Kits and methods useful for prognosis, diagnosis and treatment of prostate cancer

Abstract

Provided herein are kits and methods useful for cancer diagnosis, prognosis, research, and therapy. In particular, provided herein are methods for diagnosing, prognosing, and / or treating prostate cancer based on the expression levels of cancer markers.

Description

[Technical Field]

[0001] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under U01CA214170, P50CA186786 and R35CA231996 awarded by the National Cancer Institute. The government has certain rights in this invention.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 442,045, filed January 30, 2023, and U.S. Provisional Patent Application No. 63 / 446,596, filed February 17, 2023, each of which is incorporated herein by reference in its entirety.

[0003] Electronic Sequence Listing Reference The contents of the electronic sequence listing (LXDX-002-001WO.xml; size 50,183 bytes; and creation date: January 28, 2024) are incorporated herein by reference in their entirety.

[0004] Provided herein are kits and methods useful for cancer diagnosis, prognosis, research, and therapy. In particular, provided herein are methods for diagnosing, prognosing, and / or treating prostate cancer based on the expression levels of cancer markers. [Background technology]

[0005] Prostate cancer is the third most common urological malignancy and can originate from the prostate parenchyma or the urinary collecting system. Prostate cell carcinoma, which arises from the prostate parenchyma, is the most common malignant prostate tumor, with an incidence of 64,000 cases and approximately 14,000 deaths annually in the United States. Urothelial cell carcinoma originates from the urinary collecting system and is the most common malignancy, representing approximately 10-15% of all prostate tumors. The overall incidence of malignant prostate tumors is increasing and is now the third most common form of genitourinary cancer. Both malignant and benign prostate tumors are increasingly diagnosed incidentally with the use of advanced cross-sectional imaging. Accurate diagnosis of benign versus malignant tumor types is lacking, and patients may therefore be subjected to unnecessary or excessive treatment. Furthermore, there are currently no needle biopsy, urine, or blood diagnostic tests that accurately characterize prostate tumors or identify patients at risk for prostate tumors. The diagnostic and therapeutic approach to prostate tumors is complicated by the existence of multiple benign prostate tumor types and the fact that many small malignant prostate parenchymal tumors may be observed rather than curatively treated.

[0006] Early detection and treatment of aggressive prostate cancer is crucial to reducing its harm, but current diagnostic tests cannot reliably identify clinically significant prostate cancers (e.g., classified as Grade Group [GG] ≥ 2). The harms of serum prostate-specific antigen (PSA) as a sole diagnostic tool, with its low specificity for cancer, are well documented, and several cancer-specific biomarkers have been proposed to augment PSA. These tools demonstrate additional benefit, potentially avoiding 15–30% of biopsies performed for PSA at the cost of failing to diagnose 8–15% of GG ≥ 2 prostate cancers. MRI has also been used in this role at some academic centers. In addition to growing evidence that a proportion of GG ≥ 2 cancers are invisible on MRI, MRI is costly, resource-intensive, and subjectively interpreted, limiting its practical utility as a population-level diagnostic tool. Thus, there remains a critical need for a practical (affordable, reproducible, standardizable) non-invasive test to reliably detect aggressive prostate cancer in its localized, potentially curable state. Summary of the Invention [Problem to be solved by the invention]

[0007] Although several molecular mechanisms drive aggressive prostate cancer biology, most patients have tumors that reflect a limited number of these mechanisms. Currently, there are no accurate, user-friendly, and widely accessible screening tools at the tissue, blood, or urine level for ideal clinical management of prostate tumors. [Means for solving the problem]

[0008] (Summary of the Invention) Provided herein are methods of treating prostate cancer, comprising: a) assaying the level of expression of one or more genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject diagnosed with prostate cancer; and b) administering a prostate cancer treatment to a subject identified as having an altered level of expression of one or more of the genes compared to subjects without prostate cancer or subjects with low-grade prostate cancer.

[0009] Further provided is a method for characterizing, prognosing, or recommending treatment for prostate cancer, comprising the steps of: a) assaying the level of expression of one or more genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject diagnosed with prostate cancer; and b) identifying the subject as having high-grade prostate cancer if the subject is identified as having altered levels of expression of said genes compared to subjects without prostate cancer or subjects with low-grade prostate cancer.

[0010] Further provided is a method for informing prostate cancer survival outcome, comprising: (i) detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6, wherein the amount of expression is present in urine from the subject; (ii) determining a score based on the amount of expression, wherein the score correlates with or is indicative of the subject's likelihood of having or developing Grade Group ≧2 prostate cancer; and (iii) generating a report comprising the score.

[0011] Further provided is a method for identifying a subject who has or has a high likelihood of developing Grade Group ≥ 2 prostate cancer, comprising detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4 and HOXC6, wherein the amount of expression is present in the subject's urine and indicates whether the subject has a high likelihood of having Grade Group ≥ 2 prostate cancer with a diagnostic accuracy (AUC) of ≥ 0.75.

[0012] Further provided is a method for identifying the likelihood of detecting Grade Group ≥ 2 prostate cancer from a prostate biopsy of a subject, comprising detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4 and HOXC6, wherein the amount of expression is present in the subject's urine and indicates the likelihood that Grade Group ≥ 2 prostate cancer will be detected from the subject's prostate biopsy with a diagnostic accuracy (AUC) of ≥ 0.75.

[0013] Further provided is a method for screening for the amount of expression of at least three genes, the method comprising the steps of: (a) reacting a sample of urine from a human subject with a reagent for detecting the amount of expression of at least three genes, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; and (b) detecting the amount of expression of the at least three genes present in the sample, wherein the detecting step comprises the use of an in vitro assay.

[0014] Further provided is a method for detecting the amount of mRNA expressed by at least three genes, the method comprising the steps of: (a) synthesizing cDNA from mRNA expressed by at least three genes and present in a urine sample from a human subject, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; (b) amplifying the cDNA to provide amplified cDNA; and (c) detecting the amplified cDNA, wherein the amplified cDNA is indicative of the amount of mRNA expressed by the at least three genes.

[0015] Further provided is a method for detecting the amount of mRNA expressed by at least three genes, the method comprising: (a) isolating nucleic acids from a first composition comprising urine from a human subject to provide isolated nucleic acids; (b) reacting the isolated nucleic acids with a second composition present in the first composition and comprising reagents for detecting the amount of mRNA expressed by at least three genes, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; and (c) detecting the amount of mRNA expressed by the at least three genes.

[0016] Further provided is a kit comprising: a container containing a reagent composition for detecting the amount of expression of at least three genes; and instructions for detecting the amount of expression, wherein the amount of expression is present in the urine of a subject, and the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. [Brief explanation of the drawings]

