	0.012
EGR1	0.59	<001	0.67	0.009
EGR3	0.72	0.026	0.71	0.025
EIF5			0.76	0.025
ELK4	0.58	0.001	0.70	0.008
ENPP2	0.66	0.002	0.70	0.005
EPHA3	0.65	0.006
EPHB2	0.60	<001	0.78	0.023
EPHB4	0.75	0.046	0.73	0.006
ERBB3	0.76	0.040	0.75	0.013
ERBB4			0.74	0.023
ERCC1	0.63	<001	0.77	0.016
FAAH	0.67	0.003	0.71	0.010
FAM 107 A	0.35	<001	0.59	<001
FAM13C	0.37	<001	0.48	<001
FAS	0.73	0.019	0.72	0.008
FGF10	0.53	<001	0.58	<001

2017268510 28 Nov 2017

Table 3B	Primary Pattern	Highest	Pattern
Official Symbol	OR	p-value	OR	p-value
FGF7	0.52	<.001	0.59	<001
FGFR2 FKBP5	0.60 0.7()	<001 0Ό39	0.59 0.68	<001 o7oo3
FLNA	0.39	<001	0.56	<001
FLNC	0.33	<.001	0.52	<001
FOS	0.58	<001	0.66	0.005
FOXO1	0.57	<001	0.67	<001
FOXQ1			0.74	0.023
GADD45B	0.62	0.002	0.71	0.010
GHR	0.62	0.002	0.72	0.009
GNRHI	0.74	0.049	0.75	0.026
GPM6B	0.48	<001	0.68	<001
GPS1			0.68	0.003
GSN	0.46	<001	0.77	0.027
GSTM1	0.44	<001	0.62	<001
GSTM2	0.29	<001	0.49	<001
HGD HIR1P3	0.75	0.034	0.77	0.020
HK1	0.48	<001	0.66	0.001
HLF HNFIB	0Λ2_ 0.67	<001 0.006	0.55 0.74	<001 o.oio
HPS1	0.66	0.001	0.65	<001
HSP90ABI	0.75	0.042
HSPA5 HSPB2	0.70 0.52	0.011 <001	0.70	0.004
IGF1	0.35	<001	0.59	<001
IGF2	0.48	<001	0.70	0.005
IGFBP2 IGFBP5	0.61 0.63	<001 <001	0.77	0.044
IGFBP6	0.45	<001	0.64	<001
IL6ST	0.55	0.004	0.63	<001
ILK	0.40	<001	0.57	<001
ING5	0.56	<001	0.78	0.033
ITGA1	0.56	0.004	0.61	<001
ITGA3			0.78	0.035
ITGA5	0.71	0.019	0.75	0.017
ITGA7	0.37	<001	0.52	<001
ITGB3	0.63	0.003	0.70	0.005
ITPR1 ITPR3	0.46 0.70	<001 0.013	0.64	<001
1TSN1	0.62	0.001
JUN	0.48	<001	0.60	<001
JUNB KIT	0.72 0.51	0.025 <obT	0'68	07007

2017268510 28 Nov 2017

Table 3B	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
KLC1	0.58	<.001
KLK1 KLK2	0.69 060	0.028 <οδϊ	0.66	0.003
KLK3	0.63	<001	0.69	0.012
KRT15	0.56	<001	0.60	<001
KRT18	0.74	0.034
KRT5	0.64	<001	0.62	<001
LAMA4	0.47	<001	0.73	0.010
LAMB3	0.73	0.018	0.69	0.003
LGALS3	0.59	0.003	0.54	<001
LIG3	0.75	0.044
MAP3K7	0.66	0.003	0.79	0.031
MCM3	0.73	0.013	0.80	0.034
MGMT	0.61	0.001	0.71	0.007
MGST1			0.75	0.017
MLXIP	0.70	0.013
MMP2 MMP7	0.57 0.69	j<001 0.009	0.72	0Ό10
MPPED2	0.70	0.009	0.59	<001
MSH6 MTA1	0.78 Έ69	0.046 0Ό07
MTSS1	0.55	<001	0.54	<001
MYBPCI	0.45	<001	0.45	<001
NCAM1 NCAPD3	0.51 0.42	<001 <001	0.65 0.53	<001 <001
NCOR2	0.68	0.002
NDUFS5	0.66	0.001	0.70	0.013
NEXN NFAT5	0.55	<θθι <001	0.62 0.67	<001 0.001
NFKBIA			0.79	0.048
NRG1	0.58	0.001	0.62	0.001
OLFML3	0.42	<001	0.58	<001
OMD	0.67	0.004	0.71	0.004
OR51E2	0.65	<001	0.76	0.007
PAGE4	0.27	<001	0.46	<001
PCA3	0.68	0.004
PCDHGB7	0.70	0.025	0.65	<001
PGF	0.62	0.001
PGR PHTF2	0.63 0.69	0.028 0.033		-
PLP2	0.54	<001	0.71	0.003
PPAP2B	0.41	<001	0.54	<001
PPP1R12A PRIMA'i	0.48 0?62	<001 ’o.ooT	0.60 065	<001 <οοΓ

2017268510 28 Nov 2017

Table 3B	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
PRKARIB	0.70	0.009
PRKAR2B PRKCA	0.37	<όόϊ	0.79 0.55	0.038 <Ό0ϊ
PRKCB	0.47	<001	0.56	<001
PTCHI	0.70	0.021
PTEN	0.66	0.010	0.64	<001
PTGER3			0.76	0.015
PTGS2	0.70	0.013	0.68	0.005
PTH1R	0.48	<001
PTK2B	0.67	0.014	0.69	0.002
PYCARD	0.72	0.023
RAB27A			0.76	0.017
RAGE	0.77	0.040	0.57	<001
RARB	0.66	0.002	0.69	0.002
RECK	0.65	<001
RHOA	0.73	0.043
RHOB RND3	0.61_ a63	0.005 0Ό06	0.62 0.66	<001 <001
SDHC			0.69	0.002
SEC23A SEMA3A	0.6L ^49	<001 <001	0.74 0.55	0.010 <001
SERPINA3	0.70	0.034	0.75	0.020
SH3RF2	0.33	<001	0.42	<001
SLC22A3 SMAD4	0.23 0.33	<001 <001	0.37 039	<001 <001
SMARCC2	0.62	0.003	0.74	0.008
SMO	0.53	<001	0.73	0.009
SORBS 1 sparclI	JF40 0.42	<θθι <001	0.51 0?63	<001 <001
SRD5A2	0.28	<001	0.37	<001
ST5	0.52	<001	0.63	<001
STAT5A	0.60	<001	0.75	0.020
STAT5B	0.54	<001	0.65	c.001
STS			0.78	0.035
SUMO1	0.75	0.017	0.71	0.002
SVIL	0.45	<001	0.62	<001
TARP	0.72	0.017
TGFB1I1	0.37	<001	0.53	<001
TGFB2 TGFB3	0.61 0.46	0.025 <001	0.59 0.60	<001 <001
T1MP2	0.62	0.001
TIMP3	0.55	<001	0.76	0.019
TMPRSS2 TNF	0.71 005	OOM οόϊο	—	— -

2017268510 28 Nov 2017

Table 3B	Primary Pattern	Highest Pattern
Official Symbol	OR	p-value	OR	p-value
TNFRSF10A	0.71	0.014	0.74	0.010
TNFRSFIOB TNFSF10......	0/74	Q-Q30	0.73 0'69	0.016 0“004
TP53			0.73	0.011
TP63	0.62	<.001	0.68	0.003
TPM1	0.43	<001	0.47	<001
TPM2	0.30	<001	0.47	<001
TPP2	0.58	<001	0.69	0.001
TRA2A	0.71	0.006
TRAF3IP2	0.50	<001	0.63	<001
TRO	0.40	<001	0.59	<001
TRPC6	0.73	0.030
TRPV6			0.80	0.047
VCL	0.44	<001	0.55	<001
VEGFB	0.73	0.029
VIM	0.72	0.013
VTI1B WDR19	0.78 ΈόΓ	0.046 <00?		-
WFDC1	0.50	<001	0.72	0.010
YY1 ZFHX3	0.75 0.52	0.045 <.()()!	0.54	<001
ZFP36	0.65	0.004	0.69	0.012
ZNF827	0.59	<001	0.69	0.004

[00137]

To identify genes associated with recurrence (cRFI, bRFI) in the primary and the highest Gleason pattern, each of 727 genes were analyzed in univariate models using specimens

A1 and B2 (see Table 2, above). Tables 4A and 4B provide genes that were associated, positively or negatively, with cRFI and/or bRFI in the primary and/or highest Gleason pattern. Increased expression of genes in Table 4A is negatively associated with good prognosis, while increased expression of genes in Table 4B is positively associated with good prognosis.

Table 4A.

Genes significantly (p<0.05) associated with cRFI or bRFI in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) > 1.0 (increased expression is negatively associated with good prognosis)

Table 4A	cRFI	cRFI	bRFI	bRFI
Official Symbol	Primar HR	(Pattern p-value	Highes HR	Pattern p-value	Primary Pattern	Highest Pattern
HR	p-value	HR	p-value
AKR1C3	1.304	0.022	1.312	0.013
ANLN	1.379	0.002	1.579	<001	1.465	<001	1.623	<001
AQP2	1.184	0.027	1.276	<001
ASAP2			1.442	0.006

2017268510 28 Nov 2017

Table 4A	cRFI	cRFI	bRFI	bRFI
	Primar	y Pattern	Highest Pattern	Primary Pattern	Highest	’attem
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
ASPN	2.272	<001	2.106	<001	1.861	<001	1.895	<001
ATP5E	1.414	0.013	1.538	<001
BAG5			1.263	0.044
BAX			1.332	0.026	1.327	0.012	1.438	0.002
BGN	1.947	<001	2.061	<001	1.339	0.017
BIRC5	1.497	<001	1.567	<001	1.478	<001	1.575	<001
BMP6	1.705	<001	2.016	<001	1.418	0.004	1.541	<001
BMPR1B	1.401	0.013			1.325	0.016
BRCA2							1.259	0.007
BUB1	1.411	<001	1.435	<001	1.352	<001	1.242	0.002
CADPS					1.387	0.009	1.294	0.027
CCNB1					1.296	0.016	1.376	0.002
CCNE2	1.468	<001	1.649	<001	1.729	<001	1.563	<001
CD276	1.678	<001	1.832	<001	1.581	<001	1.385	0.002
CDC20	1.547	<001	1.671	<001	1.446	<001	1.540	<001
CDC6	1.400	0.003	1.290	0.030	1.403	0.002	1.276	0.019
CDH7	1.403	0.003	1.413	0.002
CDKN2B	1.569	<001	1.752	<001	1.333	0.017	1.347	0.006
CDKN2C	1.612	<001	1.780	<.001	1.323	0.005	1.335	0.004
CDKN3	1.384	<001	1.255	0.024	1.285	0.003	1.216	0.028
CENPF	1.578	<001	1.692	<001	1.740	<001	1.705	<001
CKS2	1.390	0.007	1.418	0.005	1.291	0.018
CLTC			1.368	0.045
COL1A1	1.873	<001	2.103	<001	1.491	<001	1.472	<001
COL1A2			1.462	0.001
COL3A1	1.827	<.001	2.005	<001	1.302	0.012	1.298	0.018
COL4A1	1.490	0.002	1.613	<001
COL8A1	1.692	<001	1.926	<001	1.307	0.013	1.317	0.010
CRISP3	1.425	0.001	1.467	<001	1.242	0.045
CTHRC1	1.505	0.002	2.025	<001	1.425	0.003	1.369	0.005
CTNND2					1.412	0.003
CXCR4 DDIT4	1.312 10543 '	0.023 ' ’ <όοΓ ’	J 355 10763	_0Ό08 <001				-
DYNLL1	1.290	0.039					1.201	0.004
EIF3H ENY2.....	’ “1361“	0.014	-	.	1.428 1.392'	0.012__ ' ”0.008	1.371	0001
EZH2			1.311	0.010
F2R	1.773	<001	1.695	<001	1.495	<001	1.277	0.018
FADD FAM171B			1.292 1.285	0.018 0.036			.--------------------------------------------------.	_----
FAP	1.455	0.004	1.560	0.001	1.298	0.022	1.274	0.038
FASN	1.263	0.035

2017268510 28 Nov 2017

Table 4A	cRFI	cRFI	bRFI	bRFI
	Primar	y Pattern	Highest Pattern	Primary Pattern	Highest	’at tern
Official Symbol	HR	p- value	HR	p-value	HR	p-value	HR	p-value
FCGR3A			1.654	<001	1.253	0.033	1.350	0.007
FGF5	1.219	0.030
GNPTAB	1.388	0.007	1.503	0.003	1.355	0.005	1.434	0.002
GPR68			1.361	0.008
GREM1	1.470	0.003	1.716	<001	1.421	0.003	1.316	0.017
HDAC1					1.290	0.025
HDAC9			1.395	0.012
HRAS	1.424	0.006	1.447	0.020
HSD17B4	1.342	0.019	1.282	0.026	1.569	<001	1.390	0.002
HSPA8	1.290	0.034
IGFBP3	1.333	0,022	1.442	0.003	1.253	0.040	1.323	0.005
INHBA	2.368	<001	2.765	<001	1.466	0.002	1.671	<001
JAG1	1.359	0.006	1.367	0.005	1.259	0.024
KCNN2	1.361	0.011	1.413	0.005	1.312	0.017	1.281	0.030
KHDRBS3	1.387	0.006	1.601	<001	1.573	<001	1.353	0.006
K1AA0196							1.249	0.037
KIF4A	1.212	0.016			1.149	0.040	1.278	0.003
KLK.14	1.167	0.023					1.180	0.007
KPNA2			1.425	0.009	1.353	0.005	1.305	0.019
KRT75							1.164	0.028
LAM A3					1.327	0.011
LAMB1			1.347	0.019
LAMCl	1.555	0.001	1.310	0.030			1.349	0.014
LIMSI							1.275	0.022
LOX					1.358	0.003	1.410	<001
LTBP2	1.396	0.009	1.656	<001	1.278	0.022
LUM			1.315	0.021
MANF					1.660	<001	1.323	0.011
MCM2					1.345	0.011	1.387	0.014
MCM6	1.307	0.023	1.352	0.008			1.244	0.039
MELK	1.293	0.014	1.401	<001	1.501	<001	1.256	0.012
MMPH MRPLl?	_,L680_	<001	1.474	<001	1.489	. _<ooi_	,1. 257	0.030 0Ό25 ”
MSH2			1.295	0.027
MYBL2 MYO6	. 1-664	. <-QQi _	_L670, 1.301	jt.001 0.033' '	1.399	<001_	1.431	< οοχ .
NETO2	1.412	0.004	1.302	0.027	1.298	0.009
NFKB1					1.236	0.050
NOX4	1.492	<001	1.507	0.001	1.555	<001	1.262	0.019
NPM1					1.287	0.036
NRIP3			1.219	0.031			1.218	0.018
NRP1			1.482	0.002			1.245	0.041

2017268510 28 Nov 2017

Table 4A	cRFI	cRFI	bRFI	bRFI
	Primar	y Pattern	Highest Pattern	Primary Pattern	Highest	’at tern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
OLFML2B			1.362	0.015
OR51E1					1.531	<001	1.488	0.003
PAK6			1.269	0.033
PATE1	1.308	<001	1.332	<001	1.164	0.044
PCNA							1.278	0.020
PEX10	1.436	0.005	1.393	0.009
PGD	1.298	0.048			1.579	<001
PGK1			1.274	0.023			1.262	0.009
PLA2G7					1.315	0.011	1.346	0.005
PLAU					1.319	0.010
PLK1	1.309	0.021	1.563	<001	1.410	0.002	1.372	0.003
PLOD2			1.284	0.019	1.272	0.014	1.332	0.005
POSTN	1.599	<001	1.514	0.002	1.391	0.005
PPP3CA					1.402	0.007	1.316	0.018
PSMD13	1.278	0.040	1.297	0.033	1.279	0.017	1.373	0.004
PTK6	1.640	<001	1.932	<001	1.369	0.001	1.406	<001
PTTG1	1.409	<001	1.510	<001	1.347	0.001	1.558	<001
RAD21	1.315	0.035	1.402	0.004	1.589	<001	1.439	<001
RAFI					1.503	0.002
RALA	1.521	0.004	1.403	0.007	1.563	<001	1.229	0.040
RALBP1					1.277	0.033
RGS7	1.154	0.015	1.266	0.010
RRM1	1.570	0.001	1.602	<001
RRM2	1.368	<001	1.289	0.004	1.396	<001	1.230	0.015
SAT1	1.482	0.016	1.403	0.030
SDC1					1.340	0.018	1.396	0.018
SEC14L1			1.260	0.048			1.360	0.002
SESN3	1.485	<001	1.631	<001	1.232	0.047	1.292	0.014
SFRP4	1.800	<001	1.814	<001	1.496	<001	1.289	0.027
SHMT2	1.807	<001	1.658	<001	1.673	<001	1.548	<001
SKIL					1.327	0.008
SLC25A21 SOX4	-	..			1398 ' 1.286	O.OOj ' '0020'	1.285 1.280	0.018 0.030
SPARC	1.539	<001	1.842	<001			1.269	0.026
SPP1 SQLE	-	.	J.322_ . -	0.022 0.020	1.27() '	. θ θ—		,
STMN1	1.402	0.007	1.446	0.005	1.279	0.031
SULF1			1.587	<001
TAF2							1.273	0.027
TFDP1			1.328	0.021	1.400	0.005	1.416	0.001
THBS2	1.812	<001	1.960	<001	1.320	0.012	1.256	0.038
THY1	1.362	0.020	1.662	<001

2017268510 28 Nov 2017

Table 4A	cRFI	cRFI	bRFI	bRFI
	Primar	y Pattern	Highest Pattern	Primary Pattern	Highest	’at tern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
TK1			1.251	0.011	1.377	<001	1.401	<001
TOP2A	1.670	<001	1.920	<001	1.869	<001	1.927	<001
TPD52	1.324	0.011			1.366	0.002	1.351	0.005
TPX2	1.884	<001	2.154	<001	1.874	<001	1.794	<001
UAP1					1.244	0.044
UBE2C	1.403	<001	1.541	<001	1.306	0.002	1.323	<001
UBE2T	1.667	<001	1.282	0.023	1.502	<001	1.298	0.005
UGT2B15			1.295	0.001			1.275	0.002
UGT2B17 111IR1 1	' 1.454	<001 ~	1.531	<001	1.257	’ 0.029	1.294	0.025
VCPIP1	1.390	0.009	1.414	0,004	1.294	0.021	1.283	0.021
WNT5A			1.274	0.038	1.298	0.020
XIAP ZMYND8		-	1.277 ’	0.048	L464	0.006	-
ZWINT	1.259	0.047

2017268510 28 Nov 2017

Table 4B.

Genes significantly (p<0.05) associated with cRFI or bRFI in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) < 1.0 (increased expression is positively associated with good prognosis)

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
AAMP	0.564	<001	0.571	<001	0.764	0.037	0.786	0.034
ABCA5	0.755	<001	0.695	<001			0.800	0.006
ABCB1	0.777	0.026
ABCG2	0.788	0.033	0.784	0.040	0.803	0.018	0.750	0.004
ABHD2			0.734	0.011
ACE			0.782	0.048
ACOX2	0.639	<001	0.631	<001	0.713	<001	0.716	0.002
ADH5	0.625	<001	0.637	<001	0.753	0.026
AKAP1	0.764	0.006	0.800	0.005	0.837	0.046
AKR1C1	0.773	0.033			0.802	0.032
AKT1			0.714	0.005
AKT3	0.811	0.015	0.809	0.021
ALDH1A2 AMPD3	,_p.6O6	<qoi_	0.498	<001	0.613 0.793	<001 0.024	0.624	<001 _
ANPEP	0.584	<001	0.493	<001
ANXA2	0.753	0.013	0.781	0.036	0.762	0.008	0.795	0.032
APRT			0.758	0.026	0.780	0.044	0.746	0.008
ATXN1	0.673	0.001	0.776	0.029	0.809	0.031	0.812	0.043
AXIN2	0.674	<001	0.571	<001	0.776	0.005	0.757	0.005
AZGPJ BAD	0.585	. <c'01 ..	J.652 ’ 0.765	<001 0.023	0.664	<001	0.746	<001
BCL2	0.788	0.033	0.778	0.036
BDKRB1	0.728	0.039
BIK BINI	^0607' '	<ooi	0.712 5.724	0,.005 .....0.002	0.726	‘ <004	‘ 0834 ’	0.034
BTG3					0.847	0.034
BTRC	0.688	0.001	0.713	0.003
C7	0.589	<001	0.639	<001	0.629	<001	0.691	<001
CADM1	0.546	<001	0.529	<001	0.743	0.008	0.769	0.015
CASP1	0.769	0.014	0.799	0.028	0.799	0.010	0.815	0.018
CAV1	0.736	0.011	0.711	0.005	0.675	<001	0.743	0.006
CAV2			0.636	0.010	0.648	0.012	0.685	0.012
CCL2	0.759	0.029	0.764	0.024
CCNH	0.689	<001	0.700	<001
CD 164 CD1A	0.664	<001	0A51	<001	0/687	0.004	-
CD44	0.545	<001	0.600	<001	0.788	0.018	0.799	0.023
CD82	0.771	0.009	0.748	0.004

