Methods for detecting and treating prostate cancer

Abstract

To provide biomarker-based tools for accurate diagnosis of prostate cancer.SOLUTION: The invention is directed to a method for detecting prostate cancer, a method for determining whether a prostate cancer is stable or progressive, low malignancy or high malignancy, a method for discriminating benign prostatic hyperplasia (BPH) from prostate cancer, a method for determining the completeness of surgery, and a method for evaluating the response to a prostate cancer therapy.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] 〔Cross - Reference to Related Applications〕 This application claims priority and benefit to U.S. Provisional Application No. 62 / 633,675, filed Feb. 22, 2018, the entire contents of which are incorporated herein by reference.

[0002] 〔Sequence Listing〕 This application includes a sequence listing submitted in ASCII format via EFS - Web, the entire contents of which are incorporated herein by reference. The name of the above - mentioned ASCII copy created on Feb. 19, 2019 is “LBIO - 005_001WO_SeqList.txt” and the size is 299 KB.

[0003] 〔Field of the Invention〕 The present invention relates to the detection of prostate cancer.

Background Art

[0004] Prostate cancer (PCA) is the fourth most frequently diagnosed cancer worldwide and the second most common cancer in men. Incidence and prevalence have been gradually decreasing, but in the United States, approximately 200,000 men are diagnosed with PCA annually. A number of factors, such as age, family composition, genetic susceptibility, and ethnicity, all contribute to the high incidence of the disease. Ninety percent of PCA is diagnosed as localized (non - metastatic), but the clinical behavior of tumors is highly variable, ranging from indolent tumors that can be monitored through observation or active surveillance (e.g., biomarkers and digital rectal examinations every six months) to malignant progression and androgen - resistant disease, metastatic seeding, and death.

[0005] Various risk stratification systems that combine clinical data and pathological information, such as the Gleason score, have been developed. Those systems, including recently developed next - generation tools, have only about 70% accuracy in predicting outcomes.

[0006] Molecular genetic information is increasingly being used to inform pathology and the information of more indolent subtypes of cancer. This information is used not only as a prognostic prediction tool, but also to stratify patients for various therapeutic interventions. For prostate cancer, mutations, DNA copy number changes, rearrangements, and gene fusions have all been investigated and identified. These are correlated with several pathological features. For example, tumors with a low Gleason score have few DNA copy number changes, while high-grade tumors show significant genome-wide copy number changes. 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 (in approximately 50% of tumors). However, tumors carrying the fusion do not show a significantly different prognosis compared to fusion-negative tumors after prostatectomy. In contrast, androgen receptor variant 7 (AR-V7) is involved in the progression to castration-resistant prostate cancer (CRPC) and is potentially useful as a therapeutic selection biomarker. However, overall, the molecular mechanisms underlying the etiology of PCA are incompletely understood, and there are no molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents. Therefore, it is very important to develop diagnostic methods that can more accurately define the disease state, identify sensitivity to treatment, and ultimately better monitor disease progression.

[0007] Surveillance is an approach that continues to monitor PCA and serves as a basis for early detection of recurrence. After potentially curative resection, monitoring can be performed through measurement of blood biomarkers and / or imaging diagnostics such as CT to detect asymptomatic metastatic disease early. The current biomarker used for monitoring is prostate-specific antigen (PSA) (also γ-seminoprotein or kallikrein-3). This glycoprotein enzyme is encoded by the KLK3 gene and is secreted from prostate epithelial cells. However, it is not an indicator specific to prostate cancer and may also detect prostatitis or benign prostatic hyperplasia (BPH). Using PSA alone can lead to unnecessary biopsies in men without cancer or inadequate diagnosis in men with serious diseases. This is based on a range of low sensitivity (20 - 40%) and low specificity (70 - 90%), resulting in a positive predictive value (positive hit rate) of only 25 - 40%. The United States Preventive Services Task Force (USPSTF) does not recommend the use of PSA for prostate cancer. However, PSA is included in clinical nomographs (calculation charts), such as the UCSF-CAPRA score for prostate cancer risk, which has some usefulness in predicting postoperative disease-free survival.

Summary of the Invention

Problems to be Solved by the Invention

[0008] There is still a continuing need for a biomarker-based tool for accurately diagnosing PCA.

Means for Solving the Problems

[0009] The present disclosure is a method for detecting prostate cancer in a subject in need thereof, comprising determining expression levels of at least 38 biomarkers from a test sample of the subject by contacting a plurality of test samples with a plurality of agents specific for detection of 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 a housekeeping gene; 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 with respect to the expression level of the housekeeping gene, 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; comparing the score with a first predetermined cut-off value;And providing a method including determining the presence of prostate cancer in a subject if the score is greater than or equal to a first predetermined cut-off value, or determining the absence of prostate cancer in the subject if the score is less than the first predetermined cut-off value.;

[0010] The present disclosure is a method for detecting prostate cancer in a subject in need thereof, comprising: (a) determining the 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 the 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 a housekeeping gene; (b) normalizing 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 with respect to the expression level of the housekeeping gene, 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, 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) Compare the score with a first predetermined cut-off value; and (e) create a report, where the report determines the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, and determines the absence of prostate cancer in the subject if the score is less than the first predetermined cut-off value, to provide a method including these steps.;

[0011] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, the method comprising determining the 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 the detection of the 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 a housekeeping gene; normalizing 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 level of the housekeeping gene, 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; inputting the respective normalized expression levels into an algorithm to generate a score;Compare the score with a first predetermined cut-off value; and if the score is less than or equal to the first predetermined cut-off value, identify that the prostate cancer is progressive, or if the score is less than the first predetermined cut-off value, identify that the prostate cancer is stable.

[0012] The present disclosure provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, 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 the respective normalized expression levels into an algorithm to generate a score;Compare the score with a first predetermined cut-off value; and (e) create a report, where the report includes determining that the prostate cancer is progressive if the score is greater than or equal to the first predetermined cut-off value, or determining that the prostate cancer is stable if the score is less than the first predetermined cut-off value.;

[0013] The present disclosure also provides a method for determining whether prostate cancer in a subject is of low malignancy or high malignancy, the method comprising determining the 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 the detection of the 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 a housekeeping gene; 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, XPC with respect to the expression level of the housekeeping gene, thereby obtaining the 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 create a score;Comparing the score with a first predetermined cut-off value; and if the score is greater than or equal to the first predetermined cut-off value, determining that the prostate cancer is of high malignancy, or if the score is less than the first predetermined cut-off value, determining that the prostate cancer is of low malignancy.