[0017] [Figure 1A] Flowchart of biomarker discovery (FIG. 1A) and sample inclusion in the MyProstateScore 2.0 (MPS2) training cohort (from University of Michigan, UM) and validation cohort (from NCI-Early Detection Research Network, EDRN) (FIG. 1B). [Figure 1B]Flowchart of biomarker discovery (FIG. 1A) and sample inclusion in the MyProstateScore 2.0 (MPS2) training cohort (from University of Michigan, UM) and validation cohort (from NCI-Early Detection Research Network, EDRN) (FIG. 1B). [Figure 2A] Model development procedure. Redundant variables (highly correlated) were removed if the variance inflation factor (VIF) was >5 (Figure 2A). To select a robust gene panel, an elastic net regression model was trained on 40 subsampled data (Figure 2B). Calibration curves for clinically significant prostate cancer for MPS2 and MPS2+ in the external validation cohort (Figure 2C). [Figure 2B] Model development procedure. Redundant variables (highly correlated) were removed if the variance inflation factor (VIF) was >5 (Figure 2A). To select a robust gene panel, an elastic net regression model was trained on 40 subsampled data (Figure 2B). Calibration curves for clinically significant prostate cancer for MPS2 and MPS2+ in the external validation cohort (Figure 2C). [Figure 2C] Model development procedure. Redundant variables (highly correlated) were removed if the variance inflation factor (VIF) was >5 (Figure 2A). To select a robust gene panel, an elastic net regression model was trained on 40 subsampled data (Figure 2B). Calibration curves for clinically significant prostate cancer for MPS2 and MPS2+ in the external validation cohort (Figure 2C). [Figure 3A] Performance evaluation of the MPS2 model in the training cohort. Receiver operating characteristic (ROC) of the original MyProstateScore (MPS) (TMPRSS2-ERG+PCA3) (Figure 3A). [Figure 3B] Performance evaluation of the MPS2 model in the training cohort. ROC of the MPS2 gene panel (Figure 3B). [Figure 3C] Performance evaluation of the MPS2 model in the training cohort. ROC of MPS2 plus clinical variables (MPS2c) (Figure 3C). [Figure 3D] Performance evaluation of the MPS2 model in the training cohort. ROC of MPS2 plus clinical variables and prostate volume (MPS2cv) (Figure 3D). [Figure 3E] Performance evaluation of the MPS2 model in the training cohort. Model calibration analysis after calibration by adjusting the slope and intercept (Figure 3E). [Figure 4A] Performance evaluation of the MPS2 model in the validation cohort. ROC and area under the curve (AUC) of the MPS2 model (Figure 4A). [Figure 4B] Performance evaluation of the MPS2 model in the validation cohort. Calibration curve of calibrated risk probability (Figure 4B). [Figure 4C] Performance evaluation of the MPS2 model in the validation cohort. Decision curve analysis demonstrating the net benefit of the MPS2 model versus "Treat All" or "Treat None" across different probability thresholds (Figure 4C). [Figure 4D] Performance evaluation of the MPS2 model in the validation cohort. Interventions avoided (biopsy) across different probability thresholds (Figure 4D). [Figure 5] Association of selected genes with high-grade prostate cancer in the TCGA PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma) cohort. 17 genes were used in the final MPS2 model: TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. [Figure 6] Evaluation of feature selection methods. Boxplots showing the area under the curve (AUC) of each feature selection method from repeated cross-validation. RFE, recursive feature exclusion. [Figure 7]Calibration curve of the MPS2 model in the validation cohort without calibration. The predicted risk is overestimated without correction for the imbalanced classes in the validation cohort. [Figure 8A] MPS2 values ​​by biopsy pathology in the external validation cohort. Box and dot plots illustrating the distribution of MPS2 (Figure 8A) and MPS2+ (Figure 8B) values ​​in men with negative biopsies, GG1 cancer on biopsy, and GG≥2 cancer on biopsy in the external validation cohort. P values ​​were ≤0.001 for pairwise comparisons of GG≥2 cancer and GG1 cancer on negative biopsies for both MPS2 models. [Figure 8B] MPS2 values ​​by biopsy pathology in the external validation cohort. Box and dot plots illustrating the distribution of MPS2 (Figure 8A) and MPS2+ (Figure 8B) values ​​in men with negative biopsies, GG1 cancer on biopsy, and GG≥2 cancer on biopsy in the external validation cohort. P values ​​were ≤0.001 for pairwise comparisons of GG≥2 cancer and GG1 cancer on negative biopsies for both MPS2 models. [Figure 9] Area under the MPS2 and MPS2+ ROC curves for clinically significant prostate cancer in the external validation cohort. Receiver operating characteristic curves and areas under the curve (AUC) for PSA (gray), PCPTrc (PCa Prevention Trial risk calculator, yellow), Prostate Health Index (PHI, purple), dmx2 (derived multiplex 2-gene model (HOXC6, DLX1), pink), dmx3 (derived multiplex 3-gene model (PCA3, ERG, SPDEF), maroon), MPS (MyProstateScore, orange), MPS2 (green), and MPS2+ (blue) in the external validation cohort. [Figure 10A]Decision curve analysis for clinically significant prostate cancer in an external validation cohort. Figure 10A shows a decision curve analysis (DCA) plot for the net clinical benefit of pre-biopsy testing with PSA (gray), PCPTrc (yellow), PHI (purple), dmx2 (pink), dmx3 (maroon), MPS (orange), MPS2 (green), and MPS2+ (blue) compared to the baseline approach of "biopsy all" (black) or "biopsy none" (dark green). FIG. 10B shows a DCA plot illustrating the net reduction in biopsies performed per 100 patients without missing a single diagnosis of GG≧2 cancer based on pre-biopsy testing with PSA (gray), PCPTrc (yellow), PHI (purple), dmx2 (pink), dmx3 (maroon), MPS (orange), MPS2 (green), and MPS2+ (blue) compared to the baseline approach of biopsying all patients. [Figure 10B] Decision curve analysis for clinically significant prostate cancer in an external validation cohort. Figure 10A shows a decision curve analysis (DCA) plot for the net clinical benefit of pre-biopsy testing with PSA (gray), PCPTrc (yellow), PHI (purple), dmx2 (pink), dmx3 (maroon), MPS (orange), MPS2 (green), and MPS2+ (blue) compared to the baseline approach of "biopsy all" (black) or "biopsy none" (dark green). FIG. 10B shows a DCA plot illustrating the net reduction in biopsies performed per 100 patients without missing a single diagnosis of GG≧2 cancer based on pre-biopsy testing with PSA (gray), PCPTrc (yellow), PHI (purple), dmx2 (pink), dmx3 (maroon), MPS (orange), MPS2 (green), and MPS2+ (blue) compared to the baseline approach of biopsying all patients. [Figure 11] Flow diagram of the NCI-EDRN external validation cohort. The external validation cohort, comprised of men undergoing prostate biopsy in the National Cancer Institute-Early Detection Research Network (NCI-EDRN) PCA3 trial, is shown. DETAILED DESCRIPTION OF THE INVENTION

[0018] definition To facilitate understanding of this disclosure, a number of terms and phrases are defined below: As used herein, the terms "detect," "detecting," or "detection" can refer to either the general act of finding or identifying a composition or the specific observation of a composition. Detecting a composition can include determining the presence or absence of the composition. Detecting can include quantifying the composition. For example, detecting can include determining the expression level of the composition. The composition can include a nucleic acid molecule. For example, the composition can include at least a portion of a cancer marker disclosed herein. Alternatively, or in addition, the composition can be a detectably labeled composition.

[0019] As used herein, the term "subject" refers to any organism screened using the diagnostic methods described herein. Such organisms preferably include, but are not limited to, mammals (e.g., mice, monkeys, horses, cows, pigs, dogs, cats, etc.), and most preferably include humans. In some embodiments, the subject is a mammal having a prostate gland. In some embodiments, the subject is a human having a prostate gland.

[0020] The term "diagnosed," as used herein, refers to the recognition of a disease by its signs and symptoms, or by genetic, pathological, histological, or other analysis.

[0021] As used herein, the phrase "characterizing cancer in a subject" refers to the identification of one or more characteristics of a cancer sample in a subject, including, but not limited to, the presence of benign, precancerous, or cancerous tissue, the stage of the cancer, and the prognosis of the subject. Cancer may be characterized by the identification of the expression of one or more cancer marker genes, including, but not limited to, the cancer markers disclosed herein.

[0022] As used herein, the term "cancer stage" refers to a qualitative or quantitative assessment of the level of cancer advancement. Criteria useful for determining the stage of cancer include, but are not limited to, tumor size and the extent of metastasis (e.g., localized or distant).

[0023] As used herein, the term "high likelihood," for example, when used in reference to the likelihood of having or developing prostate cancer (e.g., Grade Group ≥ 2 prostate cancer), refers to an increased likelihood of developing Grade Group ≥ 2 prostate cancer compared to a low-risk subject, or a high absolute likelihood of developing Grade Group ≥ 2 prostate cancer. In some embodiments, the high likelihood of developing Grade Group ≥ 2 prostate cancer is determined based on the level of expression of one or more genes described herein. In some embodiments, a "high likelihood" is a 50%, 100%, 200%, 500% or more increased likelihood compared to a healthy subject or a subject in which the expression of a gene listed herein is not altered. In some embodiments, a high likelihood refers to an absolute likelihood of developing Grade Group ≥ 2 prostate cancer. In some embodiments, a "high likelihood" is a 50% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high likelihood" is a 60% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 70% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is an 80% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 90% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 95% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 96% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 97% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 98% or greater chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject.In some embodiments, a "high chance" is a 99% or greater chance that a subject's prostate biopsy will detect Grade Group > 2 prostate cancer in the subject. In some embodiments, a "high chance" is a 100% chance that a subject's prostate biopsy will detect Grade Group > 2 prostate cancer in the subject.

[0024] As used herein, the term "low likelihood," for example, when used in reference to the likelihood of having or developing prostate cancer (e.g., Grade Group ≥ 2 prostate cancer), refers to a decreased likelihood of developing prostate cancer (e.g., Grade Group ≥ 2 prostate cancer) compared to an average-risk subject, or a low absolute likelihood of developing prostate cancer (e.g., Grade Group ≥ 2 prostate cancer). In some embodiments, the low likelihood of developing Grade Group ≥ 2 prostate cancer is determined based on the level of expression of one or more genes described herein. In some embodiments, a "low likelihood" is a 50%, 100%, 200%, 500% or more decreased likelihood compared to a healthy subject or a subject in which the expression of a gene listed herein is not altered. In some embodiments, a low likelihood refers to an absolute likelihood of developing Grade Group ≥ 2 prostate cancer. In some embodiments, a "low likelihood" is a less than 50% chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low likelihood" is a 40% or less chance that a prostate biopsy of a subject will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 30% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 20% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 10% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 5% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 4% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 3% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 2% or less chance that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject.In some embodiments, a "low chance" is a 1% or less chance that a subject's prostate biopsy will detect Grade Group > 2 prostate cancer in the subject. In some embodiments, a "low chance" is a 0% chance that a subject's prostate biopsy will detect Grade Group > 2 prostate cancer in the subject.

[0025] As used herein, the term "nucleic acid molecule" refers to any nucleic acid-containing molecule, including, but not limited to, DNA or RNA. A nucleic acid molecule can contain one or more nucleotides. The term can include nucleotide polymers in which the nucleotides and linkages between them include non-naturally occurring synthetic analogs, such as, but not limited to, phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs), and others. The term includes 4-acetylcytosine, 8-hydroxy-N6-methyladenosine, aziridinylcytosine, pseudoisocytosine, 5-(carboxyhydroxylmethyl)uracil, 5-fluorouracil, 5-bromouracil, 5-carboxymethylaminomethyl-2-thiouracil, 5-carboxymethylaminomethyluracil, dihydrouracil, inosine, N6-isopentenyladenine, 1-methyladenine, 1-methylpseudouracil, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta The present invention further encompasses sequences that can include any of the known base analogs of DNA and RNA, including, but not limited to, -D-mannosylqueosine, 5'-methoxycarbonylmethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid methyl ester, uracil-5-oxyacetic acid, oxybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, N-uracil-5-oxyacetic acid methyl ester, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.Where a nucleotide sequence is represented by a DNA sequence (i.e., A, T, G, C), it is understood that the sequence also includes an RNA sequence in which "U" replaces "T" (i.e., A, U, G, C).

[0026] The term "gene" refers to a nucleic acid (e.g., DNA) sequence that comprises coding sequences necessary for the production of a polypeptide, precursor, or RNA (e.g., rRNA, tRNA). A polypeptide can be encoded by a full-length coding sequence or by any portion of a coding sequence, so long as the desired activity or functional property of the full-length or fragment (e.g., enzymatic activity, ligand binding, signal transduction, immunogenicity, etc.) is retained. Just as "gene" corresponds to the length of a full-length mRNA, the term also encompasses the coding region of a structural gene, as well as sequences located adjacent to the coding region on both the 5' and 3' ends for a distance of about 1 kb or more on either side. Sequences located 5' of the coding region and present on the mRNA are referred to as 5' non-translated or untranslated sequences. Sequences located 3' or downstream of the coding region and present on the mRNA are referred to as 3' non-translated or untranslated sequences. The term "gene" encompasses both cDNA and genomic forms of a gene. A genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed "introns" or "intervening regions" or "intervening sequences." Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or "spliced ​​out" from the nuclear or primary transcript; therefore, introns are absent in the messenger RNA (mRNA) transcript. mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide.