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
CDC25B	0.755	0.006			0.817	0.025
CDK14	0.845	0.043
CDK2							0.819	0.032
CDK3	0.733	0.005			0.772	0.006	0.838	0.017
CDKN1A			0.766	0.041
CDKN1C	0.662	<001	0.712	0.002	0.693	<001	0.761	0.009
CHN1	0.788	0.036
COL6A1	0.608	<001	0.767	0.013	0.706	<001	0.775	0.007
CSF1 CSK	0.626	<001	0.709	0.003	0.837 '	0.029
CSRPI	0.793	0.024	0.782	0.019
CTNNBI	0.898	0.042			0.885	<001
CTSB CTSK	0.701	0-004 .	0.713	0.007	0.715 0.815	0.002_ 0Ό42	0.803	0.038
CXCL12	0.652	<001	0.802	0.044	0.711	0.001
CYP3A5	0.463	<001	0.436	<001	0.727	0.003
CYR61 DAP	0.652	0.002	0.676 0.761	0.002 0.026	0/775 '	0Ό25	' 0.802	0.048 ~
DARC					0.725	0.005	0.792	0.032
DDR2 DES						0.719	0.001	0.763	0.008
	0.619	<001	0.737	0.005	0.638	<001	0.793	0.017
DHRS9	0.642	0.003
DHX9	0.888	<001
DLC1	0.710	0.007	0.715	0.009
DLGAP1	0.613	<001	0.551	<001			0.779	0.049
DNM3	0.679	<001			0.812	0.037
DPP4	0.591	<001	0.613	<001	0.761	0.003
DPT			0.613	<001	0.576	<001	0.647	<001	0.677	<001
DUSP1	0.662	0.001	0.665	0.001			0.785	0.024
DUSP6	0.713	0.005	0.668	0.002
EDNRA	0.702	0.002	0.779	0.036
EGF EGR1	0.569 '	<Ό0Ϊ	0.738 0?577	0.028 <001'			0.782	0.022 '
EGR3	0.601	<001	0.619	<001			0.800	0.038
EIF2S3 EIF5	0.776	0.023	0.787	0.028			0.756	0.015
ELK4	0.628	<001	0.658	<001
EPHA2	0.720	0.011	0.663	0.004
EPHA3 ERBB2	0.727	0.003			0.772	0.005
0.786	0.019	0.738	0.003	0.815	0.041
ERBB3	0.728	0.002	0.711	0.002	0.828	0.043	0.813	0.023
ERCC1	0.771	0.023	0.725	0.007	0.806	0.049	0.704	0.002

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
EREG					0.754	0.016	0.777	0.034
ESR2			0.731	0.026
FAAH	0.708	0.004	0.758	0.012	0.784	0.031	0.774	0.007
FAM 107 A	0.517	<001	0.576	<001	0.642	<001	0.656	<001
FAM13C	0.568	<001	0.526	<001	0.739	0.002	0.639	<001
FAS	0.755	0.014
FASLG			0.706	0.021
FGFIO	0.653	<001			0.685	<001	0.766	0.022
FGF17 FGF7	' 0.794 ‘	0.030	0/46	0.023	0.781 0.820	0.0! 5 0.037	0.805 ‘ 0Γ8ΪΤ	0.028 0.040
FGFR2	0.683	<001	0.686	<001	0.674	<001	0.703	<001
FKBP5			0.676	0.001
FLNA FLNC	0.653 0.751	<ooi 0.029	0.741 ’ 0.779	0.010 ' 0.047	0.682 0.663 ’	<001 <001	0.771 0.725	0JH6 <.όοΊ
FLT1			0.799	0.044
FOS	0.566	<001	0.543	<001			0.757	0.006
FOXO1 FOXQl	‘ 0.753 ’	0.017	0.757	().()24	0.816 0.804 '	0Ό39 0.018	0.798	0.023
FYN	0.779	0.031
GADD45B	0.590	<001	0.619	<001
GDF15	0.759	0.019	0.794	0.048
GHR	0.702	0.005	0.630	<001	0.673	<001	0.590	<001
GNRH1					0.742	0.014
GPM6B	0.653	<001	0.633	<001	0.696	<001	0.768	0.007
GSN	0.570	<001	0.697	0.001	0.697	<.001	0.758	0.005
GSTM1	0.612	<001	0.588	<001	0.718	<001	0.801	0.020
GSTM2	0.540	<001	0.630	<001	0.602	<001	0.706	<001
HGD HIRIP3	0.796 0.753	0.020 0.011	0.736	0.002	0.824	0.050		-
HK1	0.684	<001	0.683	<001	0.799	0.011	0.804	0.014
HLA-G			0.726	0.022
HLF HNFIB	0.555 0Ϊ690 '	<001 <οδΓ '	0.582 0 585	<001 <001	0.703	<001	0.702	<001
HPS1	0.744	0.003	0.784	0.020	0.836	0.047
HSD3B2 HSP90AB1	0.801	0.036 '					0.733	0.016
HSPA5			0.776	0.034
HSPB1	0.813	0.020
HSPB2	0.762	0.037			0.699	0.002	0.783	0.034
HSPG2					0.794	0.044
ICAM1	0.743	0.024	0.768	0.040
IER3	0.686	0.002	0.663	<001

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-vaiue	HR	p-value	HR	p-value
IFIT1	0.649	<001	0.761	0.026
IGF1_______ IGF2	0.634	<001	0.537	<001	0.696 0.732	<.001 0.004	0.688	<001
IGFBP2	0.548	<001	0.620	<001
IGFBP5	0.681	<001
IGFBP6	0.577	<001			0.675	<001
IL1B	0.712	0.005	0.742	0.009
IL6	0.763	0.028
IL6R IL6ST	' 0.585	<ooT'	_079J_ 0.639	0.039 <001	0.730 '	0.002	0.76«	θΤθ06 =
IL8	0.624	<001	0.662	0.001
ILK	0.712	0.009	0.728	0.012	0.790	0.047	0.790	0.042
ING5 ITGA5	0625 0.728	<001 _ 0.006	0.658 ’ 0.803	<001 0.039	0728_	0.002	—	..-
1TGA6	0.779	0.007	0.775	0.006
ITGA7	0.584	<001	0.700	0.001	0.656	<001	0.786	0.014
ITGAD ITGB4	‘ “0.7Ϊ8Γ '	0.007	0.657 0.689	0Ό20 <.oof	0.818 '	0Ό41	-	-
ITGB5			0.801	0.050
nPRl__ jUN	0707 0.556	0.001 <001	0.574	<.oo i	- —	— -	0.754	0?008“
JUNB	0.730	0.017	0.715	0.010
KIT	0.644	0.004	0.705	0.019	0.605	<001	0.659	0.001
KLC1	0.692	0.003	0.774	0.024	0.747	0.008
KLF6	0.770	0.032	0.776	0.039
KLK1	0.646	<001	0.652	0.001	0.784	0.037
KLK10			0.716	0.006
KLK2 KLK3	0.647 0.706	<001 <001	0.628 0.748	<001 <001			0.786 0.845	0.009 0.018
KRT1							0.734	0.024
KRT15	0.627	<001	0.526	<001	0.704	<001	0.782	0.029
KRT18 KRT5	0.624 0.640” ’	<001 <δδΓ '	0.617 (4550	<001 <001	0738 0.740	0.005 <001	0.760 0798' '	0.005 0.023.....
KRT8	0.716	0.006	0.744	0.008
L1C AM LAG 3	0.738 0741	0.021 0.013 '	0.692 0729	0.009 ο.οΐΊ			0761	0.036
LAMA4	0.686	0.011			0.592	0.003
LAMA5							0.786	0.025
LAMB3	0.661	<001	0.617	<001	0.734	<001
LGALS3	0.618	<001	0.702	0.001	0.734	0.001	0.793	0.012
LIG3	0.705	0.008	0.615	<001
LRP1	0.786	0.050			0.795	0.023	0770	0.009

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
MAP3K7					0.789	0.003
MGMT	0.632	<.001	0.693	<001
MICA	0.781	0.014	0.653	<001			0.833	0.043
MPPED2	0.655	<001	0.597	<001	0.719	<001	0.759	0.006
MSH6					0.793	0.015
MTS SI	0.613	<001			0.746	0.008
MVP	0.792	0.028	0.795	0.045	0.819	0.023
MYBPC1	0.648	<001	0.496	<001	0.701	<001	0.629	<001
NCAM1 NCAPD3	' 0.574 ‘	<ooT~	0.463	‘ <00 Γ	_022-L 0.679	0.015 <001	= 0.640	<001 =
NEXN	0.701	0.002	0.791	0.035	0.725	0.002	0.781	0.016
NFAT5	0.515	<001	0.586	<001	0.785	0.017
NFATC2 NFKBIA	0.753 0.778	0.023 0.037			-	-	—	..-
NRG1	0.644	0.004	0.696	0.017	0.698	0.012
OAZ1	0.777	0.034	0.775	0.022
OLFML3 OMD	0.62£ ‘ 0.706	<.001 ().003	0.720	0.001	0.600	<001	0.626	<001
OR51E2	0.820	0.037	0.798	0.027
PAGE4	0.549	<001	0.613	<001	0.542	<001	0.628	<001
PC A3	0.684	<001	0.635	<001
PCDHGB7	0.790	0.045			0.725	0.002	0.664	<001
PGF	0.753	0.017
PGR	0.740	0.021	0.728	0.018
PIK3CG	0.803	0.024
PLAUR	0.778	0.035
PLG							0.728	0.028
PPAP2B	0.575	<001	0.629	<001	0.643	<001	0.699	<001
PPP1R12A	0.647	<001	0.683	0.002	0.782	0.023	0.784	0.030
PRIM Al	0.626	<001	0.658	<001	0.703	0.002	0.724	0.003
PRKCA	0.642	<001	0.799	0.029	0.677	0.001	0.776	0.006
PRKCB PROMI	0.675 0.603 '	0.001 0ΌΪ8			0.648 0.659 '	<001 0.014	0.747 ' 0'493	0.006 0Ό08
PTCHI	0.680	0.001			0.753	0.010	0.789	0.018
PTEf£ PTGS2	0.732 0.5G6 ‘	0.002 .. <(...-	0.747 0.610	0.005 <001	0.744	<001	0.765	0.002
PTH1R	0.767	0.042			0.775	0.028	0.788	0.047
PTHLH	0.617	0.002	0.726	0.025	0.668	0.002	0.718	0.007
PTK2B	0.744	0.003	0.679	<001	0.766	0.002	0.726	<001
PTPN1	0.760	0.020	0.780	0.042
PYCARD			0.748	0.012
RAB27A			0.708	0.004

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
RAB30	0.755	0.008
RAGE			0.817	0.048
RAP IB					0.818	0.050
RARB	0.757	0.007	0.677	<001	0.789	0.007	0.746	0.003
RASSF1	0.816	0.035
RHOB	0.725	0.009	0.676	0.001			0.793	0.039
RLN1			0.742	0.033			0.762	0.040
RND3	0.636	<001	0.647	<001
RNF144 SDC2		-	_p.749_	0.011	0.721 '	0.004
SDHC	0.725	0.003	0.727	0.006
SEMA3A	0.757	0.024	0.721	0.010
SERPINA3 SERPINB5	0.716 0.747	0.008 0.031	0.660 0.616	0.001 0.002	-	-	—	..-
SH3RF2	0.577	<001	0.458	<001	0.702	<001	0.640	<001
SLC22A3	0.565	<001	0.540	<001	0.747	0.004	0.756	0.007
SMAD4 _ SMARCD1	0346 ' 6.718	<.001 <001	0.573 0.775’	<ooi 6.017	0.636	<001	0.627	<001
SMO	0.793	0.029	0.754	0.021			0.718	0.003
SOD1	0.757	0.049	0.707	0.006
SORBS 1	0.645	<001	0.716	0.003	0.693	<001	0.784	0.025
SPARCL1	0.821	0.028			0.829	0.014	0.781	0.030
SPDEF	0.778	<001
SPINT1	0.732	0.009	0.842	0.026
SRC	0.647	<001	0.632	<001
SRD5A1					0.813	0.040
SRD5A2	0.489	<001	0.533	<001	0.544	<001	0.611	<001
ST5	0.713	0.002	0.783	0.011	0.725	<001	0.827	0.025
STAT3	0.773	0.037	0.759	0.035
STAT5A	0.695	<001	0.719	0.002	0.806	0.020	0.783	0.008
STAT5B	0.633	<001	0.655	<001			0.814	0.028
SUMO1 SViL	0.790 0.659	0.015 <οοΓ '	0.713	'6.002'	0.711 '	'67002'	6.779	6.616
TARP							0.800	0.040
TBP TFF3	0.76 L 0.734	0.010 6.616	0.659	<66i			.	..
TGFB1I1	0.618	<001	0.693	0.002	0.637	<001	0.719	0.004
TGFB2	0.679	<001	0.747	0.005	0.805	0.030
TGFB3					0.791	0.037
TGFBR2					0.778	0.035
TIMP3					0.751	0.011
TMPRSS2	0.745	0.003	0.708	<001

2017268510 28 Nov 2017

Table 4B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest Pattern	Primary Pattern	Highest Pattern
Official Symbol	HR	p- value	HR	p-value	HR	p-value	HR	p-value
TNF			0.670	0.013			0.697	0.015
TNFRSF10A	0.780	0.018	0.752	0.006	0.817	0.032
TNFRSF10B	0.576	<001	0.655	<001	0.766	0.004	0.778	0.002
TNFRSF18	0.648	0.016			0.759	0.034
TNFSF10	0.653	<001	0.667	0.004
TP53			0.729	0.003
TP63	0.759	0.016	0.636	<001	0.698	<001	0.712	0.001
TPM1	0.778	0.048	0.743	0.012	0.783	0.032	0.811	0.046
TPM2 TPP2'	J1.578 _	<001	0.634 0.775	<001 = 0.037	Oto 11	<001	Q:710_	JDtoOl
TRAF3IP2	0.722	0.002	0.690	<001	0.792	0.021	0.823	0.049
TRO	0.744	0.003	0.725	0.003	0.765	0.002	0.821	0.041
TUBB2A ΤΫΜΡ	0.639 0.786	<.001 0.039	0.625	<001
VCL	0.594	<001	0.657	0.001	0.682	<001
VEGFA			0.762	0.024
VEGFB VIM	0.795^ - -0-7j9	0.037 0.009			0.791 ’	0Ό21	-	.
WDR19							0.776	0.015
WFDC1 ΫΥΙ	0to83~	0.001	-		0.746 ~ 0.728	<001 0.()02	--	-
ZFHX3	0.684	<001	0.661	<001	0.801	0.010	0.762	0.001
ZFP36	0.605	<001	0.579	<001			0.815	0.043
ZNF827	0.624	<001	0.730	0.007	0.738	0.004

[00138] Tables 5A and 5B provide genes that were significantly associated (p<0.05), positively or negatively, with recurrence (cRFI, bRFI) after adjusting for AUA risk group in the primary and/or highest Gleason pattern. Increased expression of genes in Table 5 A is negatively associated with good prognosis, while increased expression of genes in Table 5B is positively associated with good prognosis.

Table 5A.

Gene significantly (p<0.05) associated with cRFI or bRFI after adjustment for AUA risk group in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) > 1.0 (increased expression negatively associated with good prognosis)

Table5A	cRFI Primary Pattern	cRFI Highest Pattern	bRFI Primary Pattern	bRFI Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
AKRIC3	1.315	0.018	1.283	0.024
ALOX 12							1.198	0.024

2017268510 28 Nov 2017

Table 5A	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
ANLN	1.406	<001	1.519	<001	1.485	<001	1.632	<001
AQP2	1.209	<001	1.302	<001
ASAP2			1.582	<001	1.333	0.011	1.307	0.019
ASPN	1.872	<001	1.741	<001	1.638	<001	1.691	<001
ATP5E	1.309	0.042	1.369	0.012
BAG5			1.291	0.044
BAX					1.298	0.025	1.420	0.004
BGN	1.746	<001	1.755	<001
BIRC5 BMP6	_J480_ 1.536	_ <ooi_ <001	1.470 IX15	<.001 <001	_1419_ Ί.294	<001 0.033 '	1.503^ 1429	<001 0.001 ~
BRCA2							1.184	0.037
BUB1	1.288	0.001	1.391	<001	1.254	<001	1.189	0.018
CACNA1D			1.313	0.029
CADPS					1.358	0.007	1.267	0.022
CASP3					1.251	0.037
CCNB1					1.261	0.033	1.318	0.005
CCNE2 CD276	1.345 ’ 1.482	0.005 0.002	JL438 1.668 ‘	<001 <001	1.606 1451'	<opi_ <0 0’l ’	J426 1.302	<001 0.01 i
CDC20	1.417	<001	1.547	<001	1.355	<001	1446	<001
CDC6	1.340	0.011	1.265	0.046	1.367	0.002	1.272	0.025
CDH7	1.402	0.003	1.409	0.002
CDKN2B	1.553	<001	1.746	<001	1.340	0.014	1.369	0.006
CDKN2C	1.411	<001	1.604	<001	1.220	0.033
CDKN3	1.296	0.004			1.226	0.015
CENPF	1.434	0.002	1.570	<001	1.633	<001	1.610	<001
CKS2	1.419	0.008	1.374	0.022	1.380	0.004
COL1A1	1.677	<001	1.809	<001	1.401	<001	1.352	0.003
COL1A2			1.373	0.010
COL3AI	1.669	<001	1.781	<001	1.249	0.024	1.234	0.047
COL4A1	1.475	0.002	1.513	0.002
COL8A1	1.506	0.001	1.691	<001
CRISP3 CTHRCl	1.406 1426 ”	0.004 0Ό09	1.471 Ϊ.793 ‘	5.001 <001	1.311	0.019
CTNND2					1.462	<001
DDJT4 DYNLLI	1.478 '143Ϊ ’	0.003 0.002	1.783	5.001			1.236 . „„ _	0.039 0Ό04
EIF3H					1.372	0.027
ENY2					1.325	0.023	1.270	0.017
ERG	1.303	0.041
EZH2			1.254	0.049
F2R	1.540	0.002	1.448	0.006	1.286	0.023
FADD	1.235	0.041	1.404	<001

2017268510 28 Nov 2017

Table SA	cRFI	cR	FI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
FAP	1.386	0.015	1.440	0.008	1.253	0.048
FASN	1.303	0.028
FCGR3A			1.439	0.011			1.262	0.045
FGF5	1.289	0.006
GNPTAB	1.290	0.033	1.369	0.022	1.285	0.018	1.355	0.008
GPR68			1.396	0.005
GREM1	1.341	0.022	1.502	0.003	1.366	0.006
HDAC1					1.329	0.016
HDAC9			1.378	0.012
HRAS	1.465	0.006
HSD17B4					1.442	<001	1.245	0.028
IGFBP3			1.366	0.019			1.302	0.011
INHBA	2.000	<001	2.336	<001			1.486	0.002
JAG1	1.251	0.039
KCNN2	1.347	0.020	1.524	<001	1.312	0.023	1.346	0.011
KHDRBS3			1.500	0.001	1.426	0.001	1.267	0.032
KIAA0196 KIF4A	1.199	” 07022 ”	-			-	1.272 1.262	0.028 0.004
KPNA2					1.252	0.016
LAMA3					1.332	0.004	1.356	0.010
LAMB1			1.317	0.028
LAMC1	1.516	0.003	1.302	0.040			1.397	0.007
LIMSI							1.261	0.027
LOX					1.265	0.016	1.372	0.001
LTBP2			1.477	0.002
LUM			1.321	0.020
MANF					1.647	<001	1.284	0.027
MCM2					1.372	0.003	1.302	0.032
MCM3			1.269	0.047
MCM6			1.276	0.033			1.245	0.037
MELK			1.294	0.005	1.394	<001
MKI67 MMP1T	1.253 ’'L557	0.028 <001”	1.246 7.290 ’	0.029 7035 '	1.357	' 0.005
MRPL13							1.275	0.003
MSH2 MYBL2	’ i.497 ~	’ <001	1.355 7.509 ~	0.009 <.o'<) i	. J- --	' 0.003	' 1292	0.007”
MYO6			1.367	0.010
NDRG1	1.270	0.042						1.314	0.025
NEK2			1.338	0.020			1269	0.026
NETO2	1.434	0.004	1.303	0.033	1.283	0.012
NOX4	1.413	0.006	1.308	0.037	1.444	<001
NRIP3							1.171	0.026

2017268510 28 Nov 2017

Table 5A	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
NRP1			1.372	0.020
ODC1					1.450	<001
OR51E1					1.559	<001	1.413	0.008
PAK6							1.233	0.047
PATE1	1.262	<001	1.375	<001	1.143	0.034	1.191	0.036
PCNA					1.227	0.033	1.318	0.003
PEX10	1.517	<001	1.500	0.001
PGD	1.363	0.028	1.316	0.039	1.652	<001
PGK1			1.224	0.034			1.206	0.024
PIM1					1.205	0.042
PLA2G7					1.298	0.018	1.358	0.005
PLAU					1.242	0.032
PLK1			1.464	0.001	1.299	0.018	1.275	0.031
PLOD2					1.206	0.039	1.261	0.025
POSTN	1.558	0.001	1.356	0.022	1.363	0.009
PPP3CA					1.445	0.002
PSMD13					1.301	0.017	1.411	0.003
PTK2			1.318	0.031
PTK6	1.582	<001	1.894	<001	1.290	0.011	1.354	0.003
PTTG1	1.319	0.004	1.430	<001	1.271	0.006	1.492	<001
RAD21			1.278	0.028	1.435	0.004	1.326	0.008
RAFI					1.504	<001
RALA	1.374	0.028			1.459	0.001
RGS7			1.203	0.031
RRM1	1.535	0.001	1.525	<001
RRM2	1.302	0.003	1.197	0.047	1.342	<001
SAT1	1.374	0.043
SDC1					1.344	0.011	1.473	0.008
SEC14L1							1.297	0.006
SESN3	1.337	0.002	1.495	<001			1.223	0.038
SFRP4	1.610	<001	1.542	0.002	1.370	0.009
SHMT2 SKIL	J. 567	o.ooi	J.522 .	<001	1,.485 1.303	0.001 ' 0.008	1.370	<001
SLC25A21					1.287	0.020	1.306	0.017
SLC44A1 SNRPB2	' 1.304 ~	‘ δ.όΐδ'	L308 ~	0.045		-		-
SOX4					1.252	0.031
SPARC	1.445	0.004	1.706	<001			1.269	0.026
SPP1			1.376	0.016
SQLE			1.417	0.007	1.262	0.035
STAT1							1.209	0.029
STMN1	1.315	0.029

2017268510 28 Nov 2017

Table 5A	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
SULF1			1.504	0.001
TAF2					1.252	0.048	1.301	0.019
TFDP1					1.395	0.010	1.424	0.002
THBS2	1.716	<001	1.719	<001
THY1	1.343	0.035	1.575	0.001
TK1					1.320	<001	1.304	<001
TOP2A	1.464	0.001	1.688	<001	1.715	<001	1.761	<001
TPD52					1.286	0.006	1.258	0.023
TPX2 TYMS	-J.644	<:oqi_	l-964_	<001	1.699	<001	1554 1.315	<0QX 0.014 ’
UBE2C	1.270	0.019	1.558	<001	1.205	0.027	1.333	<001
UBE2G1	1.302	0.041
UBE2T UGT2BI5	1.451	<001,	1.222	' 0.025	1.309	0.003	-
UHRF1	1.370	0.003	1.520	<001	1.247	0.020
VCPIP1			1.332	0.015
VTI1B XIAP	-				1.237 1.486	0.036 0.008
ZMYND8			1.408	0.007
ZNF3 ZWINT	“1/28 A	~ 0.028 ~	-	-		-	E284	0.018

Table 5B.

