Methods for detecting and treating prostate cancer

Abstract

The present invention is directed to methods for detecting prostate cancer, determining whether prostate cancer is stable or progressive, low grade or high grade, distinguishing benign prostatic hyperplasia (BPH) from prostate cancer, determining the completeness of surgery, and assessing response to prostate cancer therapy. [Selection diagram] Figure 1A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of U.S. Provisional Application No. 62 / 633,675, filed February 22, 2018, the entire contents of which are incorporated herein by reference.

[0002] [Sequence Listing] This application contains a Sequence Listing which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. The ASCII copy created on February 19, 2019 is entitled The file is named "LBIO-005_001WO_SeqList.txt" and is 299 KB in size.

[0003] FIELD OF THEINVENTION The present invention relates to the detection of prostate cancer. [Background technology]

[0004] Prostate cancer (PCA) is the fourth most frequently diagnosed cancer worldwide and the second most common cancer in men. Although incidence and prevalence have been slowly decreasing, approximately 200,000 men are diagnosed with PCA annually in the United States. Multiple factors, including age and family structure, genetic susceptibility, and ethnicity, all contribute to the high incidence of the disease. Although 90% of PCA are diagnosed as localized (non-disseminated), the clinical behavior of the tumor is highly variable, ranging from indolent, which can be monitored through observation or active surveillance (e.g., biomarkers or digital rectal examination every 6 months), to malignant evolution and androgen-resistant disease, metastatic dissemination, and death.

[0005] Various risk stratification systems have been developed that combine clinical data with pathological information, such as the Gleason score. These systems, including the recently developed next-generation tools, are only about 70% accurate in predicting outcome.

[0006] Molecular genetic information is increasingly being used to inform pathology and more benign subtypes of cancer. This information is being used not only as a prognostic tool but also to stratify patients for various therapeutic interventions. Mutations, DNA copy number alterations, rearrangements, and gene fusions have all been investigated and identified in prostate cancer. These correlate with several pathological features. For example, low Gleaon score tumors have few DNA copy number alterations, whereas high-grade tumors show significant copy number alterations in the whole genome. In contrast, somatic point mutations are relatively rare, with mutation frequencies ranging from 1% (IDH1) to 11% (SPOP). The most common abnormality is the androgen-regulated fusion of ERG with other ETS family members (~50% of tumors). However, tumors carrying fusions do not show a significantly different prognosis versus fusion-negative tumors after prostatectomy. In contrast, androgen receptor variant 7 (AR-V7) is implicated in progression to castration-resistant prostate cancer (CRPC) and may be potentially useful as a treatment selection biomarker. Overall, however, the molecular mechanisms underlying the pathogenesis of PCA are incompletely understood, and there are no molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents. Therefore, the development of diagnostic methods that can be used to more precisely define disease status, identify sensitivity to treatment, and ultimately better monitor disease progression is crucial.

[0007] Surveillance remains the cornerstone approach to monitor PCA and detect early recurrence. After potentially curative resection, monitoring can be performed through measurement of blood biomarkers and / or imaging such as CT scans to detect asymptomatic metastatic disease early. The current biomarker used for monitoring is prostate-specific antigen (PSA) (also gamma-seminoprotein or kallikrein-3). This glycoprotein enzyme is encoded by the KLK3 gene and is secreted by epithelial cells of the prostate. However, it is not a specific indicator of prostate cancer and may also detect prostatitis and benign prostatic hyperplasia (BPH). The use of PSA in isolation results in unnecessary biopsies in men without cancer and underdiagnosis of men with significant disease. This is based on a range of low sensitivity (20-40%) and low specificity (70-90%), resulting in a positive predictive value of only 25-40%. The United States Preventive Services Task Force (USPSTF) does not recommend the use of PSA for prostate cancer, but PSA is included in clinical nomographs, such as the UCSF-CAPRA score for prostate cancer risk, which has some utility in predicting disease-free survival after surgery. Summary of the Invention [Problem to be solved by the invention]

[0008] There remains a need for biomarker-based tools to accurately diagnose PCA. [Means for solving the problem]

[0009] The present disclosure provides a method of detecting prostate cancer in a subject in need thereof, comprising determining expression levels of at least 38 biomarkers from test samples of the subject by contacting the test samples with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS, IL-16, and / or IL-16. 25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; and AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HP The expression levels of each of N, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, and COL1A2 were normalized. obtaining a normalized expression level of each of HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; inputting each of the normalized expression levels into an algorithm to generate a score; and comparing the score to a first predetermined cutoff value;and determining the presence of prostate cancer in the subject if the score is equal to or greater than a first predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than a first predetermined cutoff value;

[0010] The present disclosure provides a method of detecting prostate cancer in a subject in need thereof, comprising: (a) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, M (b) the expression levels of the housekeeping genes, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO, FXYD8, FGFR2, FXYD9, FGFR3, FXYD1, FGFR4, FXYD2, FGFR5, FXYD3, FGFR6, FXYD4, FGFR7, FXYD5, FGFR8, FXYD6, FGFR9, FXYD7, FGFR10, FXYD8, FGFR11, FXYD9, FGFR12, FXYD1, FGFR13, FXYD14, FGFR15, FXYD16, FGFR17, FXYD18, FGFR19, FXYD20, FGFR21, FXYD22, FGFR22, FXYD24, FGFR25, FXYD26, FGFR27, FXYD28, FGFR29, FXYD30, FGFR26, FXYD40, FGFR28, FXYD50, FGFR29, FXYD60, FGFR29, FXYD70, FGFR29, FXYD80, FGFR29, FXYD90, FGFR29, FXYD10, FGFR29, FXYD11, FGFR29, FXYD12, FGFR21, FXYD14, FGFR29, FXYD15, FGFR29, FXYD16, FGFR29, FXYD18, FGFR29, FXYD18, FGFR29, FXYD19, FGFR29, The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FAP1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to those of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FAP1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, respectively. obtaining a normalized expression level for each of GFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) generating a report, wherein the report establishes the presence of prostate cancer in the subject if the score is equal to or greater than the first predetermined cutoff value, and establishes the absence of prostate cancer in the subject if the score is less than the first predetermined cutoff value.

[0011] The disclosure also provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; and determining the expression levels of the housekeeping genes, such as AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC , SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC, and the expression levels of each of them were normalized, thereby AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, and MAP1. obtaining a normalized expression level of each of SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; and inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and identifying the prostate cancer as progressive if the score is equal to or less than the first predetermined cutoff value, or identifying the prostate cancer as stable if the score is less than the first predetermined cutoff value;

[0012] The disclosure provides a method of determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) a comparison of the expression levels of said housekeeping genes with respect to the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPA The expression levels of RC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were normalized, thereby AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, and IL-17 were normalized. (c) obtaining a normalized expression level of each of SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (d) inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and (e) generating a report, wherein the report includes determining that the prostate cancer is aggressive if the score is equal to or greater than the first predetermined cutoff value, or determining that the prostate cancer is stable if the score is less than the first predetermined cutoff value;

[0013] The disclosure also provides a method for determining whether prostate cancer in a subject is low-grade or high-grade, comprising determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, and / or IL-16. , MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; and AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FX, FXR1, FXR2, FXR3, FXR4, FXR5, FXR6, FXR7, FXR8, FXR9, FXR10, FXR11, FXR12, FXR13, FXR14, FXR15, FXR16, FXR17, FXR18, FXR19, FXR20, FXR21, FXR22, FXR23, FXR24, FXR25, FXR26, FXR27, FXR28, FXR29, FXR30, FXR31, FXR40, FXR50, FXR60, FXR71, FXR82, FXR91, FXR111, FXR121, FXR131, FXR14, FXR151, FXR16, FXR18, FXR19, FXR25, FXR26, FXR18, FXR28, FXR19, FXR29, FXR211, FXR221, FXR30, FXR40, FXR50, FXR60, FX The expression levels of YD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, and the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, ED obtaining a normalized expression level for each of C4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; and inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and determining that the prostate cancer is aggressive if the score is equal to or greater than the first predetermined cutoff value, or determining that the prostate cancer is aggressive if the score is less than the first predetermined cutoff value;

[0014] The present disclosure provides a method for determining whether prostate cancer in a subject is low-grade or high-grade, the method comprising determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers; wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23. , MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; and AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FGFR3, FGFR4, FGFR5, FGFR6, FGFR7, FGFR8, FGFR9, FGFR10, FGFR11, FGFR12, FGFR13, FGFR14, FGFR15, FGFR16, FGFR17, FGFR18, FGFR19, FGFR20, FGFR21, FGFR22, FGFR23, FGFR24, FGFR25, FGFR26, FGFR27, FGFR28, FGFR29, FGFR29, FGFR28, FGFR29, FGFR29, FGFR29, FGFR21, FGFR22, FGFR25, FGFR26, FGFR27, FGFR28, FGFR29, FGFR29, FGFR29, FGFR29, FGFR21, FGFR22, FGFR23, FGFR24, FGFR25, FGFR26, FGFR27, FGFR28, FGFR29, FGFR29, FGFR29, FGFR29, FGFR29, FGFR29, FGFR29, FGFR29, FGFR The expression levels of XYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby normalizing the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, and COL1B2. obtaining a normalized expression level for each of EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; and inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and generating a report, wherein the report includes determining that the prostate cancer is aggressive if the score is equal to or greater than the first predetermined cutoff value, and determining that the prostate cancer is indolent if the score is less than the first predetermined cutoff value;