[0027] As used herein, the term "oligonucleotide" refers to a short, single-stranded polynucleotide chain. Oligonucleotides are typically less than 200 nucleotide residues long (e.g., between 15 and 100), although the term, as used herein, is intended to encompass longer polynucleotide chains. Oligonucleotides are often referred to by their length. For example, a 24-residue oligonucleotide is referred to as a "24-mer." Oligonucleotides can form secondary and tertiary structures by self-hybridizing or by hybridizing to other polynucleotides. Such structures can include, but are not limited to, duplexes, hairpins, cruciforms, bent strands, and triplexes.

[0028] The term "label," as used herein, refers to any atom or molecule that can be used to produce a detectable (preferably quantifiable) effect and that can be attached to a nucleic acid or protein. Labels include dyes; 32 Labels include, but are not limited to, radiolabels such as P; binding moieties such as biotin; haptens such as digoxigenin; luminogenic, phosphorescent, or fluorogenic moieties; and fluorescent dyes, alone or in combination with moieties that can suppress or shift their emission spectra by fluorescence resonance energy transfer (FRET). Labels can provide detectable signals by fluorescence, radioactivity, colorimetry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, and the like. Labels can be charged moieties (e.g., positively or negatively charged) or alternatively, can be neutrally charged. Labels can comprise or consist of nucleic acid or protein sequences, so long as the sequence comprising the label is detectable. In some embodiments, nucleic acids are detected directly without a label (e.g., by directly reading the sequence).

[0029] As used herein, the term "sample" includes specimens or cultures obtained from any source, as well as biological and environmental samples. Biological samples can be obtained from animals (including humans) and can encompass bodily fluids (e.g., blood, urine), solids, tissues, and gases. Biological samples can include urine, urine supernatant, and urine cell pellet, as well as blood products such as plasma, serum, and others. However, such examples should not be construed as limiting the sample types applicable to the present disclosure.

[0030] As used herein, "high-grade prostate cancer" means Grade Group > 2 prostate cancer. In some embodiments, high-grade prostate cancer is GG > 3 prostate cancer.

[0031] As used herein, "low-grade prostate cancer" means Grade Group <2 prostate cancer.

[0032] As used herein, a "score" is the likelihood that a subject's prostate biopsy will detect Grade Group ≥ 2 prostate cancer in the subject, i.e., that the subject's prostate biopsy will be positive for prostate cancer. The score is based on the level or amount of expression of one or more genes described herein present in a sample from the subject. In some embodiments, the score is a numerical value ranging from 0% to 100%. In some embodiments, the numerical value is expressed as a decimal ranging from 0.0 to 100.0. In some embodiments, the score is a qualitative readout of "low risk" or "elevated risk."

[0033] As used herein, the term "altered," e.g., in the context of "altered levels of expression of one or more genes," refers to a level of gene expression that is different (e.g., increased or decreased) from the level of expression in a subject who is free of prostate cancer or who has low-grade prostate cancer.

[0034] As used herein, the term "variant," e.g., genetic variant, refers to a sequence change that does not affect gene identity. Such sequence changes are readily recognized by those skilled in the art. In some embodiments, a variant comprises a mutation, substitution, and / or deletion. In some embodiments, a variant comprises a polymorphism. In some embodiments, a variant comprises a splice variant.

[0035] As used herein, the term "about" refers to a ±10% variation from the nominal value unless otherwise indicated or inferred. When the term "about" is used before a number, the present disclosure also includes the specific number itself unless specifically stated otherwise.

[0036] Detailed Description of the Disclosure Provided herein are kits and methods useful for cancer diagnosis, prognosis, research, and therapy. In particular, provided herein are methods for diagnosing, prognosing, and / or treating prostate cancer based on the expression levels of cancer markers.

[0037] The present disclosure is based, at least in part, on the discovery of methods for determining the likelihood that a subject has Grade Group ≧2 prostate cancer based on the amount of expression of one or more genes described herein.

[0038] Described herein are methods and kits incorporating one or more of 17 markers useful for the prognosis, diagnosis, or treatment of prostate cancer. Importantly, detection of PSA (prostate-specific antigen), the traditional method for prognosis and / or diagnosis of prostate cancer, is not a required step in the methods described herein. Elevated PSA identified during PSA screening leads to high rates of invasive and unnecessary biopsies in cancer-free men and a high frequency of overdiagnosis of low-grade, indolent cancers (Grade Group 1 (GG1)). The disclosed kits and methods provide more accurate prognosis or diagnosis of prostate cancer, helping to identify subjects who could benefit from early, aggressive therapeutic intervention while sparing subjects with indolent disease from invasive procedures such as biopsies. Thus, the present methods provide a new and unconventional set of prostate cancer biomarkers, particularly high-grade (e.g., GG≧2) prostate cancer biomarkers, independent of PSA.

[0039] Thus, provided herein are methods and kits useful for prognosing, diagnosing, or treating subjects with prostate cancer, in some embodiments, Grade Group ≧2 prostate cancer. For example, in some embodiments, provided herein are methods of treating prostate cancer, comprising: a) assaying the level of expression of one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all 17) genes selected from, for example, TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject who has been prognosed or diagnosed with prostate cancer; and b) administering a prostate cancer treatment to a subject identified as having an altered level of expression of one or more of the genes compared to a subject who does not have prostate cancer or a subject who has low-grade prostate cancer. In some embodiments, the subject has high-grade prostate cancer.

[0040] Further embodiments provide methods of characterizing, prognosing, or recommending treatment for prostate cancer, comprising: a) assaying the level of expression of one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all 17) genes selected from, e.g., TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject prognosed or diagnosed with prostate cancer; and b) identifying the subject as having high-grade prostate cancer if the subject is identified as having altered levels of expression of said genes compared to subjects without prostate cancer or subjects with low-grade prostate cancer. In some embodiments, the method further comprises administering to the subject a prostate cancer treatment. In some embodiments, the method further comprises administering to the subject a treatment for Grade Group > 2 prostate cancer.

[0041] In some embodiments, the method further comprises performing a prostate biopsy on the subject. In some embodiments, the method further comprises recommending to the subject or the subject's healthcare provider that the subject undergo a prostate biopsy. In some embodiments, the prostate biopsy indicates that the subject has Grade Group > 2 prostate cancer. In some embodiments, the prostate biopsy indicates that the subject does not have Grade Group > 2 prostate cancer.

[0042] In some embodiments, the method further comprises the step of recommending to the subject or the subject's healthcare provider that the subject not undergo a prostate biopsy.

[0043] In some embodiments, the method does not include performing a prostate biopsy in the subject.

[0044] The methods described herein are useful for identifying subjects with high-grade prostate cancer for treatment and allowing subjects identified as not having high-grade prostate cancer to avoid biopsy or treatment and the associated side effects. The methods provided herein are useful for reducing the number of unnecessary prostate biopsies and sparing healthy subjects from costly invasive procedures.

[0045] I. Methods of Assaying Marker Expression As described herein, embodiments of the present disclosure provide methods for prognosis, diagnosis, or treatment that utilize detection of the expression or level of one or more genes selected from, for example, TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all 17). Illustrative, non-limiting methods are described herein.

[0046] Genes for detection In some embodiments, the level or amount of expression of one or more genes is determined. In some embodiments, the level or amount of expression is the level or amount of mRNA or protein expressed by the gene.

[0047] In some embodiments, the one or more genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

[0048] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the TMPRSS2-ERG gene. TMPRSS2-ERG gene fusions overexpress the transcription factor ERG, which is present in both early- and late-stage prostate cancer. Numerous variants of the TMPRSS2-ERG fusion have been identified, with the most common involving exon 1 of TMPRSS2 and exons 4-11 of ERG. In some embodiments, the TMPRSS2-ERG gene fusion comprises a fusion of the nucleotide sequences of Ensembl gene identifiers ENSG00000184012 and ENSG00000157554. In some embodiments, the TMPRSS2-ERG gene fusion comprises the nucleotide sequence of SEQ ID NO: 1 or a variant thereof.

[0049] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the SCHLAP1 gene. SCHLAP1 is a long non-coding RNA that is overexpressed in a subset of prostate cancers. SCHLAP1 antagonizes the genome-wide localization and regulatory function of the SWI / SNF chromatin-modifying complex. In some embodiments, the SCHLAP1 gene comprises a nucleotide sequence provided by the HUGO Gene Nomenclature Committee (HGNC). In some embodiments, the HGNC identifier for SCHLAP1 is 48603. In some embodiments, the SCHLAP1 gene is located at chromosome location 2q31.3. In some embodiments, the SCHLAP1 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000281131. In some embodiments, the SCHLAP1 gene comprises the nucleotide sequence of SEQ ID NO: 2 or a variant thereof.

[0050] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the OR51E2 gene. OR51E2 is an olfactory receptor (OR) that represents the largest G protein-coupled receptor (GPCR) family in the human genome. Activation of human ORs can affect cell proliferation. Specifically, OR51E2 has been identified as being involved in regulating cell growth, migration, and invasiveness of melanocytes, melanoma cells, and prostate cancer cells. In some embodiments, the OR51E2 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for OR51E2 is 15195. In some embodiments, the OR51E2 gene is located at chromosome location 11p15.4. In some embodiments, the OR51E2 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000167332. In some embodiments, the OR51E2 gene comprises the nucleotide sequence of SEQ ID NO: 3 or a variant thereof.

[0051] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the APOC1 gene. APOC1 is the smallest apolipoprotein and is a component of both triglyceride-rich lipoprotein and high-density lipoprotein. APOC1 is involved in various biological processes and is associated with the progression of several diseases, such as diabetic nephropathy, Alzheimer's disease, and glomerulosclerosis. Recent studies have shown that APOC1 may be associated with the development of cancer, including breast cancer, pancreatic cancer, lung cancer, and prostate cancer. In some embodiments, the APOC1 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for APOC1 is 607. In some embodiments, the APOC1 gene is located at chromosome location 19q13.32. In some embodiments, the APOC1 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000130208. In some embodiments, the APOC1 gene comprises the nucleotide sequence of SEQ ID NO: 4 or a variant thereof.