Genes significantly (p<0.05) associated with cRFI or bRFI after adjustment for

AUA risk group in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) < 1.0 (increased expression is positively associated with good prognosis)

Table 5B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
AAMP	0.535	<001	0.581	<001	0.700	0.002	0.759	0.006
ABCA5	0.798	0.007	0.745	0.002			0.841	0.037
ABCC1			0.800	0.044
ABCC4			0.787	0.022
ABHD2			0.768	0.023
ACOX2	0.678	0.002	0.749	0.027	0.759	0.004
ADH5	0.645	<001	0.672	0.001
AGTR1	0.780	0.030
AKAP1	0.815	0.045	0.758	<001
AKT1			0.732	0.010
ALDH1A2	0.646	<001	0.548	<001	0.671	<001	0.713	0.001
ANPEP	0.641	<001	0.535	<001

2017268510 28 Nov 2017

Table 5B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest Pattern	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
ANXA2	0.772	0.035			0.804	0.046
ATXN1	0.654	<001	0.754	0.020	0.797	0.017
AURKA			0.788	0.030
AX1N2	0.744	0.005	0.655	<001
AZGP1	0.656	<001	0.676	<001	0.754	0.001	0.791	0.004
BAD			0.700	0.004
BINI	0.650	<001	0.764	0.013	0.803	0.015
BTG3					0.836	0.025
BTRC____ cT	0.730 0.617	J)O05_ ' <001 ”	“ft680~	<00T	0.667	<001	0.755	0.005
CADMl	0.559	<001	0.566	<001	0.772	0.020	0.802	0.046
CASP1	0.781	0.030	0.779	0.021	0.818	0.027	0.828	0.036
CAV1					0.775	0.034
CAV2			0.677	0.019
CCL2			0.752	0.023
CCNH	0.679	<001	0.682	<001
CD164 CDIA	0.721	0.002	0.724	0.005	' 0.710 ’	0.014	-	-
CD44	0.591	<001	0.642	<001
CD82 __ _ CDC25B	_0/779_ 0.778	0.021 0.035 '	0.771	0.024	0.818	0Ό23
CDK14	0.788	0.011
CDK3	0.752	0.012			0.779	0.005	0.841	0.020
CDKN1A	0.770	0.049	0.712	0.014
CDKN1C	0.684	<001			0.697	<001
CHN1	0.772	0.031
COL6A1	0.648	<001	0.807	0.046	0.768	0.004
CSF1	0.621	<001	0.671	0.001
CTNNB1					0.905	0.008
CTSB	0.754	0.030	0.716	0.011	0.756	0.014
CXCL12	0.641	<001	0.796	0.038	0.708	<001
CYP3A5 CYR6I	0.503 0 639 '	<001 'o.oof	0.528 0?659	<001 oiooT '	0.791	0.028	0.797	0.048
DARC					0.707	0.004
DDR2 DES	0.657 '	<.001	'1X758	0022	0.750 ' 0.699	0.01J ' <001 '
DHRS9	0.625	0.002
DHX9	0.846	<001
DIAPH1	0.682	0.007	0.723	0.008	0.780	0.026
DLC1	0.703	0.005	0.702	0.008
DLGAPI	0.703	0.008	0.636	<001
DNM3	0.701	0.001			0.817	0.042

2017268510 28 Nov 2017

Table 5B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
DPP4	0.686	<001	0.716	0.001
DPT	0.636	<001	0.633	<001	0.709	0.006	0.773	0.024
DUSP1	0.683	0.006	0.679	0.003
DUSP6	0.694	0.003	0.605	<001
EDN1					0.773	0.031
EDNRA	0.716	0.007
EGR1	0.575	<001	0.575	<001			0.771	0.014
EGR3	0.633	0.002	0.643	<001			0.792	0.025
EIF4E	0.722	0.002
ELK4	0.710	0.009	0.759	0.027
ENPP2	0.786	0.039
EPHA2			0.593	0.001
EPHA3	0.739	0.006			0.802	0.020
ERBB2			0.753	0.007
ERBB3	0.753	0.009	0.753	0.015
ERCC1							0.727	0.001
EREG					0.722	0.012	0.769	0.040
ESRI			0.742	0.015
FABP5	0.756	0.032
FAM 107 A	0.524	<001	0.579	<001	0.688	<001	0.699	0.001
FAM 130	0.639	<001	0.601	<001	0.810	0.019	0.709	<001
FAS	0.770	0.033
FASLG	0.716	0.028	0.683	0.017
FGF10					0.798	0.045
FGF17			0.718	0.018	0.793	0.024	0.790	0.024
FGFR2	0.739	0.007	0.783	0.038	0.740	0.004
FGFR4			0.746	0.050
FKBP5			0.689	0.003
FLNA	0.701	0.006	0.766	0.029	0.768	0.037
FLNC					0.755	<001	0.820	0.022
FLT1			0.729	0.008
FOS FOXQ1	0.572 0*778	<001 '0.033	0.536	<001	' 0.820	.. .	0.750	0.005
FYN	0.708	0.006
GADD45B GDFI5	0.577 0.757 ’	<001 ' 0ΌΪ3	0.589 0.743	<001 0.006 ’
GHR			0.712	0.004			0.679	0.001
GNRH1					0.791	0.048
GPM6B	0.675	<001	0.660	<001	0.735	<001	0.823	0.049
GSK3B	0.783	0.042
GSN	0.587	<001	0.705	0.002	0.745	0.004	0.796	0.021
GSTM1	0.686	0.001	0.631	<001	0.807	0.018

2017268510 28 Nov 2017

Table 5B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
GSTM2	0.607	<001	0.683	<001	0.679	<001	0.800	0.027
HIRIP3	0.692	<001			0.782	0.007
HKi	0.724	0.002	0.718	0.002
HLF	0.580	<001	0.571	<001	0.759	0.008	0.750	0.004
HNF1B			0.669	<.001
HPS1	0.764	0.008
HSD17B10	0.802	0.045
HSD17B2					0.723	0.048
HSD3B2	0/780”						0.709	0.010
HSP90AB1	0.034			0.809	0.041
HSPA5			0.738	0.017
HSPB1	0.770	0.006	0.801	0.032
HSPB2					0.788	0.035
ICAM1	0.728	0.015	0.716	0.010
IER3	0.735	0.016	0.637	<001			0.802	0.035
IFIT1	0.647	<001	0.755	0.029
IGF1 IGF2	0.675	<001	0.603 _	<001	0.762 0.761	0.006 0.011	0.770	0.030
IGFBP2	0.601	<001	0.605	<001
IGFBP5 IGFBP6	0.702 0.628	<001 <.δοί	-	—	().726	”θΌΟ3”
IL1B	0.676	0.002	0.716	0.004
IL6	0.688	0.005	0.766	0.044
IL6R			0.786	0.036
1L6ST	0.618	<001	0.639	<001	0.785	0.027	0.813	0.042
IL8	0.635	<001	0.628	<001
ILK	0.734	0.018	0.753	0.026
1NG5	0.684	<001	0.681	<.001	0.756	0.006
ITGA4	0.778	0.040
ITGA5	0.762	0.026
ITGA6			0.811	0.038
ITGA7 ITGAD	0.592 .	<001.	0.715 (1576	0.006 “0Ό06 ’	0.710	0.002
ITGB4			0.693	0.003
ITPR1 J LN	0.789 0.572	0.029 ’ <001	o3~8 ί	<001 '			0.777 '	0 019
JUNB	0.732	0.030	0.707	0.016
KCTD12	0.758	0.036
KIT					0.691	0.009	0.738	0.028
KLC1	0.741	0.024			0.781	0.024
KLF6	0.733	0.018	0.727	0.014
KLK1			0.744	0.028

2017268510 28 Nov 2017

Table SB	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
KLK2	0.697	0.002	0.679	<001
KLK3	0.725	<001	0.715	<001			0.841	0.023
KRT15	0.660	<001	0.577	<001	0.750	0.002
KRT18	0.623	<001	0.642	<001	0.702	<001	0.760	0.006
KRT2					0.740	0.044
KRT5	0.674	<001	0.588	<001	0.769	0.005
KRT8	0.768	0.034
LI CAM	0.737	0.036
LAG3	0.711	0.013	0.748	0.029
LAMA4					0.649	0.009
LAMB3	0.709	0.002	0.684	0.006	0.768	0.006
LGALS3	0.652	<001	0.752	0.015	0.805	0.028
LIG3	0.728	0.016	0.667	<001
LRP1							0.811	0.043
MDM2			0.788	0.033
MGMT	0.645	<001	0.766	0.015
MICA__ MPPED2	0.796 ~ 0.675 '	0.043 <001 '	0.676 _ 0.616	<001 <δοΓ '	().750 ’	0Ό06	-	-
MRC1							0.788	0.028
MTS SI	0.654	<001			0.793	0.036
MYBPC1	0.706	<001	0.534	<001	0.773	0.004	0.692	<001
NCAPD3	0.658	<001	0.566	<001	0.753	0.011	0.733	0.009
NCOR1			0.838	0.045
NEXN	0.748	0.025			0.785	0.020
NFAT5	0.531	<001	0.626	<001
NFATC2			0.759	0.024
OAZ1			0.766	0.024
OLFML3	0.648	<001	0.748	0.005	0.639	<001	0.675	<001
OR51E2	0.823	0.034
PAGE4	0.599	<001	0.698	0.002	0.606	<001	0.726	<001
PCA3	0.705	<001	0.647	<001
PCDHGB7 PGF................. ’	0.790 ’	'0.039					0.712	<001
PLG							0.764	0.048
PLP2 PPAP2B	0.589 ’	<.001	0.766 0.647	0.037 <001 '	' 0.691	’ <;όδΐ'	0.765 '	0.013
PPP1RI2A	0.673	0.001	0.677	0.001			0.807	0.045
PRIM Al	0.622	<001	0.712	0.008	0.740	0.013
PRKCA	0.637	<001			0.694	<001
PRKCB	0.741	0.020			0.664	<001
PROMI	0.599	0.017	0.527	0.042	0.610	0.006	0.420	0.002
PTCHI	0.752	0.027			0.762	0.011

2017268510 28 Nov 2017

Table SB	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest Pattern	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
PTEN	0.779	0.011	0.802	0.030	0.788	0.009
PTGS2 PTHLH	0.639 0.632	<001 0.007	0.606 0.739	<.001 0.043	0.654	ΠΟΌΟΓ	0.740	0.015
PTK2B			0.775	0.019	0.831	0.028	0.810	0.017
PTPN1	0.721	0.012	0.737	0.024
PYCARD			0.702	0.005
RAB27A			0.736	0.008
RAB30	0.761	0.011
RARB__ RASSF1	ΊΰοΓ	~0?043 '	0.746	0,010	---			-----
RHOB	0.755	0.029	0.672	0.001
RLN1	0.742	0.036	0.740	0.036
RND3	0.607	<.001	0.633	<001
RNF114	0.782	0.041	0.747	0.013
SDC2					0.714	0.002
SDHC	0.698	<001	0.762	0.029
SERP1NA3 SERPINB5			0.752 0.669	0Ό30 0.014	-		-	-
SH3RF2	0.705	0.012	0.568	<001			0.755	0.016
SLC22A3 SMAD4	0.650 0.636	<001 <001	0.582- 0.684	_<00_l _ 0.002	0.741	0.007	0.738	ΠΪ007“
SMARCD1	0.757	0.001
SMO	0.790	0.049					0.766	0.013
SOD1	0.741	0.037	0.713	0.007
SORBS 1	0.684	0.003	0.732	0.008	0.788	0.049
SPDEF	0.840	0.012
SPINT1			0.837	0.048
SRC	0.674	<001	0.671	<001
SRD5A2	0.553	<001	0.588	<001	0.618	<001	0.701	<001
ST5	0.747	0.012	0.761	0.010	0.780	0.016	0.832	0.041
STAT3			0.735	0.020
STAT5A STAT5B	0.731 0.708 '	0.005 . .<θ-!	0.743 0'696	0.009 “0.001 '			0.817	0.027
SUMOI	0.815	0.037
SVIL TBP	0.689^ 0.792 '	0.003 0.037	0.739	0.008	0/761	.. ρ-θιι.
TFF3	0.719	0.007	0.664	0.001
TGFB1I1	0.676	0.003	0.707	0.007	0.709	0.005	0.777	0.035
TGFB2 TGFBR2	0.741	0.010	0.785	0.017	0.759	0.022	--	----
TIMP3					0.785	0.037
TMPRSS2	0.780	0.012	0.742	<001

2017268510 28 Nov 2017

Table SB	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest Pattern	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
TNF			0.654	0.007			0.682	0.006
TNFRSF10 B	0.623	<001	0.681	<001	0.801	0.018	0.815	0.019
TNFSF10 TP53	0.721	0.004	0/759 '	ooTi				=
TP63			0.737	0.020	0.754	0.007
TPM2 TRAF3IP2	0.609 0.795	<001 0.041	0.671 0.727	<001 0.005	0.673	<001	0.789	0.031
TRO	0.793	0.033	0.768	0.027	0.814	0.023
TUBB2A	0.626	<001	0.590	<001
VCL VIM	0.613 0.716	<.00£ ' 0Ό05	0.701	0.011	6/792	' 0.025
WFDC1					0.824	0.029
YY1	0.668	<001	0.787	0.014	0.716	0.001	0.819	0.011
ZFHX3	0.732	<001	0.709	<001
ZFP36	0.656	0.001	0.609	<001			0.818	0.045
ZNF827	0.750	0.022

[00139] Tables 6A and 6B provide genes that were significantly associated (p<0.05), positively or negatively, with recurrence (cRFI, bRFI) after adjusting for Gleason pattern in the primary and/or highest Gleason pattern. Increased expression of genes in Table 6A is negatively associated with good prognosis, while increased expression of gene in Table 6B is positively associated with good prognosis.

Table 6A.

Genes significantly (p<0.05) associated with cRFI or bRFI after adjustment for

Gleason pattern in the primary Gleason pattern or highest Gleason pattern with a hazard ratio (HR) > 1.0 (increased expression is negatively associated with good prognosis)

Table 6A	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
AKR1C3	1.258	0.039
ANLN	1.292	0.023			1.449	<001	1.420	0.001
AQP2	1.178	0.008	1.287	<001
ASAP2			1.396	0.015
ASPN	1.809	<001	1.508	0.009	1.506	0.002	1.438	0.002
BAG5			1.367	0.012
BAX							1.234	0.044
BGN	1.465	0.009	1.342	0.046
B1RC5	1.338	0.008			1.364	0.004	1.279	0.006

2017268510 28 Nov 2017

Table 6A	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
BMP6	1.369	0.015	1.518	0.002
BUB1	1.239	0.024	1.227	0.001	1.236	0.004
CACNA1D			1.337	0.025
CADPS					1.280	0.029
CCNE2			1.256	0.043	1.577	<001	1.324	0.001
CD276	1.320	0.029	1.396	0.007	1.279	0.033
CDC20	1.298	0.016	1.334	0.002	1.257	0.032	1.279	0.003
CDH7	1.258	0.047	1.338	0.013
CDKN2B CDKN2C	J. 342 L344 ’	0.032 ’ 04)10 ’	1.488 = 1.450	JK009 <001
CDKN3	1.284	0.012
CENPF	1.289	0.048			1.498	0.001	1.344	0.010
COL1A1 COL3A1	1.481 1.459 '	0.003 0.004	1.506 1.430	0.002 0.013			-
COL4A1	1.396	0.015
COL8A1	1.413	0.008
CRISP3 cthrcT	1.346	0.012	1.310 ’ Ϊ.588	0.025^ 67602	-	..	-	-
DDIT4	1.363	0.020	1.379	0.028
DICER 1__ ΕΝΫ2	—		-		1.269 '	0.024	1.294	0.008
FADD			1.307	0.010
FAS							1.243	0.025
FGF5	1.328	0.002
GNPTAB							1.246	0.037
GREM1	1.332	0.024	1.377	0.013	1.373	0.011
HDAC1					1.301	0.018	1.237	0.021
HSD17B4 IFN-γ	-	-	-		L219	0.048	L277	CLOU
IMMT					1.230	0.049
INHBA	1.866	<001	1.944	<001
JAG1			1.298	0.030
KCNN2			1.378	0.020			1.282	0.017
KHDRBS3			1.353	0.029	1.305	0.014
LAMA3					1.344	<001	1.232	0.048
LAMC1 LIMSI	1.396	0.015					1.337	0.004
LOX					1.355	0.001	1.341	0.002
LTBP2 MAGEA4	1215	‘ 6.024	1,304 =	0.045				-
MANF					1.460	<001
MCM6			1.287	0.042			1.214	0.046

2017268510 28 Nov 2017

Table 6A	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest	Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
MELK					1.329	0.002
MMP11	1.281	0.050
MRPL13							1.266	0.021
MYBL2	1.453	<001			1.274	0.019
MYC					1.265	0.037
MYO6			1.278	0.047
NETO2	1.322	0.022
NFKB1							1.255	0.032
NOX4 OR51E1		-			1,266 _ 1.566’	0.041_ <001	1.428	0.003 =
PATE1	1.242	<001	1.347	<001			1.177	0.011
PCNA							1.251	0.025
PEX10 PGD		-	1.302 1.335	0.028 0.045	' 1379 ~	0.014	1.274 '	0.025 ’
PIM1					1.254	0.019
PLA2G7					1.289	0.025	1.250	0.031
PLAU_ PSMD13		.	-	-	1.267	0.031	1333 '	0.005 ~
PTK6	1.432	<001	1.577	<001	1.223	0.040
PTTG1					1.279	0.013	1308	0.006
RAGE							1.329	0.011
RALA	1.363	0.044			1.471	0.003
RGS7	1.120	0.040	1.173	0.031
RRM1	1.490	0.004	1.527	<001
SESN3			1.353	0.017
SFRP4	1.370	0.025
SHMT2	1.460	0.008	1.410	0.006	1.407	0.008	1.345	<001
SKIL					1.307	0.025
SLC25A21					1.414	0.002	1330	0.004
SMARCC2					1.219	0.049
SPARC			1.431	0.005
TFDP1 THBS2	1.456	' 0.005	1.431 ”	' 0.012 ’	1.283	0.046 _	1.345	0.003
TK1					1.214	0.015	1.222	0.006
TOP2A IPX2	1.513	' 0.001	1367 ’ *1.607	0.018 . < θθ-	1518 ' 1.588 '	0.001 <001 '	1.480 1.481”	<001 <.()() ί
UBE2T	1.409	0.002			1.285	0.018
UGT2B15			1.216	0.009			1.182	0.021
XIAP					1.336	0.037	1.194	0.043

Table 6B.