[0015] The present disclosure provides a method for determining whether prostate cancer in a subject is a low Gleason score (≦6) or high Gleason score (≧7) prostate cancer, the method comprising: (a) (b) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were measured with respect to the expression levels of the housekeeping genes. Normalize the current levels to obtain normalized expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC;(c) inputting each of the normalized expression levels into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) determining that the prostate cancer is a high Gleason score prostate cancer if the score is equal to or greater than the first predetermined cutoff value and determining that the prostate cancer is a low Gleason score prostate cancer if the score is less than the first predetermined cutoff value;

[0016] The present disclosure provides a method for determining whether prostate cancer in a subject is a low Gleason score (≦6) or high Gleason score (≧7) prostate cancer, the method comprising: (a) (b) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were compared with those of the housekeeping genes. bells to obtain normalized expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC;(c) inputting each of the normalized expression levels into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) generating a report, wherein the report includes determining that the prostate cancer is aggressive if the score is equal to or greater than the first predetermined cutoff value and determining that the prostate cancer is indolent if the score is less than the first predetermined cutoff value;

[0017] The disclosure also provides a method of determining the completeness of surgery in a subject having prostate cancer, the method comprising determining expression levels of at least 38 biomarkers from a test sample of the subject after surgery by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; and AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, F The expression levels of XYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were normalized, and the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, COL2B2, COL1B1, COL2C1, COL2D1, COL2D2, COL3D1, COL4D1, COL5D1, COL6D1, COL7D1, COL8D1, COL9D1, COL1D2, COL1D3, COL1D4, COL1D5, COL1D6, COL1D7, COL1D8, COL1D9, COL2D1, COL3D1, COL4D1, COL5D1, COL6D1, COL7D2, COL1D1, COL1D2, COL1D3, COL1D4, COL1D5, COL1D6, COL1D7, COL1D8, COL1D1, COL1D1, COL1D1, COL1D2 ... obtaining a normalized expression level for each of EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; and inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and determining that the prostate cancer has not been completely removed if the score is equal to or greater than the first predetermined cutoff value, or determining that the prostate cancer has been completely removed if the score is less than the first predetermined cutoff value;

[0018] The disclosure provides a method of determining completeness of surgery in a subject having prostate cancer, the method comprising: (a) determining expression levels of at least 38 biomarkers from a test sample from the subject after surgery by contacting the test sample with a plurality of agents specific for detecting expression levels of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS245, PRPR2_2TR , XPC, and housekeeping genes; (b) a comparison of the expression levels of the housekeeping genes with respect to the expression levels of the housekeeping genes, including AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, S The expression levels of MC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby detecting the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, PPRC2, PPRC1 ... obtaining a normalized expression level of each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) generating a report, wherein the report includes determining that the prostate cancer has not been completely removed if the score is equal to or greater than the first predetermined cutoff value, and determining that the prostate cancer has been completely removed if the score is less than the first predetermined cutoff value;

[0019] The present disclosure provides a method for distinguishing prostate cancer from benign prostatic hyperplasia in a subject having prostatic hyperplasia, the method comprising determining an expression level of at least 38 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific for detecting an expression level of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, and / or IL-16. , FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes. AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQ The expression levels of LE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby indicating that the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, and IL-17 were normalized. obtaining a normalized expression level for each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; and inputting each of the normalized expression levels into an algorithm to generate a score;comparing the score to a first predetermined cutoff value; and determining the presence of prostate cancer in the subject when the score is equal to or greater than the first predetermined cutoff value, and determining the presence of benign prostatic hyperplasia in the subject when the score is less than the first predetermined cutoff value;

[0020] The present disclosure provides a method of distinguishing benign prostatic hyperplasia from prostate cancer in a subject having prostatic hyperplasia, the method comprising determining expression levels of at least 38 biomarkers from a test sample from a subject after surgery by contacting the test sample with a plurality of agents specific for detecting expression levels of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS245, PRPR2_2TR , XPC, and housekeeping genes; (b) a comparison of the expression levels of the housekeeping genes with respect to the expression levels of the housekeeping genes, including AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, S The expression levels of MC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby detecting the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, PPRC2, PPRC1 ... obtaining a normalized expression level of each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) generating a report, wherein the report identifies the presence of prostate cancer in the subject when the score is equal to or greater than the first predetermined cutoff value, and identifies the presence of benign prostatic hyperplasia in the subject when the score is less than the first predetermined cutoff value.

[0021] The present disclosure provides a method of assessing a response of a subject having prostate cancer to a first line therapy, comprising: (1) determining, at a first time point, expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_2, TRPR2, TMPRSS2_3, TRPR2_4, TRPR2_5, TRPR2_6, TRPR2_7, TRPR2_8, TRPR2_9, TRPR2_10, TRPR2_12, TRPR2_13, TRPR2_24, TRPR2_14, TRPR2_15, TRPR2_25, TRPR2_36, TRPR2_16, TRPR2_17, TRPR2_28, TRPR2_18, TRPR2_29, TRPR2_37, TRPR2_19, TRPR2_26, TRPR2_28, TRPR2_38, TRPR2_19, TRPR2_29, TRPR2_39, TRPR2_18, TRPR2_29, TRPR2_38, TRPR2_19, TRPR2_29, TRPR2_38, TRPR2_19, TRPR2_29, TRPR2_ (b) comparing the expression levels of the housekeeping genes, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRI, and the like, with respect to the expression levels of the housekeeping genes, By normalizing the expression levels of P1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, respectively, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, and MAP1. (c) obtaining a normalized expression level of each of SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a first score;(2) at a second time point, the second time point being after the first time point and after administering a primary therapy to the subject: (d) determining an expression level of at least 38 biomarkers from the subject's test sample by contacting the test sample with a plurality of agents specific for detecting the expression level of at least 38 biomarkers; (e) The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with those of the housekeeping genes. (f) normalize the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (3) inputting each of the normalized expression levels into an algorithm to generate a second score; (4) comparing the first score to the second score; and (5) determining that the subject is responsive to the first therapy if the second score is significantly decreased compared to the first score, and determining that the subject is non-responsive to the first therapy if the second score is not significantly decreased compared to the first score.

[0022] The method may further include continuing to administer the primary therapy to the subject if the second score is significantly reduced compared to the first score. The method may further include discontinuing administration of the primary therapy to the subject if the second score is not significantly reduced compared to the first score.

[0023] The disclosure provides a method of assessing a response to a first line therapy in a subject having prostate cancer, the method comprising: (1) determining, at a first time point, expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of the at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SQL1ARC, SLC18A2, SMC4 , STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, S The expression levels of QLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were normalized, thereby AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, obtaining a normalized expression level of each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a first score;(2) at a second time point, the second time point being later than the first time point and subsequent to administration of the therapy to the subject: (d) determining an expression level of the at least 38 biomarkers from a test sample of the subject by contacting the test sample with an agent specific for detecting expression of the at least 38 biomarkers; and (e) The expression levels of the housekeeping genes AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC (f) inputting each of the normalized expression levels into an algorithm to generate a second score; (3) comparing the first score to the second score; and (4) normalizing the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC to obtain a normalized expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC. generating a report, wherein the report comprises determining the subject as responsive to the first therapy if the second score is significantly reduced compared to the first score, and determining the subject as non-responsive to the first therapy if the second score is not significantly reduced compared to the first score;

[0024] The method may further include continuing to administer the primary therapy to the subject if the second score is significantly reduced compared to the first score. The method may further include ceasing to administer the primary therapy to the subject if the second score is not significantly reduced compared to the first score. The method may further include administering a secondary therapy to the subject if the second score is not significantly reduced compared to the first score.

[0025] The disclosure provides a method of treating prostate cancer in a subject in need thereof, comprising measuring expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, S QLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; and AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC for expression of said housekeeping genes. normalizing the expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, thereby obtaining a normalized expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; inputting each of the normalized expression levels into an algorithm to generate a score; and comparing the score to a first predetermined cutoff value;and administering at least a primary therapy to the subject if the score is equal to or greater than a first predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than a first predetermined cutoff value;

[0026] In the disclosed methods, the housekeeping gene can be selected from the group consisting of ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1. The housekeeping gene can be TOX4.

[0027] In the methods of the present disclosure, the predetermined cutoff value is at least 33% on a scale of 0 to 100%, or at least 50% on a scale of 0 to 100%, or at least 50% on a scale of 0 to 100%.

[0028] The methods of the present disclosure may further include administering a therapy to the subject.The methods of the present disclosure may further include administering a primary therapy to the subject.The methods of the present disclosure may further include administering a secondary therapy to the subject.

[0029] In the methods of the present disclosure, the therapy can include active surveillance, radiation therapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone metastasis therapy, or any combination thereof. The primary therapy can include active surveillance, radiation therapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone metastasis therapy, or any combination thereof. The secondary therapy can include active surveillance, radiation therapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone metastasis therapy, or any combination thereof.

[0030] In some embodiments, the hormone therapy may include androgen deprivation therapy. In some embodiments, the chemotherapy may include docetaxel, cabazitaxel, mitoxantrone, estramustine, or a combination thereof. In some embodiments, the vaccine therapy may include sipuleucel-T. In some embodiments, the bone metastasis treatment may include bisphosphonates, denosumab, corticosteroids, or a combination thereof.

[0031] In the method of the present disclosure, the algorithm can be XGB, RF, glmnet, cforest, CART, teebag, knn, nnet, SVM-radial, SVM-linear, NB, or mlp. The algorithm can be XGB.

[0032] In the methods of the present disclosure, the first time point can be either before or after administering a treatment to the subject. The first time point can be either before or after administration of a primary therapy to the subject.