[0052] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the PCAT14 gene. PCAT14 is a long non-coding RNA that exhibits both cancer and lineage specificity. PCAT14 is transcriptionally regulated by the androgen receptor (AR), and overexpression of endogenous PCAT14 suppresses cell invasion. In some embodiments, the PCAT14 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for PCAT14 is 48977. In some embodiments, the PCAT14 gene is located at chromosome location 22q11.23. In some embodiments, the PCAT14 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000280623. In some embodiments, the PCAT14 gene comprises the nucleotide sequence of SEQ ID NO: 5 or a variant thereof.

[0053] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the CAMKK2 gene. CAMKK2 is a direct target of AR, and regulation can vary across disease stages. CAMKK2 has been identified as a driver of prostate cancer progression. In some embodiments, the CAMKK2 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for CAMKK2 is 1470. In some embodiments, the CAMKK2 gene is located at chromosome location 12q24.31. In some embodiments, the CAMKK2 gene comprises the nucleotide sequence of Ensembl gene ENSG00000110931. In some embodiments, the CAMKK2 gene comprises the nucleotide sequence of SEQ ID NO: 6 or a variant thereof.

[0054] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the PCA3 gene. PCA3 is a non-coding gene associated with prostate cancer. In some embodiments, the PCA3 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for PCA3 is 8637. In some embodiments, the PCA3 gene is located at chromosome location 9q21.2. In some embodiments, the PCA3 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000225937. In some embodiments, the PCA3 gene comprises the nucleotide sequence of SEQ ID NO: 7 or a variant thereof.

[0055] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the NKAIN1 gene. NKAIN1 is a sodium / potassium transporting ATPase. In some embodiments, the NKAIN1 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for NKAIN1 is 25743. In some embodiments, the NKAIN1 gene is located at chromosomal location 1p35.2. In some embodiments, the NKAIN1 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000084628. In some embodiments, the NKAIN1 gene comprises the nucleotide sequence of SEQ ID NO: 8 or a variant thereof.

[0056] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the B3GNT6 gene. B3GNT6 is a member of the O-GlcNAc transferase (OGT) family and is responsible for the production of core 3 structures of O-glycans. In some embodiments, the B3GNT6 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for B3GNT6 is 24141. In some embodiments, the B3GNT6 gene is located at chromosome location 11q13.5. In some embodiments, the B3GNT6 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000198488. In some embodiments, the B3GNT6 gene comprises the nucleotide sequence of SEQ ID NO: 9 or a variant thereof.

[0057] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the TFF3 gene. TFF3 is a trefoil factor, a secreted peptide produced by normal intestinal mucosa. Members of the trefoil family are overexpressed in various cancers and are associated with tumor invasion, resistance to apoptosis, and metastasis. In some embodiments, the TFF3 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for TFF3 is 11757. In some embodiments, the TFF3 gene is located at chromosome location 21q22.3. In some embodiments, the TFF3 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000160180. In some embodiments, the TFF3 gene comprises the nucleotide sequence of SEQ ID NO: 10 or a variant thereof.

[0058] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the SPON2 gene. SPON2 belongs to the F-spondin family of secreted extracellular matrix proteins and is deregulated in some tumors, including prostate cancer. In some embodiments, the SPON2 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for SPON2 is 11253. In some embodiments, the SPON2 gene is located at chromosomal location 4p16.3. In some embodiments, the SPON2 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000159674. In some embodiments, the SPON2 gene comprises the nucleotide sequence of SEQ ID NO: 11 or a variant thereof.

[0059] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the PCGEM1 gene. PCGEM1 is a long non-coding RNA that is a prostate-specific transcript. In some embodiments, the PCGEM1 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for PCGEM1 is 30145. In some embodiments, the PCGEM1 gene is located at chromosome location 2q32.3. In some embodiments, the PCGEM1 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000227418. In some embodiments, the PCGEM1 gene comprises the nucleotide sequence of SEQ ID NO: 12 or a variant thereof.

[0060] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the TRGV9 gene. TRGV9 is encoded by the TRG locus, which rearranges to encode a TCR gamma chain containing 14 variable genes, only six of which are functional, comprising TRGV9. In some embodiments, the TRGV9 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for TRGV9 is 12295. In some embodiments, the TRGV9 gene is located at chromosome location 7p14.1. In some embodiments, the TRGV9 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000211695. In some embodiments, the TRGV9 gene comprises the nucleotide sequence of SEQ ID NO: 13 or a variant thereof.

[0061] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the TMSB15A gene. TMSB15A is an isoform of human thymosin beta 15, an actin-binding protein. TMSB15A is expressed in normal human prostate and prostate cancer tissues. In some embodiments, the TMSB15A gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for TMSB15A is 30744. In some embodiments, the TMSB15A gene is located at chromosome location Xq22.1. In some embodiments, the TMSB15A gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000158164. In some embodiments, the TMSB15A gene comprises the nucleotide sequence of SEQ ID NO: 14 or a variant thereof.

[0062] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the ERG gene. ERG is a transcriptional regulator that is overexpressed in prostate cancer. In some embodiments, the ERG gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for ERG is 3446. In some embodiments, the ERG gene is located at chromosome location 21q22.2. In some embodiments, the ERG gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000157554. In some embodiments, the ERG gene comprises the nucleotide sequence of SEQ ID NO: 15 or a variant thereof.

[0063] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the KLK4 gene. KLK4 is a member of the kallikrein (KLK) family of highly conserved serine proteases that play key roles in various physiological and pathological processes. KLKs are secreted proteins with extracellular substrates and functions. KLK4 is overexpressed in prostate cancer. In some embodiments, the KLK4 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for KLK4 is 6365. In some embodiments, the KLK4 gene is located at chromosome location 19q13.41. In some embodiments, the KLK4 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000167749. In some embodiments, the KLK4 gene comprises the nucleotide sequence of SEQ ID NO: 16 or a variant thereof.

[0064] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of the HOXC6 gene. HOXC6 is a homeobox (HOX) gene. HOX genes are involved in organogenesis and homeostasis and have been shown to be involved in normal prostate and prostate cancer development. HOXC6 is overexpressed in prostate cancer. In some embodiments, the HOXC6 gene comprises a nucleotide sequence provided by HGNC. In some embodiments, the HGNC identifier for HOXC6 is 5128. In some embodiments, the HOXC6 gene is located at chromosome location 12q13.13. In some embodiments, the HOXC6 gene comprises the nucleotide sequence of Ensembl gene identifier ENSG00000197757. In some embodiments, the HOXC6 gene comprises the nucleotide sequence of SEQ ID NO: 17 or a variant thereof.

[0065] Illustrative nucleotide sequences of TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 are presented in Table A.

[0066] [Table 1] TIFF2026503720000003.tif224169TIFF2026503720000004.tif220169TIFF202 6503720000005.tif221169TIFF2026503720000006.tif215169TIFF2026503720 000007.tif220169TIFF2026503720000008.tif221169TIFF2026503720000009. tif220169TIFF2026503720000010.tif221169TIFF2026503720000011.tif22216 9TIFF2026503720000012.tif221169TIFF2026503720000013.tif221169TIFF20 26503720000014.tif221169TIFF2026503720000015.tif216169TIFF2026503720 000016.tif222169TIFF2026503720000017.tif220169TIFF2026503720000018. tif220169TIFF2026503720000019.tif223169TIFF2026503720000020.tif54169

[0067] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

[0068] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least three genes are TMPRSS2-ERG, PCA3, and PCAT14.

[0069] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least four genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and OR51E2. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and TRGV9. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and ERG. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and TFF3. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and SCHLAP1. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and HOXC6. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and SPON2. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and TMSB15A. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and APOC1. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and B3GNT6. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and KLK4. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and CAMKK2. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and NKAIN1. In some embodiments, the at least four genes are TMPRSS2-ERG, PCA3, PCAT14, and PCGEM1.

[0070] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least five genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, OR51E2, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and OR51E2. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, TFF3, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, OR51E2, and TFF3. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, SCHLAP1, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, OR51E2, and SCHLAP1. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, HOXC6, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, OR51E2, and HOXC6. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, SPON2, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and SPON2. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, TMPSB15A, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and TMSB15A.In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, APOC1, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and APOC1. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, B3GNT6, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and B3GNT6. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, KLK4, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and KLK4. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, CAMKK2, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and CAMKK2. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, NKAIN1, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and NKAIN1. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, PCGEM1, and TRGV9. In some embodiments, the at least five genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, and PCGEM1.

[0071] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least six genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and OR51E2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, NKAIN1, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, TRGV9, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, CAMKK2, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and TCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and TFF3. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and SCHLAP1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and HOXC6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and SPON2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and TMSB15A. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, and APOC1.In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, and APOC1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, and B3GNT6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, B3GNT6, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, KLK4, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, CAMKK2, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, KLK4, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and CAMKK12. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and SCHLAP1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and HOXC6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and SPON2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and TMSB15A.In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and APOC1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, and B2GNT6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, B3GNT6, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, B3GNT6, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and HOXC6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and SPON2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and TMSB15A. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and APOC1.In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and B3GNT6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and B3GNT6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and SPON2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and TMSB15A. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, and PCGEM1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, and APOC1.In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, and B3GNT6. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, and KLK4. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, and CAMKK2. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, and NKAIN1. In some embodiments, the at least six genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, and PCGEM1.

[0072] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least seven genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and TFF3. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, NKAIN1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, CAMKK2, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and NKAIN1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, KLK4, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and CAMKK2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, and HOXC6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, B3GNT6, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and KLK4.In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, HOXC6, and SPON2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, APOC1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and B3GNT6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TMSB15A, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, and APOC1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, TFF3, and NKAIN1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, HOXC6, and SPON2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, TFF3, and CAMKK2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, SPON2, and TMPSB15A. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, TFF3, and KLK4. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, SCHLAP1, and PCGEM1.In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, SPON2, and TMSB15A. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, SCHLAP1, and NKAIN1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, TMSB15A, and APOC1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TFF3, SCHLAP1, and CAMKK2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, TMSB15A, and APOC1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, HOXC6, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, APOC1, and B3GNT6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SCHLAP1, HOXC6, and NKAIN1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, APOC1, and B3GNT6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, APOC1, and B3GNT6. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, B3GNT6, and KLK4. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, HOXC6, SPON2, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, B3GNT6, and KLK4. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, KLK4, and CAMKK2.In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, SPON2, NKAIN1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, KLK4, and CAMKK2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, CAMKK2, and NKAIN1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TMSB15A, NKAIN1, and PCGEM1. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, NKAIN1, and CAMKK2. In some embodiments, the at least seven genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, APOC1, NKAIN1, and PCGEM1.