Genes significantly (p<0.05) associated with cRFI or bRFI after adjustment for

Gleason pattern in the primary Gleason pattern or highest Gleason pattern with hazard ration (HR) < 1.0 (increased expression is positively associated with good prognosis)

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
AAMP	0.660	0.001	0.675	<001			0.836	0.045
ABCA5	0.807	0.014	0.737	<001			0.845	0.030
ABCC1	0.780	0.038	0.794	0.015
ABCG2							0.807	0.035
ABHD2			0.720	0.002
ADH5	0.750	0.034
AKAPI			0.721	<001
ALDH1A2	0.735	0.009	0.592	<001	0.756	0.007	0.781	0.021
ANGPT2					0.741	0.036
ANPEP	0.637	<001	0.536	<001
ANXA2			0.762	0.044
APOE			0.707	0.013
APRT ATXN1	' 0.725	0013	0.727 _	0.004		-	0.771	0.006 _
AURKA	0.784	0.037	0.735	0.003
AXIN2	0.744	0.004	0.630	<001
AZGP1	0.672	<001	0.720	<001	0.764	0.001
BAD			0.687	<001
BAK1			0.783	0.014
BCL2	0.777	0.033	0.772	0.036
B1K			0.768	0.040
BINI	0.691	<001
BTRC			0.776	0.029
C7 CADMl	0.707 0.587 ‘	0.004 <001	’ '0593 '	<ΓόδΤ '	0.791	0.024	-	-
CASP1	0.773	0.023			0.820	0.025
CAVI					0.753	0.014
CAV2			0.627	0.009			0.682	0.003
CCL2			0.740	0.019
CCNH	0.736	0.003
CCR1			0.755	0.022
CD1A					0.740	0.025
CD44	0.590	<001	0.637	<001
CD68	0.757	0.026
CD82	0.778	0.012	0.759	0.016
CDC25B	0.760	0.021
CDK3	0.762	0.024			0.774	0.007
CDKN1A			0714	0.015

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
CDKN1C	0.738	0.014			0.768	0.021
COL6A1	0.690	<001	0.805	0.048
CSF1	0.675	0.002	0.779	0.036
CSK					0.825	0.004
CTNNB1	0.884	0.045			0.888	0.027
CTSB	0.740	0.017	0.676	0.003	0.755	0.010
CTSD	0.673	0.031	0.722	0.009
CTSK					0.804	0.034
CTSL2			0.748	0.019
CXCL12	0.731	0.017
CYP3A5	0.523	<001	0.518	<001
CYR61	0.744	0.041
DAP					0.755	0.011
DARC					0.763	0.029
DDR2							0.813	0.041
DES	0.743	0.020
DHRS9 DHX9	0.606 ‘ 0.916	0.001 0.021	-			-	-	-
DIAPH1	0.749	0.036	0.688	0.003
DLGAP1	0.758	0.042	0.676	0.002
DLL4							0.779	b.oib
DNM3	0.732	0.007
DPP4	0.732	0.004	0.750	0.014
DPT			0.704	0.014
DUSP6	0.662	<001	0.665	0.001
EBNA1BP2							0.828	0.019
EDNRA	0.782	0.048
EGF			0.712	0.023
EGR1	0.678	0.004	0.725	0.028
EGR3	0.680	0.006	0.738	0.027
EIF2C2			0.789	0.032
EIF2S3 ELK4	0/745	‘ 1/024					0.759	0.012
EPHA2			0.661	0.007
EPHA3 ERBB2	0/781 = 0.791	0.026 0/022	0.760	--------	0.789 '	0/006	0.828 _	. Q·037
ERBB3			0.757	0.009
ERCC1							0.760	0.008
ESRI			0.742	0.014
ESR2			0.711	0.038
ETV4			0.714	0.035
FAM 107 A	0.619	<001	0.710	0.011			0.781	0.019

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
FAM13C	0.664	<001	0.686	<001			0.813	0.014
FAM49B FASLG	0.670	<001	0.793 0.616	0.014 0.004	0.815	0.044	0.843 0.813	0.047 0.038
FGF10	0.751	0.028			0.766	0.019
FGF17			0.718	0.031	0.765	0.019
FGFR2	0.740	0.009			0.738	0.002
FKBP5			0.749	0.031
FLNC					0.826	0.029
FLT1 FLT4		0.779	0.045	0.729	0.006
							0.815	0.024
FOS	0.657	0.003	0.656	0.004
FSDI							0.763	0.017
FYN	0.716	0.004			0.792	0.024
GADD45B	0.692	0.009	0.697	0.010
GDF15			0.767	0.016
GHR			0.701	0.002	0.704	0.002	0.640	<001
GNRH1 GPM6B	‘ ().749 '	' ~δ.δϊδ	' 0/750 '	0.010	0.778 0.827	0Ό39 0.037 '	-	-
GRB7			0.696	0.005
GSK3B__ GSN	0.726 0.660	0.005 <001	0.752	0.019	- —	—		- -
GSTM1	0.710	0.004	0.676	<001
GSTM2	0.643	<001			0.767	0.015
HK1	0.798	0.035
HLA-G			0.660	0.013
HLF	0.644	<001	0.727	0.011
HNF1B			0.755	0.013
HPS1	0.756	0.006	0.791	0.043
HSD17B10	0.737	0.006
HSD3B2							0.674	0.003
HSP90AB1			0.763	0.015
HSPB1 hspeT	0.787	. 0-020	0.778 0 /794	0.015 . 9-
1CAM1			0.664	0.003
ER3 IFITl	0.699 07621	0.003 <001	0.693 0.733	0Ό10 ' 0.027			-
IGF1	0.751	0.017	0.655	<001
IGFBP2	0.599	<001	0.605	<001
IGFBP5	0.745	0.007	0.775	0.035
IGFBP6	0.671	0.005
IL1B	0.732	0.016	0.717	0.005
IL6	0.763	0.040

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
IL6R			0.764	0.022
IL6ST IL8		0.647	<001	0.739	0.012
	0.711	0.015	0.694	0.006
ING5	0.729	0.007	0.727	0.003
ITGA4			0.755	0.009
ITGA5	0.743	0.018	0.770	0.034
ITGA6	0.816	0.044	0.772	0.006
ITGA7	0.680	0.004
ITGAD			0.590	0.009
ITGB4	0.663	<001	0.658	<001	0.759	0.004
JUN	0.656	0.004	0.639	0.003
KIAA0196	0.737	0.011
KIT KLC1					0.730	0.021	0.724	0.008
0.755	0.035
KLK1	0.706	0.008
KLK2	0.740	0.016	0.723	0.001
KLK3 KRT1	. 0565 _	0.006	0.740 _	0.002		-	0574 ’	* 0Ό42 '
KRT15	0.658	<001	0.632	<001	0.764	0.008
KRT18 KRT5		0.703	0.004	0.672	<001	0.779	0.015	0.811	0.032
	0.686	<001	0.629	<001	0.802	0.023
KRT8	0.763	0.034	0.771	0.022
L1CAM	0.748	0.041
LAG3	0.693	0.008	0.724	0.020
LAMA4					0.689	0.039
LAMB3	0.667	<001	0.645	<001	0.773	0.006
LGALS3	0.666	<001			0.822	0.047
LIG3			0.723	0.008
LRP1	0.777	0.041					0.769	0.007
MDM2			0.688	<001
MET	0.709	0.010	0.736	0.028	0.715	0.003
MGMT MICA	0.751	. 0-03.1	0.705'	' 0.002
MPPED2	0.690	0.001	0.657	<001	0.708	<001
MRC1 MSH6							0.812 07860	0.049 04)49
MTSS1	0.686	0.001
MVP	0.798	0.034	0.761	0.033
MYBPC1	0.754	0.009	0.615	<001
NCAPD3	0.739	0.021	0.664	0.005
NEXN			0.798	0.037
NFAT5	0.596	<001	0.732	0.005

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary	Pattern	Highest	Pattern	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
NFATC2	0.743	0.016	0.792	0.047
NOS3	0.730	0.012	0.757	0.032
OAZ1	0.732	0.020	0.705	0.002
OCLN					0.746	0.043	0.784	0.025
OLFML3	0.711	0.002			0.709	<001	0.720	0.001
OMD	0.729	0.011	0.762	0.033
OSM							0.813	0.028
PAGE4	0.668	0.003	0.725	0.004	0.688	<001	0.766	0.005
PCA3	0.736	0.001	0.691	<001
PCDHGB7					0.769	0.019	0.789	0.022
PIK3CA			0.768	0.010
PIK3CG	0.792	0.019	0.758	0.009
PLG							0.682	0.009
PPAP2B	0.688	0.005			0.815	0.046
PPPIR12A	0.731	0.026	0.775	0.042
PRIM Al	0.697	0.004	0.757	0.032
PRKCA PRKCB	0.743 ‘ 0.756 '	0.019 0.036	-		0.767	0.029 '	-	-
PROMI	0.640	0.027			0.699	0.034	0.503	0.013
PTCHI	0.730	0.018
ΡΊΈΝ	0.779	0.015			0.789	0.007
PTGS2	0.644	<001	0.703	0.007
PTHLH	0.655	0.012	0.706	0.038	0.634	0.001	0.665	0.003
PTK2B	0.779	0.023	0.702	0.002	0.806	0.015	0.806	0.024
PYCARD			0.659	0.001
RAB30	0.779	0.033	0.754	0.014
RARB	0.787	0.043	0.742	0.009
RASSF1	0.754	0.005
RHOA			0.796	0.041			0.819	0.048
RND3	0.721	0.011	0.743	0.028
SDC1			0.707	0.011
SDC2 SDHC	0Y50	' 0ΌΪ 3			0.745	0.002
SERPINA3			0.730	0.016
SERPINB5 SH3RF2			0.715 '0'698	0O41_ ' 0.025			-
SIPA1L1			0.796	0.014			0.820	0.004
SLC22A3	0.724	0.014	0.700	0.008
SMAD4	0.668	0.002			0.771	0.016
SMARCD1	0.726	<001	0.700	0.001			0.812	0.028
SMO							0.785	0.027
SOD1			0.735	0.012

2017268510 28 Nov 2017

Table 6B	cRFI	cRFI	bRFI	bRFI
	Primary Pattern	Highest Pattern	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value	HR	p-value
SORBS 1			0.785	0.039
SPDEF SPINT1	0.818 0.761	0.002 0.024	0.773	Ίλοδβ	-	--	-	-- —
SRC	0.709	<001	0.690	<001
SRD5A1	0.746	0.010	0.767	0.024	0.745	0.003
SRD5A2	0.575	<001	0.669	0.001	0.674	<001	0.781	0.018
ST5	0.774	0.027
STAT1	0.694	0.004
STAT5A_ STΛΤ5 B	0.719 ' 0704	0.004 0.00 f	0.765 0.744	0.006 0.012		-	0.834	0.049
SUMO1	0.777	0.014
SVIL			0.771	0.026
TBP TFF3	0.774 0.742 ’	0.031 0.015	0.719	0.024				=
TGFB1I1	0.763	0.048
TGFB2	0.729	0.011	0.758	0.002
TMPRSS2 TN1;	0.810	0.034	0.692	<001		-	0727 ’	‘ 0Ό22
TNFRSF10A			0.805	0.025
TNFRSF10B TNFSFIO ”	0,581 ' 0.751	<001 0.015	0738 0.700 ~	0.014 <001	0.809_	0.034	-	-
TP63			0.723	0.018	0.736	0.003
TPM2	0.708	0.010	0.734	0.014
TRAF3IP2			0.718	0.004
TRO			0.742	0.012
TSTA3			0.774	0.028
TUBB2A	0.659	<001	0.650	<001
TYMP VCL	0.695 0.683	0.002 0.008	-	-	-	-		-
VIM	0.778	0.040
WDR19							0775	0.014
XRCC5 ΥΎΪ	0.793 0.751	0.042’ 0 ()25					οίδϊο	0.008
ZFHX3	0.760	0.005	0.726	0.001
ZFP36 ZNF827	0707 0~667	0.008 0.002	0,672	0.003	0.792	0.039 '	-

[00140] Tables 7A and 7B provide genes significantly associated (p<0.05), positively or negatively, with clinical recurrence (cRFI) in negative TMPRSS fusion specimens in the primary or highest Gleason pattern specimen. Increased expression of genes in Table 7A is negatively associated with good prognosis, while increased expression of genes in Table 7B is positively associated with good prognosis.

2017268510 28 Nov 2017

Table 7A.

Genes significantly (p<0.05) associated with cRFI for TMPRSS2-ERG fusion negative in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) > 1.0 (increased expression is negatively associated with good prognosis)

Table 7A	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
ANLN AQP2	1.42 1.25	0.012 0.033	1.36	0.004
ASPN	2.48	<001	1.65	<001
BGN	2.04	<001	1.45	0.007
BIRC5 BMP6	1.59 1.95	<001 <ooi	1.37 Ϊ.43	0.005 0.()12
BMPR1B	1.93	0.002
BUB1 CCNE2	1.51 Ϊ.48	_<00j 0.007	1.35	<001
CD276	1.93	<001	1.79	<001
CDC20	1.49	0.004	1.47	<001
CDC6	1.52	0.009	1.34	0.022
CDKN2B	1.54	0.008	1.55	0.003
CDKN2C	1.55	0.003	1.57	<001
CDKN3	1.34	0.026
CENPF	1.63	0.002	1.33	0.018
CKS2	1.50	0.026	1.43	0.009
CLTC			1.46	0.014
COL1A1	1.98	<001	1.50	0.002
COL3A1 COL4A1	2.03 i.81	<001 0.002	1.42	0.007
COLS A l	1.63	0.004	1.60	0.001
CRISP3 CTHRCl	L67	0.006	1.31 Ϊ.48	0.016 07005
DDIT4	1.49	0.037
ENY2			1.29	0.039
EZH2 F2R	1.46	0.034	1.35 1.46	(1016 0.007
FAP	1.66	0.006	1.38	0.012
FGF5			1.46	0.001
GNPTAB	1.49	0.013
HSD17B4	1.34	0.039	1.44	0.002
INHBA	2.92	<001	2.19	<001
JAG1	1.38	0.042
KCNN2	1.71	0.002	1.73	<001
KHDRBS3			1.46	0.015
KLK14	1.28	0.034

2017268510 28 Nov 2017

Table 7A	Primary Pattern	Highest	Pattern
Official Symbol	HR	p-value	HR	p-value
KPNA2	1.63	0.016
LAMC1 LOX	1.41	0.044	1.29	0.036
LTBP2	1.57	0.017
MELK	1.38	0.029
MMP11	1.69	0.002	1.42	0.004
MYBL2	1.78	<001	1.49	<001
NETO2	2.01	<001	1.43	0.007
NME1			1.38	0.017
PATE1	1.43	<001	1.24	0.005
PEX10	1.46	0.030
PGD	1.77	0.002
POSTN	1.49	0.037	1.34	0.026
PPFIA3	1.51	0.012
PPP3CA	1.46	0.033	1.34	0.020
PTK6	1.69	<001	1.56	<001
PTTG1 RAD51	1.35 1.32	0.028 0Ό48	-	—
RALBP1			1.29	0.042
RGS7 RRMl	1.18 1.57	0.012 0.016	1.32 1.32	0.009 0.041
RRM2	1.30	0.039
SAT1	1.61	0.007
SESN3 SFRP4	JL76 1.55	<001 0.016	1.36 1.48	0.020 0.002
SHMT2	2.23	<001	1.59	<001
SPARC	1.54	0.014
SQLE STMN1	L86 2.14	0.003 <o6f		-
THBS2	1.79	<001	1.43	0.009
TK1	1.30	0.026
TOP2A	2.03	<001	1.47	0.003
TPD52	1.63	0.003
TPX2	2.11	<001	1.63	<001
TRAP1	1.46	0.023
UBE2C	1.57	<001	1.58	<001
UBE2G1	1.56	0.008
UBE2T	1.75	<001
UGT2B15 UHRF1	1.31 1.46	0.036 0.007	1.33	0.004
UTP23	1.52	0.017

2017268510 28 Nov 2017

Table 7B.

Genes significantly (p<0.05) associated with cRFI for TMPRSS2-ERG fusion negative in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) <1.0 (increased expression is positively associated with good prognosis)

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
AAMP	0.56	<001	0.65	0.001
ABCA5	0.64	<001	0.71	<001
ABCB1	0.62	0.004
ABCC3			0.74	0.031
ABCG2			0.78	0.050
ABHD2	0.71	0.035
ACOX2	0.54	<001	0.71	0.007
ADH5	0.49	<001	0.61	<001
AKAP1	0.77	0.031	0.76	0.013
AKR1C1	0.65	0.006	0.78	0.044
AKT1			0.72	0.020
AKT3	0.75	<001
ALDH1A2	0.53	<001	0.60	<001
AMPD3	0.62	<001	0.78	0.028
ANPEP	0.54	<001	0.61	<001
ANXA2	0.63	0.008	0.74	0.016
ARHGAP29	0.67	0.005	0.77	0.016
ARHGDIB	0.64	0.013
ATP5J	0.57	0.050
ATXN1	0.61	0.004	0.77	0.043
AXIN2	0.51	<001	0.62	<001
AZGP1	0.61	<001	0.64	<001
BCL2	0.64	0.004	0.75	0.029
BINI	0.52	<001	0.74	0.010
BTG3	0.75	0.032	0.75	0.010
BTRC	0.69	0.011
C7	0.51	<001	0.67	<001
CADMI	0.49	<001	0.76	0.034
CASP1	0.71	0.010	0.74	0.007
CAVI			0.73	0.015
CCL5	0.67	0.018	0.67	0.003
CCNH	0.63	<001	0.75	0.004
CCR1			0.77	0.032
CD 164	0.52	<001	0.63	<001
CD44	0.53	<001	0.74	0.014
CDH10	0.69	0.040
CDHI8	0.40	0.011
CDK14	0.75	0.013
CDK2			0.81	0.031
CDK3	0.73	0.022

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
CDKN1A	0.68	0.038
CDKN1C	0.62	0.003	0.72	0.005
COL6A1	0.54	<.001	0.70	0.004
COL6A3	0.64	0.004
CSF1	0.56	<001	0.78	0.047
CSRP1	0.40	<001	0.66	0.002
CTGF	0.66	0.015	0.74	0.027
CTNNB1	0.69	0.043
CTSB	0.60	0.002	0.71	0.011
CTSS	0.67	0.013
CXCL12	0.56	<001	0.77	0.026
CYP3A5	0.43	<001	0.63	<001
CYR61	0.43	<001	0.58	<001
DAG1			0.72	0.012
DARC	0.66	0.016
DDR2	0.65	0.007
DES	0.52	<001	0.74	0.018
DHRS9	0.54	0.007
DICER 1	0.70	0.044
DLC1			0.75	0.021
DLGAP1	0.55	<001	0.72	0.005
DNM3	0.61	0.001
DPP4	0.55	<001	0.77	0.024
DPT	0.48	<001	0.61	<001
DUSP1	0.47	<001	0.59	<001
DUSP6	0.65	0.009	0.65	0.002
DYNLL1			0.74	0.045
EDNRA	0.61	0.002	0.75	0.038
EFNB2	0.71	0.043
EGR1	0.43	<001	0.58	<001
EGR3	0.47	<001	0.66	<001
EIF5			0.77	0.028
ELK4	0.49	<001	0.72	0.012
EPHA2			0.70	0.007
EPHA3	0.62	<001	0.72	0.009
EPHB2	0.68'	0.009
ERBB2	0.64	<001	0.63	<001
ERBB3	0.69	0.018
ERCC1	0.69	0.019	0.77	0.021
ESR2	0.61	0.020
FAAH	0.57	<001	0.77	0.035
FABP5	0.67	0.035
FAM 107 A	0.42	<001	0.59	<001
FAM13C	0.53	<001	0.59	<001

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
FAS	0.71	0.035
FASLG	0.56	0.017	0.67	0.014
FGF10	0.57	0.002
FGF17	0.70	0.039	0.70	0.010
FGF7	0.63	0.005	0.70	0.004
FGFR2	0.63	0.003	0.71	0.003
FKBP5			0.72	0.020
FLNA	0.48	<001	0.74	0.022
FOS	0.45	<001	0.56	<001
FOXO1	0.59	<001
FOXQ1	0.57	<001	0.69	0.008
FYN	0.62	0.001	0.74	0.013
G6PD			0.77	0.014
GADD45A	0.73	0.045
GADD45B	0.45	<001	0.64	0.001
GDF15	0.58	<001
GHR	0.62	0.008	0.68	0.002
GPM6B	0.60	<001	0.70	0.003
GSK3B	0.71	0.016	0.71	0.006
GSN	0.46	<001	0.66	<001
GSTM1	0.56	<001	0.62	<001
GSTM2	0.47	<001	0.67	<001
HGD			0.72	0.002
HTRTP3	0.69	0.021	0.69	0.002
HK1	0.68	0.005	0.73	0.005
HLA-G	0.54	0.024	0.65	0.013
HLF	0.41	<001	0.68	0.001
HNF1B	0.55	<001	0.59	<001
HPS1	0.74	0.015	0.76	0.025
HSD17B3	0.65	0.031
HSPB2	0.62	0.004	0.76	0.027
ICAM1	0.61	0.010
IER3	0.55	<001	0.67	0.003
IFIT1	0.57	<001	0.70	0.008
IFNG			0.69	0.040
IGF1	0.63	<001	0.59	<001
IGF2	0.67	0.019	0.70	0.005
IGFBP2	0.53	<001	0.63	<001
IGFBP5	0.57	<001	0.71	0.006
IGFBP6	0.41	<001	0.71	0.012
IL10	0.59	0.020
IL1B	0.53	<001	0.70	0.005
IL6	0.55	0.001
IL6ST	0.45	<001	0.68	<001

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
IL8	0.60	0.005	0.70	0.008
ILK	0.68	0.029	0.76	0.036
ING5	0.54	<001	0.82	0.033
ITGA1	0.66	0.017
ITGA3	0.70	0.020
ITGA5	0.64	0.011
ITGA6	0.66	0.003	0.74	0.006
ITGA7	0.50	<001	0.71	0.010
ITGB4	0.63	0.014	0.73	0.010
ITPR1	0.55	<001
ITPR3			0.76	0.007
JUN	0.37	<001	0.54	<001
JUNB	0.58	0.002	0.71	0.016
KCTD12	0.68	0.017
ΚΓΤ	0.49	0.002	0.76	0.043
KLC1	0.61	0.005	0.77	0.045
KLF6	0.65	0.009
KLK1	0.68	0.036
KLK10			0.76	0.037
KLK2	0.64	<001	0.73	0.006
KLK3	0.65	<001	0.76	0.021
KLRK1	0.63	0.005
KRT15	0.52	<001	0.58	<001
KRT18	0.46	<001
KRT5	0.51	<001	0.58	<001
KRT8	0.53	<001
L1CAM	0.65	0.031
LAG3	0.58	0.002	0.76	0.033
LAMA4	0.52	0.018
LAMB3	0.60	0.002	0.65	0.003
LGALS3	0.52	<001	0.71	0.002
LIG3	0.65	0.011
LRP1	0.61	0.001	0.75	0.040
MGMT	0.66	0.003
MICA	0.59	0.001	0.68	0.001
MLXIP	0.70	0.020
MMP2	0.68	0.022
MMP9	0.67	0.036
MPPED2	0.57	<001	0.66	<001
MRC1	0.69	0.028
MTSS1	0.63	0.005	0.79	0.037
MVP	0.62	<001
MYBPC1	0.53	<001	0.70	0.011
NCAM1	0.70	0.039	0.77	0.042

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
NCAPD3	0.52	<001	0.59	<001
NDRG1 NEXN	’θ'.62	().002	0.69	0.008
NFAT5	0.45	<001	0.59	<001
NFATC2	0.68	0.035	0.75	0.036
NFKBIA	0.70	0.030
NRG1	0.59	0,022	0.71	0.018
OAZ1	0.69	0.018	0.62	<001
OLFML3	0.59	<001	0.72	0.003
OR51E2	0.73	0.013
PAGE4	0.42	<001	0.62	<001
PCA3	0.53	<001
PCDHGB7	0.70	0.032
PGF	0.68	0.027	0.71	0.013
PGR			0.76	0.041
PIK3C2A			0.80	<001
PIK3CA	0.61	<001	0.80	0.036
PIK3CG	0.67	0.001	0.76	0.018
PLP2	0.65	0.015	0.72	0.010
PPAP2B	0.45	<001	0.69	0.003
PPP1R12A	0.61	0.007	0.73	0.017
PRIMA1	0.51	<001	0.68	0.004
PRKCA	0.55	<001	0.74	0.009
PRKCB	0.55	<001
PROMI			0.67	0.042
PROS!	0.73	0.036
PTCHI	0.69	0.024	0.72	0.010
ΡΊΈΝ	0.54	<001	0.64	<001
PTGS2	0.48	<001	0.55	<001
PTH1R	0.57	0.003	0.77	0.050
PTHLH	0.55	0.010
PTK2B	0.56	<001	0.70	0.001
PYCARD			0.73	0.009
RAB27A	0.65	0.009	0.71	0.014
RAB30	0.59	0.003	0.72	0.010
RAGE			0.76	0.011
RARB	0.59	<001	0.63	<001
RASSF1	0.67	0.003
RBI	0.67	0.006
RFX1	0.71	0.040	0.70	0.003
RHOA	0.71	0.038	0.65	<001
RHOB	0.58	0.001	0.71	0.006
RND3	0.54	<001	0.69	0.003
RNF114	0.59	0.004	0.68	0.003