[0033] In the methods of the present disclosure, the second score is significantly decreased as compared to the first score if the second score is at least 25% less than the first score.

[0034] The methods of the present disclosure may have a sensitivity of at least 92%. The methods of the present disclosure may have a specificity of at least 95%.

[0035] In the methods of the present disclosure, at least one of the at least 38 biomarkers can be RNA, cDNA, or protein.

[0036] In the methods of the disclosure, when the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the expression level of the produced cDNA can be detected.

[0037] In the methods of the disclosure, the expression level of a biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.

[0038] In the methods of the disclosure, when the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody.

[0039] In the method of the present disclosure, when biomarker is RNA or cDNA, RNA or cDNA can be detected by forming a complex between said RNA or cDNA and labeled nucleic acid probe or primer.For example, the label can be a fluorescent label.The complex between RNA or cDNA and labeled nucleic acid probe or primer can be a hybridization complex.

[0040] In the methods of the present disclosure, the predetermined cut-off value can be derived from a number of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. The neoplastic disease can be prostate cancer.

[0041] In the methods of the present disclosure, the test sample can be blood, serum, plasma, or tumor tissue, and the reference sample can be blood, serum, plasma, or non-tumor tissue.

[0042] In the methods of the present disclosure, the subject has at least one symptom of prostate cancer. In the methods of the present disclosure, the subject can have a predisposition or family history of developing prostate cancer.

[0043] In the methods of the present disclosure, the subject may have been previously diagnosed with prostate cancer or is being tested for prostate cancer recurrence.

[0044] In the methods of the present disclosure, the subject can be a human.

[0045] Any of the above aspects can be combined with any other aspect.

[0046] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs. In this specification, the plural form is also included unless the context clearly indicates otherwise. By way of example, the terms "a" and "the" are to be interpreted as singular or plural, and "or" is to be interpreted as inclusive. As an example, "an element" means one or more elements. Throughout this specification, the term "comprising" or variations of "comprise" or "comprising" should be understood to mean the inclusion of the indicated element, integer or step, or group of elements, integers or steps, and not the exclusion of any other element, integer or step, or group of elements, integers or steps. "About" can be interpreted as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the indicated numerical value. Unless the context clearly dictates otherwise, all numerical values ​​given herein are modified by the term "about."

[0047] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, exemplary methods and materials are described. Other features, objects, and advantages will be apparent from the detailed description and claims. In the specification and the appended claims, the singular encompasses the plural unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. All patents and publications cited in this specification are incorporated herein by reference in their entirety. [Brief description of the drawings]

[0048] [Figure 1A]Figure 1A is a graph showing the visualization of 30 putative marker genes identified by the Random Forest algorithm in a derivation cohort of n=595 tissue specimens. (Figure 1A) Expression in E-GEOD-46691. [Figure 1B] Same as Fig. 1A. (Fig. 1B) Expression in E-GEOD-46602.

[0049] [Diagram 2] Figure 2 shows a scatter plot of the mean predictive importance versus Kruskal-Wallis p-value. The vertical and horizontal lines represent the p-value of 0.05 and the median predictive importance, respectively.

[0050] [Diagram 3] Figure 3 is a graph showing hierarchical clustering of gene expression clearly showing that PCA tumor tissues and control tissues clustered separately. Gene expression was significantly higher in PCA tissues.

[0051] [Figure 4] FIG. 4 is a graph showing that normalized gene expression clustering separates prostate-derived cell lines into their tissue of origin (ie, normal, regional, metastatic tissue).

[0052] [Diagram 5] Figure 5 is a graph showing gene expression in CON (controls, n=201), BPH (benign prostatic hyperplasia, n=26) and PCA (prostate cancer cases, n=125). Identified expression levels were significantly (p<0.0001) elevated in cases (PCA: 63±19%) versus benign prostatic hyperplasia (BPH: 17±13%) and controls (CONT: 8±9%).

[0053] [Figure 6] Figure 6 is a graph showing the receiver operating characteristic curve analysis. The AUROC was 0.97 and the Youden J index was 0.94. The Z statistic was highly significant (38.9; p<0.0001).

[0054] [Figure 7] Figure 7 is a graph showing ProstaTest's metrics for determining PCA in the 92-99% range.

[0055] [Figure 8] Figure 8 is a probit analysis risk assessment plot determining that a ProstaTest score >30 was 60% accurate (predictive value) in predicting prostate cancer in blood samples. This increased to >80% for test scores >34. Dotted lines represent the 95% confidence interval.

[0056] [Figure 9] Figure 9 is a graph showing a receiver operating characteristic curve analysis comparing ProstaTest with PSA measurements for predicting high-grade (≧Gleason score 7) compared to low-grade (Gleason scores 5 and 6) tumors. The AUROC for ProstaTest was 0.98 and the Youden J index was 0.87. For PSA, the AUROC was 0.64 and the Youden J index was 0.42. ProstaTest was significantly superior to PSA for predicting grade (Z statistic: 3.05, p=0.002).

[0057] [Figure 10] Figure 10 is a graph showing the effect of surgery on ProstaTest. Pre-surgery levels were high (80±18%). Surgery reduced levels to 22±7% (p<0.0001), which was not different from control levels.

[0058] [Figure 11] Figure 11 is a graph showing the effect of treatment on ProstaTest. Pre-treatment levels are elevated (74±18%). In patients who responded to hormone therapy, levels decreased to 18±7% (p<0.0001). In patients requiring chemotherapy, levels decreased to 27±10% (p<0.001). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0059] Further details of the invention are set forth in the accompanying description below.

[0060] Described herein are methods for quantifying (scoring) circulating prostate cancer molecular signatures with high sensitivity and specificity, which are used for purposes including, but not limited to, detecting prostate cancer, determining whether prostate cancer is stable or progressive, distinguishing benign prostatic hyperplasia (BPH) from prostate cancer, determining the completeness of surgery, and assessing response to prostate cancer therapy. Specifically, the present invention is based on the discovery that the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC, normalized by the expression levels of housekeeping genes, are elevated in subjects with prostate cancer compared to healthy subjects or subjects with BPH.

[0061] Symptoms of prostate cancer include problems urinating, bleeding in the urine or semen, erectile dysfunction, pain in the buttocks, back (spine), chest (ribs), or pain in the area affected by the cancer down to the bone, weakness or numbness in the legs or feet, or even inability to control the bladder or bowels from cancer pressing on the spinal cord.

[0062] As described in the Examples, "ProstaTest", a measurement of circulating prostate cancer transcripts, is a method for diagnosing prostate cancer, where a decrease in ProstaTest score correlates with the effectiveness of therapeutic interventions such as surgery or chemotherapy. Target gene expression profiles of prostate cancer RNA can be isolated from the patient's peripheral blood. The expression profile is evaluated by an algorithm and converted into an output (score), which diagnoses and identifies active disease, and provides an assessment of treatment response in conjunction with standard clinical evaluation and imaging.

[0063] Accordingly, the present invention provides a method of detecting prostate cancer in a subject in need thereof, the method comprising: (a) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of the at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_2, TRPR2, TMPRSS2 XPC, and housekeeping genes; and (b) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of the at least 38 biomarkers. The expression levels of the housekeeping genes were compared with those of the following: AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQL E, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to their respective expression levels, thereby AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, and IL-11 were normalized to their respective expression levels. obtaining a normalized expression level of each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) determining the presence of prostate cancer in the subject if the score is equal to or greater than the first predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than the first predetermined cutoff value.

[0064] In some embodiments of the above methods, the method can include a step (e) of generating a report that establishes the presence of prostate cancer in the subject if the score is equal to or greater than a first predetermined cutoff value, or that establishes the absence of prostate cancer in the subject if the score is less than a first predetermined cutoff value.

[0065] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 33% on a scale of 0 to 100%.

[0066] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0067] The disclosure also provides a method of determining whether prostate cancer in a subject is stable or progressive, the method comprising: (a) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, RE PIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) a comparison of the expression levels of said housekeeping genes with respect to the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPA Normalize the expression levels of each of RC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, thereby obtaining the normalized expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC;(c) inputting each of the normalized expression levels into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) determining that the prostate cancer is progressive if the score is equal to or greater than the first predetermined cutoff value, or determining that the prostate cancer is stable if the score is less than the first predetermined cutoff value.

[0068] In some embodiments of the above method, the method can include a step (e) of generating a report that determines the prostate cancer to be aggressive if the score is equal to or greater than a first predetermined cutoff value, or that the prostate cancer is stable if the score is less than a first predetermined cutoff value.

[0069] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 50% on a scale of 0 to 100%.

[0070] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0071] The present invention also provides a method for determining whether prostate cancer in a subject is low grade or high grade, the method comprising: (a) (b) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with those of the housekeeping genes. (c) normalizing the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC to generate a score; (d) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) determining that the prostate cancer is aggressive if the score is equal to or greater than the first predetermined cutoff value and determining that the prostate cancer is aggressive if the score is less than the first predetermined cutoff value;

[0072] In some embodiments of the above method, the method may include a step (e) of generating a report, the report determining that the prostate cancer is aggressive if the score is equal to or greater than a first predetermined cutoff value, or determining that the prostate cancer is indolent if the score is less than a first predetermined cutoff value.

[0073] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 50% on a scale of 0 to 100%.

[0074] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0075] In some embodiments, low-grade prostate cancer is prostate cancer with a Gleason score below 6. In some embodiments, high-grade prostate cancer is prostate cancer with a Gleason score above 7.