[0014] The present disclosure provides a method for determining whether prostate cancer in a subject is of low malignancy or high malignancy, the method comprising determining the 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 the detection of the 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 a housekeeping gene; 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 relative to the expression level of said housekeeping gene, thereby obtaining the 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, REP1, REPIN1, 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;Compare the score with a first predetermined cut-off value; and generate a report, where the report includes determining that the prostate cancer has a high malignancy if the score is greater than or equal to the first predetermined cut-off value, and determining that the prostate cancer has a low malignancy if the score is less than the first predetermined cut-off value.

[0015] The present disclosure provides a method for determining whether prostate cancer in a subject is prostate cancer with a low Gleason score (≤6) or prostate cancer with a high Gleason score (≥7), the method comprising: (a) determining the 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 the 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 a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, thereby obtaining the 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) Input each of the normalized expression levels into an algorithm to generate a score; (d) compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer is high Gleason score prostate cancer, and if the score is less than the first predetermined cut-off value, determine that the prostate cancer is low Gleason score prostate cancer.;

[0016] The present disclosure provides a method for determining whether prostate cancer in a subject is low Gleason score (≤6) prostate cancer or high Gleason score (≥7) prostate cancer, the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, thereby obtaining the 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, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC;(c) Input each of the normalized expression levels into the algorithm to generate a score; (d) Compare the score with a first predetermined cut-off value; and (e) Create a report, where the report determines that the prostate cancer is of high malignancy if the score is greater than or equal to the first predetermined cut-off value, and determines that the prostate cancer is of low malignancy if the score is less than the first predetermined cut-off value.;

[0017] The present disclosure also provides a method for determining surgical completeness in a subject having prostate cancer, the method comprising determining the 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 the detection of the 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 a housekeeping gene; normalizing 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 level of the housekeeping gene, 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; inputting each of the normalized expression levels into an algorithm to generate a score;Compare the score with a first predetermined cut-off value; and if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer has not been completely removed, or if the score is less than the first predetermined cut-off value, determine that the prostate cancer has been completely removed.

[0018] The present disclosure provides a method for determining surgical completeness in a subject having prostate cancer, the method comprising: (a) determining the 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 the expression levels 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_45, PRPR2_2TR, XPC, and a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, 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, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting the respective normalized expression levels into an algorithm to generate a score;(d) Compare the score with a first predetermined cut-off value; and (e) create a report, where the report includes determining that the prostate cancer has not been completely removed if the score is greater than or equal to the first predetermined cut-off value, and determining that the prostate cancer has been completely removed if the score is less than the first predetermined cut-off value.;

[0019] The present disclosure provides a method for distinguishing prostate cancer from benign prostatic hyperplasia in a subject having benign prostatic hyperplasia, the method comprising determining the expression levels 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 the expression levels 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 a housekeeping gene; normalizing 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 level of the housekeeping gene, 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, 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;Compare the score with a first predetermined cut-off value; and determine the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, and determine the presence of benign prostatic hyperplasia in the subject if the score is less than the first predetermined cut-off value.

[0020] The present disclosure provides a method for differentiating benign prostatic hyperplasia from prostate cancer in a subject having benign prostatic hyperplasia, the method comprising: (a) determining the 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 the expression levels 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_45, PRPR2_2TR, XPC, and a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, 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, 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) Compare the score with a first predetermined cut-off value; and (e) create a report, where the report discriminates the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, and discriminates the presence of benign prostatic hyperplasia in the subject if the score is less than the first predetermined cut-off value.;

[0021] The present disclosure is a method for evaluating the response of a subject having prostate cancer to primary therapy, comprising: (1) at a first time point: (a) determining the 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 the detection of the 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 XPC, and a housekeeping gene; (b) normalizing 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 with respect to the expression level of the housekeeping gene to obtain 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 first score;(2) At a second time point that is after the first time point and after the subject has received primary therapy, at the second time point: (d) determining the 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 the expression levels of at least 38 biomarkers; (e) 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 with respect to the expression level of the housekeeping gene, thereby obtaining the 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; (f) inputting each of the normalized expression levels into an algorithm to generate a second score; (3) comparing the first score with the second score; and (4) if the second score is significantly decreased compared to the first score, determining that the subject responds to primary therapy, and if the second score is not significantly decreased compared to the first score, determining that the subject does not respond (is non-responsive) to primary therapy.;

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

[0023] The present disclosure provides a method for evaluating the response of a subject with prostate cancer to primary therapy, the method comprising, at a first time point: (1) (a) determining the 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 the detection of the 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, SQL1ARC, SLC18A2, SMC4, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and a housekeeping gene; (b) normalizing 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 with respect to the expression level of the housekeeping gene, thereby obtaining the 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; (c) inputting each of the normalized expression levels into an algorithm to generate a first score;(2) At a second time point, which is after the first time point and after the administration of the therapy to the subject: (d) determining the expression levels of at least 38 biomarkers from a test sample of the subject by contacting the test sample with an agent specific for detecting the expression of at least 38 biomarkers; (e) 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 with respect to the expression level of the housekeeping gene, 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, 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 with the second score; and (4) creating a report, where the report determines that the subject responds to primary therapy if the second score is significantly decreased compared to the first score, and determines that the subject does not respond (is non-responsive) to primary therapy if the second score is not significantly decreased compared to the first score.;

[0024] The above method may further include continuing to administer primary therapy to the subject if the second score is significantly decreased compared to the first score. The above method may further include discontinuing the administration of primary therapy to the subject if the second score is not significantly decreased compared to the first score. The above method may further include administering secondary therapy to the subject if the second score is not significantly decreased compared to the first score.

[0025] The present disclosure provides a method for treating prostate cancer in a subject in need thereof, the method comprising measuring the 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 the detection of the 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 a housekeeping gene; 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 against the expression of the housekeeping gene, thereby obtaining the 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 create a score; comparing the score with a first predetermined cut-off value;And when the score is greater than or equal to a first predetermined cut-off value, subject the subject to at least primary therapy, or when the score is less than the first predetermined cut-off value, confirm the absence of prostate cancer in the subject.;

[0026] In the method of the present disclosure, 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 method of the present disclosure, the predetermined cut-off value is at least 33% on a scale of 0 - 100%, or at least 50% on a scale of 0 - 100%, or at least 50% on a scale of 0 - 100%.

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

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

[0030] In some embodiments, hormonal therapy may include androgen suppression therapy. In some embodiments, chemotherapy may include docetaxel, cabazitaxel, mitoxantrone, estramustine, or combinations thereof. In some embodiments, vaccine therapy may include sipuleucel-T. In some embodiments, bone metastasis treatment may include bisphosphonates, denosumab, corticosteroids, or combinations thereof.

[0031] In the methods 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 treating the subject. The first time point can be either before or after administration of primary therapy to the subject.