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
SCUBE2			0.77	0.046
SDHC	0.72	0.028	0.76	0.025
SEC23A			0.75	0.029
SEMA3A	0.61	0.004	0.72	0.011
SEPT9	0.66	0..013	0.76	0.036
SERPINB5			0.75	0.039
SH3RF2	0.44	<.001	0.48	<001
SHH			0.74	0.049
SLC22A3	0.42	<001	0.61	<001
SMAD4	0.45	<.001	0.66	<001
SMARCD1	0.69	0.016
SOD1	0.68	0.042
SORBS 1	0.51	<.001	0.73	0.012
SPARCL1	0.58	<001	0.77	0.040
SPDEF	0.77	<001
SPINT1	0.65	0.004	0.79	0.038
SRC	0.61	<001	0.69	0.001
SRD5A2	0.39	<001	0.55	<001
ST5	0.61	<001	0.73	0.012
STAT1	0.64	0.006
STAT3	0.63	0.010
STAT5A	0.62	0.001	0.70	0.003
STAT5B	0.58	<001	0.73	0.009
SUMO1	0.66	<001
SVIL	0.57	0.001	0.74	0.022
TBP	0.65	0.002
TFF1	0.65	0.021
TFF3	0.58	<001
TGFB1I1	0.51	<001	0.75	0.026
TGFB2	0.48	<001	0.62	<001
TGFBR2	0.61	0.003
TIAM1	0.68	0.019
TIMP2	0.69	0.020
TIMP3	0.58	0.002
TNFRSF10A	0.73	0.047
TNFRSF10B	0.47	<001	0.70	0.003
TNFSFIO	0.56	0.001
TP63			0.67	0.001
TPM1	0.58	0.004	0.73	0.017
TPM2	0.46	<001	0.70	0.005
TRA2A	0.68	0.0 J 3
TRAF3IP2	0.73	0.041	0.71	0.004
TRO	0.72	0.016	0.71	0.004
TUBB2A	0.53	<001	0.73	0.021

2017268510 28 Nov 2017

Table 7B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
TYMP	0.70	0.011
VC AMI veil·	0.69 046	0.041 <όδϊ		...
VEGFA			0.77	0.039
VEGFB	0.71	0.035
VIM	0.60	0.001
XRCC5			0.75	0.026
YY1	0.62	0.008	0.77	0.039
ZFHX3	0.53	<001	0.58 0.54	<001 <001
ZFP36	0.43	<.001
ZNF827	0.55	0.001

[00141] Tables 8A and 8B provide genes that were significantly associated (p<0.05), positively or negatively, with clinical recurrence (cRFI) in positive TMPRSS fusion specimens in the primary or highest Gleason pattern specimen. Increased expression of genes in Table 8 A is negatively associated with good prognosis, while increased expression of genes in Table 8B is positively associated with good prognosis.

Table 8A.

Genes significantly (p<0.05) associated with cRFI for TMPRSS2-ERG fusion positive in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) >1.0 (increased expression is negatively associated with good prognosis)

Table 8A	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
ACTR2 AKR1C3	1.78 1.44	0.017 0.013		.
ALCAM			1.44	0.022
ANLN	1.37	0.046	1.81	<001
APOE AQP2	1.49	0-023	1.66 130	0.005 0.013
ARHGDIB	1.55	0.021
ASPN	2.13	<001	2.43	<001
ATP5E BGN	1.69 1.92	0.013 <001	1.58 2.55	0.014 <001
BIRC5	1.48	0.006	1.89	<001
BMP6	1.51	0.010	1.96	<001
BRCA2			1.41	0.007
BUB1	1.36	0.007	1.52	<001
CCNE2	1.55	0.004	1.59	<001
CD276			1.65	<001
CDC20	1.68	<001	1.74	<001
CDH11			1.50	0.017
CDH18	1.36	<001

2017268510 28 Nov 2017

Table 8A	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
CDH7	1.54	0.009	1.46	0.026
CDKN2B CDKN2C	1.68 2.()1	0.008 <001	1.93 1.77	0.001 <001
CDKN3	1.51	0.002	1.33	0.049
CENPF	1.51	0.007	2.04	<001
CKS2	1.43	0.034	1.56	0.007
COL1A1	2.23	<001	3.04	<001
COL1A2	1.79	0.001	2.22	<001
COL3A1	1.96	<.001	2.81	<001
COL4A1		1.52	0.020
COL5A1			1.50	0.020
COL5A2	1.64	0.017	1.55	0.010
COL8A1	1.96	<.001	2.38	<001
CRISP3	1.68	0.002	1.67	0.002
CTHRC1			2.06	<001
CTNND2	1.42	0.046	1.50	0.025
CTSK CXCR4	1.82	0.001	L43_ 1.64	j0.049^ 0'007
DDIT4	1.54	0.016	1.58	0.009
DLL4 DYNLL1	1.50	0.021	1.51 1.22	0.007 0.002
F2R	2.27	<001	2.02	<001
FAP			2.12	<001
FCGR3A FGF5	1.23	0Ό47	1.94	0.002
FOXP3	1.52	0.006	1.48	0.018
GNPTAB			1.44	0.042
GPR68 GREM1	1.91	<001	1.51 2.38	0.011 <obf
HDAC1			1.43	0.048
HDAC9	1.65	<001	1.67	0.004
HRAS	1.65	0.005	1.58	0.021
IGFBP3	1.94	<001	1.85	<001
INHBA	2.03	<001	2.64	<001
JAG1	1.41	0.027	1.50	0.008
KCTD12			1.51	0.017
KHDRBS3	1.48	0.029	1.54	0.014
KPNA2			1.46	0.050
LAMA3 LAMC1	1.35 1.77	0.040 0.012		.
LTBP2			1.82	<001
LUM	1.51	0.021	1.53	0.009
MELK MKI67	J.38	0.020	1.49 1.37	o.oojl 0.014

2017268510 28 Nov 2017

Table 8A	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
MMPI1	1.73	<001	1.69	<001
MRPL13 MYBL2’	1.56	<001	1.30 1.72	0.046 <6of
MYLK3			1.17	0.007
NOX4	1.58	0.005	1.96	<001
NRIP3			1.30	0.040
NRP1			1.53	0.021
OLFML2B			1.54	0.024
OSM	1.43	0.018
PATE1	1.20	<.001	1.33	<001
PCNA			1.64	0.003
PEX10	1.41	0.041	1.64	0.003
PIK3CA	1.38	0.037
PLK1	1.52	0.009	1.67	0.002
PLOD2			1.65	0.002
POSTN	1.79	<001	2.06	<001
PTK6_ PTTG1	1.67 1.56	0.002 0.002	2.38_ 1.54	^.001 03.)03
RAD21	1.61	0.036	1.53	0.005
RAD51 RALA	1.95	0.004	1.33 1.60	0.009 0.007
REG4			1.43	0.042
ROBO2	1.46	0.024
RRM1 RRM2	L50	0Ό03	1.44 1.48	0.033 <001
SAT1	1.42	0.009	1.43	0.012
SEC14L1			1.64	0.002
SFRP4 SHMT2	2.07 1.52	<001 0.030	2.40 1.60	<001 δ.οόϊ
SLC44A1			1.42	0.039
SPARC	1.93	<001	2.21	<001
SULF1	1.63	0.006	2.04	<001
THBS2	1.95	<001	2.26	<001
THY1	1.69	0.016	1.95	0.002
TK1			1.43	0.003
TOP2A	1.57	0.002	2.11	<001
TPX2	1.84	<001	2.27	<001
UBE2C	1.41	0.011	1.44	0.006
UBE2T	1.63	o.ooi
UHRF1	1.51	0.007	1.69	<001
WISP1	1.47	0.045
WNT5A	1.35	0.027	1.63	0.001
ZWINT	1.36	0.045

2017268510 28 Nov 2017

Table SB.

Genes significantly (p<0.05) associated with cRFI for TMPRSS2-ERG fusion positive in the primary Gleason pattern or highest Gleason pattern with hazard ratio (HR) <1.0 (increased expression is positively associated with good prognosis)

Table 8B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
AAMP	0.57	0.007	0.58	<001
ABCA5			0.80	0.044
ACE	0.65	0.023	0.55	<001
ACOX2			0.55	<001
ADH5			0.68	0.022
AKA Pl__ AlDill A 2	0.72	0.036	0.81 0.43	0.043 <Ό0Ϊ
ANPEP	0.66	0.022	0.46	<001
APRT ΑΧΪΝ2		.	0.73 0.60	0.040 <δόϊ
AZGP1	0.57	<001	0.65	<001
BCL2			0.69	0.035
BIK	0.71 0.71	0.045	0.71	0.009
BINI	0.004
BTRC	0.66	0.003	0.58	<001
C7			0.64	0.006
CADM1	0.61	<001	0.47	<001
CCL2			0.73	0.042
CCNH	0.69	0.022
CD44	0.56	<001	0.58	<001
CD82			0.72	0.033
CDC25B	0.74	0.028
CDH1	0.75	0.030	0.72	0.010
CDH19 CDK3	..	..	0.56 0.78	0.015 0.045
CDKNIC	0.74	0.045	0.70	0.014
CSFI			0.72	0.037
CTSB			0.69	0.048
CTSL2			0.58	0.005
CYP3A5	0.51	<001	0.30	<001
DHX9	0.89	0.006	0.87	0.012
DLC1 _ DLGAP1	0.69	Ο.ΟΪΟ	0-Q4 0.49	0.023 <.001
DPP4	0.64	<001	0.56	<001
DPT EGR1			0.-63 0.69	0.003 0.035
EGR3			0.68	0.025
EIF2S3			0.70	0.021
EIF5	0.71	0.030
ELK4	0.71	0.041	0.60	0.003

2017268510 28 Nov 2017

Table 8B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
EPHA2	0.72	0.036	0.66	0.011
EPHB4 ERCC1		.	0.65 Cl 68	0.007 0.023
ESR2			0.64	0.027
FAM 107 A	0.64	0.003	0.61	0.003
FAM13C	0.68	0.006	0.55	<001
FGFR2	0.73	0.033	0.59	<001
FKBP5			0.60	0.006
FLNC	0.68	0.024	0.65	0.012
FLT1			0.71	0.027
FOS			0.62	0.006
FOXO1			0.75	0.010
GADD45B			0.68	0.020
GHR			0.62	0.006
GPM6B			0.57	<001
GSTM1	0.68	0.015	0.58	<001
GSTM2 HGD ~	J1.65 0.63	0.005 0.001	0.47 0.71	<oo l 0.020
HK1	0.67	0.003	0.62	0.002
HLF HNF1B	0.66	0.004	0.59 0.61	<001 0.001
IER3			0.70	0.026
IGF1	0.63	0.005	0.55	<001
IGF1R IGFBP2	0.59	0.007	0.76 0.64	0.049 0.003
IL6ST			0.65	0.005
IL8	0.61	0.005	0.66	0.019
ILK 'ING5	0.73'	“0Ό33’	0.64 0.70	0.015 0.009
ITGA7	0.72	0.045	0.69	0.019
1TGB4			0.63	0.002
KLC1			0.74	0.045
KLK1	0.56	0.002	0.49	<001
KLK10			0.68	0.013
KLK11			0.66	0.003
KLK2	0.66	0.045	0.65	0.011
KLK3	0.75	0.048	0.77	0.014
KRT15	0.71	0.017	0.50	<001
KRT5	0.73	0.031	0.54	<001
LAMA5			0.70	0.044
LAMB3	0.70	0.005	0.58	<001
LGALS3			0.69	0.025
LIG3			0.68	0.022
MDK	0.69	0.035

2017268510 28 Nov 2017

Table 8B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
MGMT	0.59	0.017	0.60	<001
MGST1 MICA		.	0.73 0.70	0.042 0Ό09
MPPED2	0.72	0.031	0.54	<001
MTSS1	0.62	0.003
MYBPC1			0.50	<001
NCAPD3	0.62	0.007	0.38	<001
NCOR1			0.82	0.048
NFAT5	0.60	0.001	0.62	<001
NRG1	0.66	0.040	0.61	0.029
NUP62	0.75	0.037
OMD	0.54	<001
PAGE4			0.64	0.005
PCA3			0.66	0.012
PCDHGB7			0.68	0.018
PGR			0.60	0.012
PPAP2B PPP1R12A	^73”	0.031	0.62 0.58	0.010 0.003
PRIM Al			0.65	0.013
PROM! PTCH1	0Λ1 0.64	0.013 0.006
PTEN			0.75	0.047
PTGS2			0.67	0.011
PTK2B PTPN1			0.66 0.71	0.005 0.026
RAGE	0.70	0.012
RARB			0.68	0.016
RGS10 RHOB			0-84 0.66	0.034 0.016
RND3			0.63	0.004
SDHC	0.73	0.044	0.69	0.016
SERPINA3	0.67	0.011	0.51	<001
SERPINB5			0.42	<001
SH3RF2	0.66	0.012	0.51	<001
SLC22A3	0.59	0.003	0.48	<001
SMAD4	0.64	0.004	0.49	<001
SMARCC2			0.73	0.042
SMARCD1	0.73	<001	0.76	0.035
SMO	-		0.64	0.006
SNAI1	0.53	0.008
SOD1			0.60	0.003
SRC	0.64	<001	0.61	<001
SRD5A2 STAT3	0.63_	-0.004	0.59 0.64	_<qoi, 07014

2017268510 28 Nov 2017

Table 8B	Primary Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value
STAT5A			0.70	0.032
STAT5B SVIL	0.74	0.034	0.63 0.71	0.003 0.028
TGFB1I1			0.68	0.036
TMPRSS2	0.72	0.015	0.67	<001
TNFRSF10A			0.69	0.010
TNFRSF10B	0.67	0.007	0.64	0.001
TNFRSF18	0.38	0.003
TNFSF10	0.68	0.004	0.71 0.57	0.025 <001
TP53
TP63	0.75	0.049	0.52	<001
TPM2			0.62	0.007
TRAF3IP2	0.71	0.017	0.68	0.005
TRO			0.72	0.033
TUBB2A			0.69	0.038
VCL			0.62	<001
VEGFA WWOX	..		0.71 0.65	0.037 0.004
ZFHX3	0.77	0.011	0.73	0.012
ZFP36 ZNF827	0.68	0.013	0.69 0A9	0.018 <.001
[00142] Tables 9A and 9B provide genes significantly associated (p<0.05), positively or

negatively, with TMPRSS fusion status in the primary Gleason pattern. Increased expression of genes in Table 9A are positively associated with TMPRSS fusion positivity, while increased expression of genes in Table 10A are negatively associated with TMPRSS fusion positivity.

Table 9A.

Genes significantly (p<0.05) associated with TMPRSS fusion status in the primary

Gleason pattern with odds ratio (OR) > 1.0 (increased expression is positively associated with TMPRSS fusion positivity

Table 9A Official Symbol	p-value	Odds Ratio	Official Symbol	p-value	Odds Ratio
ABCC8	<001	1.86	MAP3K5	<001	2.06
ALDH18A1	0.005	1.49	MAP7	<001	2.74
ALKBH3	0.043	1.30	MSH2	0.005	1.59
ALOX5	<001	1.66	MSH3	0.006	1.45
AMPD3	<001	3.92	MUC1	0.012	1.42
APEX1	<001	2.00	MYO6	<001	3.79
ARHGDIB	<001	1.87	NCOR2	0.001	1.62
ASAP2	0.019	1.48	NDRG1	<001	6.77
ATXN1	0.013	1.41	NETO2	<001	2.63
BMPR1B	<001	2.37	ODC1	<001	1.98
CACNA1D	<001	9.01	OR51E1	<001	2.24

2017268510 28 Nov 2017

Table 9A Official Symbol CADPS	p-value 0.015’	Odds Ratio ' L39	Official Symbol PDE9A	p-value ” <ooT’	Odds Ratio ’ ” ’ 2.21
CD276	0.003	2.25	PEX10	<001	3.41
CDH1	0.016	1.37	PGK1	0.022	1.33
CDH7	<.001	2.22	PLA2G7	<001	5.51
CDK7	0.025	1.43	PPP3CA	0.047	1.38
COL9A2	<001	2.58	PSCA	0.013	1.43
CRISP3	<001	2.60	PSMD13	0.004	1.51
CTNND1	0.033	1.48	PTCHI	0.022	1.38
ECE1	<001	2.22	PTK2	0.014	1.38
EIF5	0.023	1.34	PTK6	<001	2.29
EPHB4	0.005	1.51	PTK7	<001	2.45
ERG	<.001	14.5	PTPRK	<001	1.80
FAM 17 IB	0.047	1.32	RAB30	0.001	1.60
FAM73A	0.008	1.45	REG4	0.018	1.58
FASN	0.004	1.50	RELA	0.001	1.62
GNPTAB	<001	1.60	RFX1	0.020	1.43
GPS1	0.006	1.45	RGSI0	<001	1.71
GRB7	0.023	1.38	SCUBE2	0.009	1.48
HDAC1	<001	4.95	SEPT9	<001	3.91
HGD	<001	1.64	SH3RF2	0.004	1.48
HIP1	<001	1.90	SH3YL1	<001	1.87
HNF1B	<001	3.55	SHH	<001	2.45
II SPAS	0.041	1.32	STM2	<001	1.74
IGF1R	0.001	1.73	SIPA1L1	0.021	1.35
ILF3	<001	1.91	SLC22A3	<001	1.63
IMMT	0.025	1.36	SLC44A1	<001	1.65
ITPR1	<001	2.72	SPINT1	0.017	1.39
ITPR3	<001	5.91	TFDP1	0.005	1.75
JAG1	0.007	1.42	TMPRSS2ERGA	0.002	14E5
KCNN2	<001	2.80	TMPRSS2ERGB	<001	1.97
KHDRBS3	<001	2.63	TRIM 14	<001	1.65
KIAA0247	0.019	1.38	TSTA3	0.018	1.38
KLK11	<001	1.98	UAP1	0.046	1.39
LAMC1	0.008	1.56	UBE2G1	0.001	1.66
LAMC2	<001	3.30	UGDH	<001	2.22
LOX	0.009	1.41	XRCC5	<001	1.66
LRP1	0.044	1.30	ZMYND8	<001	2.19

Tabic 9B.