[0073] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least eight genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and SCHLAP1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, NKAIN1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, CAMKK2, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, SCHLAP1, and HOXC6. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, KLK4, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and CAMKK2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, HOXC6, and SPON2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, B3GNT6, and PCGEM1.In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and KLK4. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, SPON2, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, APOC1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, and B2GNT6. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SCHLAP1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, HOXC6, and SPON2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SCHLAP1, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SPON2, and TMSB15A. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SCHLAP1, and CAMKK2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SCHLAP1, and KLK4.In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, SPON2, and TMSB15A. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, HOXC6, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, TMSB15A, and APOC1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, HOXC6, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SCHLAP1, HOXC6, and CAMKK2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, HOXC6, TMSB15A, and APOC1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, HOXC6, APOC1, and B3GNT6. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, HOXC6, SPON2, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SPON2, APOC1, and B3GNT6. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SPON2, B3GNT6, and KLK4. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, SPON2, TMSB15A, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TMSB15A, B3GNT6, and KLK4.In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TMSB15A, KLK4, and CAMKK2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TMSB15A, NKAIN1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, APOC1, KLK4, and CAMKK2. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, APOC1, CAMKK2, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, APOC1, NKAIN1, and PCGEM1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, B3GNT6, CAMKK2, and NKAIN1. In some embodiments, the at least eight genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, B3GNT6, NKAIN1, and PCGEM1.

[0074] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least nine genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and HOXC6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and pCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and NKAIN1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and SPON2. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and CAMKK2. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and HOXC6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and TMSB15A. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and KLK4. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, and B3GNT6.In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, NKAIN1, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, NKAIN1, and KLK4. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, NKAIN1, and B3GNT6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, NKAIN1, and SPON2. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, NKAIN1, and TMSB15A. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, and B3GNT6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, and APOC1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, and HOXC6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, and SPON2. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, and PCGEM1.In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, and TMSB15A. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, and APOC1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, and KLK4. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, and APOC1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, and B3GNT6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, TMSB15A, and B3GNT6. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, TMSB15A, and KLK4. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, TMSB15A, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, TMSB15A, and APOC1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, APOC1, and KLK4.In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, APOC1, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, KLK4, and PCGEM1. In some embodiments, the at least nine genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, B3GNT6, and PCGEM1.

[0075] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, and SPON2. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, NKAIN1, and pCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, CAMKK2, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, and NKAIN1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, and TMSB15A. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, KLK4, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, and CAMKK2.In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, TMBS15A, and APOC1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, B3GNT6, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, and KLK4. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, and B3GNT6. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, TMSB15A, and KLK4. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, APOC1, and NKAIN1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, CAMKK2, HOXC6, and SPON2. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, KLK4, SPON2, and B3GNT6.In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, SPON2, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, TMSB15A, and APCOC1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, SPON2, and NKAIN1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, APOC1, and B3GNT6. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, APOC1, and B3GNT6. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, TMSB15A, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SPON2, TMSB15A, and NKAIN1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, B3GNT6, APOC1, and TMSB15A. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, APOC1, NKAIN1, and PCGEM1.In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, APOC1, TMSB15A, and PCGEM1. In some embodiments, the at least 10 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, TMSB15A, NKAIN1, and PCGEM1.

[0076] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 11 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, and TMSB15A. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, B3GNT6, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, and KLK4. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, CAMKK2, KLK4, and NKAIN1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, CAMKK2, B3GNT6, and KLK4.In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, CAMKK2, SPON2, and TMSB15A. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, B3GNT6, and KLK4. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, NKAIN1, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, TMSB15A, and B3GNT6. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, SPON2, and TMSB15A. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, TMSB15A, and NKAIN1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, APOC1, B3GNT6, and PCGEM1. In some embodiments, the at least 11 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, TMSB15A, KLK4, and PCGEM1.

[0077] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 12 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, CAMKK2, B3GNT6, and KLK4. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, CAMKK2, KLK$, and NKAIN1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, CAMKK2, SPON2, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, CAMKK2, SPON2, and TMSB15A. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, NKAIN1, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, and PCGEM1.In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, and APOC1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, and NKAIN1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, and B3GNT6. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, KLK4, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, and B3GNT6. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, B3GNT6, and KLK4. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, and NKAIN1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, and KLK4.In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least 12 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, and PCGEM1.

[0078] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 13 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, and B3GNT6. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, NKAIN1, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, KLK4, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, and CAMKK2. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, KLK4, and CAMKK2. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, and KLK4. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, and NKAIN1.In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, CAMKK2, and NKAIN1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, KLK4, and CAMKK2. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, KLK4, and PGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, B3GNT6, KLK4, NKAIN1, and CAMKK2. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, B3GNT6, KLK4, and CAMKK2. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, B3GNT6, KLK$, and PCGEM1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, KLK4, and NKAIN1. In some embodiments, the at least 13 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, B3GNT6, CAMKK2, and PCGEM1.

[0079] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 14 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, SPON2, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, SPON2, TMSB15A, CAMKK2, and NKAIN1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, SPON2, TMSB15A, APOC1, and NKAIN1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, SPON2, TMSB15A, APOC1, and B3GNT6. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, KLK4, and APOC1.In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, B3GNT6, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, KLK4, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, APOC1, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, B3GNT6, and APOC1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, KLK4, B3GNT6, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, KLK4, TMSB15A, and SPON1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, B3GNT6, APOC1, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, KLK4, B3GNT6, APOC1, and SPON2.In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, KLK4, B3GNT6, and APOC1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, KLK4, B3GNT6, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, APOC1, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKIAN1, B3GNT6, APOC1, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, KLK4, B3GNT6, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, KLK4, B3GNT6, and APOC1. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, KLK4, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, APOC1, and SPON2.In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, B3GNT6, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, CAMKK2, B3GNT6, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, NKAIN1, KLK4, APOC1, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, B3GNT6, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, B3GNT6, APOC1, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, CAMKK2, KLK4, B3GNT6, and TMSB15A. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, PCGEM1, KLK4, APOC1, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, B3GNT6, APOC1, TMSB15A, and SPON2.In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, CAMKK2, APOC1, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, CAMKK2, KLK4, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, CAMKK2, KLK4, B3GNT6, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, CAMKK2, KLK4, APOC1, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, KLK4, B3GNT6, APOC1, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, NKAIN1, KLK4, APOC1, TMSB15A, and SPON2. In some embodiments, the at least 14 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, KLK4, B3GNT6, APOC1, TMSB15A, and SPON2.

[0080] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 15 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, and PCEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, and CAMKK2.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, KLK4, NKAIN1, and PCGEM1.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK$, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, S. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, CAMKK2, and NKAIN1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3NGTZ6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 15 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1.

[0081] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of at least 16 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and NKAIN1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, B3NGT6, KLK4, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, TFF3, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, OR51E2, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, TRGV9, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, ERG, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1.In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, PCAT14, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1. In some embodiments, the at least 16 genes are TMPRSS2-ERG, PCA3, ERG, TRGV9, OR51E2, TFF3, SCHLAP1, HOXC6, SPON2, TMSB15A, APOC1, B3GNT6, KLK4, CAMKK2, NKAIN1, and PCGEM1.

[0082] In some embodiments, the methods and kits described herein are useful for detecting the level or amount of expression of TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

[0083] In some embodiments, the level or amount of expression of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 genes described herein is higher in subjects with or at risk of developing Grade Group ≥ 2 prostate cancer compared to subjects with or at risk of developing Grade Group < 2 prostate cancer or subjects without prostate cancer. In some embodiments, the level or amount of expression of at least one of TMPRSS2-ERG, SCHLAP1, OR51E2, PCAT14, PCA3, B3GNT6, TFF3, SPON2, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 is higher in subjects at risk of having Grade Group ≥ 2 prostate cancer compared to subjects with or at risk of developing Grade Group < 2 prostate cancer or subjects without prostate cancer. In some embodiments, the level or amount of expression of each of TMPRSS2-ERG, SCHLAP1, OR51E2, PCAT14, PCA3, B3GNT6, TFF3, SPON2, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 is higher in subjects at risk of having Grade Group ≧2 prostate cancer than in subjects at risk of having or developing Grade Group <2 prostate cancer or subjects without prostate cancer. In some embodiments, the total level or amount of expression of TMPRSS2-ERG, SCHLAP1, OR51E2, PCAT14, PCA3, B3GNT6, TFF3, SPON2, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 is higher in subjects at risk of having Grade Group ≧2 prostate cancer than in subjects at risk of having or developing Grade Group <2 prostate cancer or subjects without prostate cancer.

[0084] In some embodiments, the level or amount of expression of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 genes described herein is lower in subjects with or at risk of developing Grade Group ≥ 2 prostate cancer compared to subjects with or at risk of developing Grade Group < 2 prostate cancer or subjects without prostate cancer. In some embodiments, the level or amount of expression of at least one of APOC1, CAMKK2, NKAIN1, and PCGEM1 is lower in subjects at risk of having Grade Group ≥ 2 prostate cancer than in subjects with or at risk of developing Grade Group < 2 prostate cancer or subjects without prostate cancer. In some embodiments, the level or amount of expression of each of APOC1, CAMKK2, NKAIN1, and PCGEM1 is lower in subjects at risk of having Grade Group ≥ 2 prostate cancer than in subjects with or at risk of developing Grade Group < 2 prostate cancer or subjects without prostate cancer. In some embodiments, the total level or amount of expression of APOC1, CAMKK2, NKAIN1 and PCGEM1 is lower in subjects at risk of having Grade Group ≧2 prostate cancer than in subjects at risk of having or developing Grade Group <2 prostate cancer or subjects without prostate cancer.