[0076] The Gleason grading system is widely used in the art as a prognostic parameter and is often used in conjunction with other prognostic factors or tests for prostate cancer. A prostate biopsy specimen (sample) may be examined, for example, by microscopy, and a Gleason score may be determined by a pathologist based on the architectural pattern of the prostate tumor. The Gleason score is based on the degree of loss of normal glandular tissue architecture (i.e., shape, size, and glandular differentiation). Samples are assigned a grade of 1 for the most common tumor pattern and a grade of 2 for the next most common pattern. Following the first or most common pattern, there may be a second or second most common pattern that can be distinguished; alternatively, they may only be a single grade. The Gleason patterns are associated with the following features: Pattern 1 - the cancerous prostate closely resembles normal prostate tissue. The glands are small, well-formed, and tightly packed. Pattern 2 - the tissue still has well-formed glands, but they are larger and have more tissue between them. Pattern 3 - the tissue still has distinguishable glands, but the cells are darker. At higher magnification, some of these cells have detached from the glands and have begun to invade the surrounding tissue. Pattern 4 - The tissue has few discernible glands. Many cells have invaded the surrounding tissue. Pattern 5 - The tissue has no discernible glands. Often there are only sheets of cells throughout the surrounding tissue. The two grades are added together to get the Gleason score, also called the Gleason sum.

[0077] The disclosure also provides a method of determining whether prostate cancer in a subject is a low Gleason score (≦6) or high Gleason score (≧7) prostate cancer, the method comprising: (a) (b) determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were compared with those of the housekeeping genes. bells to obtain normalized expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC;(c) inputting each of the normalized expression levels into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) determining that the prostate cancer is a high Gleason score prostate cancer if the score is equal to or greater than the first predetermined cutoff value and determining that the prostate cancer is a low Gleason score prostate cancer if the score is less than the first predetermined cutoff value;

[0078] In some embodiments of the above method, the method may include a step (e) of generating a report, wherein the report includes determining that the prostate cancer is a high Gleason score prostate cancer if the score is equal to or greater than a first predetermined cutoff value, and determining that the prostate cancer is a low Gleason score prostate cancer if the score is less than a first predetermined cutoff value.

[0079] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 50% on a scale of 0 to 100%.

[0080] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0081] The present disclosure also provides a method of determining completeness of surgery in a subject having prostate cancer, the method comprising: (a) (b) determining expression levels of at least 38 biomarkers from a test sample of the post-surgery subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with those of the housekeeping genes. (c) normalizing the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC to generate a score; (d) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) determining that the prostate cancer has not been completely removed if the score is equal to or greater than the first predetermined cutoff value, or determining that the prostate cancer has been completely removed if the score is less than the first predetermined cutoff value.

[0082] Some aspects of the above methods can include a step (e) of generating a report, wherein the report includes determining that the prostate cancer has not been completely removed if the score is equal to or greater than a first predetermined cutoff value, or determining that the prostate cancer has been completely removed if the score is less than a first predetermined cutoff value.

[0083] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 33% on a scale of 0 to 100%.

[0084] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0085] A method of distinguishing benign prostatic hyperplasia from prostate cancer in a subject having prostatic hyperplasia is provided, the method comprising: (a) determining expression levels of at least 38 biomarkers from a test sample from a subject after surgery by contacting the test sample with a plurality of agents specific for detecting expression levels of at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS245, PRPR2_2TR, and / or a combination of these. , XPC, and housekeeping genes; (b) a comparison of the expression levels of the housekeeping genes with respect to the expression levels of the housekeeping genes, including AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, S The expression levels of MC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized, thereby detecting the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, PPRC2, PPRC1 ... obtaining a normalized expression level of each of REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) comparing the score to a first predetermined cutoff value; and (e) determining the presence of prostate cancer in the subject if the score is equal to or greater than the first predetermined cutoff value, and determining the presence of benign prostatic hyperplasia in the subject if the score is less than the first predetermined cutoff value.

[0086] In some embodiments of the aforementioned methods, the method can include a step (e) of generating a report, wherein the report comprises determining the presence of prostate cancer in the subject if the score is equal to or greater than a first predetermined cutoff value, or determining the presence of benign prostatic hyperplasia in the subject if the score is less than a first predetermined cutoff value.

[0087] In some embodiments of the aforementioned methods, the first predetermined cutoff value can be 33% on a scale of 0 to 100%.

[0088] In some aspects, the methods can further include administering a primary therapy to the subject.The methods can further include administering a primary therapy to the subject if the score is equal to or greater than a predetermined cutoff value.

[0089] The present disclosure provides a method of treating prostate cancer in a subject in need thereof, the method comprising: (a) administering to a subject a therapeutically effective amount of (b) measuring expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC for the housekeeping genes. (c) normalizing the expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC to obtain a normalized expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (d) inputting the normalized expression level of each of AAMP, ANO7, AR,(d) comparing the score to a first predetermined cutoff value; and (e) administering at least a first line therapy to the subject if the score is equal to or greater than the first predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than the first predetermined cutoff value.

[0090] The disclosure provides a method of assessing the response of a subject having prostate cancer to a first line therapy, the method comprising: (1) determining, at a first time point, expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with a plurality of agents specific for detecting expression of the at least 38 biomarkers, wherein the at least 38 biomarkers are AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_2, TRPR2, TMPRSS2_3, TRPR2_4, TRPR2_5, TRPR2_6, TRPR2_7, TRPR2_8, TRPR2_9, TRPR2_10, TRPR2_12, TRPR2_13, TRPR2_14, TRPR2_15, TRPR2_26, TRPR2_16, TRPR2_17, TRPR2_28, TRPR2_18, TRPR2_29, TRPR2_30, TRPR2_29, TRPR2_31, TRPR2_19, TRPR2_20, TRPR2_32, TRPR2_18, TRPR2_21, TRPR2_22, TRPR2_33, TRPR2_19, TRPR2_23, TRPR2_24, TRPR2_25, TRPR2_34, TRPR2_19, TRPR2_25, TRPR2_35, TRPR (b) comparing the expression levels of the housekeeping genes, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRI, and the like, with respect to the expression levels of the housekeeping genes, By normalizing the expression levels of P1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, respectively, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, and MAP1. (c) obtaining a normalized expression level of each of SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (d) inputting each of the normalized expression levels into an algorithm to generate a first score;(2) at a second time point, the second time point being after the first time point and after administering the first therapy to the subject: (d) determining an expression level of at least 38 biomarkers from the subject's test sample by contacting the test sample with a plurality of agents specific for detecting the expression level of at least 38 biomarkers; (e) The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC relative to the expression levels of the housekeeping genes. (f) to obtain normalized expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, respectively; (3) inputting each of the normalized expression levels into an algorithm to generate a second score; (4) comparing the first score to the second score; and (5) determining that the subject is responsive to the first therapy if the second score is significantly decreased compared to the first score, and determining that the subject is non-responsive to the first therapy if the second score is not significantly decreased compared to the first score.

[0091] In some embodiments of the above methods, the method can include a step (e) of generating a report that includes determining that the subject is responsive to the primary therapy if the second score is significantly reduced compared to the first score, and determining that the subject is non-responsive to the primary therapy if the second score is not significantly reduced compared to the first score.

[0092] In some embodiments, the method can further include continuing to administer the primary therapy to the subject if the second score is significantly reduced compared to the first score. The method can further include ceasing to administer the primary therapy to the subject if the second score is not significantly reduced compared to the first score. The method can further include administering a secondary therapy to the subject if the second score is not significantly reduced compared to the first score.

[0093] In some embodiments of the above method, the second score is considered to be significantly decreased compared to the first score if the second score is at least about 10% less than the first score, at least about 20% less than the first score, at least about 25% less than the first score, at least about 30% less than the first score, at least about 40% less than the first score, at least about 50% less than the first score, at least about 60% less than the first score, at least about 75% less than the first score, at least about 80% less than the first score, at least about 90% less than the first score, at least 95% less than the first score, or at least about 99% less than the first score. In some embodiments, if the second score is not significantly decreased compared to the first score, the subject is considered to be unresponsive to the therapy.

[0094] In some aspects of the aforementioned methods, the first time point can be prior to administering the primary therapy to the subject. The first time point can be after administering the primary therapy to the subject.

[0095] In some embodiments of the methods of the present disclosure, the housekeeping genes include, but are not limited to, ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1. In some embodiments, the housekeeping gene is TOX4.

[0096] In some embodiments of the methods of the present disclosure, the predetermined cutoff value can be about 33% on a scale of 0 to 100%. In some embodiments of the methods of the present disclosure, the predetermined cutoff value can be about 50% on a scale of 0 to 100%. The predetermined cutoff value can be about 60% on a scale of 0 to 100%. The predetermined cutoff value can be about 10%, or about 20%, or about 30%, or about 40%, or about 70%, or about 80%, or about 90% on a scale of 0 to 100%.

[0097] The disclosed methods can have a sensitivity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The disclosed methods can have a sensitivity of greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0098] The disclosed methods can have a specificity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The disclosed methods can have a specificity of greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0099] The disclosed methods can have an accuracy (precision) of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The disclosed methods can have an accuracy of more than about 50%, or more than about 60%, or more than about 70%, or more than about 75%, or more than about 80%, or more than about 85%, or more than about 90%, or more than about 95%, or more than about 99%.

[0100] In some embodiments of the methods of the present disclosure, the predetermined cut-off value, e.g., the first predetermined cut-off value, is derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. The plurality of reference samples can be about 2-500, 2-200, 10-100, or 20-80. Each reference sample generates a score using an algorithm, and the first predetermined cut-off value can be, for example, the arithmetic mean of those scores. Each reference sample can be blood, serum, plasma, or non-neoplastic tissue. In some embodiments, each reference sample is blood. In some embodiments, each reference sample is of the same type as the test sample.