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

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

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

[0036] In the methods of the present disclosure, if 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 present disclosure, the expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.

[0038] In the method of the present 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 the biomarker is RNA or cDNA, the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. For example, the label can be a fluorescent label. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.

[0040] In the method of the present disclosure, a predetermined cut-off value can be derived from a plurality of reference samples obtained from subjects without a neoplastic disease or not diagnosed with a neoplastic disease. The neoplastic disease can be prostate cancer.

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

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

[0043] In the method 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 method of the present disclosure, the subject can be human.

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

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In this specification, the singular forms also include the plural forms unless the context clearly dictates otherwise. By way of example, the terms "a", "an", and "the" are to be construed to be singular or plural, and the term "or" is to be construed as inclusive. As an example, "an element" means one or more elements. Throughout this specification, the term "comprising", or variations such as "comprises" or "comprising", are to be understood to imply the inclusion of the stated element, integer, or step, or group of elements, integers, or steps, but not the exclusion of any other element, integer, or step, or group of elements, integers, or steps. "About" can be construed to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise apparent from the context, all numerical values given in this specification are modified by the term "about".

[0047] Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, but exemplary methods and materials will be described. Other features, objects, and advantages will be apparent from the detailed description and the claims. In the specification and the appended claims, the singular forms also include the plural forms unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill 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

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Figure 1A

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BRIEF DESCRIPTION OF THE INVENTION

[0059] The details of the present invention are set forth in the following accompanying description.

[0060] A method for quantitatively (scoring) a circulating prostate cancer molecular signature with high sensitivity and high specificity is described herein, and the method is used for purposes including, but not limited to, detecting prostate cancer, determining whether the prostate cancer is stable or progressive, distinguishing benign prostatic hyperplasia (BPH) from prostate cancer, determining the completeness of surgery, and evaluating the response to prostate cancer therapy. Specifically, the present invention is based on the finding 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 level of a housekeeping gene, are elevated in subjects with prostate cancer compared to healthy subjects or subjects with BPH.

[0061] Symptoms of prostate cancer include problems with urination, bleeding in the urine or semen, erectile dysfunction, pain in the hips, back (spine), chest (ribs), or the affected area of cancer that has spread to the bone, weakness or numbness in the lower limbs or feet, or further, loss of bladder or bowel control from cancer compressing the spinal cord.

[0062] As described in the examples, "ProstaTest", a method for measuring circulating prostate cancer transcripts, is a method for diagnosing prostate cancer, and a decrease in the ProstaTest score correlates with the effect of therapeutic interventions such as surgery and chemotherapy. The target gene expression profile 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). It diagnoses and identifies active disease and provides an assessment of treatment response in collaboration with standard clinical evaluation and imaging.

[0063] Accordingly, the present invention provides a method for detecting prostate cancer in a subject in need thereof, the method comprising: (a) determining the 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 the 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 XPC, and a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, 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, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting the respective normalized expression levels into an algorithm to generate a score;(d) Compare the score with a first predetermined cut-off value; and (e) determine the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, or determine the absence of prostate cancer in the subject if the score is less than the first predetermined cut-off value.;

[0064] In some embodiments of the method described above, step (e) of generating a report can be included, the report determining the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, or determining the absence of prostate cancer in the subject if the score is less than the first predetermined cut-off value.

[0065] In some embodiments of the method described above, the first predetermined cut-off value can be 33% on a scale of 0 - 100%.

[0066] In some embodiments, the method described above can further include administering primary therapy to the subject. The method described above can further include administering primary therapy to the subject if the score is greater than or equal to a predetermined cut-off value.

[0067] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, 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) Input each of the normalized expression levels into an algorithm to generate a score; (d) compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer is progressive, or if the score is less than the first predetermined cut-off value, determine that the prostate cancer is stable.

[0068] In some embodiments of the method described above, step (e) of creating a report can be included, the report determining that the prostate cancer is progressive if the score is greater than or equal to the first predetermined cut-off value, or determining that the prostate cancer is stable if the score is less than the first predetermined cut-off value.

[0069] In some embodiments of the method described above, the first predetermined cut-off value can be 50% on a scale of 0 - 100%.

[0070] In some embodiments, the method described above can further include subjecting the primary therapy thereto. The method described above can further include subjecting the primary therapy thereto if the score is greater than or equal to a predetermined cut-off value.

[0071] The present invention also provides a method for determining whether prostate cancer in a subject is of low malignancy or high malignancy, the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) normalizing 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 with respect to the expression level of the housekeeping gene, 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) Compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer is of high malignancy, and if the score is less than the first predetermined cut-off value, determine that the prostate cancer is of low malignancy.

[0072] In some embodiments of the method described above, step (e) of creating a report can be included, and the report determines that the prostate cancer is of high malignancy if the score is greater than or equal to the first predetermined cut-off value, or determines that the prostate cancer is of low malignancy if the score is less than the first predetermined cut-off value.

[0073] In some aspects of the method described above, the first predetermined cut-off value can be 50% on a scale of 0 - 100%.

[0074] In some aspects, the method described above can further include subjecting to primary therapy. The method described above can further include subjecting to primary therapy if the score is greater than or equal to a predetermined cut-off value.

[0075] In some aspects, low-grade prostate cancer is prostate cancer having a Gleason score of 6 or less. In some aspects, high-grade prostate cancer is prostate cancer having a Gleason score of 7 or more.

[0076] The Gleason grading system is widely used in the art as a prognostic parameter and is often used in combination with other prognostic factors or tests for prostate cancer. A prostate biopsy specimen (sample) can be examined, for example, microscopically, and a Gleason score can be determined by a pathologist based on the structural pattern of the prostate tumor. The Gleason score is based on the degree of loss of normal glandular tissue structure (i.e., shape, size, and glandular differentiation). The sample is assigned grade 1 to the most common tumor pattern, and then grade 2 to the next most common pattern. Following the first pattern, i.e., the most common pattern, there is a second pattern, i.e., the second most common pattern, which can be distinguished; alternatively, they may be only of 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-shaped, and tightly packed. Pattern 2 - The tissue still has well-shaped glands, but they are larger and more tissue is seen between them. Pattern 3 - The tissue still has distinguishable glands, but the cells are darker. At high magnification, some of these cells are separating from the glands and beginning to invade the surrounding tissue. Pattern 4 - Few distinguishable glands are seen in the tissue. Many cells are invading the surrounding tissue. Pattern 5 - No distinguishable glands are present in the tissue. Often only a sheet of cells is seen throughout the surrounding tissue. Adding the two grades gives a Gleason score, also called the Gleason sum.