Genes significantly (p<0.05) associated with TMPRSS fusion status in the primary

Gleason pattern with odds ratio (OR) <1.0 (increased expression is negatively associated with TMPRSS fusion positivity)

2017268510 28 Nov 2017

Table 9B Officials ymbol ABCC4	p-valuc 0Ό45	OddsRatio Ο.77' '
ABHD2	<001	0.38
ACTR2	0.027	0.73
ADAMTS1	0.024	0.58
ADH5	<001	0.58
AGTR2	0.016	0.64
AKAP1	0.013	0.70
AKT2	0.015	0.71
ALCAM	<001	0.45
ALDH1A2	0.004	0.70
ANPEP	<001	0.43
ANXA2	0.010	0.71
APC	0.036	0.73
APOC1	0.002	0.56
APOE	<001	0.44
ARF1	0.041	0.77
ATM	0.036	0.74
AURKB	<001	0.62
AZGP1	<001	0.54
BBC3	0.030	0.74
BCL2	0.012	0.70
BINI	0.021	0.74
BTG1	0.004	0.67
BTG3	0.003	0.63
C7	0.023	0.74
CADM1	0.007	0.69
CAS Pl	0.011	0.70
CAVI	0.0 II	0.71
CCND1	0.019	0.72
CCR1	0.022	0.73
CD44	<001	0.57
CD68	<001	0.54
CD82	0.002	0.66
CDH5	0.007	0.66
CDKN1A	<001	0.60
CDKN2B	<001	0.54
CDKN2C	0.012	0.72
CDKN3	0.037	0.77
CHNI	0.038	0.75
CKS2	<001	0.48

2017268510 28 Nov 2017

Table 9B Official Symbol	p-value	Odds Ratio
COL11A1	0.017	0.72
COL1A1	<001	0.59
COL1A2	0.001	0.62
COL3A1	0.027	0.73
COL4A1	0.043	0.76
COL5A1	0.039	0.74
COL5A2	0.026	0.73
COL6A1	0.008	0.66
COL6A3	<001	0.59
COL8A1	0.022	0.74
CSF1	0.011	0.70
CTNNB1	0.021	0.69
CTSB	<001	0.62
CTSD	0.036	0.68
CTSK	0.007	0.70
CTSS	0.002	0.64
CXCL12	<001	0.48
CXCR4	0.005	0.68
CXCR7	0.046	0.76
CYR61	0.004	0.65
DAP	0.002	0.64
DARC	0.021	0.73
DDR2	0.021	0.73
DHRS9	<001	0.52
DIAPH1	<001	0.56
DICER1	0.029	0.75
DLC1	0.013	0.72
DLGAP1	<001	0.60
DLL4	<001	0.57
DPT	0.006	0.68
DUSP1	0.012	0.68
DUSP6	0.001	0.62
DVL1	0.037	0.75
EFNB2	<001	0.32
EGR1	0.003	0.65
ELK4	<001	0.60
ERBB2	<001	0.61
ERBB3	0.045	0.76
ESR2	0.010	0.70
ETV1	0.042	0.74
FABP5	<001	0.21
FAM13C	0.006	0.67
FCGR3A	0.018	0.72
FGF17	0.009	0.71

2017268510 28 Nov 2017

Table 9B Official Symbol	p-value	Odds Ratio
FGF6	0.011	0.70
FGF7	0.003	0.63
FNI	0.006	0.69
FOS	0.035	0.74
FOXP3	0.010	0.71
GABRG2	0.029	0.74
GADD45B	0.003	0.63
GDF15	<001	0.54
GPM6B	0.004	0.67
GPNMB	0.001	0.62
GSN	0.009	0.69
HLA-G	0.050	0.74
HLF	0.018	0.74
HPS1	<001	0.48
HSD17B3	0.003	0.60
HSD17B4	<001	0.56
HSPBI	<001	0.38
HSPB2	0.002	0.62
ΠΤ30	0.049	0.75
IFNG	0.006	0.64
1GF1	0.016	0.73
IGF2	0.001	0.57
IGFBP2	<001	0.51
1GFBP3	<001	0.59
1GFBP6	<001	0.57
IL10	<001	0.62
IL17A	0.012	0.63
ILIA	0.011	0.59
IL2	0.001	0.63
IL6ST	<001	0.52
INSL4	0.014	0.71
ITGA1	0.009	0.69
ITGA4	0.007	0.68
JUN	<001	0.59
KIT	<001	0.64
KRT76	0.016	0.70
LAG3	0.002	0.63
LAPTM5	<001	0.58
LGALS3	<001	0.53
LTBP2	0.011	0.71
LUM	0.012	0.70
MAOA	0.020	0.73
MAP4K4	0.007	0.68
MGST1	<001	0.54

2017268510 28 Nov 2017

Table 9B Official Symbol	p-value <001	Odds Ratio 0.61
MMP2
MPPED2	<001	0.45
MRC1	0.005	0.67
MTPN	0.002	0.56
MTSS1	<001	0.53
MVP	0.009	0.72
MYBPC1	<001	0.51
MYLK3	0.001	0.58
NCAM1	<001	0.59
NCAPD3	<001	0.40
NCOR1	0.004	0.69
NFKBIA	<001	0.63
NNMT	0.006	0.66
NPBWR1	0.027	0.67
OAZ1	0.049	0.64
OLFML3	<001	0.56
OSM	<001	0.64
PAGE1	0.012	0.52
PDGFRB	0.016	0.73
PECAM1	<001	0.55
PGR	0.048	0.77
PIK3CA	<001	0.55
PIK3CG	0.008	0.71
PLAU	0.044	0.76
PLK1	0.006	0.68
PLOD2	0.013	0.71
PLP2	0.024	0.73
PNLIPRP2	0.009	0.70
PPAP2B	<001	0.62
PRKAR2B	<001	0.61
PRKCB	0.044	0.76
PROS1	0.005	0.67
PTEN	<001	0.47
PTGER3	0.007	0.69
PTH1R	0.011	0.70
PTK2B	<001	0.61
PTPN1	0.028	0.73
RAB27A	<001	0.21
RAD51	<001	0.51
RAD9A	0.030	0.75
RARB	<001	0.62
RASSF1	0.038	0.76
RECK	0.009	0.62
RHOB	0.004	0.64

2017268510 28 Nov 2017

Table 9B Official Symbol	p-value <001	Odds Ratio 0.56
RHOC
RLN1	<001	0.30
RND3	0.014	0.72
SI OOP	0.002	0.66
SDC2	<001	0.61
SEMA3A	0.001	0.64
SMAD4	<001	0.64
SPARC	<001	0.59
SPARCL1	<001	0.56
SPINK 1	<001	0.26
SRD5A1	0.039	0.76
STAT1	0.026	0.74
STS	0.006	0.64
SULF1	<001	0.53
TFF3	<001	0.19
TGFA	0.002	0.65
TGFB1I1	0.040	0.77
TGFB2	0.003	0.66
TGFB3	<001	0.54
TGFBR2	<001	0.61
THY1	<001	0.63
TIMP2	0.004	0.66
ΊΊΜΡ3	<001	0.60
TMPRSS2	<001	0.40
TNFSF11	0.026	0.63
TPD52	0.002	0.64
TRAM1	<001	0.45
TRPC6	0.002	0.64
TUBB2A	<001	0.49
VCL	<001	0.57
VEGFB	0.033	0.73
VEGFC	<001	0.61
VIM	0.012	0.69
WISP1	0.030	0.75
WNT5A	<001	0.50

A molecular field effect was investigated, and determined that the expression [00143] levels of histologically normal-appearing cells adjacent to the tumor exhibited a molecular signature of prostate cancer. Tables 10A and 10B provide genes significantly associated (p<0.05), positively or negatively, with cRFI or bRFI in non-tumor samples. Table 10A is negatively associated with good prognosis, while increased expression of genes in Table Ι 0Β is positively associated with good prognosis.

Table 10A

Genes significantly (p<0.05) associated with cRFI or bRFI in Non-Tumor Samples with hazard ratio (HR) >1.0 (increased expression is negatively associated with good prognosis)

2017268510 28 Nov 2017

Table 10A	cRFI	bRFI
Official Symbol	HR	p-value	HR	p-value
ALCAM			1.278	0.036
ASPN BAG5	1.309 1.458	0.032 0.004
BRCA2	1.385	<001
CACNA1D			1.329	0.035
„CD164 CDKN2B	1.398	0.014	1.339	0.020
COL3A1	1.300	0.035
COL4A1 CTNND2	1.358	0.019	1.370	OOOl
DARC	1.451	0.003
DICER 1			1.345	<001
DPP4 EFNB2			1.358 1.323	0.008 0.007
FASN			1.327	0.035
GHR			1.332	0.048
HSPA5			1.260	0.048
INHBA	1.558	<001
KCNN2			1.264	0.045
KRT76			1.115	<001
LAMC1	1.390	0.014
LAMC2			1.216	0.042
LIG3			1.313	0.030
MAOA MCM6	1.307	0.036	1.405	0.013
MKI67	1.271	0.008
NEK2			1.312	0.016
NPBWRI	1.278	0.035
ODC1			1.320	0.010
PEX10			1.361	0.014
PGK1	1.488	0.004
PLA2G7 POSIN'	1.306	0.043	1.337	0.025
PTK6			1.344	0.005
REG4 RGS7			1.348 1.144	0.009 0.047
SFRP4	1.394	0.009
TARP			1.412	0.011
TFF1			1.346	0.010
TGFBR2	1.310	0.035

2017268510 28 Nov 2017

Table 10A	cRFI	bRFI
Official Symbol	HR	p-value	HR	p-value
THYI	1.300	0.038
TMPRSS 2ERGA TPD52			1.333 1.374	<001 0.015
TRPC6	1.272	0.046
UBE2C	1.323	0.007
UHRF1	1.325	0.021

Table 10B

Genes significantly (p<0.05) associated with cRFI or bRFI in Non-Tumor Samples with hazard ratio (HR) < 1,0 (increased expression is positively associated with good prognosis)

Table 10B	cRFI	bB	.11
Official Symbol	HR	p-value	HR	p-value
ABCA5	0.807	0.028
ABCC3	0.760	0.019	0.750	0.003
ABHD2	0.781	0.028
ADAM15	0.718	0.005
AKAP1	0.740	0.009
AMPD3			0.793	0.013
ANGPT2			0.752	0.027
ANXA2 A PC	0/755	adiT	0.776	0,035
APRT	0.762	0.025
AR	0.752	0.015
ARHGDIB			0.753	<001
BINI	0.738	0.016
CADM1	0.711	0.004
CCNH	0.820	0.041
CCR1			0.749	0.007
CDK14			0.772	0.014
CDK3	0.819	0.044
CDKN1C______ CHAF1A	0.808 0.634	0.038 0.002	0*779 ”	~ 0.045
CHN1			0.803	0.034
CHRAC1	0.751	0.014	0.779	0.021
COL5A1 COL5A2	-		0.736 0/762~	0.012 0.013 '
COLA Al			0.757	0.032
COL6A3			0.757	0.019
CSK	0.663	<001	0.698	<001
CTSK			0.782	0.029
CXCL12			0.771	0.037
CXCR7			0.753	0.008

2017268510 28 Nov 2017

Table 10B	cRFI	bRFI
Official Symbol	HR	p-value	HR	p-value
CYP3A5	0.790	0.035
DDIT4 DIAPH1			0.725 0.771	0.017 OX) 15
DLC1	0.744	0.004	0.807	0.015
DLGAP1	0.708	0.004
DUSP1	0.740	0.034
EDN1			0.742	0.010
EGR1	0.731	0.028
EIF3H	0.761	0.024
EIF4E	0.786	0.041
ERBB2	0.664	0.001
ERBB4	0.764	0.036
ERCC1	0.804	0.041
ESR2			0.757	0.025
EZH2			0.798	0.048
FAAH	0.798	0.042
FAM13C FA.VU71B	0.764	0.012	0.755	0.005
FAM49B			0.811	0.043
FAM73A F AS LG	0.778	0.015	0.757	0.041
FGFR2	0.735	0.016
FOS	0.690	0.008
FYN GPNMB	0.788	0.035	0.777 0.762	0.011 0.011
GSK3B	0.792	0.038
HGD	0.774	0.017
U1R1P3 HSP90ABT	°J02_ 0.753	0.033 0.013
HSPBI	0.764	0.021
HSPE1	0.668	0.001
IFI30			0.732	0.002
IGF2			0.747	0.006
IGFBP5			0.691	0.006
IL6ST			0.748	0.010
IL8			0.785	0.028
IMMT			0.708	<001
ITGA6	0.747	0.008
ITGAV ITGB3			0792 0.814	0.016 0.034
ITPR3	0.769	0.009
JUN	0.655	0.005
KHDRBS3 KLF6~'	0.714	<001	0.764	0.012

2017268510 28 Nov 2017

Table 10B	cRFI	bRFI
Official Symbol	HR	p-value	HR	p-value
KLK2	0.813	0.048
LAMA4 LAMA5	0.744	0.011	0.702	0.009
LAPTM5			0.740	0.009
LGALS3	0.773	0.036	0.788	0.024
LIMSI			0.807	0.012
MAP3K5			0.815	0.034
MAP3K7			0.809	0.032
MAP4K4	0.735	0.018	0.761	0.010
MAPKAPK3	0.754	0.014
MICA	0.785	0.019
MT Al			0.808	0.043
MVP__ MYLK3		-	0.691 0.730	__οροί__ 0.039
MYO6	0.780	0.037
NCOA1			0.787	0.040
NCOR1 NDRG1	0.761	<001	0.876	0.020
NFAT5	0.770	0.032
NFKBIA NME2			0.799 6’753	0.018 6.005
NUP62			0.842	0.032
OAZ1			0.803	0.043
OLFML2B OLFML3			0/745 0.743	0.023 0.009
OSM			0.726	0.018
PCA3	0.714	0.019
PEC AMI PIK3C2A	.		0.774 0.768	0.023 o.odi
PIM1	0.725	0.011
PLOD2			0.713	0.008
PPP3CA	0.768	0.040
PROMI			0.482	<.001
PTEN			0.807	0.012
PTGS2	0.726	0.011
PTTG1			0.729	0.006
PYCARD			0.783	0.012
RAB30			0.730	0.002
RAGE RFX1	0.792 0.789	0.012 0Ό16	0.792	0.010
RGS10	0.781	0.017
RUNX1			0.747	0.007
SDHC SEC23A		-	0.827 6.752	0.036 0.0 io

2017268510 28 Nov 2017

Table 10B	cRFI	bRFI
Official Symbol	HR	p-value	HR	p-value
SEPT9			0.889	0.006
SERPINA3 SLC25A21		-	0.738 0.788	0.013 0.045
SMARCD1	0.788	0.010	0.733	0.007
SMO	0.813	0.035
SRC	0.758	0.026
SRD5A2			0.738	0.005
ST5			0.767	0.022
STAT5A TGFB2	0.771	0Ό27	0.784	0.039
TGFB3			0.752	0.036
THBS2			0.751	0.015
TNFRSF10B	0.739	0.010
TPX2			0.754	0.023
TRAF3IP2			0.774	0.015
TRAM1	0.868	<001	0.880	<001
TRIM 14 TUBB2A	0.785- 0705	0.047 0ΌΪδ
TYMP			0.778	0.024
UAP1 UTP23	0.721 0.763	0.013 0.007	0.826	0.018
VCL			0.837	0.040
VEGF A	0.755	0.009
WDR19 YBXl	0.724	0.005	0.786	0.027
ZFP36	0.744	0.032
ZNF827	0.770	0.043

[00144] Table 11 provides genes that are significantly associated (p<0.05) with cRFI or bRFI after adjustment for Gleason pattern or highest Gleason pattern.

Table 11

Genes significantly (p<0.05) associated with cRFI or bRFI after adjustment for Gleason pattern in the primary Gleason pattern or highest Gleason pattern Some HR <= 1.0 and some HR > 1.0

Table 11	cRFI	bRFI	bRFI
	Highest Pattern	Primary	Pattern	Highest Pattern
Official Symbol	HR	p-value	HR	p-value	HR	p-value
HSPA5	0.710	0,009	1.288	0.030
ODC1	0.741	0.026	1.343	0.004	1.261	0.046

[00145] Tables 12A and 12B provide genes that are significantly associated (p<0.05) with prostate cancer specific survival (PCSS) in the primary Gleason pattern. Increased expression of genes in Table 12A is negatively associated with good prognosis, while increased expression of genes in Table 12B is positively associated with good prognosis.

Table 12A

Genes significantly (p<0.05) associated with prostate cancer specific survival (PCSS)

2017268510 28 Nov 2017 in the Primary Gleason Pattern HR > 1.0 (Increased expression is negatively associated with good prognosis)____________________________

Table 12A Official Symbol	HR	p-value	Official Symbol	HR	p-value
AKR1C3	1.476	0.016	GREM1	1.942	<001
ANLN	1.517	0.006	IFI30	1.482	0.048
APOC1	1.285	0.016	IGFBP3	1.513	0.027
APOE	1.490	0.024	INHBA	3.060	<001
ASPN	3.055	<001	KIF4A	1.355	0.001
ATP5E	1.788	0.012	KLK14	1.187	0.004
AURKB	1.439	0.008	LAPTM5	1.613	0.006
BGN	2.640	<001	LTBP2	2.018	<001
BIRC5	1.611	<001	MMP11	1.869	<001
BMP6	1.490	0.021	MYBL2	1.737	0.013
BRCA1	1.418	0.036	NEK2	1.445	0.028
CCNB1	1.497	0.021	NOX4	2.049	<001
CD276	1,668	0.005	OLFML2B	1.497	0.023
CDC20	1.730	<001	PLK1	1.603	0.006
CDH11	1.565	0.017	POSTN	2.585	<.001
CDH7	1.553	0.007	PPFIA3	1.502	0.012
CDKN2B	1.751	0.003	PTK6	1.527	0.009
CDKN2C	1.993	0.013	PTTG1	1.382	0.029
CDKN3	1.404	0.008	RAD51	1.304	0.031
CENPF	2.031	<001	RGS7	1.251	<001
CHAF1A	1.376	0.011	RRM2	1.515	<001
CKS2	1.499	0.031	SAT1	1.607	0.004
COL1A1	2.574	<001	SDC1	1.710	0.007
COL1A2	1.607	0.0 IT	SESN3	1.399	0.045
COL3A1	2.382	<001	SFRP4	2.384	<001
COL4A1	1.970	<001	SHMT2	1.949	0.003
COL5A2	1.938	0.002	SPARC	2.249	<001
COL8A1	2.245	<001	STMN1	1.748	0.021
CTHRC1	2.085	<001	SULF1	1.803	0.004
CXCR4	1.783	0.007	THBS2	2.576	<001
DDIT4	1.535	0.030	THY1	1.908	0.001
DYNLL1	1.719	0.001	TK1	1.394	0.004
F2R	2.169	<001	TOP2A	2.119	<001
FAM171B	1.430	0.044	TPX2	2.074	0.042
FAP	1.993	0.002	UBE2C	1.598	<001

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Table 12A Official Symbol	HR	p-value	Official Symbol	HR	p-value
FCGR3A	2.099	<001	UGT2B15	1.363	0.016
FN1	1.537	0.024	UHRF1	1.642	0.001
GPR68	1.520	0.018	ZW1NT	1.570	0.010

Table 12B

Genes significantly (p<0.05) associated with prostate cancer specific survival (PCSS) in the Primary Gleason Pattern HR < 1.0 (Increased expression is positively associated with good prognosis)

Table 12B Official Symbol	HR	p-value	Official Symbol	HR	p-value
AAMP	0.649	0.040	IGFBP6	0.578	0.003
ABCA5	0.777	0.015	IL2	0.528	0.010
ABCG2	0.715	0.037	IL6ST	0.574	<001
AC0X2	0.673	0.016	IL8	0.540	0.001
ADH5	0.522	<001	ING5	0.688	0.015
ALDH1A2	0.561	<001	ITGA6	0.710	0.005
AMACR	0.693	0.029	ITGA7	0.676	0.033
AMPD3	0.750	0.049	JUN	0.506	0.001
ANPEP	0.531	<001	KIT	0.628	0.047
ATXN1	0.640	0.011	KLK1	0.523	0.002
AXIN2	0.657	0.002	KLK2	0.581	<001
AZGP1	0.617	<001	KLK3	0.676	<001
BDKRB1	0.553	0.032	KRT15	0.684	0.005
BINI	0.658	<001	KRT18	0.536	<001
BTRC	0.716	0.011	KRT5	0.673	0.004
C7	0.531	<001	KRT8	0.613	0.006
CADMI	0.646	0.015	LAMB3	0.740	0.027
CASP7	0.538	0.029	LGALS3	0.678	0.007
CCNH	0.674	0.001	MGST1	0.640	0.002
CD 164	0.606	<001	MPPED2	0.629	<001
CD44	0.687	0.016	MTSS1	0.705	0.041
CDK3	0.733	0.039	MY BPCI	0.534	<001
CHN1	0.653	0.014	NCAPD3	0.519	<001
C0L6A1	0.681	0.015	NFAT5	0.536	<001
CSF1	0.675	0.019	NRG1	0.467	0.007
CSRP1	0.711	0.007	OLFML3	0.646	0.001
CXCL12	0.650	0.015	OMD	0.630	0.006
CYP3A5	0.507	<001	OR51E2	0.762	0.017
CYR61	0.569	0.007	PAGE4	0.518	<001
DLGAP1	0.654	0.004	PCA3	0.581	<001
DNM3	0.692	0.010	PGF	0.705	0.038
DPP4	0.544	<001	PPAP2B	0.568	<001
DPT	0.543	<001	PPP1R12A	0.694	0.017
DUSP1	0.660	0.050	PRIM Al	0.678	0.014

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Table 12B Official Symbol	HR	p-value	Official Symbol	HR	p-value
DUSP6	0.699	0.033	PRKCA	0.632	0.001
EGR1	0.490	<001	PRKCB	0.692	0.028
EGR3	0.561	<001	PROMI	0.393	0.017
EIF5	0.720	0.035	PTEN	0.689	0.002
ERBB3	0.739	0.042	PTGS2	0.611	0.004
FAAH	0.636	0.010	PTH1R	0.629	0.031
FAM 107 A	0.541	<001	RAB27A	0.721	0.046
FAM13C	0.526	<001	RND3	0.678	0.029
FAS	0.689	0.030	RNF114	0.714	0.035
FGF10	0.657	0.024	SDHC	0.590	<001
FKBP5	0.699	0.040	SERPINA3	0.710	0.050
FLNC	0.742	0.036	SH3RF2	0.570	0.005
FOS	0.556	0.005	SLC22A3	0.517	<001
FOXQ1	0.666	0.007	SMAD4	0.528	<001
GADD45B	0.554	0.002	SMO	0.751	0.026
GDF15	0.659	0.009	SRC	0.667	0.004
GHR	0.683	0.027	SRD5A2	0.488	<001
GPM6B	0.666	0.005	STAT5B	0.700	0.040
GSN	0.646	0.006	SVIL	0.694	0.024
GSTM1	0.672	0.006	TFF3	0.701	0.045
GSTM2	0.514	<001	TGFB1I1	0.670	0.029
HGD	0.771	0.039	TGFB2	0.646	0.010
HIRIP3	0.730	0.013	TNFRSF10B	0.685	0.014
HK1	0.778	0.048	TNFSF10	0.532	<001
HLF	0.581	<001	TPM2	0.623	0.005
HNF1B	0.643	0.013	TRO	0.767	0.049
HSD17B10	0.742	0.029	TUBB2A	0.613	0.003
IER3	0.717	0.049	VEGFB	0.780	0.034
IGF1	0.612	<001	ZFP36	0.576	0.001
			ZNF827	0.644	0.014

[00146] Analysis of gene expression and upgrading/upstaging was based on univariate ordinal logistic regression models using weighted maximum likelihood estimators for each gene in the gene list (727 test genes and 5 reference genes). P-values were generated using a Wald test of the null hypothesis that the odds ratio (OR) is one. Both unadjusted p-values and the qvalue (smallest FDR at which the hypothesis test in question is rejected) were reported. Unadjusted p-values <0.05 were considered statistically significant. Since two tumor specimens were selected for each patient, this analysis was performed using the 2 specimens from each patient as follows: (1) analysis using the primary Gleason pattern specimen from each patient

101 (Specimens Al and B2 as described in Table 2); and (2) analysis using the highest Gleason pattern specimen from each patient (Specimens A1 and Bl as described in Table 2). 200 genes were found to be significantly associated (p<0.05) with upgrading/upstaging in the primary

Gleason pattern sample (PGP) and 203 genes were found to be significantly associated (p<0.05) with upgrading/upstaging in the highest Gleason pattern sample (HGP).

[00147] Tables 13A and 13B provide genes significantly associated (p<0.05), positively or negatively, with upgrading/upstaging in the primary and/or highest Gleason pattern. Increased expression of genes in Table 13A is positively associated with higher risk of upgrading/upstaging (poor prognosis), while increased expression of genes in Table 13B is negatively associated with risk of upgrading/upstaging (good prognosis).