[0101] In some aspects of the disclosed methods, the test sample can include any biological fluid obtained from a subject. In some aspects, the test sample can include blood, serum, plasma, tumor tissue, or any combination thereof. In some aspects, the test sample includes blood. In some aspects, the test sample includes serum. In some aspects, the test sample includes plasma.

[0102] In some aspects of the methods of the present disclosure, the reference sample can include any biological fluid obtained from a subject. In some aspects, the reference sample includes blood, serum, plasma, tumor tissue, or any combination thereof. In some aspects, the reference sample includes blood. In some aspects, the reference sample includes serum. In some aspects, the reference sample includes plasma.

[0103] Each biomarker disclosed herein may have one or more transcript variants. The methods disclosed herein can measure the expression level of any one of such transcript variants for each biomarker.

[0104] The expression level can be measured in a variety of ways, including, but not limited to, measuring the mRNA encoded by a particular gene; measuring the amount of a protein encoded by a particular gene; and measuring the activity of a protein encoded by a particular gene.

[0105] The biomarker can be RNA, cDNA or protein. If the biomarker is RNA, the RNA is reverse transcribed to generate cDNA (e.g., by RT-PCR) and the expression level of the generated cDNA is detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer. If the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and a labeled nucleic acid probe or primer can be a hybridization complex.

[0106] Gene expression can also be detected by microarray analysis. Differential gene expression can also be identified or confirmed using microarray technology. Thus, expression profile biomarkers can be measured in either fresh or fixed tissues using microarray technology. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) are coated or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is typically total RNA isolated from biological samples, and corresponding normal tissues or cell lines can be used to measure differential expression.

[0107] In some embodiments of the microarray technique, PCR-amplified cDNA clone inserts are applied to a substrate in the form of a high-density array. In some embodiments, at least 10,000 nucleotide sequences are applied to the substrate. The microarray genes immobilized on the microchip at a density of 10,000 elements each are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from the tissue of interest. The labeled cDNA probes immobilized on the chip hybridize specifically to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by a device such as a confocal laser microscope or by another detection method such as a CCD camera. Quantification of hybridization of each array element allows assessment of the abundance of the corresponding mRNA. Using dual-color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized to the array in a pairwise fashion. In this way, the relative abundance of the transcripts from the two sources corresponding to each particular gene is determined simultaneously. Microarray analysis can be performed by commercially available equipment following the manufacturer's protocols.

[0108] In some embodiments, biomarkers can be detected in biological samples using qRT-PCR. The first step in gene expression profiling by RT-PCR is to extract RNA from a biological sample, followed by reverse transcription of the RNA template into cDNA and amplification by PCR reaction. The reverse transcription step is generally primed with specific primers, random hexamers, or oligo-dT primers, depending on the final goal of expression profiling. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0109] When the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be any label, such as a fluorescent label, a chemiluminescent label, a radioactive label, etc. Typical protein detection methods include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, the biomarker can be detected in an ELISA in which the biomarker antibody is bound to a solid phase and an enzyme-antibody complex is used to detect and / or quantify the biomarker present in the sample. Alternatively, a Western blot assay can be used in which the solubilized and separated biomarker is bound to nitrocellulose paper. The combination of a highly specific and stable liquid complex and a highly sensitive chromogenic substrate allows for rapid and accurate identification of the sample.

[0110] In some aspects of the disclosed methods, the methods described herein can have a specificity, sensitivity and / or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0111] In some aspects of the disclosed methods, the labeled probe, labeled primer, labeled antibody, or labeled nucleic acid can comprise a fluorescent label.

[0112] Any algorithm capable of assigning a score to a sample can be used in the method of the present disclosure, which can be created by evaluating where the sample's value falls within the range of a predictive model that is constructed using various techniques, such as decision trees. The algorithm analyzes the data (i.e., expression levels) and then assigns a score. In some embodiments, the algorithm can be a machine learning algorithm. Exemplary algorithms that can be used in the method of the present disclosure include, but are not limited to, XGBoost (XGB), Random Forest (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-Neaest Neighbors (kNN), Neural Networks (nnet), Support Vector Machines Radial (SVM-Radial), Support Vector Machines Linear (SVM-Linear), Naive Bayes (NB), Layered Perceptron (mlp), or any combination thereof.

[0113] In some aspects of the disclosed methods, the algorithm can be XGB (also called XGBoost), which is an implementation of gradient boosting decision trees designed for improved analysis speed and performance.

[0114] In some aspects of the methods of the present disclosure, the therapy, e.g., the primary or secondary therapy, can include active surveillance, surgery, radiation therapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone metastasis treatment, immunotherapy, or any combination thereof.

[0115] In some aspects of the disclosed methods, active surveillance can include a doctor's visit with a prostate specific antigen blood test and digital rectal examination about every six months. Active surveillance can also include a prostate biopsy, which can be performed annually.

[0116] In some aspects of the methods of the present disclosure, the surgery may include a radical prostatectomy.

[0117] In some aspects of the disclosed methods, radiation therapy can include external beam radiation therapy and brachytherapy.

[0118] Cryotherapy, also called cryosurgery or cryoablation, is the use of extremely low temperatures to freeze and kill prostate cancer cells.

[0119] In some aspects of the disclosed method, hormone therapy can include androgen deprivation therapy or androgen suppression therapy. The goal is to reduce the body's levels of male hormones called androgens or to stop them from acting on prostate cancer cells. Hormonal therapy can include orchiectomy. Hormonal therapy can also include administration of compounds that reduce androgen levels, such as luteinizing hormone releasing hormone (LHRH) agonists, LHRH antagonists and CYP17 inhibitors. Known LHRH agonists include, but are not limited to, leuprolide, goserelin, triptorelin, and histrelin. Known LHRH antagonists include degarelix. Known CYP17 inhibitors include abiraterone. Hormonal therapy can also include administration of antiandrogens, such as flutamide, bicalutamide, nilutamide, and enzalutamide. Hormonal therapy may also include administration of androgen suppressants, such as estrogen and ketoconazole.

[0120] In some aspects of the methods of the present disclosure, chemotherapy can include docetaxel, cabazitaxel, mitoxantrone, estramustine, or combinations thereof.

[0121] In some aspects of the methods of the present disclosure, the vaccine therapy may include Sipuleucel-T.

[0122] If the cancer has grown outside the prostate, the main goal of treatment is to prevent or slow the spread of the cancer to the bone. Treatment for bone metastases can include bisphosphonates (e.g., zoledronic acid), denosumab, corticosteroids, external beam radiation therapy, radiopharmaceuticals (e.g., strontium-89, samarium-153, or radium-223), and painkillers.

[0123] The response of a subject with prostate cancer to a therapy can also be evaluated by comparing the scores determined by the same algorithm at different time points of the therapy. For example, the first time point can be before or after the subject is administered the therapy; the second time point is after the first time point and after the subject is administered the therapy. A first score is generated at the first time point, and a second score is generated at the second time point. If the second score is significantly reduced compared to the first score, the subject is considered to be responsive to the therapy. In some embodiments, the second score is significantly reduced compared to the first score if it is at least 10% less than the first score, at least 20% less than the first score, at least 25% less than the first score, at least 40% less than the first score, at least 50% less than the first score, at least 75% less than the first score, or at least 90% less than the first score. If the second score is not significantly reduced compared to the first score, the subject is considered to be unresponsive to the therapy.

[0124] Sequence information for prostate cancer biomarkers and housekeeping genes is shown in Table 1 below.

[0125] Table 1. Prostate cancer biomarker / housekeeping sequence information [Table 1-1] [Table 1-2]

Table 1-3

Table 1-4

Table 1-5

Table 1-6

Table 1-7

Table 1-8

Table 1-9

Table 1-10

Table 1-11

Table 1-12

Table 1-13

Table 1-14

Table 1-15

Table 1-16

Table 1-17

Table 1-18

Table 1-19

Table 1-20

Table 1-21

Table 1-22

Table 1-23

Table 1-24

Table 1-25

Table 1-26

Table 1-27

Table 1-28

Table 1-29

Table 1-30

Table 1-31

Table 1-32

Table 1-33

Table 1-34

Table 1-35

Table 1-36

Table 1-37

Table 1-38

Table 1-39

Table 1-40

Table 1-41

Table 1-42

Table 1-43

Table 1-44

Table 1-45

Table 1-46

Table 1-47

Table 1-48

Table 1-49

Table 1-50

Table 1-51

Table 1-52

Table 1-53

Table 1-54

Table 1-55

Table 1-56

Table 1-57

Table 1-58

Table 1-59

Table 1-60

Table 1-61

Table 1-62

Table 1-63

Table 1-64

Table 1-65

Table 1-66

Table 1-67

Table 1-68

Table 1-69

[0126] [Definition] The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0127] The term "and / or" is used herein to mean either "and" or "or," unless otherwise indicated.