[0077] The present disclosure also provides a method for determining whether prostate cancer in a subject is prostate cancer with a low Gleason score (≤6) or prostate cancer with a high Gleason score (≥7), the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) normalizing 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 with respect to the expression level of the housekeeping gene, thereby obtaining the 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, REP1, REPIN1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC;(c) Input the respective normalized expression levels into an algorithm to generate a score; (d) compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer is high-Gleason score prostate cancer, and if the score is less than the first predetermined cut-off value, determine that the prostate cancer is low-Gleason score prostate cancer.

[0078] In some aspects of the method described above, step (e) of creating a report can be included, where the report determines that the prostate cancer is high-Gleason score prostate cancer if the score is greater than or equal to the first predetermined cut-off value, and determines that the prostate cancer is low-Gleason score prostate cancer if the score is less than the first predetermined cut-off value.

[0079] In some aspects of the method described above, the first predetermined cut-off value can be 50% on a scale of 0 - 100%.

[0080] In some aspects, the method described above can further include being applied to primary therapy. The method described above can further include being applied to primary therapy when the score is greater than or equal to a predetermined cut-off value.

[0081] The present disclosure also provides a method for determining surgical completeness in a subject having prostate cancer, the method comprising: (a) determining the 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 the detection of the 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 a housekeeping gene; (b) 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 with respect to the expression level of the housekeeping gene, thereby obtaining the 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, 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) Compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, determine that the prostate cancer has not been completely removed, or if the score is less than the first predetermined cut-off value, determine that the prostate cancer has been completely removed.

[0082] In some aspects of the foregoing method, step (e) of creating a report can be included, where the report determines that the prostate cancer has not been completely removed if the score is greater than or equal to the first predetermined cut-off value, or determines that the prostate cancer has been completely removed if the score is less than the first predetermined cut-off value.

[0083] In some aspects of the foregoing method, the first predetermined cut-off value can be 33% on a scale of 0 to 100%.

[0084] In some aspects, the foregoing method can further include subjecting the primary therapy. The foregoing method can further include subjecting the primary therapy when the score is greater than or equal to a predetermined cut-off value.

[0085] Provided is a method for distinguishing benign prostatic hyperplasia from prostate cancer in a subject having benign prostatic hyperplasia, the method comprising: (a) determining the 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 the expression levels 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, TMPRSS245, PRPR2_2TR, XPC, and a housekeeping gene; (b) normalizing 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 level of the housekeeping gene, thereby obtaining the 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; (c) inputting each of the normalized expression levels into an algorithm to generate a score;(d) Compare the score with a first predetermined cut-off value; and (e) determine the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, and determine the presence of benign prostatic hyperplasia in the subject if the score is less than the first predetermined cut-off value.

[0086] In some aspects of the method described above, the step (e) of generating a report can be included, where the report determines the presence of prostate cancer in the subject if the score is greater than or equal to the first predetermined cut-off value, or determines the presence of benign prostatic hyperplasia in the subject if the score is less than the first predetermined cut-off value.

[0087] In some aspects of the method described above, the first predetermined cut-off value can be 33% on a scale of 0 - 100%.

[0088] In some aspects, the method described above can further include administering primary therapy to the subject. The method described above can further include administering primary therapy to the subject if the score is greater than or equal to a predetermined cut-off value.

[0089] The present disclosure provides a method for treating prostate cancer in a subject in need thereof, the method comprising: (a) measuring the 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 the 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 a housekeeping gene; (b) 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 with respect to the housekeeping gene, thereby obtaining the 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; (c) inputting each of the normalized expression levels into an algorithm to create a score;(d) Compare the score with a first predetermined cut-off value; and (e) if the score is greater than or equal to the first predetermined cut-off value, subject the subject to at least primary therapy, or if the score is less than the first predetermined cut-off value, confirm the absence of prostate cancer in the subject.;

[0090] The present disclosure provides a method for evaluating the response of a subject having prostate cancer to primary therapy, the method comprising, at a first time point: (1) (a) determining the 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 the detection of the 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 XPC, and a housekeeping gene; (b) normalizing 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 level of the housekeeping gene to obtain 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 the respective normalized expression levels into an algorithm to generate a first score;(2) At a second time point after the first time point and after the subject has been administered a first therapy, at the second time point: (d) determining the 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 the expression levels of at least 38 biomarkers; (e) 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 with respect to the expression level of the housekeeping gene, 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, 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 with the second score; and (4) if the second score is significantly decreased compared to the first score, determining that the subject responds to the primary therapy, and if the second score is not significantly decreased compared to the first score, determining that the subject does not respond (is non-responsive) to the primary therapy.;

[0091] In some aspects of the foregoing method, the step (e) of creating a report can be included, and the report determines that the subject responds to primary therapy if the second score is significantly decreased compared to the first score, and determines that the subject does not respond (is non-responsive) to primary therapy if the second score is not significantly decreased compared to the first score.

[0092] In some aspects, it can further include continuing to administer primary therapy to the subject if the second score is significantly decreased compared to the first score. The foregoing method can further include discontinuing the administration of primary therapy to the subject if the second score is not significantly decreased compared to the first score. The foregoing method can further include administering secondary therapy to the subject if the second score is not significantly decreased compared to the first score.

[0093] In some aspects of the foregoing 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% smaller than the first score, at least about 25% smaller than the first score, at least about 30% smaller than the first score, at least about 40% smaller than the first score, at least about 50% smaller than the first score, at least about 60% smaller than the first score, at least about 75% smaller than the first score, at least about 80% smaller than the first score, at least about 90% smaller than the first score, at least about 95% smaller than the first score, or at least about 99% smaller than the first score. In some aspects, if the second score is not significantly decreased compared to the first score, the subject is considered non-responsive to the therapy.

[0094] In some aspects of the foregoing method, the first time point can be before administering primary therapy to the subject. The first time point can be after administering primary therapy to the subject.

[0095] In some aspects of the methods of the present disclosure, 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 aspects, the housekeeping gene is TOX4.

[0096] In some aspects of the methods of the present disclosure, a predetermined cut-off value can be about 33% on a scale of 0 - 100%. In some aspects of the methods of the present disclosure, a predetermined cut-off value can be about 50% on a scale of 0 - 100%. The predetermined cut-off value can be about 60% on a scale of 0 - 100%. The predetermined cut-off 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 - 100%.

[0097] The methods of the present disclosure 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 methods of the present disclosure can have a sensitivity 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 method of the present disclosure 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 method of the present disclosure 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 method of the present disclosure can have an accuracy 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 method of the present disclosure can have an accuracy 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%.