[0085] Methods for detecting gene expression The level or amount of expression of one or more genes of the present disclosure may be detected using any of a variety of nucleic acid techniques, including, but not limited to, nucleic acid sequencing; nucleic acid hybridization; and nucleic acid amplification.

[0086] In some embodiments, nucleic acid sequencing methods are utilized (e.g., for detection of amplified nucleic acids). In some embodiments, the techniques provided herein find use in second-generation (i.e., next-generation or Next-Gen), third-generation (i.e., Next-Next-Gen), or fourth-generation (i.e., N3-Gen) sequencing technologies, including, but not limited to, pyrosequencing, sequencing-by-ligation, single-molecule sequencing, sequence-by-synthesis (SBS), semiconductor sequencing, massively parallel clonal, massively parallel single-molecule SBS, massively parallel single-molecule real-time, massively parallel single-molecule real-time nanopore technologies, and the like. Morozova and Marra provide a review of some such technologies in Genomics, 92:255 (2008), the entire contents of which are incorporated herein by reference. Those skilled in the art will recognize that because RNA is unstable within cells and prone to nuclease attack in experiments, RNA may be reverse transcribed into DNA before sequencing.

[0087] Numerous DNA sequencing techniques are suitable for use with the present methods, including fluorescence-based sequencing methodologies (see, e.g., Birren et al., Genome Analysis: Analyzing DNA, 1, Cold Spring Harbor, NY, which is incorporated herein by reference in its entirety). In some embodiments, the sequencing is an automated sequencing technique as understood in the art. In some embodiments, the sequencing is parallel sequencing of split amplicons (PCT Publication No. WO2006084132, Kevin McKernan et al., which is incorporated herein by reference in its entirety). In some embodiments, the sequencing is DNA sequencing by parallel oligonucleotide extension (see, e.g., U.S. Pat. No. 5,750,341, Macevicz et al. and U.S. Pat. No. 6,306,597, Macevicz et al., both of which are incorporated herein by reference in their entirety).Further examples of sequencing techniques include the Church polony technique (Mitra et al., 2003, Analytical Biochemistry 320, pp. 55-65; Shendure et al., 2005 Science 309, pp. 1728-1732; U.S. Patent Nos. 6,432,360, 6,485,944, and 6,511,803, which are incorporated by reference in their entireties), the 454 picotiter pyrosequencing technique (Margulies et al., 2005 Nature 309, pp. 1728-1732, which are incorporated by reference in their entireties), and the 454 picotiter pyrosequencing technique (Margulies et al., 2005 Nature 309, pp. 1728-1732, which are incorporated by reference in their entireties). 437, pp. 376-380; U.S. Patent Application Publication No. 20050130173), Solexa single base addition technology (Bennett et al., 2005, Pharmacogenomics, 6, pp. 373-382; U.S. Patent No. 6,787,308; U.S. Patent No. 6,833,246, which are incorporated by reference in their entireties), Lynx massively parallel signature sequencing technology (Brenner et al. (2000). Nat. Biotechnol. 18:630-634; U.S. Patent No. 5,695,934; U.S. Patent No. 5,714,330, which are incorporated by reference in their entireties), and Adessi PCR colony technology (Adessi et al. (2000). Nucleic Acid Res. 28, E87; WO 00018957, which are incorporated by reference in their entireties).

[0088] Illustrative, non-limiting examples of nucleic acid hybridization techniques include, but are not limited to, in situ hybridization (ISH), microarrays, and Southern or Northern blots.

[0089] In situ hybridization (ISH) is a type of hybridization that uses labeled complementary DNA or RNA strands as probes to localize specific DNA or RNA sequences in tissue sections or sections (in situ) or in whole tissues (whole-mount ISH) if the tissue is sufficiently small. DNA ISH can be used to determine chromosome structure. RNA ISH can be used to measure and localize mRNA and other transcripts (e.g., cancer markers) within tissue sections or whole mounts. Sample cells and tissues can be treated to fix target transcripts in place and increase probe access. Probes hybridize to target sequences at elevated temperatures, and excess probe is then washed away. Probes labeled with either radioactive, fluorescent, or antigen-labeled bases are localized and quantified in tissues using autoradiography, fluorescence microscopy, or immunohistochemistry, respectively. ISH can also detect two or more transcripts simultaneously using two or more probes labeled with radioactive or other non-radioactive labels.

[0090] In the methods described herein, one or more cancer markers can be detected by performing one or more hybridization reactions. The one or more hybridization reactions can include one or more hybridization arrays, hybridization reactions, hybridization chain reactions, isothermal hybridization reactions, nucleic acid hybridization reactions, or combinations thereof. The one or more hybridization arrays can include hybridization array genotyping, hybridization array proportional sensing, DNA hybridization arrays, macroarrays, microarrays, high-density oligonucleotide arrays, genomic hybridization arrays, comparative hybridization arrays, or combinations thereof.

[0091] Different types of biological assays are called microarrays, including, but not limited to, DNA microarrays (e.g., cDNA microarrays and oligonucleotide microarrays); protein microarrays; tissue microarrays; transfection or cell microarrays; chemical microarrays; and antibody microarrays. DNA microarrays, commonly known as gene chips, DNA chips, or biochips, are collections of microscopic DNA spots attached to a solid surface (e.g., glass, plastic, or silicon chip) to form an array for the purpose of simultaneously profiling or monitoring the expression levels of thousands of genes. The attached DNA segments are known as probes, and thousands of probes can be used in a single DNA microarray. Microarrays can be used to identify disease genes or transcripts (e.g., cancer markers) by comparing gene expression in diseased and normal cells. Microarrays can be fabricated using a variety of techniques, including, but not limited to, printing with finely pointed pins on glass slides; photolithography using prefabricated masks; photolithography using dynamic micromirror devices; inkjet printing; or electrochemistry on microelectrode arrays.

[0092] The methods disclosed herein may include performing one or more amplification reactions. Nucleic acids (e.g., cancer markers) may be amplified prior to or simultaneously with detection. Performing one or more amplification reactions may include one or more PCR-based amplifications, non-PCR-based amplifications, or combinations thereof. Illustrative, non-limiting examples of nucleic acid amplification techniques include, but are not limited to, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), nested PCR, linear amplification, multiple displacement amplification (MDA), real-time SDA, rolling circle amplification, circle-to-circle amplification, transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence-based amplification (NASBA). Those skilled in the art will recognize that certain amplification techniques (e.g., PCR) require RNA to be reverse transcribed into DNA prior to amplification (e.g., RT-PCR), while other amplification techniques directly amplify RNA (e.g., TMA and NASBA).

[0093] The polymerase chain reaction, commonly referred to as PCR (U.S. Pat. Nos. 4,683,195, 4,683,202, 4,800,159, and 4,965,188, each of which is incorporated by reference in its entirety), uses multiple cycles of denaturation, annealing of primer pairs to opposite strands, and primer extension to exponentially increase the copy number of a target nucleic acid sequence. In a variant called RT-PCR, reverse transcriptase (RT) is used to create complementary DNA (cDNA) from mRNA, and the cDNA is then amplified by PCR to produce multiple copies of the DNA. For various other permutations of PCR, see, e.g., U.S. Pat. Nos. 4,683,195, 4,683,202, and 4,800,159; Mullis et al., Meth. Enzymol. 155:335 (1987); and Murakawa et al., DNA 7:287 (1988), each of which is incorporated by reference in its entirety.

[0094] Transcription-mediated amplification, commonly referred to as TMA (U.S. Pat. Nos. 5,480,784 and 5,399,491, each of which is incorporated by reference in its entirety), autocatalytically synthesizes multiple copies of a target nucleic acid sequence under substantially constant conditions of temperature, ionic strength, and pH, where multiple RNA copies of the target sequence autocatalytically generate additional copies. See, e.g., U.S. Pat. Nos. 5,399,491 and 5,824,518, each of which is incorporated by reference in its entirety. In a variant described in U.S. Patent Application Publication No. 20060046265 (incorporated by reference in its entirety), TMA optionally incorporates the use of blocking moieties, terminators, and other modifying moieties to improve the sensitivity and accuracy of the TMA process.

[0095] The ligase chain reaction, commonly referred to as LCR (Weiss, R., Science 254:1292 (1991), incorporated herein by reference in its entirety), uses two sets of complementary DNA oligonucleotides that hybridize to adjacent regions of a target nucleic acid. The DNA oligonucleotides are covalently joined by DNA ligase in repeated cycles of thermal denaturation, hybridization, and ligation to produce a detectable double-stranded ligated oligonucleotide product.

[0096] Strand displacement amplification, commonly referred to as SDA (Walker, G. et al., Proc. Natl. Acad. Sci. USA 89:392-396 (1992); U.S. Pat. Nos. 5,270,184 and 5,455,166, each of which is incorporated by reference in its entirety), uses cycles of annealing a pair of primer sequences to opposite strands of a target sequence, primer extension in the presence of dNTPαS to produce a double-stranded hemiphosphorothioated primer extension product, endonuclease-mediated nicking of a hemimodified restriction endonuclease recognition site, and polymerase-mediated primer extension from the 3' end of the nick to displace the existing strand and produce a strand for the next round of primer annealing, nicking, and strand displacement, resulting in geometric amplification of the product. Thermophilic SDA (tSDA) uses thermophilic endonucleases and polymerases at higher temperatures in essentially the same method (EP 0684315).

[0097] Other amplification methods include, for example: nucleic acid sequence-based amplification, commonly referred to as NASBA (U.S. Pat. No. 5,130,238, incorporated herein by reference in its entirety); a method that uses an RNA replicase to amplify the probe molecule itself, commonly referred to as Qβ replicase (Lizardi et al., BioTechnol. 6:1197 (1988), incorporated herein by reference in its entirety); a transcription-based amplification method (Kwoh et al., Proc. Natl. Acad. Sci. USA 86:1173 (1989)); and self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87:1874 (1990), each of which is incorporated herein by reference in its entirety). For a further discussion of known amplification methods, see Persing, David H., "In Vitro Nucleic Acid Amplification Techniques," in Diagnostic Medical Microbiology: Principles and Applications (Persing et al., eds.), pp. 51-87 (American Society for Microbiology, Washington, DC (1993)).