[0128] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to mean a polymeric form of nucleotides of at least 10 bases or base pairs in length, either ribonucleotides or deoxyribonucleotides, or modified forms of either type of nucleotide, and are meant to include single-stranded or double-stranded forms of DNA. As used herein, the nucleic acid molecule or nucleic acid sequence that serves as a probe in a microarray analysis preferably comprises a strand of nucleotides, more preferably a DNA and / or RNA strand. In another embodiment, the nucleic acid molecule or nucleic acid sequence comprises other types of nucleic acid structures, such as DNA / RNA helices, peptide nucleic acid (PNA), locked nucleic acid (LNA) and / or ribozymes. Thus, as used herein, the term "nucleic acid molecule" also encompasses strands that comprise non-natural nucleotides, modified nucleotides and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0129] As used herein, the terms "hybridize," "hybridizing," "hybridize," and the like, when used with respect to polynucleotides, refer to hybridization under conventional hybridization conditions, such as in 50% formamide / 6xSSC / 0.1% SDS / 100 μg / mL ssDNA, where the hybridization temperature is above about 37°C (degrees Celsius) and the washing temperature in 0.1xSSC / 0.1% SDS is above about 55°C, and are preferably meant to refer to stringent hybridization conditions.

[0130] As used herein, the term "normalization" or "normalizer" refers to the expression of a differential value relative to a standard value to adjust for effects resulting from technical variations due to sample handling, sample preparation, and measurement methodology rather than biological variations in the concentration of a biomarker in a sample. For example, when measuring the expression of a differentially expressed protein (a differentially expressed protein), the absolute value of the expression of the protein can be expressed relative to the absolute value of the expression of a standard protein that is substantially constant during expression.

[0131] The terms "diagnosis" and "diagnostic method" encompass the terms "prognosis" and "prognostic method," respectively, as well as the application of such procedures across two or more time points to monitor diagnosis and / or prognosis over time, and statistical modeling based thereon. Furthermore, the term diagnosis encompasses a. prediction (determining whether a patient is prone to develop aggressive disease (highly proliferative / invasive)), b. prognosis (predicting whether a patient is prone to have a benign or malignant outcome at a predetermined time in the future), c. treatment selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0132] "Accuracy (precision)" refers to the closeness of fit of a measured or calculated quantity (test-reported value) to its actual (or true) value. Clinical accuracy refers to the ratio of true outcomes (true positive (TP) or true negative (TN)) to incorrectly classified outcomes (false positive (FP) or false negative (FN)), and it can be presented as sensitivity, specificity, positive predictive value (positive predictive value; PPV) or negative predictive value (negative predictive value; NPV), among other measures, or also as likelihood, odds ratio.

[0133] The term "biological sample" as used herein refers to any sample of biological origin that potentially contains one or more biomarkers. Examples of biological samples include tissues, organs, or bodily fluids, such as whole blood, plasma, serum, tissue, lavage fluid, or any other specimen used to detect disease.

[0134] The term "subject" as used herein refers to a mammal, particularly a human. In some embodiments, the subject has at least one symptom of prostate cancer. In some embodiments, the subject has a tendency or family history of developing prostate cancer. The subject may have been previously diagnosed with prostate cancer and is examined for recurrence of cancer. In some embodiments, the subject has benign prostatic hyperplasia.

[0135] The terms "treating" or "treatment" as used herein with respect to a condition can refer to slowing the onset or rate of development of the condition, reducing the risk of occurrence of the condition, preventing or delaying the onset of symptoms associated with the condition, attenuating or terminating symptoms associated with the condition, causing complete or partial regression of the condition, or some combination thereof.

[0136] Biomarker levels may change due to treatment of a disease. Changes in biomarker levels can be measured by the methods of the present disclosure. Changes in biomarker levels can be used to monitor disease progression or treatment.

[0137] "Altered," "altered," or "significantly different" refers to a detectable change or difference from a nearly equivalent state, profile, measurement, etc. Such changes can be all or nothing. They can be incremental and not necessarily linear. They can be by orders of magnitude. The changes can be an increase or decrease of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or any value between 0% and 100%. Alternatively, the change can be 1-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold or more, or any value between 1-fold and 5-fold. The change can be statistically significant at a p-value of 0.1, 0.05, 0.001, or 0.0001.

[0138] The term "stable disease" refers to a diagnosis of the presence of prostate cancer, but the prostate cancer has been treated and remains stable, i.e., not progressive, as determined by imaging data and / or best clinical judgment.

[0139] The term "progressive disease" refers to a diagnosis of the presence of prostate cancer in a highly active state, i.e., the subject is untreated and not stable, is treated but is not responding to treatment, or is treated but has persistent active disease, as determined by imaging data and / or best clinical judgment.

[0140] The term "neoplastic disease" refers to any abnormal growth of cells or tissues, either benign (non-cancerous) or malignant (cancerous). For example, the neoplastic disease can be prostate cancer.

[0141] The term "neoplastic tissue" refers to a mass of abnormally proliferating cells.

[0142] The term "non-neoplastic tissue" refers to a mass of normally proliferating cells.

[0143] The term "immunotherapy" can refer to immunostimulatory therapy or immunosuppressive therapy. As understood by those skilled in the art, immunostimulatory therapy refers to the use of therapeutic agents that induce, enhance or boost immune response, such as T cell response, while immunosuppressive therapy refers to the use of therapeutic agents that interfere with, suppress or inhibit immune response, such as T cell response. Immunostimulatory therapy can include the use of checkpoint inhibitors. Immunostimulatory therapy can include administering to a subject therapeutic agents that activate stimulatory checkpoint molecules. Stimulatory checkpoint molecules include, but are not limited to, CD27, CD28, CD40, CD122, CD137, OX40, GITR and ICOS. Therapeutic agents that activate stimulatory checkpoint molecules include, but are not limited to, MEDI0562, TGN1412, CDX-1127, lipocalin.

[0144] The term "antibody" is used herein in the broadest sense and encompasses a variety of antibody structures, including but not limited to monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments, so long as they exhibit the desired antigen-binding activity. An antibody that binds to a target refers to an antibody that can bind to a target with sufficient affinity such that the antibody is useful as a diagnostic and / or therapeutic agent by targeting the target. In one embodiment, the extent of binding of an anti-target antibody to an unrelated non-target protein is less than about 10% of the binding of the antibody to the target, as measured, for example, by radioimmunoassay (RIA) or Biacore® assay. In certain embodiments, an antibody that binds to a target has a dissociation constant (Kd) (e.g., 10 -8 M or less, e.g. 10 -8 M to 10 -13 M, for example 10 -9 M to 10 -13 In certain embodiments, the anti-target antibody binds to an epitope of the target that is conserved among different species.

[0145] A "blocking antibody" or "antagonist antibody" partially or completely blocks, inhibits, interferes with, or neutralizes the normal biological activity of the antigen to which it binds. For example, an antagonist antibody can block signaling through an immune cell receptor (e.g., a T cell receptor) to restore a functional response by a T cell to an antigen (e.g., proliferation, cytokine production, target cell killing) from a dysfunctional state.

[0146] An "agonist antibody" or "activating antibody" is an antibody that mimics, promotes, stimulates, or enhances the normal biological activity of the antigen to which it binds. An agonist antibody can also enhance or initiate signaling by the antigen to which it binds. In some embodiments, an agonist antibody initiates or activates signaling without the presence of a native ligand. For example, an agonist antibody can increase memory T cell proliferation, increase cytokine production by memory T cells, inhibit regulatory T cell function, and / or inhibit effector T cell function, such as regulatory T cell suppression of effector T cell proliferation and / or cytokine production.