[0100] In some aspects of the method of the present disclosure, a predetermined cut-off value, for example a first predetermined cut-off value, is derived from a plurality of reference samples obtained from subjects without a neoplastic disease or not diagnosed with a neoplastic disease. The plurality of reference samples can be about 2 to 500, 2 to 200, 10 to 100, or 20 to 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 aspects, each reference sample is blood. In some aspects, each reference sample is of the same type as the test sample.

[0101] In some aspects of the methods of the present disclosure, a test sample can include any biological fluid obtained from a subject. In some aspects, the test sample can include blood, serum, plasma, neoplastic 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, a reference sample can include any biological fluid obtained from a subject. In some aspects, the reference sample includes blood, serum, plasma, neoplastic 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 various ways, including, but not limited to, measuring the mRNA encoded by a particular gene; measuring the amount of protein encoded by a particular gene; and measuring the activity of the protein encoded by a particular gene.

[0105] A biomarker can be RNA, cDNA, or protein. When 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. When 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 the 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 using microarray technology in either fresh or fixed tissue. In this method, the polynucleotide sequences of interest (including cDNA and oligonucleotides) are applied or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes derived from the cells or tissues of interest. The origin of the mRNA is typically total RNA isolated from a biological sample, and differential expression can be measured using a corresponding normal tissue or cell line.

[0107] In some embodiments of the microarray technology, the insert fragments of PCR amplified cDNA clones 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. Microarray genes immobilized on a 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 specifically hybridize to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by an apparatus such as a confocal laser microscope or by another detection method such as a CCD camera. By quantifying the hybridization of each array element, it becomes possible to evaluate the abundance of the corresponding mRNA. By using two-color fluorescence, separately labeled cDNA probes generated from RNA of two origins are hybridized to the array in a paired format. In this way, the relative abundances of transcripts from two origins corresponding to each specific gene are determined simultaneously. Microarray analysis can be carried out by commercially available devices according to the manufacturer's protocol.

[0108] In some embodiments, biomarkers can be detected in biological samples using qRT-PCR. The first step of 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 a PCR reaction. The reverse transcription reaction step is generally primed using specific primers, random hexamers or oligo dT primers depending on the ultimate purpose of the 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 (radioimmunoassay, RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, a biomarker can be detected in an ELISA where the biomarker antibody is bound to a solid phase and the biomarker present in the sample is detected and / or quantified using an enzyme-antibody complex. 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 enables rapid and accurate identification of the sample.

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

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

[0112] Any algorithm that can assign a score to a sample can be used in the method of the present disclosure, and the algorithm can be created by evaluating that the numerical value of the sample falls within the range of a prediction model 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 (CART) for machine learning, treebag, K-Nearest Neighbors (kNN), neural network (nnet), Support Vector Machine - Radial (SVM - radial), Support Vector Machine - Linear (SVM - linear), Naive Bayes (NB), multilayer perceptron (mlp), or any combination thereof.

[0113] In some aspects of the method of the present disclosure, the algorithm can be XGB (also referred to as XGBoost). XGB is an implementation of gradient - boosting decision trees designed for improved analysis speed and performance.

[0114] In some aspects of the method of the present disclosure, a therapy, such as a primary therapy or a 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 method of the present disclosure, active surveillance can include a doctor's examination accompanied by a blood test for prostate - specific antigen and a digital rectal examination approximately every six months. Active surveillance can also include a prostate biopsy that can be performed annually.

[0116] In some aspects of the method of the present disclosure, surgery can include radical prostatectomy.

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

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

[0119] In some aspects of the methods of the present disclosure, hormone therapy can include androgen blockade therapy or androgen suppression therapy. The goal is to reduce the body levels of male hormones called androgens, or to stop those hormones from acting on prostate cancer cells. Hormone therapy can include orchiectomy. Hormone therapy can also include the administration of compounds that reduce the levels of androgens, 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. A known LHRH antagonist includes degarelix. A known CYP17 inhibitor is abiraterone. Hormone therapy can also include the administration of anti-androgens, such as flutamide, bicalutamide, nilutamide, and enzalutamide. Hormone therapy can also include the 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, vaccine therapy can include Sipuleucel-T.

[0122] When cancer has grown outside the prostate, preventing or delaying the cancer's metastasis to the bone is the main goal of treatment. Bone metastasis treatment can include bisphosphonates (such as zoledronic acid), denosumab, corticosteroids, external beam radiation therapy, radiopharmaceuticals (such as strontium-89, samarium-153, or radium-223), and analgesics.

[0123] The response of a subject with prostate cancer to a therapy can also be evaluated by comparing scores determined by the same algorithm at different time points of that therapy. For example, the first time point can be before or after the administration of the therapy to the subject; the second time point is after the first time point and after the administration of the therapy to the subject. A first score is created at the first time point, and a second score is created at the second time point. If the second score is significantly decreased compared to the first score, the subject is considered to be responsive to the therapy. In some embodiments, the second score 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, then it is significantly decreased compared to the first score. If the second score is not significantly decreased compared to the first score, the subject is considered non-responsive (not responsive) to the therapy.

[0124] The sequence information of prostate cancer biomarkers and housekeeping genes is shown in Table 1 below.

[0125] Table 1. Prostate Cancer Biomarker / Housekeeping Gene 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

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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

Table 1-70

Table 1-71

Table 1-72

Table 1-73

Table 1-74

Table 1-75

[0126] 〔Definition〕 The articles "a" and "an" are used herein to refer to one or more (i.e., at least one) of the grammatical objects 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 herein.

[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 include single-stranded and double-stranded forms of DNA. As used herein, a nucleic acid molecule or nucleic acid sequence that functions as a probe in 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 species of nucleic acid structures, such as DNA / RNA helices, peptide nucleic acids (PNA), locked nucleic acids (LNA) and / or ribozymes. Thus, as used herein, the term "nucleic acid molecule" also includes strands containing unnatural nucleotides, modified nucleotides and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0129] As used herein, terms such as "hybridizes", "hybridized" and "hybridizing" with respect to polynucleotides refer to hybridization under normal hybridization conditions, such as hybridization in 50% formamide / 6×SSC / 0.1% SDS / 100 μg / mL ssDNA, where the hybridization temperature is above about 37 °C (degrees Celsius) and the washing temperature in 0.1×SSC / 0.1% SDS is above about 55 °C, and preferably refers to stringent hybridization conditions.

[0130] As used herein, the terms "normalization" or "normalization group" refer to an expression of differential values for a standard value for adjusting for effects resulting from technical biases caused by sample handling, sample preparation and measurement methods rather than biological biases in biomarker concentrations in a sample. For example, when measuring the expression of differentially expressed proteins (expression-variable proteins), the absolute value of the protein expression can be expressed relative to the absolute value of the expression of a standard protein that is substantially constant during the expression.