TABLE 13A

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Genes significantly (p<0.05) associated with upgrading/upstaging in the Primary Gleason Pattern (PGP) and Highest Gleason Pattern (HGP) OR > 1.0 (Increased expression is positively associated with higher risk of upgrading/upstaging (poor prognosis))

Table 13A	PGP	HGP
Gene	OR	p-value	OR	p-value
ALCAM	1.52	0.0179	1.50	0.0184
ANLN	1.36	0.0451
APOE	1.42	0.0278	1.50	0.0140
ASPN	1.60	0.0027	2.06	0.0001
AURKA	1.47	0.0108
AURKB			1.52	0.0070
BAX			1.48	0.0095
BGN	1.58	0.0095	1.73	0.0034
BIRC5	1.38	0.0415	.
BMP6	1.51	0.0091	1.59	0.0071
BUB1	1.38	0.0471	1.59	0.0068
CACNA1D	1.36	0.0474	1.52	0.0078
CASP7		»	1.32	0.0450
CCNE2	1.54	0.0042
CD276			1.44	0.0265
CDC20	1.35	0.0445	1.39	0.0225
CDKN2B		.	1.36	0.0415
CENPF	1.43	0.0172	1.48	0.0102
CLTC	1.59	0.0031	1.57	0.0038

102

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Table 13A	PGP	HGP
Gene	OR	p-value	OR	p-value
COL1A1	1.58	0.0045	1.75	0.0008
COL3A1	1.45	0.0143	1.47	0.0131
COL8A1	1.40	0.0292	1.43	0.0258
CRISP3			1.40	0.0256
CTHRC1			1.56	0.0092
DBN1	1.43	0.0323	1.45	0.0163
DIAPH1	1.51	0.0088	1.58	0.0025
DICER 1	.		1.40	0.0293
DIO2		.	1.49	0.0097
DVL1			1.53	0.0160
F2R	1.46	0.0346	1.63	0.0024
FAP	1.47	0.0136	1.74	0.0005
FCGR3A			1.42	0.0221
HPN			1.36	0.0468
HSD17B4			1.47	0.0151
HSPA8	1.65	0.0060	1.58	0.0074
IL 11	1.50	0.0100	1.48	0.0113
IL1B	1.41	0.0359
INHBA	1.56	0.0064	1.71	0.0042
KHDRBS3	1.43	0.0219	1.59	0.0045
KIF4A			1.50	0.0209
KPNA2	1.40	0.0366		.
KRT2		.	1.37	0.0456
KRT75			1.44	0.0389
MANF		I	1.39	0.0429
MELK	1.74	0.0016	.	.
MKI67	1.35	0.0408
MMP11			1.56	0.0057
NOX4	1.49	0.0105	1.49	0.0138
PLAUR	1.44	0.0185		-
PLK1			1.41	0.0246
PTK6			1.36	0.0391
RAD51	.	.	1.39	0.0300
RAFI			1.58	0.0036
RRM2	1.57	0.0080
SESN3	1.33	0.0465
SFRP4	2.33	<0.0001	2.51	0.0015
SKIL	1.44	0.0288	1.40	0.0368
S0X4	1.50	0.0087	1.59	0.0022

103

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Table 13A	PGP	HGP
Gene	OR	p-value	OR	p-value
SPINK 1	1.52	0.0058	«
SPP1	«		1.42	0.0224
THBS2			1.36	0.0461
TK1			1.38	0.0283
TOP2A	1.85	0.0001	1.66	0.0011
TPD52	1.78	0.0003	1.64	0.0041
TPX2	1.70	0.0010
UBE2G1	1.38	0.0491	.	.
UBE2T	1.37	0.0425	1.46	0.0162
UHRF1			1.43	0.0164
VCPIP1			1.37	0.0458

TABLE 13B

Genes significantly (p<0.05) associated with upgrading/upstaging in the Primary Gleason Pattern (PGP) and Highest Gleason Pattern (HGP) OR < 1.0 (Increased expression is negatively associated with higher risk of upgrading/upstaging (good prognosis))

Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
ABCC3			0.70	0.0216
ABCC8	0.66	0.0121
ABCG2	0.67	0.0208	0.61	0.0071
ACE			0.73	0.0442
ACOX2	0.46	0.0000	0.49	0.0001
ADH5	0.69	0.0284	0.59	0.0047
AIG1			0.60	0,0045
AKR1C1			0.66	0,0095
ALDH1A2	0.36	<0.0001	0.36	<0.0001
ALKBH3	0.70	0.0281	0.61	0.0056
ANPEP	.	«	0.68	0.0109
ANXA2	0.73	0.0411	0.66	0.0080
APC			0.68	0.0223
ATXN1	.	,	0.70	0.0188
AXIN2	0.60	0.0072	0.68	0.0204
AZGP1	0.66	0.0089	0.57	0.0028
BCL2	.	.	0.71	0.0182
BINI	0.55	0.0005
BTRC	0.69	0.0397	0.70	0.0251
C7	0.53	0.0002	0.51	<0.0001

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Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
CADM1	0.57	0.0012	0.60	0.0032
CASP1	0.64	0.0035	0.72	0.0210
CAV1	0.64	0.0097	0.59	0.0032
CAV2			0.58	0.0107
CD164			0.69	0.0260
CD82	0.67	0.0157	0.69	0.0167
CDH1	0.61	0.0012	0.70	0.0210
CDK14	0.70	0.0354	.	.
CDK3	.		0.72	0.0267
CDKN1C	0.61	0.0036	0.56	0.0003
CHN1	0.71	0.0214
COL6A1	0.62	0.0125	0.60	0.0050
COL6A3	0.65	0.0080	0.68	0.0181
CSRP1	0.43	0.0001	0.40	0.0002
CTSB	0.66	0.0042	0.67	0.0051
CTSD	0.64	0.0355
CTSK	0.69	0.0171
CTSL1	0.72	0.0402
CUL1	0.61	0.0024	0.70	0.0120
CXCL12	0.69	0.0287	0.63	0.0053
CYP3A5	0.68	0.0099	0.62	0.0026
DDR2	0,68	0.0324	0.62	0.0050
DES	0.54	0.0013	0.46	0,0002
DHX9	0.67	0.0164
DLGAP1			0.66	0.0086
DPP4	0.69	0.0438	0.69	0.0132
DPT	0.59	0.0034	0.51	0.0005
DUSP1			0.67	0.0214
EDN1	.		0.66	0.0073
EDNRA	0.66	0.0148	0.54	0.0005
EIF2C2			0.65	0.0087
ELK4	0.55	0.0003	0.58	0.0013
ENPP2	0.65	0.0128	0.59	0.0007
EPHA3	0.71	0.0397	0.73	0.0455
EPHB2	0.60	0.0014
EPHB4	0.73	0.0418		.
EPHX3			0.71	0.0419
ERCC1	0.71	0.0325
FAM 107 A	0.56	0.0008	0.55	0.0011

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Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
FAM13C	0.68	0.0276	0.55	0.0001
FAS	0.72	0.0404	.	.
FBN1	0.72	0.0395
FBXW7	0.69	0.0417
FGF10	0.59	0.0024	0.51	0.0001
FGF7	0.51	0.0002	0.56	0.0007
FGFR2	0.54	0.0004	0.47	<0.0001
FLNA	0.58	0.0036	0.50	0.0002
FLNC	0.45	0.0001	0.40	<0.0001
FLT4	0.61	0.0045
FOXO1	0.55	0.0005	0.53	0.0005
FOXP3	0.71	0.0275	0.72	0.0354
GHR	0.59	0.0074	0.53	0.0001
GNRH1	0.72	0.0386
GPM6B	0.59	0.0024	0.52	0.0002
GSN	0.65	0.0107	0.65	0.0098
GSTM1	0.44	<0.0001	0.43	<0.0001
GSTM2	0.42	<0.0001	0.39	<0.0001
ULF	0.46	<0.0001	0.47	0.0001
HPS1	0.64	0.0069	0.69	0.0134
HSPA5	0.68	0.0113
HSPB2	0.61	0.0061	0.55	0.0004
HSPG2	0.70	0.0359
ID3			0.70	0.0245
IGF1	0.45	<0.0001	0.50	0.0005
IGF2	0.67	0.0200	0.68	0.0152
IGFBP2	0.59	0.0017	0.69	0.0250
IGFBP6	0.49	<0.0001	0.64	0.0092
IL6ST	0.56	0.0009	0.60	0.0012
ILK	0.51	0.0010	0.49	0.0004
ITGA1	0.58	0.0020	0.58	0.0016
ITGA3	0.71	0.0286	0.70	0.0221
ITGA5	.	.	0.69	0.0183
ITGA7	0.56	0.0035	0.42	<0.0001
ITGB1	0.63	0.0095	0.68	0.0267
ITGB3	0.62	0.0043	0.62	0,0040
ITPR1	0.62	0.0032
JUN	0.73	0.0490	0.68	0.0152
KIT	0.55	0.0003	0.57	0.0005

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Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
KLC1	.	«	0.70	0.0248
KLK1	.	«	0.60	0.0059
KRT15	0.58	0.0009	0.45	<0.0001
KRT5	0.70	0.0262	0.59	0.0008
LAMA4	0.56	0.0359	0.68	0.0498
LAMB3			0.60	0.0017
LGALS3	0.58	0.0007	0.56	0.0012
LRP1	0.69	0.0176	.	.
MAP3K7	0.70	0.0233	0.73	0.0392
MCM3	0.72	0.0320
MMP2	0.66	0.0045	0.60	0.0009
MMP7	0.61	0.0015	0.65	0.0032
MMP9	0.64	0.0057	0.72	0.0399
MPPED2	0.72	0.0392	0.63	0.0042
MT Al			0.68	0.0095
MTSS1	0.58	0.0007	0.71	0.0442
MVP	0.57	0.0003	0.70	0.0152
MYBPC1			0.70	0.0359
NCAM1	0.63	0.0104	0.64	0.0080
NCAPD3	0.67	0.0145	0.64	0.0128
NEXN	0.54	0.0004	0.55	0.0003
NFAT5	0.72	0.0320	0.70	0.0177
NUDT6	0.66	0.0102
OLFML3	0.56	0.0035	0.51	0.0011
OMD	0.61	0.0011	0.73	0.0357
PAGE4	0.42	<0.0001	0.36	<0.0001
PAK6	0.72	0.0335
PCDHGB7	0.70	0.0262	0.55	0.0004
PGF	0.72	0.0358	0.71	0.0270
PLP2	0.66	0.0088	0.63	0.0041
PPAP2B	0.44	<0.0001	0.50	0.0001
PPP1R12A	0.45	0.0001	0.40	<0.0001
PR IM Al	.	.	0.63	0.0102
PRKAR2B	0.71	0.0226
PRKCA	0.34	<0.0001	0.42	<0.0001
PRKCB	0.66	0.0120	0.49	<0,0001
PROMI	0.61	0.0030
PTEN	0.59	0.0008	0.55	0.0001
PTGER3	0.67	0.0293

107

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Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
PTH1R	0.69	0.0259	0.71	0.0327
PTK2	0.75	0.0461	.	.
PTK2B	0.70	0.0244	0.74	0.0388
PYCARD	0.73	0.0339	0.67	0.0100
RAD9A	0.64	0.0124
RARB	0.67	0.0088	0.65	0.0116
RGS10	0.70	0.0219
RHOB		.	0.72	0.0475
RND3	.		0.67	0.0231
SDHC	0.72	0.0443
SEC23A	0.66	0.0101	0.53	0.0003
SEMA3A	0.51	0.0001	0.69	0.0222
SH3RF2	0.55	0.0002	0.54	0.0002
SLC22A3	0.48	0.0001	0.50	0.0058
SMAD4	0.49	0.0001	0.50	0.0003
SMARCC2	0.59	0.0028	0.65	0.0052
SMO	0.60	0.0048	0.52	<0.0001
SORBS1	0.56	0.0024	0.48	0.0002
SPARCL1	0.43	0.0001	0.50	0.0001
SRD5A2	0.26	<0.0001	0.31	<0.0001
ST5	0.63	0.0103	0.52	0.0006
STAT5A	0,60	0.0015	0.61	0.0037
STAT5B	0.54	0.0005	0.57	0,0008
SUMO1	0.65	0.0066	0.66	0.0320
SV1L	0.52	0.0067	0.46	0.0003
TGFB1I1	0.44	0.0001	0.43	0.0000
TGFB2	0.55	0.0007	0.58	0.0016
TGFB3	0.57	0.0010	0.53	0.0005
TIMP1	0.72	0.0224	.	.
TIMP2	0.68	0.0198	0.69	0.0206
TIMP3	0.67	0.0105	0.64	0.0065
TMPRSS2			0.72	0.0366
TNFRSF10A	0.71	0.0181
TNFSFIO	0.71	0.0284
TOP2B	0.73	0.0432
TP63	0.62	0.0014	0.50	<0,0001
TPM1	0.54	0.0007	0.52	0.0002
TPM2	0.41	<0.0001	0.40	<0.0001
TPP2	0.65	0.0122

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Table 13B	PGP	HGP
Gene	OR	p-value	OR	p-value
TRA2A	0.72	0.0318		«
TRAF3IP2	0.62	0.0064	0.59	0.0053
TRO	0.57	0.0003	0.51	0.0001
VCL	0.52	0.0005	0.52	0.0004
VIM	0.65	0.0072	0.65	0.0045
WDR19	0.66	0.0097
WFDC1	0.58	0.0023	0.60	0.0026
ZFHX3	0.69	0.0144	0.62	0.0046
ZNF827	0.62	0.0030	0.53	0.0001

Example 3: Identification of MicroRNAs Associated with Clinical Recurrence and Death Due to Prostate Cancer [00148] MicroRNAs function by binding to portions of messenger RNA (mRNA) and changing how frequently the mRNA is translated into protein. They can also influence the turnover of mRNA and thus how long the mRNA remains intact in the cell. Since microRNAs function primarily as an adjunct to mRNA, this study evaluated the joint prognostic value of microRNA expression and gene (mRNA) expression. Since the expression of certain microRNAs may be a surrogate for expression of genes that are not in the assessed panel, we also evaluated the prognostic value of microRNA expression by itself.

Patients and Samples [00149] Samples from the 127 patients with clinical recurrence and 374 patients without clinical recurrence after radical prostatectomy described in Example 2 were used in this study. The final analysis set comprised 416 samples from patients in which both gene expression and microRNA expression were successfully assayed. Of these, 106 patients exhibited clinical recurrence and 310 did not have clinical recurrence. Tissue samples were taken from each prostate sample representing (1) the primary Gleason pattern in the sample, and (2) the highest Gleason pattern in the sample. In addition, a sample of histologically normal-appearing tissue adjacent to the tumor (NAT) was taken. The number of patients in the analysis set for each tissue type and the number of them who experienced clinical recurrence or death due to prostate cancer are shown in Table 14.

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Table 14. Number of Patients and Events in Analysis Set
	Patients	Clinical Recurrences	Deaths Due to Prostate Cancer
Primary Gleason Pattern Tumor Tissue	416	106	36
Highest Gleason Pattern Tumor Tissue	405	102	36
Normal Adjacent	364	81	29
Tissue

Assay Method [00150] Expression of 76 test microRNAs and 5 reference microRNAs were determined from RNA extracted from fixed paraffin-embedded (FPE) tissue. MicroRNA expression in all three tissue type was quantified by reverse transcriptase polymerase chain reaction (RT-PCR) using the crossing point (C_p) obtained from the Taqman® MicroRNA Assay kit (Applied Biosystems, Inc., Carlsbad, CA).

Statistical Analysis [00151] Using univariate proportional hazards regression (Cox DR, Journal of the Royal Statistical Society, Series B 34:187-220, 1972), applying the sampling weights from the cohort sampling design, and using variance estimation based on the Lin and Wei method (Lin and Wei, Journal of the American Statistical Association 84:1074-1078, 1989), microRNA expression, normalized by the average expression for the 5 reference microRNAs hsa-miR- 106a, hsa-miR146b-5p, hsa-miR-191, hsa-miR-19b, and hsa-miR-92a, and reference-normalized gene expression of the 733 genes (including the reference genes) discussed above, were assessed for association with clinical recurrence and death due to prostate cancer. Standardized hazard ratios (the proportional change in the hazard associated with a change of one standard deviation in the co variate value) were calculated.

[00152] This analysis included the following classes of predictors:

[00153] 1. MicroRNAs alone [00154] 2. MicroRNA-gene pairs Tier 1

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[00155]	3. MicroRNA-gene pairs Tier 2
[00156]	4. MicroRNA-gene pairs Tier 3
[001571	5. All other microRNA-gene pairs Tier 4
[001581	The four tiers were pre-determined based on the likelihood (Tier 1 representing

the highest likelihood) that the gene-microRNA pair functionally interacted or that the microRNA was related to prostate cancer based on a review of the literature and existing microarray data sets.

[00159]

False discovery rates (FDR) (Benjamini and Hochberg, Journal of the Royal

Statistical Society, Series B 57:289-300, 1995) were assessed using Efron’s separate class methodology (Efron, Annals of Applied Statistics 2:197-223., 2008). The false discovery rate is the expected proportion of the rejected null hypotheses that are rejected incorrectly (and thus are false discoveries). Efron’s methodology allows separate FDR assessment (q-values) (Storey, Journal of the Royal Statistical Society, Series B 64:479-498, 2002) within each class while utilizing the data from all the classes to improve the accuracy of the calculation. In this analysis, the q-value for a microRNA or microRNA-gene pair can be interpreted as the empirical Bayes probability that the microRNA or microRNA-gene pair identified as being associated with clinical outcome is in fact a false discovery given the data. The separate class approach was applied to a true discovery rate degree of association (TDRDA) analysis (Crager, Statistics in Medicine 29:33-45, 2010) to determine sets of microRNAs or microRNA-gene pairs that have standardized hazard ratio for clinical recurrence or prostate cancer-specific death of at least a specified amount while controlling the FDR at 10%. For each microRNA or microRNA-gene pair, a maximum lower bound (MLB) standardized hazard ratio was computed, showing the highest lower bound for which the microRNA or microRNA-gene pair was included in a TDRDA set with 10% FDR. Also calculated was an estimate of the true standardized hazard ratio corrected for regression to the mean (RM) that occurs in subsequent studies when the best predictors are selected from a long list (Crager, 2010 above). The RM-corrected estimate of the standardized hazard ratio is a reasonable estimate of what could be expected if the selected microRNA or microRNA-gene pair were studied in a separate, subsequent study.

[00160] These analyses were repeated adjusting for clinical and pathology covariates available at the time of patient biopsy; biopsy Gleason score, baseline PSA level, and clinical T111

2017268510 28 Nov 2017 stage (T1-T2A vs. T2B or T2C) to assess whether the microRNAs or microRNA-gene pairs have predicti ve value independent of these clinical and pathology covariates.

Results [00161] The analysis identified 21 microRNAs assayed from primary Gleason pattern tumor tissue that were associated with clinical recurrence of prostate cancer after radical prostatectomy, allowing a false discovery rate of 10% (Table 15). Results were similar for microRNAs assessed from highest Gleason pattern tumor tissue (Table 16), suggesting that the association of microRNA expression with clinical recurrence does not change markedly depending on the location within a tumor tissue sample. No microRNA assayed from normal adjacent tissue was associated with the risk of clinical recurrence at a false discovery rate of 10%. The sequences of the microRNAs listed in Tables 15-21 are shown in Table B.

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Table 15. MicroRNAs Associated with Clinical Recurrence of Prostate Cancer

Primary Gleason Pattern Tumor Tissue

Absolute Standardized Hazard Ratio

MicroRNA	p-value	(/-value3 (FDR)	Direction of Association’’	Uncorrected Estimate	95% Confidence Interval	Max. Lower Bound @10% FDR	RM- Correctcd Estimate”
hsannR93	<0.0001	0.0%	(+)	1.79	(1.38, 2.32)	1.19	1.51
hsa-miR-106b	<0.0001	0.1%	(+)	1.80	(1.38, 2.34)	1.19	1.51
hsa-miR-30e-5p	<0.0001	0.1%	(-)	1.63	(1.30, 2.04)	1.18	1.46
hsa-miR-21	<0.0001	0.1%	(+)	1.66	(1.31,2.09)	1.18	1.46
hsa-miR-133a	<0.0001	0.1%	(-)	1.72	(1.33,2.21)	1.18	1.48
hsa-miR-449a	<0.0001	0.1%	(+)	1.56	(1.26, 1.92)	1.17	1.42
hsa-iniR-30a	0.0001	0.1%	(-)	1.56	(1.25, 1.94)	1.16	1.41
hsa-miR-182	0.0001	0.2%	(+)	1.74	(1.31,2.31)	1.17	1.45
hsa-miR-27a	0.0002	0.2%	(+)	1.65	(1.27,2.14)	1.16	1.43
hsa-niiR-222	0.0006	0.5%	(-)	1.47	(1.18, 1.84)	1.12	1.35
hsa-miR-103	0.0036	2.1%	(+)	1.77	(1.21,2.61)	1.12	1.36
hsa-miR-1	0.0037	2.2%	(-)	1.32	(1.10, 1.60)	1.07	1.26
hsa-miR-145	0.0053	2.9%	(-)	1.34	(1.09, 1.65)	1.07	1.27
hsa-miR-141	0.0060	3.2%	(+)	1.43	(1.11, 1.84)	1.07	1.29
hsa-miR-92a	0.0104	4.8%	(+)	1.32	(1.07, 1.64)	1,05	1.25
hsa-miR-22	0.0204	7.7%	(+)	1.31	(1.03, 1.64)	1.03	1.23
hsa-miR-29b	0.0212	7.9%	(+)	1.36	(1.03, 1.76)	1.03	1.24
hsa-miR-210	0.0223	8.2%	(+)	1.33	(1.03, 1.70)	1.00	1.23
hsa-miR-486-5p	0.0267	9.4%	(-)	1.25	(1.00, 1.53)	1.00	1.20
hsa-miR-19b	0.0280	9.7%	(-)	1.24	(1.00, 1.50)	1.00	1.19
hsa-miR-205	0.0289	10.0%	(-)	1.25	(1.00, 1.53)	1.00	1.20

“The (/-value is the empirical Bayes probability that the microRNA’s association with clinical recurrence is a false discovery, given the data.

^bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of clinical recurrence.

'RM: regression to the mean.