[0147] "Antibody fragment" refers to a molecule that is not a complete antibody, but contains a portion of the complete antibody that binds to the antigen that the complete antibody binds. Examples of antibody fragments include, but are not limited to, Fv, Fab, Fab', Fab'-SH, F(ab')2; diabodies; linear antibodies; single-chain antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0148] Administering chemotherapy to a subject can include administering a therapeutically effective amount of at least one chemotherapeutic agent, including, but not limited to, 13-cis-retinoic acid, 2-CdA, 2-chlorodeoxyadenosine, 5-azacytidine, 5-fluorouracil (5-FU), 6-mercaptopurine (6-MP), 6-thioguanine (6-TG), abemaciclib, abiraterone acetate, abraxane, accutane, actinomycin D, adcetris, adotrastuzumab, emtansine, adriamycin, adrsil, afatinib, afinitor, agrilin, alaquat, aldesleukin, alemtuzumab, and argenteuil. Sensa, Alectinib, Alimta, Alitretinoin, Alkaban-AQ, Alkeran, All-trans retinoic acid, α-interferon, Altretamine, Alumbrig, Amethopterin, Amifostine, Aminoglutethimide, Anagrelide, Anandrone, Anastrozole, Apalutamide, Arabinosylcytosine, Ara-C, Aranesp, Aredia, Arimidex, Aromasin, Alanone, Arsenic trioxide, Alzera, Asparaginase, Atezolizumab, Atra, Avastin, Avelumab, Axicabtagenesis Loleucel, Axitinib, Azacitidine, Babencio, Bcg, Beleodac, Belinostat, Bendamustine, Bendeca, Besponsa, Bevacizumab, Bexarotene, Bexar, Bicalutamide, BiCNU, Blenoxane, Bleomycin, Blinatumomab, Brincito, Bortezomib, Bosulif, Bosutinib, Brentuximab Vedotin, Brigatinib, Busulfan, Busulfex, C225, Cabazitaxel, Cabozantinib, Calcium Leucovorin, Campath, Camptosar, Camptothecin-11, Ca Pecitabine, Caprelsa, Carac, Carboplatin, Carfilzomib, Carmustine, Carmustine Wafer, Casodex, CCI-779, Ccnu, Cddp, Ceenu, Ceritinib, Cervizin, Cetuximab, Chlorambucil, Cisplatin, Citrovorum Factor, Cladribine, Clofarabine, Chlorar, Cobimetinib, Cometrik, Cortisone, Cosmegen, Cotellic, Cpt-11, Crizotinib, Cyclophosphamide, Ciramza, Cytadren, Cytarabine, Cytarabine Liposomal, Cytosar U,Cytoxan, Dabrafenib, Dacarbazine, Dacogen, Dactinomycin, Daratumumab, Darbepoetin alfa, Darzalex, Dasatinib, Daunomycin, Daunorubicin, Daunorubicin cytarabine (liposomal), Daunorubicin hydrochloride, Daunorubicin liposomal, Daunoxome, Decadron, Decitabine, Degarelix, δ-Cortef, Deltasone, Denileukin diftitox, Denosumab, DepoCyt, Dexamethasone, Dexamethasone acetate, Dexamethasone sodium phosphate, Dexasone, Dexrazoxane, Dhad, D ic, Geodex, docetaxel, doxil, doxorubicin, doxorubicin liposome, droxia, DTIC, Dtic-Dome, Duralon, durvalumab, eculizumab, efdex, erence, elotuzumab, eloxatin, erspar, eltrombopag, Emcyt, emplici, enasidenib, enzalutamide, epirubicin, epoetin alfa, erbitux, eribulin, elivege, erlida, erlotinib, erwinia L-asparaginase, estramustine, ethiol, etopofos, etoposide, eto Poside phosphate, Eurexin, Everolimus, Evista, Exemestane, Fareston, Farydak, Faslodex, Femara, Filgrastim, Filmagon, Floxuridine, Fludara, Fludarabine, Fluoplex, Fluorouracil, Fluorouracil (cream), Fluoxymesterone, Flutamide, Folinic acid, Folotin, Fudr, Fulvestrant, G-Csf, Gazyva, Gefitinib, Gemcitabine, Gemtuzumab ozogamicin, Gemzar, Gilotrif, Gleevec, Gleastin, Gliadelweiss Her, Gm-Csf, Goserelin, Granix, Granulocyte colony-stimulating factor, Granulocyte-macrophage colony-stimulating factor, Halaven, Halotestin, Herceptin, Hexadrol, Hexalen, Hexamethylmelamine, Hmm, Hycamtin, Hydrea, Hydrocort acetate, Hydrocortisone, Hydrocortisone sodium phosphate, Hydrocortisone sodium succinate, Hydrocortone phosphate, Hydroxyurea, Ibrance, Ibritumomab, Ibritumomab tiuxetan, Ibrutinib, Iclusig, Idamycin, Idarubicin,Idelalisib, Idifur, Ifex, IFN-α, Ifosfamide, IL-11, IL-2, Imbruvica, Imatinib mesylate, Imfinzi, Imidazole carboxamide, Imligic, Inrita, Inotuzumab ozogamicin, Interferon-α, Interferon α-2b (PEG conjugate), Interleukin-2, Interleukin-11, Intron A (Interferon α-2b), Ipilimumab, Iressa, Irinotecan, Irinotecan (liposomal), Isotretinoin, Istodax, Ixabepilone , Ixazomib, Ixempra, Jakafi, Jevtana, Kadcyla, Keytruda, Quidrolase, Kisqali, Kymriah, Kyprolis, Lanacort, Lanreotide, Lapatinib, Latorvo, L-Asparaginase, Lubrance, Lcr, Lenalidomide, Lenvatinib, Lenvima, Letrozole, Leucovorin, Leukeran, Leukin, Leuprolide, Leulocristine, Leustatin, Liposomal Ara-C, Liquid Pred, Lomustine, Lonsurf, L-PAM, L-Sarcolysin, Lupron, Lupron Depot, Lynparza, Marchivo, Matulan, Maxidex, mechlorethamine, mechlorethamine hydrochloride, medralone, medrol, megas, megestrol, megestrol acetate, mekinist, mercaptopurine, mesna, mesnex, methotrexate, methotrexate sodium, methylprednisolone, methycortene, midostaurin, mitomycin, mitomycin-C, mitoxantrone, M-prednisolone, MTC, MTX, mustalgen, mustine, mutamycin, myleran, mirocel, mylotarg, navelbine, necitumumab, nelarabine, neosal, neratinib, ne Lulinx, Neulasta, Neumega, Neupogen, Nexavar, Nilandrone, Nilotinib, Nilutamide, Ninlaro, Nipent, Niraparib, Nitrogen Mustard, Nivolumab, Nolvadex, Novantron, Nplate, Obinutuzumab, Octreotide, Octreotide acetate, Odomzo, Ofatumumab, Olaparib, Olaratumab, Omacetaxine, Oncospar, Oncovin, Onivyde, Ontac, Onxar, Opdivo, Oprelvekin, Olapred, Orazon, Osimertinib, Otrexap, Oxaliplatin,Paclitaxel, paclitaxel protein-bound, palbociclib, pamidronate, panitumumab, panobinostat, panretin, paraplatin, pazopanib, pediapred, peginterferon, pegaspargase, pegfilgrastim, pegintron, PEG-L-asparaginase, pembrolizumab, pemetrexed, pentostatin, perjeta, pertuzumab, phenylalanine mustard, platinol, platinol-AQ, pomalidomide, pomalyst, ponatinib, portraza, pralatrexate, prednisolone, pred Nisone, Prelon, Procarbazine, Procrit, Proleukin, Prolia, Carmustine Implant Containing Prolife Prospan 20, Promacta, Provenge, Prinethol, Radium 223 Dichloride, Raloxifene, Ramucirumab, Lasbo, Regorafenib, Revlimid, Rheumatrex, Ribociclib, Rituxan, Rituxan Hycera, Rituximab, Rituximab Hyalurodinase, Roferon-A (Interferon α-2a), Romidepsin, Romiplostim, Rubex, Rubidomycin Hydrochloride, Rubraca, Rucaparib, Luxo Litinib, Rhydapt, Sandostatin, Sandostatin LAR, Sargramostim, Siltuximab, Sipuleucel-T, Soliris, Sol-Cortef, Sol-Medrol, Somatulin, Sonidegib, Sorafenib, Sprycel, Sti-571, Stivarga, Streptozocin, SU11248, Sunitinib, Stent, Silvant, Synribo, Tafinral, Tagrisso, Talimogene laherparepvec, Tamoxifen, Tarceva, Targretin, Tasigna, Taxol, Taxotere, Tecentriq, Temodar, Temozolomide, Temsirolimus, Teni Poside, Tespa, Thalidomide, Talomi, Terasis, Thioguanine, Thioguanine tabloid, Thiophosphoamide, Thioplex, Thiotepa, Tice, Tisagenlecleucel, Toposal, Topotecan, Toremifene, Tolicel, Tositumomab, Trabectedin, Trametinib, Trastuzumab, Treanda, Trelstar, Tretinoin, Trexal, Trifluridine / Tipiricil, Triptorelin pamoate, Trisenox, Tspa, T-VEC, Tykerb, Valrubicin, Valstar, Vandetanib, VCR, Vectibix, Velban,Velcade, Vemurafenib, Venclexta, Venetoclax, Bepcid, Besenio, Vesanoid, Viadur, Vidaza, Vinblastine, Vinblastine Sulfate, Vincasar Pfs, Vincristine, Vincristine Liposomal, Vinorelbine, Vinorelbine Tartrate, Vismodegib, Vlb, VM-26, Vorinostat, Votrient, VP-16, Vumon, Vyxeos, Xalkori Capsules, Xeroda, Xygeva, Xofigo, Xitandi, Yervoy, Escata, Yondelis, Zaltrap, Zanosar, Zalxio, Zejula, Zelboraf, Zevalin, Zinecard, Ziv-Aflibercept, Zoladex, Zoledronic Acid, Zolinza, Zometa, Zydelig, Zykadia, Zytiga, or any combination thereof.

[0149] The terms "effective amount" and "therapeutically effective amount" of an agent or compound are used in the broadest sense to refer to a nontoxic but sufficient amount of an active agent or compound to provide a desired effect or benefit.

[0150] The term "benefit" is used in the broadest sense and refers to any desired effect, specifically including clinical benefit as defined herein.Clinical benefit can be measured by evaluating various endpoints, including, for example, inhibition of disease progression to some extent, for example slowing down and complete halt, reduction in the number of disease episodes and / or symptoms; reduction in lesion size; inhibition of diseased cell infiltration into nearby peripheral organs and / or tissues (i.e. reduction, slowing down or complete halt); inhibition of disease spread (i.e. reduction, slowing down or complete halt); reduction in autoimmune reaction, which may lead to regression or ablation of lesions, but does not necessarily have to; alleviation of one or more symptoms associated with the disorder to some extent; increase in the length of disease-free presentation period after treatment, for example, increase in disease-free survival rate; increase in overall survival rate; high response rate; and / or reduction in mortality at a given time point after treatment.

[0151] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. This definition includes benign and malignant cancers. Examples of cancer include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia. More specific examples of such cancers include adrenocortical carcinoma, bladder urothelial carcinoma, invasive breast cancer, cervical squamous cell carcinoma, cervical squamous cell carcinoma, cervical adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, renal clear cell carcinoma, kidney papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, uterine carcinosarcoma, and uveal melanoma. Other examples include breast cancer, lung cancer, lymphoma, melanoma, liver cancer, colorectal cancer, ovarian cancer, bladder cancer, renal cancer or gastric cancer. Further examples of cancer include neuroendocrine cancer, non-small cell lung cancer (NSCLC), small cell lung cancer, thyroid cancer, endometrial cancer, bile duct cancer, esophageal cancer, anal cancer, salivary gland cancer, vulvar cancer, or cervical cancer.