[0131] The terms "diagnosis" and "diagnostic method" each encompass the terms "prognosis" and "prognostic method", and in addition encompass the application of such procedures over two or more time points for monitoring a diagnosis and / or prognosis over a period of time, and statistical modeling based thereon. Further, the term diagnosis encompasses: a. prediction (determining whether a patient has a tendency to develop an aggressive disease (high proliferative / aggressive)), b. prognosis (speculating whether a patient has a tendency to have a benign or malignant outcome at a predetermined future time point), c. treatment option selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0132] "Accuracy" refers to the degree of conformity of a measured or calculated quantity (test reported value) to an actual (or true) value. Clinical accuracy refers to the ratio of true outcomes (true positive (TP) or true negative (TN)) to misclassified outcomes (false positive (FP) or false negative (FN)), which is presented, among other metrics, as sensitivity, specificity, positive predictive value (positive likelihood ratio; PPV) or negative predictive value (negative likelihood ratio; NPV), or as likelihood, odds ratio.

[0133] As used herein, the term "biological sample" refers to any sample of biological origin that potentially contains one or more biomarkers. Examples of biological samples include tissues, organs, or body fluids such as whole blood, plasma, serum, tissues, washings, or any other specimen used for the detection of disease.

[0134] As used herein, the term "subject" 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 be pre-diagnosed with prostate cancer and tested for cancer recurrence. In some embodiments, the subject has benign prostatic hyperplasia.

[0135] As used herein with respect to a condition, the terms "treating" or "treatment" 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 occurrence of symptoms associated with the condition, attenuating or terminating the symptoms associated with the condition, causing complete or partial regression of the condition, or some combination thereof.

[0136] Biomarker levels can vary with treatment of a disease. Changes in biomarker levels can be measured by the methods of the present disclosure. Changes in biomarker levels can be utilized to monitor disease progression or treatment regimens.

[0137] "Changed," "modified," or "significantly different" refers to a detectable change or difference from substantially equivalent states, profiles, measurements, etc. Such changes can be all or none. They can be incremental and need not be linear. They can be by an order of magnitude of size. The change can be an increase or decrease of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or an increase or decrease of 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 with a p-value of 0.1, 0.05, 0.001, or 0.0001.

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

[0139] The term "progressive disease" refers to the diagnosis of the presence of high - activity prostate cancer, i.e., when determined by imaging data and / or best clinical judgment, the subject has not been treated and is not stable, is being treated but not responding to treatment, or is being treated but active disease remains.

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

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

[0142] The term "non - neoplastic tissue" refers to a mass of normally growing 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 augment an immune response, such as a T - cell response, while immunosuppressive therapy refers to the use of therapeutic agents that interfere with, suppress, or inhibit an immune response, such as a T - cell response. Immunostimulatory therapy can include the use of checkpoint inhibitors. Immunostimulatory therapy can include administering to a subject a therapeutic agent that activates 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 in the broadest sense herein and encompasses various antibody structures, such as, but not limited to, monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments as long as they exhibit the desired antigen-binding activity. An antibody that binds to a target refers to an antibody that can bind to the 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 degree of binding of an anti-target antibody to an irrelevant non-target protein is less than about 10% of the binding of the antibody to the target as measured, for example, by a radioimmunoassay (RIA) or a Biacore® assay. In certain embodiments, the antibody that binds to the target has a dissociation constant (Kd) of <1 μM, <100 nM, <10 nM, <1 nM, <0.1 nM, <0.01 nM, or <0.001 nM (e.g., 10 -8 M or less, e.g., 10 -8 M to 10 -13 M, e.g., 10 -9 M to 10 -13 M). In certain embodiments, the anti-target antibody binds to an epitope of the target that is conserved across 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 signal transduction through an immune cell receptor (e.g., a T cell receptor) to restore a functional response (e.g., proliferation, cytokine production, target cell killing) by T cells to an antigen 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 signal transduction by the antigen to which it binds. In some embodiments, the agonist antibody elicits or activates signal transduction in the absence of the native ligand. For example, the agonist antibody can increase memory T cell proliferation, increase cytokine production by memory T cells, inhibit regulatory T cell function, and / or inhibit regulatory T cell suppression of effector T cell function, such as effector T cell proliferation and / or cytokine production.