[0098] In some embodiments, the amplification method is a real-time quantitative PCR method (QPCR). Real-time polymerase chain reaction (real-time PCR or qPCR) is a molecular biology laboratory technique based on polymerase chain reaction (PCR). It monitors the amplification of target DNA molecules during PCR (i.e., in real time), rather than at the end as in conventional PCR. Real-time PCR can be used quantitatively (quantitative real-time PCR) and semi-quantitatively (i.e., above / below a certain amount of DNA molecules) (semi-quantitative real-time PCR). Two common methods for detecting PCR products in real-time PCR are (1) non-specific fluorescent dyes that intercalate into any double-stranded DNA, and (2) sequence-specific DNA probes consisting of oligonucleotides labeled with a fluorescent reporter that allows detection only after hybridization of the probe with its complementary sequence.

[0099] Illustrative, non-limiting examples of immunoassays include, but are not limited to, immunoprecipitation; Western blot; ELISA; immunohistochemistry; immunocytochemistry; flow cytometry; and immuno-PCR. Polyclonal or monoclonal antibodies that are detectably labeled using a variety of techniques known to those skilled in the art (e.g., colorimetric, fluorescent, chemiluminescent, or radioactive) are suitable for use in immunoassays.

[0100] Immunoprecipitation is a technique that uses an antibody specific for an antigen to precipitate that antigen from solution. This process can be used to identify protein complexes present in cell extracts by targeting proteins believed to be present in the complex. The complex is carried out of solution by insoluble antibody-binding proteins, such as protein A and protein G, originally isolated from bacteria. The antibody may be coupled to Sepharose beads, which can be easily isolated from solution. After washing, the precipitate can be analyzed using mass spectrometry, Western blotting, or any number of other methods to identify the components in the complex.

[0101] Western blots, or immunoblots, are methods for detecting proteins in a given sample of tissue homogenate or extract. They use gel electrophoresis to separate denatured proteins by mass. Proteins are then transferred from the gel onto a membrane, typically polyvinyldifluoride or nitrocellulose, and probed using antibodies specific to the protein of interest. As a result, researchers can examine the amount of protein in a given sample and compare levels between several groups.

[0102] ELISA, short for Enzyme-Linked ImmunoSorbent Assay, is a biochemical technique for detecting the presence of antibodies or antigens in a sample. It utilizes a minimum of two antibodies, one specific for the antigen and the other coupled to an enzyme. The second antibody induces a chromogenic or fluorogenic substrate to produce a signal. Variations of ELISA include sandwich ELISA, competitive ELISA, and ELISPOT. ELISA can be performed to assess either the presence of antigen or antibody in a sample, making it a useful tool for both determining serum antibody concentrations and detecting the presence of antigens.

[0103] Immunohistochemistry and immunocytochemistry refer to the process of localizing proteins in tissue sections or cells, respectively, based on the principle that antigens in the tissue or cells bind to their respective antibodies. Visualization is made possible by tagging the antibodies with chromogenic or fluorescent tags. Typical examples of chromogenic tags include, but are not limited to, horseradish peroxidase and alkaline phosphatase. Typical examples of fluorophore tags include, but are not limited to, fluorescein isothiocyanate (FITC) or phycoerythrin (PE).

[0104] Immuno-polymerase chain reaction (IPCR) utilizes nucleic acid amplification techniques to increase signal generation in antibody-based immunoassays. Because there is no protein equivalent of PCR, i.e., proteins cannot be replicated in the same manner as nucleic acids are replicated in PCR, the only way to increase detection sensitivity is through signal amplification. The target protein is bound by an antibody directly or indirectly conjugated to an oligonucleotide. Unbound antibodies are washed away, and the remaining bound antibodies amplify their own oligonucleotides. Protein detection occurs through detection of the amplified oligonucleotides using standard nucleic acid detection methods, including real-time methods.

[0105] In some embodiments, the level or amount of mRNA is detected using RT-qPCR analysis, which provides a Ct (cycle threshold value) for each detected mRNA. In real-time PCR assays, a positive reaction is detected by the accumulation of a fluorescent signal. The Ct value is defined as the number of cycles required for the fluorescent signal to cross the threshold (i.e., exceed the background level). The Ct level is inversely proportional to the amount of target nucleic acid in the sample (i.e., the lower the Ct value, the higher the amount of mRNA in the sample).

[0106] In some embodiments, the expression level or amount of any one of the genes described herein is normalized to the expression level or amount of a reference gene. In some embodiments, the expression amount of mRNA is normalized to the expression level or amount of the mRNA of a reference gene. Reference genes suitable for normalization are known to those skilled in the art and include, but are not limited to, KLK3, CYPB561A3, EEF1A2, GAPDH, HPN, KLK2, KLK4, LBH, NUDT8, SPDEF, or TRGV. In some embodiments, the reference gene is KLK3.

[0107] Compositions for use in the methods described herein, such as reagent compositions, include, but are not limited to, antibodies, probes, amplification oligonucleotides, and the like.

[0108] The compositions and kits can include one or more, two or more, three or more, or four or more antibodies, probes, probe pairs, amplification oligonucleotide pairs, or sequencing primers.

[0109] The probes or primers can hybridize to one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, or twenty or more target molecules. The target molecules can be RNA, DNA, cDNA, mRNA, portions or fragments thereof, or combinations thereof. In some examples, at least a portion of the target molecule is a cancer marker. The probes can hybridize to one or more or two or more of the cancer markers disclosed herein.

[0110] Typically, the probe or primer comprises a target-specific sequence, which may be complementary to at least a portion of a target molecule, which may be at least about 50% or more, 55% or more, 60% or more, 65% or more, 70% or more, 75% or more, 80% or more, 85% or more, 90% or more, 95% or more, 97% or more, 98% or more, or 100% complementary to at least a portion of the target molecule.

[0111] A target-specific sequence can be at least about 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, or 20 or more nucleotides in length. In some examples, the target-specific sequence is about 8 to about 20 nucleotides, 10 to about 18 nucleotides, or 12 to about 16 nucleotides in length.

[0112] The compositions and kits can include a plurality of probes or primers, where two or more of the plurality of probes include the same target-specific sequence. The compositions and kits can include a plurality of probes, where two or more of the plurality of probes include different target-specific sequences.

[0113] The probe can further comprise a unique sequence. The unique sequence is non-complementary to the cancer marker. The unique sequence can comprise a label, a barcode, or a unique identifier. The unique sequence can comprise a random sequence, a non-random sequence, or a combination thereof. The unique sequence can be at least about 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 22 or more, 24 or more, 26 or more, 28 or more, or 30 or more nucleotides in length. In some examples, the unique sequence is about 8 to about 20 nucleotides, 10 to about 18 nucleotides, or 12 to about 16 nucleotides in length.

[0114] The probe may further comprise a universal sequence. The universal sequence may comprise a primer binding site. The universal sequence may allow for detection of the target sequence. The universal sequence may allow for amplification of the target sequence. The universal sequence may allow for transcription or reverse transcription of the target sequence. The universal sequence may allow for sequencing of the target sequence.

[0115] The probe or primer composition of the present disclosure can be mounted on a solid support. The solid support can include one or more types of beads, plates, solid surfaces, wells, chips, or combinations thereof. The beads can be magnetic, antibody-coated, protein A-crosslinked, protein G-crosslinked, streptavidin-coated, oligonucleotide-conjugated, silica-coated, or combinations thereof. Examples of beads include, but are not limited to, Ampure beads, AMPure XP beads, streptavidin beads, agarose beads, magnetic beads, Dynabeads®, MACS® microbeads, antibody-conjugated beads (e.g., anti-immunoglobulin microbeads), protein A-conjugated beads, protein G-conjugated beads, protein A / G-conjugated beads, protein L-conjugated beads, oligo-dT-conjugated beads, silica beads, silica-like beads, anti-biotin microbeads, anti-fluorescent dye microbeads, and BcMag™ carboxy-terminated magnetic beads.

[0116] The compositions and kits can include primers and primer pairs capable of amplifying a target molecule, or a fragment or subsequence or complement thereof. The nucleotide sequence of the target molecule can be provided in a computer-readable medium for in silico applications and as a basis for designing appropriate primers for amplification of one or more target molecules.

[0117] Primers based on the nucleotide sequence of a target molecule can be designed for use in amplifying the target molecule. Primer pairs can be used for use in amplification reactions such as PCR. The exact composition of the primer sequence is not critical to this disclosure, but for most applications, the primers can hybridize to a specific sequence of the target molecule or a universal sequence of a probe under stringent conditions, particularly high stringency conditions, as known in the art. Primer pairs are typically selected to generate amplification products of at least about 15 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more, 200 or more, 250 or more, 300 or more, 350 or more, 400 or more, 450 or more, 500 or more, 600 or more, 700 or more, 800 or more, 900 or more, or 1000 or more nucleotides. Algorithms for selecting primer sequences are generally known and are available in commercial software packages.Such primers can be used in standard quantitative or qualitative PCR-based assays to assess the transcript expression level of target molecules.Alternatively, such primers can be used in combination with probes such as molecular beacons in real-time PCR amplification.

[0118] The nucleotide sequence of the entire length of a primer does not need to be derived from the target sequence. Thus, for example, a primer can contain nucleotide sequences at the 5' and / or 3' ends that are not derived from the target molecule. The nucleotide sequence that is not derived from the nucleotide sequence of the target molecule can provide additional functionality to the primer. For example, it can provide a restriction enzyme recognition sequence or a "tag" that facilitates detection, isolation, purification, or immobilization on a solid support. Alternatively, the additional nucleotides can provide a self-complementary sequence that causes the primer to adopt a hairpin configuration. Such a configuration may be necessary for certain primers that can be used in solution hybridization techniques, such as molecular beacons and Scorpion primers.