[0152] The term "tumor" refers to all tumorigenic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The terms "cancer," "cancerous," "cell proliferative disorder," "proliferative disorder," and "tumor" are not mutually exclusive as referred to herein.

[0153] The term "about" when used in conjunction with numerical values ​​and / or ranges herein generally refers to numerical values ​​and / or ranges that are close to the stated numerical values ​​and / or ranges. In some cases, the term "about" can mean within ±10% of the stated value. For example, in some cases, "about 100 [units]" can mean within ±10% of 100 (e.g., from 90 to 110). EXAMPLES

[0154] The present disclosure is further illustrated by the following examples, which should not be construed as limiting the disclosure in scope or spirit to the specific procedures described herein. It should be understood that the examples are provided to illustrate specific embodiments, and no limitation to the scope of the present disclosure is intended thereby. It should be further understood that it may be necessary to resort to other various embodiments, modifications, and their equivalents that may be suggested to those skilled in the art without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0155] Example 1. Derivation of a 38-marker gene panel

[0156] Two microarray datasets (E-GEOD-46691 and E-GEOD-46602, Table 2) were used as the derivation cohort (n=595 samples). A Random Forest algorithm was applied to each dataset to identify the most significant set of transcripts that were predictors of phenotypic diversity within each set. Each microarray dataset consisted of 22,011 and 54,675 probe sets, respectively. The marker selection algorithm driving the Random Forest identified n=129 transcripts as predictors of disease progression across the two datasets. Of these, n=30 displayed high predictive importance scores within both datasets (Figures 1A-1B). We then validated the prostate cancer predictive signature using three microarray datasets (E-GEOD-62116, E-GEOD-62667, and E-GEOD-72220, Table 2). Predictive importance and Kruskal-Wallis p-values ​​were obtained for each transcript across the validation cohort (n=564 samples) and averaged across the three datasets (Figure 2). A search of the literature identified ARv7 variants, ERG-TRMS22 fusion genes, and AR1 / AR2 signaling as additional gene sets to be included and evaluated.

[0157] Evaluation of the transcripts (n=20) in a preliminary dataset of blood samples from prostate cancer (n=20) and matched normal blood (n=20) confirmed the expression of 37 genes as markers of PCA (Table 3). These genes were demonstrated to be highly expressed in PCA tumor tissues compared to normal prostate, and this fact could be used to effectively distinguish tumors from controls (Figure 3). The genes also identified seven different PCA cell lines, namely, 22Rv1 and E006AA-hT (localized); VCaP; PC-3; LNCaP; DU145 and MDA PCa2b (all metastatic) from two normal prostate epithelial cell lines PWR-1E and RWPE-1 (Figure 4). These data demonstrate that the candidate target transcripts are produced by neoplastically transformed prostate epithelial cells and are detectable in blood.

[0158] Normalized gene expression of those 37 markers in whole blood from control (n=100) and PCA (n=21) samples was used to build an artificial intelligence model of prostate cancer disease. The dataset was randomly split into training and testing partitions for model building and validation. Twelve algorithms were evaluated (XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, and mlp). The best performing algorithm (XGB - "Gradient Boosting") best predicted the training data. In the test set, XGB generated a probability score to predict a sample. Each probability score reflects the algorithm's "certainty" that an unknown sample belongs to either the "control" or "PCA" class. For example, an unknown sample S1 may have the following probability vector [control=20%, PCA=80%]. This sample is considered a PCA sample.

[0159] Table 2. Summary of all public microarray datasets used to derive the prostate cancer specific gene signature. [Table 2]

[0160] Table 3. 37 PCA marker gene panel (housekeeping genes not included). [Table 3-1] [Table 3-2] [Table 3-3]

[0161] Example 2. Clinical utility

[0162] ProstaTest scores were significantly (p<0.001) elevated in PCA patients (63±19%) compared to men with benign prostatic hyperplasia (17±13%) and controls (8±9%) (Figure 5). No differences were observed between controls and hypertrophy patients. Data (Receiver Operating Characteristic Curve Analysis and Metric Data) on the utility of the test to distinguish prostate cancer patients (n=125) from controls (n=201) in the validation are included in Figure 6. The score showed an area under the curve (AUROC) of 0.97. The metric is sensitivity: 92% and specificity: 99% (Figure 7). Youden index J was 0.94 and the Z statistic for discriminating controls was 38.9.

[0163] A probit-risk assessment plot identified that a ProstaTest score >30 was 50% accurate (precision) in predicting PCA in blood samples (Figure 8). This increased to 60% for ProstaTest scores of ≥32 and >80% for scores of >34. Thus, this tool can accurately discriminate between controls and prostate cancer disease.

[0164] ProstaTest scores were significantly (p<0.001) elevated in the PCA of high-grade (Gleason score ≥7; 70±19%) compared to the PCA of low-grade (Gleason score 5+6) (41±7%). Data on the utility of the test for distinguishing high-grade from low-grade scores (Receiver Operating Characteristic Curve Analysis and Metric Data) are given in Figure 9. The score showed an area under the curve (AUROC) of 0.98. Metrics are sensitivity: 100% and specificity: 88%. Youden index J is 0.87. PSA levels, in comparison, showed an AUROC of 64%. Sensitivity and specificity were 54% and 87%, respectively. The Z statistic for distinguishing ProstaTest from PSA was 3.05 (p=0.002).

[0165] Specific evaluation of the pre- and post-operative PCA cohort determined that complete removal of tumor and absence of evidence of disease was associated with a significant decrease (p<0.0001) in ProstaTest. No significant differences in levels were observed between controls or patients with prostatic hyperplasia. Evaluation of another cohort revealed that patients who received and responded to treatment showed significantly lower scores (p<0.001) than those diagnosed with disease (Figure 11). Treatment included hormonal therapy and chemotherapy. Thus, this tool can accurately identify treatment response in prostate cancer.

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[0197] While the present invention has been described in conjunction with the specific embodiments set forth above, many alternatives, modifications and other variations will be apparent to those skilled in the art. All such alternatives, modifications and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. 1. An in vitro method of analyzing a blood sample to aid in detecting prostate cancer in a subject, comprising: determining expression levels of at least 38 biomarkers from a blood sample of the subject by contacting the blood sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with those of the housekeeping genes.

3. normalize the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; inputting each of said normalized expression levels into an algorithm to generate a probability score, wherein the algorithm is a predictive model generated using one or more machine learning algorithms; comparing the probability score to a first predetermined cutoff value; and A method comprising determining that there is a high possibility of prostate cancer cells being present in the subject if the probability score is equal to or greater than a first predetermined cutoff value, or determining that there is a low possibility of prostate cancer cells being present in the subject if the probability score is less than a first predetermined cutoff value.

2. 1. An in vitro method for analyzing a blood sample to assist in determining the likelihood of residual prostate cancer cells remaining in a subject having prostate cancer after surgery, comprising: determining expression levels of at least 38 biomarkers from a blood sample of the subject by contacting the blood sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of the housekeeping genes were compared with those of the following: AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STR Normalizing the expression levels of each of IP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, thereby obtaining normalized expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; inputting each of said normalized expression levels into an algorithm to generate a probability score, wherein the algorithm is a predictive model generated using one or more machine learning algorithms; comparing the probability score to a first predetermined cutoff value; and A method comprising: determining that there is a high possibility that prostate cancer cells remain if the probability score is equal to or greater than a first predetermined cutoff value; or determining that there is a low possibility that prostate cancer cells remain if the probability score is less than a first predetermined cutoff value.

3. 1. An in vitro method of analyzing a blood sample to aid in distinguishing benign prostatic hyperplasia from prostate cancer in a subject with prostatic hyperplasia, comprising: determining expression levels of at least 38 biomarkers from a blood sample of the subject by contacting the blood sample with a plurality of agents specific for detecting expression of at least 38 biomarkers, wherein said at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and housekeeping genes; The expression levels of the housekeeping genes were compared with those of the following: AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STR Normalizing the expression levels of each of IP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC, thereby obtaining normalized expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; inputting each of said normalized expression levels into an algorithm to generate a probability score, wherein the algorithm is a predictive model generated using one or more machine learning algorithms; comparing the probability score to a first predetermined cutoff value; and The method comprises determining that there is a high possibility that prostate cancer cells are present in the prostate hyperplasia in the subject if the probability score is equal to or greater than a first predetermined cutoff value, or determining that there is a low possibility that prostate cancer cells are present in the prostate hyperplasia in the subject if the probability score is less than a first predetermined cutoff value.

4. The method of any one of claims 1 to 3, wherein the housekeeping genes are selected from the group consisting of ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1.

5. The method of claim 4, wherein the housekeeping gene is TOX4.

6. The method according to any one of claims 1 to 5, wherein said first predetermined cut-off value is at least 33% on a scale of 0 to 100%.

7. 7. The method of claim 6, wherein the first predetermined cutoff value is at least 50% on a scale of 0 to 100%.

8. The method according to any one of claims 1 to 7, having a sensitivity of at least 92%.

9. The method according to any one of claims 1 to 8, having a specificity of at least 95%.

10. The method of any one of claims 1 to 9, wherein at least one of the at least 38 biomarkers is RNA, cDNA or protein.

11. The method of claim 10, wherein the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the cDNA produced is detected.

12. The method of claim 10, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

13. The method according to any one of claims 1 to 12, wherein the first predetermined cut-off value is derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease.

14. The method of claim 13, wherein the neoplastic disease is prostate cancer.

15. The method according to any one of claims 1 to 14, wherein the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB or mlp.

16. The method of claim 15, wherein the algorithm is XGB.

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