113

Table 16. MicroRNAs Associated with Clinical Recurrence of Prostate Cancer

Highest Gleason Pattern Tumor Tissue

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Absolute Standardized Hazard Ratio

MicroRNA	p-value	(?-valuea (FDR)	Direction of Association1’	Uncorrected Estimate	95% Confidence Interval	Max. Lower Bound @10% FDR	RM- Corrected Estimalec
hsa-miR-93	<0.0001	0.0%	(+)	1.91	(1.48, 2.47)	1.24	1.59
hsa-miR-449a	<0.0001	0.0%	(+)	1.75	(1.40, 2.18)	1.23	1.54
hsa-tniR-205	<0.0001	0.0%	(-)	1.53	(1.29, 1.81)	1.20	1.43
hsa-tniR-19b	<0.0001	0.0%	(-)	1.37	(1.19, 1.57)	1.15	1.32
hsa-miR-106b	<0.0001	0.0%	(+)	1.84	(1.39, 2.42)	1.22	1.51
hsa-iniR-21	<0.0001	0.0%	(+)	1.68	(1.32, 2.15)	1.19	1.46
hsa-tniR-30a	0.0005	0.4%	(-)	1.44	(1.17, 1.76)	1.13	1.33
hsa-miR-30e-5p	0.0010	0.6%	(-)	1.37	(1.14, 1.66)	1.11	1.30
hsa-miR-133a	0.0015	0.8%	(-)	1.57	(1.19, 2.07)	1.13	1.36
hsa-miR-1	0.0016	0.8%	(-)	1.42	(1.14, 1.77)	1.11	1.31
hsa-miR-103	0.0021	1.1%	(+)	1.69	(1.21,2.37)	1.13	1.37
hsa-miR-210	0.0024	1.2%	(+)	1.43	(1.13, 1.79)	1.11	1.31
hsa-miR-182	0.0040	1.7%	(+)	1.48	(1.13, 1.93)	1.11	1.31
hsa-miR-27a	0.0055	2.1%	(+)	1.46	(1.12, 1.91)	1.09	1.30
hsa-tniR-222	0.0093	3.2%	(-)	1.38	(1.08, 1.77)	1.08	1.27
hsa-miR-331	0.0126	3.9%	(+)	1.38	(1.07, 1.77)	1.07	1.26
hsa-miR-191*	0.0143	4.3%	(+)	1.38	(1.06, 1.78)	1.07	1.26
hsa+niRY25	0.0151	4.5%	(+)	1.40	¢1.06.1.83)	1.07	1.26
hsa-tniR-31	0.0176	5.1%	¢-)	1.29	(1.04, 1.60)	1.05	1.22
hsa-miR-92a	0.0202	5.6%	¢+)	1.31	(1.03. 1.65)	1.05	1.23
hsa-miR-155	0.0302	7.6%	(-)	1.32	(1.00, 1.69)	1.03	1.22
hsa-miR-22	0.0437	9.9%	(+)	1.30	(1.00, 1.67)	1.00	1.21

“The g-value is the empirical Bayes probability that the microRNA’s association with death due to prostate cancer is a false discovery, given the data.

'’Direction of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of clinical recurrence.

^CRM: regression to the mean.

114 [00162] Table 17 shows microRNAs assayed from primary Gleason pattern tissue that were identified as being associated with the risk of prostate-cancer-specific death, with a false discovery rate of 10%. Table 18 shows the corresponding analysis for microRNAs assayed from highest Gleason pattern tissue. No microRNA assayed from normal adjacent tissue was associated with the risk of prostate-cancer-specific death at a false discovery rate of 10%.

Table 17. MicroRNAs Associated with Death Due to Prostate Cancer

Primary Gleason Pattern Tumor Tissue

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Absolute Standardized Hazard Ratio

MicroRNA	p-value	(/-value3 (FDR)	Direction of Association1’	Uncorrectcd Estimate	95% Gonfidence Interval	Max. Lower Bound @10% FDR	RM- Corrected Estimate'
hsa-miR-30e-5p	0.0001	0.6%	(-)	1.88	(1.37, 2.58)	1.15	1.46
hsa-miR-30a	0.01X11	0.7%	(-)	1.78	(1.33,2.40)	1.14	1.44
hsa-miR-!33a	0.0005	1.2%	(-)	1.85	(1.31,2.62)	1.13	1.41
hsa-miR-222	0.0006	1.4%	(-)	1.65	(1.24, 2.20)	1.12	1.38
hsa-miR-J06b	0.0024	2.7%	(+)	1.85	(1.24, 2.75)	1.11	1.35
hsamiR-1	0.0028	3.0%	(-)	1.43	(1.13, 1.81)	1.08	1.30
hsa-miR-21	0.0034	3.3%	(+)	1.63	(1.17, 2.25)	1.09	1.33
hsa-miR-93	0.0044	3.9%	(+)	1.87	(1.21,2.87)	1.09	1.32
hsa-miR-26a	0.0072	5.3%	(-)	1.47	(1.11, 1.94)	1.07	1.29
hsa-miR-152	0.0090	6.0%	(-)	1.46	(1.10, 1.95)	1.06	1.28
hsa-tniR-331	0.0105	6.5%	(+)	1.46	(1.09, 1.96)	1.05	1.27
hsa-miR-150	0.0159	8.3%	(+)	1.51	(1.07,2.10)	1.03	1.27
hsa-miR-27b	0.0160	8.3%	(+)	1.97	(1.12,3.42)	1.05	1.25

^aThe (/-value is the empirical Bayes probability that the microRNA's association with death due to prostate cancer endpoint is a false discovery, given the data.

^bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of death due to prostate cancer.

^CRM: regression to the mean.

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	Table 18.	MicroRNAs Associated with Death Due to Prostate Cancer
Highest Gleasoi	ii Pattern Tumor Tissue
					Absolute Standardized Hazard Ratio
MicroRNA	p-value	r/-valuea (FDR)	Direction of Association1’	Uncorrected Estimate	95% Confidence Interval	Max. Lower Bound @10% FDR	RM-Corrected Estimate17
hsa-miR-27b	0.0016	6.1%	(+)	2.66	(1.45,4.88)	1.07	1.32
hsa-iniR-21	0.0020	6.4%	(+)	1.66	(1.21,2.30)	1.05	1.34
hsa-miR-lOa	0.0024	6.7%	(+)	1.78	(1.23,2.59)	1.05	1.34
hsa-miR-93	0.0024	6.7%	(+)	1.83	(1.24, 2.71)	1.05	1.34
hsa-miR-106b	0.0028	6.8%	(+)	1.79	(1.22, 2.63)	1.05	1.33
hsa-miR-150	0.0035	7.1%	¢+)	1.61	(1.17,2.22)	1.05	1.32
hsa-miR-l	0.0104	9.0%	(-)	1.52	(1.10, 2.09)	1.00	1.28

^aThe (/-value is the empirical Bayes probability that the microRNA’s association with clinical endpoint is a false discovery, given the data.

^bDirection of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of death due to prostate cancer.

^CRM: regression to the mean.

[00163] Table 19 and Table 20 shows the microRNAs that can be identified as being associated with the risk of clinical recurrence while adjusting for the clinical and pathology covariates of biopsy Gleason score, baseline PSA level, and clinical T-stage. The distributions of these covariates are shown in Figure 1. Fifteen (15) of the microRNAs identified in Table 15 are also present in Table 19, indicating that these microRNAs have predictive value for clinical recurrence that is independent of the Gleason score, baseline PSA, and clinical T-stage.

[00164] Two microRNAs assayed from primary Gleason pattern tumor tissue were found that had predictive value for death due to prostate cancer independent of Gleason score, baseline PSA, and clinical T-stage (Table 21).

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Table 19. MicroRNAs Associated with Clinical Recurrence of Prostate Cancer

Adjusting for Biopsy Gleason Score, Baseline PSA Level, and Clinical T-Stage Primary Gleason Pattern Tumor Tissue

Absolute Standardized Hazard Ratio

Max. Lower

MicroRNA	p-value	i/-valuea (FDR)	Direction of Association6	(Incorrect ed Estimate	95% Confidence Interval	Bound @10% FDR	RM- Corrccled Estiinatec
hsa-miR-30e-5p	<0.0001	0.0%	(-)	1.80	(1.42, 2.27)	1.23	1.53
hsa-miR-30a	<0.0001	0.0%	(-)	1.75	(1.40, 2.19)	1.22	1.51
hsa-miR-93	<0.0001	0.1%	(+)	1.70	(1.32, 2.20)	1.19	1.44
hsa-miR-449a	0.0001	0.1%	¢+)	1.54	(1.25, 1.91)	1.17	1.39
hsa-miR-l 33a	0.0001	0.1%	(-)	1.58	(1.25,2.00)	1.17	1.39
hsa-miR-27a	0.0002	0.1%	(+)	1.66	(1.28, 2.16)	1.17	1.41
hsa-miR-21	0.0003	0.2%	(+)	1.58	(1.23, 2.02)	1.16	1.38
hsa-miR-l 82	0.0005	0.3%	(+)	1.56	(1.22, 1.99)	1.15	1.37
hsa-miR-l 06b	0.0008	0.5%	(+)	1.57	(1.21,2.05)	1.15	1.36
hsa-miR-222	0.0028	1.1%	(-)	1.39	(1.12, 1.73)	1.11	1.28
hsa-miR-103	0.0048	1.7%	(+)	1.69	(1.17,2.43)	1.13	1.32
hsa-miR-486-5p	0.0059	2.0%	(-)	1.34	(1.09, 1.65)	1.09	1.25
hsa-miR-l	0.0083	2.7%	(-)	1.29	(1.07, 1.57)	1.07	1.23
hsa-miR-141	0.0088	2.8%	(+)	1.43	(1.09, 1.87)	1.09	1.27
hsa-miR-200c	0.0116	3.4%	(+)	1.39	(1.07, 1.79)	1.07	1.25
hsa-miR-l 45	0.0201	5.1%	(-)	1.27	(1.03, 1.55)	1.05	1.20
hsa-miR-206	0.0329	7.2%	(-)	1.40	(1.00, 1.91)	1.05	1.23
hsa-miR-29b	0.0476	9.4%	(+)	1.30	(1.00, 1.69)	1.00	1.20

“The (/-value is the empirical Bayes probability that the niicroRNA’s association with clinical recurrence is a false discovery, given the data.

^Direction of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of clinical recurrence.

‘’RM: regression to the mean.

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Table 20. MicroRNAs Associated with Clinical Recurrence of Prostate Cancer

Adjusting for Biopsy Gleason Score, Baseline PSA Level, and Clinical T-Stage Highest Gleason Pattern Tumor Tissue

Absolute Standardized Hazard Ratio

MicroRNA	p-value	«/-value3 (PDR)	Direction of Associationb	Uncorrected Estimate	95% Confidence Interval	Max. Lower Bound @10% EDR	RM- Correcied Estimate'
hsa-miR-30a	<0.0001	0.0%	(-)	1.62	(1.32, 1.99)	1.20	1.43
hsa-miR-30e-5p	<0.0001	0.0%	(-)	1.53	(1.27, 1.85)	1.19	1.39
hsa-miR-93	<0.0001	0.0%'	(+)	1.76	(1.37, 2.26)	1.20	1.45
hsa-miR-205	<0.0001	0.0%'	(-)	1.47	(1.23, 1.74)	1.18	1.36
hsa-miR-449a	0.0001	0.1%	(+)	1.62	(1.27, 2.07)	1.18	1.38
hsa-miR-l 06b	0.0003	0.2%	(+)	1.65	(1.26, 2.16)	1.17	1.36
hsa-miR-133a	0.0005	0.2%	(-)	1.51	(1.20, 1.90)	1.16	1.33
hsa-miR-l	0.0007	0.3%	(-)	1.38	(1.15, 1.67)	1.13	1.28
hsa-miR-210	0.0045	1.2%	(+)	1.35	(1.10, 1.67)	1.11	1.25
hsa-miR-l 82	0.0052	1.3%	(+)	1.40	(1.10, 1.77)	1.11	1.26
hsa-miR-425	0.0066	1.6%'	(+)	1.48	(1.12, 1.96)	1.12	1.26
hsa-miR-l 55	0.0073	1.8%	(-)	1.36	(1.09, 1.70)	1.10	1.24
hsa-miR-21	0.0091	2.1%	(+)	1.42	(1.09, 1.84)	1.10	1.25
hsa-miR-222	0.0125	2.7%	(-)	1.34	(1.06, 1.69)	1.09	1.23
hsa-miR-27a	0.0132	2.8%	(+)	1.40	(1.07, 1.84)	1.09	1.23
hsa-miR-191*	0.0150	3.0%	(+)	1.37	(1.06, 1.76)	1.09	1.23
hsa-miR-l 03	0.0180	3.4%	(+)	1.45	(1.06, 1.98)	1.09	1.23
hsa-miR-31	0.0252	4.3%	(-)	1.27	(1.00, 1.57)	1.07	1.19
hsa-miR-l 9b	0.0266	4.5%	(-)	1.29	(1.00, 1.63)	1.07	1.20
hsa-miR-99a	0.0310	5.0%	(-)	1.26	(1.00, 1.56)	1.06	1.18
hsa-miR-92a	0.0348	5.4%	¢+)	1.31	(1.00, 1.69)	1.06	1.19
hsa-miR-!46b-5p	0.0386	5.8%>	(-)	1.29	(1.00, 1.65)	1.06	1.19
hsa-miR-145	0.0787	9.7%	(-)	1.23	(1.00, 1.55)	1.00	1.15

^aThc (/-value is the empirical Bayes probability that the microRNA’s association with clinical clinical recurrence is a false discovery, given the data.

^Direction of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of clinical recurrence. ‘RM: regression to the mean.

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Table 21, MicroRNAs Associated with Death Due to Prostate Cancer Adjusting for Biopsy Gleason Score, Baseline PSA Level, and Clinical T-Stage Primary Gleason Pattern Tumor Tissue

Absolute Standardized Hazard Ratio

Max.

Lower

MicroRNA	p-value	(/-value-1 (FDR)	Direction of Association15	Uncorrected Estimate	95% Confidence Interval	Bound @10% FDR	RM- Corrected Estimate'
hsa-miR-30e-5p	0.0001	2.9%	(-)	1.97	(1.40,2.78)	1.09	1.39
hsa-miR-30a	0.0002	3.3%	(-)	1.90	(1.36, 2.65)	1.08	1.38

“The (/-value is the empirical Bayes probability that the microRNA’s association with clinical recurrence is a false discovery, given the data.

^Direction of association indicates where higher microRNA expression is associated with higher (+) or lower (-) risk of clinical recurrence.

^CRM: regression to the mean.

[00165] Accordingly, the normalized expression levels of hsa-miR-93; hsa-miR-106b; hsa-miR-21; hsa-miR-449a; hsa-miR-182; hsa-miR-27a; hsa-miR-103; hsa-miR-141; hsa-miR92a; hsa-miR-22; hsa-miR-29b; hsa-miR-210; hsa-miR-331; hsa-miR-191; hsa-miR-425; and hsa-miR-200c are positively associated with an increased risk of recurrence; and hsa-miR-30e5p; hsa-miR-133a; hsa-miR-30a; hsa-miR-222; hsa-miR-1; hsa-miR-145; hsa-miR-486-5p; hsamiR-19b; hsa-miR-205; hsa-miR-31; hsa-miR-155; hsa-miR-206; hsa-miR-99a; and hsa-miR146b-5p are negatively associated with an increased risk of recurrence.

[00166] Furthermore, the noimalized expression levels of hsa-miR-106b; hsa-miR-21; hsa-miR-93; hsa-miR-331; hsa-miR-150; hsa-miR-27b; and hsa-miR-lOa are positively associated with an increased risk of prostate cancer specific death; and the normalized expression levels of hsa-miR-30e-5p; hsa-miR-30a; hsa-miR-133a; hsa-miR-222; hsa-miR-1; hsa-miR-26a; and hsa-miR-152 are negatively associated with an increased risk of prostate cancer specific death.

[00167] Table 22 shows the number of microRNA-gene pairs that were grouped in each tier (Tiers 1-4) and the number and percentage of those that were predictive of clinical recurrence at a false discovery rate of 10%.

119

2017268510 28 Nov 2017

Table 22.

Tier	Total Number of MicroRNA-Gene Pairs	Number of Pairs Predictive of Clinical Recurrence at False Discovery Rate 10% (%)
Tier 1	80	46 (57.5%)
Tier 2	719	591 (82.2%)
Tier 3	3,850	2,792 (72.5%)
Tier 4	54,724	38,264 (69.9%)

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2017268510 28 Nov 2017

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2017268510 28 Nov 2017

TABLEB

mfetrfRNA	Sequence	SEQ tD HO
hsa-miR-l	UGGAAUGUAAAGAAGUAUGUAU	2629
hsa-miR-103	GCAGCAUUGUACAGGGCUAUGA	2630
hsa-miR-106b	UAAAG UGCUGACAGUG CAGAU	2631
hsa-miR-10a	UACCCUGUAGAUCCGAAUUUGUG	2632
hsa-miR-133a	U U UGGUCCCC U UCAACCAGCUG	2633
hsa-miR-141	UAACACUGUCUGGUAAAGAUGG	2634
hsa-miR-145	GUCCAGUUUUCCCAGGAAUCCCU	2635
hsa-miR-Ι 46b-5p	UGAGAACUGAAUUCCAUAGGCU	2636
hsa-miR-150	UCUCCCAACCCUUGUACCAGUG	2637
hsa-miR-152	UCAGUGCAUGACAGAACUUGG	2638
hsa-miR-155	UUAAUGCUAAUCGUGAUAGGGGU	2639
hsa-miR-1S2	U U UGGCAA UGGUAG AACUCAC ACU	2640
hsa-miR-191	CAACGGAAUCCCAAAAGCAGCUG	2641
hsa-miR-19b	UG UAAACAUCCUCGACU GGAAG	2642
hsa-miR-200c	UAAUACUGCCGGGUAAUGAUGGA	2643
hsa-miR-205	UCCUUCAUUCCACCGGAGUCUG	2644
hsa-miR-206	UGGAAUGUAAGGAAGUGUGUGG	2645
hsa-miR-21	UAGCU UAUCAGACUGA UG UUGA	2646
hsa-miR-210	CUGUGCGUGUGACAGCGGCUGA	2647
hsa-miR-2 2	AAGCUGCCAGUUGAAGAACUGU	2648
hsa-miR-222	AGCUACAUCUGGCUACUGGGU	2649
hsa-miR-26a	UUCAAGUAAUCCAGGAUAGGCU	2650
hsa-miR-27a	U UCACAG UG GC UAAGU U CCGC	2651
hsa-miR-27b	UUCACAGUGGCUAAGUUCUGC	2652
hsa-miR-29b	UAGCACCAUUUGAAAUCAGUGUU	2653
hsa-miR-30a	CU UUCAG UCGGAUG U U UGCAGC	2654
hsa-miR-3 Oe-5p	CUUUCAGUCGGAUGUUUACAGC	2655
hsa-miR-31	AGGCAAGAUGCUGGCAUAGCU	2656
hsa-miR-331	GCCCCUGGGCCUAUCCUAGAA	2657
hsa-miR-425	AA UGACACGAUCACUCCCG UU G A	2658
hsa-miR-449a	UGGCAGUGUAUUGUUAGCUGGU	2659
hsa-miR-486-5p	UCCUG UACUGAGCUGCCCCGAG	2660
hsa-miR-92a	UAUUGCACUUGUCCCGGCCUGU	2661
hsa-miR-93	CAAAGUGCUGUUCGUGCAGGUAG	2662
hsa-miR-99a	AACCCGUAGAUCCGAUCUUGUG	2663

139

2017268510 28 Nov 2017 [00168] Throughout this specification, unless the context requires otherwise, the word comprise, or variations such as comprises or comprising, will be understood to imply the inclusion of a stated step or element or integer or group of steps or elements or integers but not the exclusion of any other step or element or integer or group of elements or integers.

[00169] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims.

Claims (15)

1. A method for determining a likelihood of cancer recurrence in a human patient with prostate cancer, comprising:

measuring an expression level of an RNA transcript of KLK2 in a biological sample comprising prostate tissue obtained from the patient: predicting a likelihood of cancer recurrence for the patient based on the measured expression level of the RNA transcript ofKLK2;

wherein an increased expression level of KLK2 RNA is negatively associated with an increased risk of recurrence.

2. The method of claim 1, further comprising normalizing said RNA expression level to obtain a normalized KLK2 RNA expression level, wherein an increased expression level of normalized KLK2 RNA is negatively associated with an increased risk of recurrence.

3. The method of any one of claims 1 or 2, further comprising generating a report based on the KLK2 RNA expression level.

4. The method of any one of claims 1-3, wherein the likelihood of cancer recurrence is based on clinical recurrence-free interval (cRFI).

5. The method of any one of claims 1-4, wherein the likelihood of cancer recurrence is based on biochemical recurrence-free interval (bRFI).

6. The method of any one of claims 1-5, wherein the biological sample has a positive TMPRSS2 fusion status.

7. The method of any one of claims 1-5, wherein the biological sample has a negative TMPRSS2 fusion status.

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2017268510 16 Jul 2019

8. The method of any one of claims 1-7, wherein the patient has early-stage prostate cancer.

9. The method of any one of claims 1-8, wherein the biological sample comprises prostate tumor tissue with the primary Gleason pattern for said prostate tumor.

10. The method of any one of claims 1-8, wherein the biological sample comprises prostate tumor tissue with the highest Gleason pattern for said prostate tumor.

11. The method of any one of claims 1-9, wherein the biological sample is prostate tumor tissue.

12. The method of any one of claims 1-6, wherein the biological sample comprises non-tumor prostate tissue.

13. The method of any one of claims 1-12, further comprising measuring an expression level of INHBA, wherein an increased expression level of INHBA is positively associated with increased risk of recurrence.

14. The method of any one of claims 1-13, further comprising determining a likelihood of upgrading or upstaging in the patient with prostate cancer, wherein an increased expression level of KLK2 RNA is negatively associated with an increased risk of upgrading/upstaging.

15. The method of any one of claims 1-14, wherein the biological sample is a fixed, paraffin-embedded tissue sample.