[0147] An "antibody fragment" refers to a molecule that is not a full antibody and includes a portion of a full antibody that binds to the antigen to which the full 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] The administration of chemotherapy to a subject can include administering a therapeutically effective amount of at least one chemotherapeutic agent. Chemotherapeutic agents include, but are not limited to, 13-cis-retinoic acid, 2-CdA, 2-chlorodeoxyadenosine, 5-azacitidine, 5-fluorouracil (5-FU), 6-mercaptopurine (6-MP), 6-thioguanine (6-TG), abemaciclib, abiraterone acetate, abraxane, acutinine, actinomycin D, adcetris, ado-trastuzumab emtansine, adriamycin, adrucil, afatinib, afinitor, agrilin, ara-Cot, aldesleukin, alemtuzumab, alisertib, alectinib, alimta, alitretinoin, alkaban-AQ, alkeran, all-trans retinoic acid, α-interferon, altretamine, alunbrig, amethopterin, amifostine, aminoglutethimide, anagrelide, androstanolone, anastrozole, apalutamide, arabinosyl cytosine, Ara-C, aranesp, aredia, arimidex, aromasin, aranone, arsenic trioxide, arzerra, asparaginase, atezolizumab, atrasentan, avastin, avelumab, axicabtagene ciloleucel, axitinib, azacitidine, bavencio, Bcg, beleodaq, belinostat, bendamustine, bendeka, besponsa, bevacizumab, bexarotene, bexar, bicalutamide, BiCNU, brexan, bleomycin, blinatumomab, blincyto, bortezomib, bosulif, bosutinib, brentuximab vedotin, brigatinib, busulfan, busulfex, C225, cabazitaxel, cabozantinib, calcium leucovorin, campath, camptosar, camptothecin-11, capecitabine, caprelsa, carac, carboplatin, carfilzomib, carmustine, carmustine wafer, casodex, CCI-779, Ccnu, Cddp, Ceenu, ceritinib, selvidgin, cetuximab, chlorambucil, cisplatin, citrovorum factor, cladribine, clofarabine, chloral, cobimetinib, cometrix, cortisone, cosmege, cotelic, Cpt-11, crizotinib, cyclophosphamide, cylamza, cytadrene, cytarabine, cytarabine liposome, cytoxan U,Cytosine arabinoside, Dabrafenib, Dacarbazine, Dacogen, Dactinomycin, Daratumumab, Darbepoetin alfa, Darzalex, Dasatinib, Daunomycin, Daunorubicin, Daunorubicin cytarabine (liposomal), Daunorubicin hydrochloride, Daunorubicin liposome, Daunoxome, Decadron, Decitabine, Degarelix, δ-Cortef, Deltasone, Denileukin diftitox, Denosumab, DepoCyt, Dexamethasone, Dexamethasone acetate, Dexamethasone sodium phosphate, Dexazone, Dexrazoxane, Dhad, Dic, Diadex, Docetaxel, Doxyl, Doxorubicin, Doxorubicin liposome, Doxira, DTIC, Dtic-Dome, Duralon, Durvalumab, Eculizumab, Efudex, Elence, Elotuzumab, Erlotinib, Erwinia L-asparaginase, Estramustine, Ethiol, Etophos, Etoposide, Etoposide phosphate, Eurexin, Everolimus, Evista, Exemestane, Fareston, Faridac, Faslodex, Femara, Filgrastim, Filmagon, Floxuridine, Fludara, Fludarabine, Fluoplex, Fluorouracil, Fluorouracil (cream), Fluoxymesterone, Flutamide, Folic acid, Folotyn, Fudr, Fulvestrant, G-Csf, Gaziva, Gefitinib, Gemcitabine, Gemtuzumab ozogamicin, Gemzar, Gilotrif, Gleevac, Gliadel wafer, Gm-Csf, Goserelin, Granix, Granulocyte colony-stimulating factor, Granulocyte macrophage colony-stimulating factor, Halaven, Halotestin, Herceptin, Hexadrol, Hexalen, Hexamethylmelamine, Hmm, Hycamtin, Hydrea, Hydrocortisone acetate, Hydrocortisone, Hydrocortisone sodium phosphate, Hydrocortisone sodium succinate, Hydrocortisone phosphate, Hydroxyurea, Ibrance, Ibritumomab, Ibritumomab tiuxetan, Ibrutinib, Iclusig, Idamycin, Idarubicin,idelalisib, idifab, ifex, IFN-α, ifosfamide, IL-11, IL-2, imbruvica, imatinib mesylate, imfinzi, imidazole carboxamide, imlygic, inlyta, inotuzumab ozogamicin, interferon-α, interferon α-2b (PEG conjugate), interleukin-2, interleukin-11, intron A (interferon α-2b), ipilimumab, ireessa, irinotecan, irinotecan (liposome), isotretinoin, istodax, ixabepilone, ixazomib, xeloda, jakafi, jevtana, kadcyla, ketruda, kidrolase, kisqali, kymriah, kaiprolis, lanacort, lanreotide, lapatinib, latruculum, L-asparaginase, rubraca, Lcr, lenalidomide, lenvatinib, lenvima, letrozole, leucovorin, leukeran, leukine, leuprolide, leurocristine, leustatin, liposomal Ara-C, liquid pred, romidepsin, ronsurf, L-PAM, L-sarcolysin, lupron, lupron depot, lymphasar, markib, matulane, maxidex, mechlorethamine, mechlorethamine hydrochloride, medralone, medrol, megace, megestrol, megestrol acetate, mekinist, mercaptopurine, mesna, mesnex, methotrexate, methotrexate sodium, methylprednisolone, methylcortene, midostaurin, mitomycin, mitomycin-C, mitoxantrone, M-prednisone, MTC, MTX, mustargen, mustine, mutamycin, myleran, mylosel, mylotarg, navelbine, necitumumab, nelarabine, neosar, neratinib, nerlynx, neulasta, neomeg, neupogen, nexavar, nilandron, nilotinib, nilutamide, ninlaro, nipent, niraparib, nitrogen mustard, nivolumab, norvasc, novantrone, enplate, obinutuzumab, octreotide, octreotide acetate, odomzo, ofatumumab, olaparib, olartuzumab, omacetaxine, oncospar, oncovin, onivyde, ontak, onxal, opdivo, oprelvekin, oral pred, orasone, osimertinib, otrexup, oxaliplatinPaclitaxel, 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, Pomalis, Ponatinib, Portrazza, Pralatrexate, Prednisolone, Prednisone, Prelone, Procarbazine, Procrit, Proleukin, Prolia, Carmustine Implant-Containing Prolifeprosspan 20, Promacta, Probenecid, Purinethol, Radium 223 Dichloride, Raloxifene, Ramucirumab, Rasburicase, Regorafenib, Lenalidomide, Rituxan, Rituxan Hycela, Rituximab, Rituximab Hyaluronidase, Roferon-A (Interferon α-2a), Romidepsin, Romiplostim, Rubex, Rubidomycin Hydrochloride, Rubraca, Rucaparib, Luxolutinib, Ridapt, Sandostatin, Sandostatin LAR, Sargramostim, Cetuximab, Sipuleucel-T, Soliris, Solu-Cortef, Solu-Medrol, Somatropin, Sonidegib, Sorafenib, Sprycel, Sti-571, Stibarga, Streptozocin, SU11248, Sunitinib, Stent, Sylvan, Simulect, Tafinlar, Tagrisso, Talimogene Laherparepvec, Tamoxifen, Tarceva, Targretin, Tysabri, Taxol, Taxotere, Tcentriq, Temodal, Temozolomide, Temsirolimus, Teniposide, Tespa, Thalidomide, Talomid, Teresis, Thioguanine, Thioguanine Tabloid, Thiofoshamide, Thioplex, Thiotepa, Tys, Tisagenlecleucel, Toposal, Topotecan, Toremifene, Trisel, Tositumomab, Trabectedin, Trametinib, Trastuzumab, Treanda, Treleasta, Tretinoin, Trexall, Trifluridine / Tipiracil, Triptorelin Pamoate, Trisenox, Tspa, T-VEC, Tykerb, Valrubicin, Valtrex, Vandetanib, VCR, Vectibix, Velban,Belotecan, Bemlafetinib, Bencrexat, Venetoclax, Veposide, Bezanio, Besanoid, Viadur, Vidaza, Vinblastine, Vinblastine Sulfate, Vincasar Pfs, Vincristine, Vincristine Liposome, Vinorelbine, Vinorelbine Tartrate, Bismodegib, Vlb, VM-26, Vorinostat, Botrient, VP-16, Vumon, Vyxeos, Xalkori Capsule, Xeloda, Xgeva, Xofigo, Xtandi, Yervoy, Escarter, Yondelis, Zaltrap, Zanosar, Zalxio, Zejula, Zelboraf, Zevalin, Dinecard, Ziv-aflibercept, Zoladex, Zoledronic Acid, Zolinza, Zometa, Zydelig, Dicardia, 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 an amount of the active agent or compound that is non-toxic yet sufficient to provide the desired effect or benefit.,

[0150] The term "benefit" is used in the broadest sense and refers to any desirable effect, specifically including the clinical benefits defined herein. Clinical benefits can be measured by evaluating various endpoints, such as some inhibition of disease progression, such as slowing and complete cessation, reduction in the number of disease episodes and / or symptoms; reduction in lesion size; inhibition (i.e., reduction, slowing or complete cessation) of infiltrating diseased cells into adjacent peripheral organs and / or tissues; inhibition (i.e., reduction, slowing or complete cessation) of disease spread; reduction in autoimmune reactions, which may or may not result in regression or excision of lesions; some alleviation of one or more symptoms associated with the disorder; increase in the length of the disease-free presentation period after treatment, such as 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 a physiological state in mammals that is typically characterized by unregulated cell growth. This definition includes both benign and malignant cancers. Examples of cancers include, but are not limited to, carcinomas, lymphomas, blastomas, sarcomas, and leukemias. More specific examples of such cancers include adrenocortical carcinoma, bladder urothelial carcinoma, invasive breast cancer, cervical squamous cell carcinoma, squamous cell carcinoma of the neck, endocervical adenocarcinoma, cholangiocarcinoma, colorectal adenocarcinoma, lymphoid neoplastic diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, chromophobic renal cell carcinoma, clear cell renal carcinoma, papillary renal cell carcinoma of the kidney, acute myeloid leukemia, low-grade glioma of the brain, hepatocellular carcinoma of the liver, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, uterine carcinosarcoma, uveal melanoma. Other examples include breast cancer, lung cancer, lymphoma, melanoma, liver cancer, colorectal cancer, ovarian cancer, bladder cancer, kidney cancer, or gastric cancer. Further examples of cancers include neuroendocrine cancer, non-small cell lung cancer (NSCLC), small cell lung cancer, thyroid cancer, endometrial cancer, cholangiocarcinoma, esophageal cancer, anal cancer, salivary gland cancer, vulvar cancer, or cervical cancer.

[0152] The term "tumor" refers to all neoplastic cell growth and proliferation, and all precancerous and cancerous cells and tissues, regardless of malignancy or benignity. The terms "cancer", "cancerous", "cell proliferative disorder", "proliferative disorder", and "tumor" are not mutually exclusive as referred to herein.

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

Examples

[0154] The present disclosure will be further described by the following examples, which should not be construed as limiting this disclosure in terms of scope or spirit to the specific procedures described herein. It should be understood that the examples are provided to illustrate specific embodiments and that no limitation to the scope of the present disclosure is thereby intended. Furthermore, it should be understood that it may be necessary to rely on various other 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 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). The Random Forest algorithm was applied to each dataset to identify the set of most important transcripts that are 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 prediction importance scores within both datasets (Figures 1A - 1B). Next, three microarray datasets (E-GEOD-62116, E-GEOD-62667, E-GEOD-72220, Table 2) were used to validate the prostate cancer prediction signature (characteristics). For each transcript (n = 564 samples) across the entire validation cohort, the prediction importance and the Kruskal-Wallis p-value were obtained and averaged across the three datasets (Figure 2). A literature search identified the ARv7 variant, the ERG-TRMS22 fusion gene, and the AR1 / AR2 signaling as additional gene sets to be included and evaluated.

[0157] Results of the evaluation of 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 PCA markers (Table 3). These genes were demonstrated to be highly expressed in PCA tumor tissues compared to normal prostate, and this fact was used to effectively distinguish tumors from controls (Figure 3). Those genes also discriminated between 7 different PCA cell lines, namely, 22Rv1 and E006AA-hT (localized); VCaP; PC-3; LNCaP; DU145 and MDA PCa2b from 2 normal prostate epithelial cell lines PWR-1E and RWPE-1 (all metastatic) (Figure 4). These data demonstrate that candidate target transcripts are produced by neoplastically transformed prostate epithelial cells and are detectable in blood.

[0158] Using the normalized gene expression of those 37 markers in whole blood from control (n = 100) and PCA (n = 21) samples, an artificial intelligence model for prostate cancer disease was constructed. The dataset was randomly split into a training partition and a test partition for model construction and validation. Twelve algorithms were evaluated (XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, and mlp). The algorithm with the best performance (XGB - "gradient boosting") predicted the training data best. In the test set, XGB generated a probability score for predicting samples. Each probability score reflects the "certainty" of the algorithm as to whether an unknown sample belongs to either the "control" or "PCA" class. For example, an unknown sample S1 could have the following probability vector [control = 20%, PCA = 80%]. This sample is considered a PCA sample.

[0159] Table 2. Summary of all publicly available microarray datasets used to derive prostate cancer-specific gene signatures.

Table 2

[0160] Table 3. 37 PCA marker gene panel (excluding housekeeping genes). [Table 3-1] [Table 3-2] [Table 3-3]

[0161] Example 2. Clinical utility

[0162] The ProstaTest score was significantly increased (p < 0.001) in PCA patients (63 ± 19%) compared to men with benign prostatic hyperplasia (17 ± 13%) and controls (8 ± 9%) (Figure 5). No difference was observed between controls and hyperplastic patients. Data (receiver operating characteristic curve analysis and metric data) on the usefulness of the test to distinguish prostate cancer patients (n = 125) from controls (n = 201) in validation are included in Figure 6. The score showed an area under the curve (AUROC) of 0.97. The metric (measurement criterion) was sensitivity: 92% and specificity: 99% (Figure 7). The Youden index J was 0.94, and the Z statistic for discriminating controls was 38.9.

[0163] The 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% with a ProstaTest score of ≥ 32 and > 80% with a score > 34. Therefore, this tool can accurately discriminate between controls and prostate cancer disease.

[0164] The ProstaTest score was significantly increased (p<0.001) in high-grade PCA (Gleason score ≥7; 70±19%) compared to low-grade (Gleason score 5+6) PCA (41±7%). Data on the usefulness of the test for distinguishing between low- and high-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. The metric (measurement criterion) was sensitivity: 100% and specificity: 88%. The Youden index J was 0.87. The PSA level showed an AUROC of 64% for comparison. The 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 cohorts established that complete removal of the tumor and absence of disease evidence were associated with a significant decrease in ProstaTest (p<0.0001). No significant difference in levels was observed between controls or patients with benign prostatic hyperplasia. Evaluation of another cohort revealed that patients who received treatment and responded to it had significantly lower scores (p<0.001) than those diagnosed with the disease (Figure 11). Treatment modalities included hormonal therapy and chemotherapy. Thus, this tool can accurately identify treatment response in prostate cancer.

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[0197] Although the invention has been described in connection with the specific embodiments described 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 be within the spirit and scope of the 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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