[0119] Probes or primers can, if desired, incorporate moieties useful in detection, isolation, purification, or immobilization. Such moieties are well known in the art (see, e.g., Ausubel et al. (1997 & ed.) Current Protocols in Molecular Biology, Wiley & Sons, New York) and are selected so as not to affect the ability of the probe to hybridize with its target molecule.

[0120] Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin / streptavidin, biotin, haptens, enzyme cofactors / substrates, enzymes, and the like.

[0121] A label may optionally be attached to or incorporated into a probe or primer to enable detection and / or quantification of a target polynucleotide representing the target molecule of interest. The target polynucleotide may be the expressed target molecule RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be either a plus or minus strand, as long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.

[0122] In certain multiplex formats, the labels used to detect different target molecules may be distinguishable. The labels may be attached directly (e.g., via covalent linkage) or indirectly, for example, via a bridging molecule or series of molecules (e.g., via a molecule or complex that can bind to an assay component, or via a member of a binding pair, e.g., biotin-avidin or streptavidin, that can be incorporated into an assay component). Many labels are commercially available in activated forms that can be readily used for such conjugation (e.g., by amine acylation), or the labels may be attached by known or determinable conjugation schemes, many of which are known in the art.

[0123] Labels useful in the present disclosure described herein include any substance that can be detected when bound to or incorporated into a target molecule. Any effective detection method may be used, including optical, spectroscopic, electrical, piezoelectric, magnetic, Raman scattering, surface plasmon resonance, colorimetry, calorimetry, and the like. Labels are typically selected from chromophores, lumiphores, fluorophores, one member of a quenching system, chromogens, haptens, antigens, magnetic particles, materials exhibiting nonlinear optics, semiconductor nanocrystals, metal nanoparticles, enzymes, antibodies or binding moieties or equivalents thereof, aptamers, and one member of a binding pair, as well as combinations thereof. Quenching schemes may also be used, whereby a quencher and a fluorophore as members of a quenching pair can be used in a probe such that a change in an optical parameter that occurs after binding to the target induces or quenches a signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher / fluorophore systems are known in the art. Labels can be attached via a variety of intermediate linkages. For example, the target polynucleotide can include a biotin-binding species, and an optically detectable label can be conjugated to the biotin and then bound to the labeled target polynucleotide. Similarly, the polynucleotide sensor can include an immunological species, such as an antibody or fragment, and a secondary antibody containing an optically detectable label can be added.

[0124] Chromophores useful in the methods described herein include any substance capable of absorbing energy and emitting light. For multiplexed assays, multiple different signal-generating chromophores with detectably different emission spectra can be used. Chromophores can be lumophores or fluorophores. Exemplary fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes, and green fluorescent protein.

[0125] Coding schemes can optionally be used, including coded particles and / or coded tags associated with different polynucleotides of the present disclosure. A variety of different coding schemes are known in the art, including fluorophores, deposited metals, and RF tags, including SCNCs. Subjects and samples The methods and kits described herein are suitable for detecting the level or amount of expression of one or more of the genes described herein in a sample from a subject. In some embodiments, the subject from whom the sample is obtained may be selected by a skilled practitioner. In some embodiments, the selection of the subject is based on consideration or analysis of one or more factors. Such factors for consideration include, but are not limited to, a family history of a specific disease, a genetic predisposition to a disease, an increased risk of a disease, physical symptoms indicative of a disease, or environmental reasons. Environmental reasons may include, but are not limited to, lifestyle habits or exposure to agents that cause or contribute to a specific disease. In some embodiments, the selection of the subject is based on the subject's previous medical history, a positive diagnosis before or after therapy, treatment of a disease, or remission or recovery from a disease.

[0126] In some embodiments, the sample for use in the kit and method of the present disclosure contains nucleic acids suitable for providing RNA expression information. In principle, the biological sample from which expressed RNA is obtained and analyzed for target molecule expression can be any material suspected of containing cancer tissue or cells. The sample can be a biological sample that is directly used in the method of the present disclosure. Alternatively, the sample can be a sample prepared from a biological sample.

[0127] In some embodiments, a sample or portion of a sample containing or suspected of containing cancerous tissue or cells can be any source of biological material, including cells, tissues, secretions, or bodily fluids, including biological fluids. Non-limiting examples of sample sources include aspirates, needle biopsies, cytology pellets, bulk tissue preparations or sections thereof obtained, for example, by surgery or autopsy, lymph, blood, plasma, serum, tumors, and organs. Alternatively or additionally, the sample source can be urine, bile, feces, sweat, tears, spinal fluid, and feces. In some embodiments, the sample source is a secretion. In some embodiments, the secretion is an exosome. In some embodiments, the sample is a urine sample. In some embodiments, the urine sample is obtained after a digital rectal examination (DRE) of the subject. In some embodiments, the urine sample is obtained within 30 minutes after the subject's DRE. In some embodiments, the urine sample is obtained 30 to 60 minutes after the subject's DRE. In some embodiments, the urine sample is obtained 30 to 180 minutes after the subject's DRE. In some embodiments, the urine sample is obtained within 1 hour after the subject's DRE. In some embodiments, the urine sample is obtained within 2 hours after the subject's DRE. In some embodiments, the urine sample is obtained within 3 hours after the subject's DRE. In some embodiments, the urine sample is obtained from a subject who has not undergone a DRE.

[0128] Without wishing to be bound by theory, it is believed that DRE increases the concentration of mRNA or protein expressed by one or more of TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample, such as a urine sample. This increased concentration facilitates detection of the mRNA or protein expressed by the one or more genes.

[0129] In some embodiments, the sample is combined with a buffer, for example, for processing. In some embodiments, the amount of expression of one or more genes described herein is determined from a composition, for example, a solution or suspension, comprising the sample and a buffer. Suitable buffers for the sample are known to those of skill in the art and can be determined based on the type of sample being collected. In some embodiments, the composition further comprises a preservative for proper sample stability. In some embodiments, the buffer to sample ratio is 2:5. In some embodiments, the buffer to sample ratio is 1:5, 2:5, 3:5, or 4:5.

[0130] The sample may be an archival sample with a known and established medical outcome, or may be a sample from a current patient whose ultimate medical outcome is not yet known.

[0131] In some embodiments, samples may be dissected prior to molecular analysis. Samples may be prepared by macrodissection of bulk tumor specimens or portions thereof, or may be processed by microdissection, e.g., laser capture microdissection (LCM).

[0132] Samples may initially be provided in a variety of states, such as fresh tissue, fresh-frozen tissue, or fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state to facilitate tissue storage. Various fixatives may be used to fix tissue and stabilize cellular morphology, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinkers, alcohols, acetone, Bouin's solution, Zenker's solution, Hely's solution, osmic acid solution, and Carnoy's solution.

[0133] A cross-linking fixative can contain any agent suitable for forming two or more covalent bonds, such as an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde, or formalin. Preferably, the cross-linking agent contains formaldehyde, which can be included in its native form or in the form of paraformaldehyde or formalin. Those skilled in the art will recognize that for samples in which a cross-linking fixative is used, special preparatory steps, including, for example, a heating step and proteinase-K digestion, may be required.

[0134] One or more alcohols can be used, alone or in combination with other fixatives, to fix the tissue. Exemplary alcohols used for fixation include methanol, ethanol, and isopropanol.

[0135] Formalin fixation is frequently used in medical laboratories. Formalin contains both an alcohol, typically methanol, and formaldehyde, both of which can act to fix biological samples.

[0136] Whether fixed or unfixed, biological samples can optionally be embedded in an embedding medium. Exemplary embedding media used in histology include paraffin, Tissue-Tek® VIP™, Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away paraffin embedding wax, polyester wax, Carbowax polyethylene glycol, Polyfin™, tissue freezing medium TFMFM, Cryo-Gef™, and OCT compound (Electron Microscopy Sciences, Hatfield, PA). Prior to molecular analysis, the embedding material can be removed by any suitable technique known in the art. For example, if the sample is embedded in wax, the embedding material can be removed by extraction with organic solvent(s), such as xylene. Kits for removing embedding medium from tissue are commercially available. The sample or its sections can be subjected to further processing steps, such as serial hydration or dehydration steps, as needed.

[0137] In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin-embedded (FFPE) tissue.

[0138] In some embodiments, the subject is prostate biopsy naive, i.e., the subject has not undergone a prostate biopsy. In some embodiments, the subject has had a previous negative prostate biopsy result. In some embodiments, the prostate biopsy result is negative for Grade Group ≧2 prostate cancer. In some embodiments, one or more additional clinical variables are associated with the subject. In some embodiments, the method comprises assaying one or more additional clinical variables (e.g., including but not limited to, the subject's prostate volume, PSA level or amount, PSA density, biopsy Gleason score, race, family history of prostate cancer, previous negative prostate biopsy, or abnormal DRE). In some embodiments, the one or more additional clinical variables are associated with a subject who has had a previous negative prostate biopsy result.

[0139] Determination of likelihood of having or developing Grade Group ≥ 2 prostate cancer In some embodiments, the level or amount of expression of one or more genes described herein determines the likelihood of detecting prostate cancer in a subject. In some embodiments, the likelihood of detecting prostate cancer in a subject is based on a prostate biopsy of the subject. In some embodiments, the level or amount of expression of one or more genes described herein determines the likelihood of detecting Grade Group ≥ 2 prostate cancer in a subject. In some embodiments, the likelihood is presented as a score based on the amount or level of expression of one or more genes described herein present in a sample from the subject. In some embodiments, the likelihood of detecting Grade Group ≥ 2 prostate cancer is provided as a score ranging from 0% to 100%. In some embodiments, the likelihood of detecting Grade Group ≥ 2 prostate cancer is provided as a score ranging from 0.0 to 100.0. In some embodiments, a biopsy-naive subject receiving a score of 0-7.5% means that there is a low risk or low likelihood of Grade Group ≥ 2 prostate cancer being detected from a prostate biopsy in the subject. In some embodiments, a subject with a previous negative prostate biopsy result receiving a score of 0-5.4% means that there is a low risk or low likelihood of Grade Group ≥ 2 prostate cancer being detected from a prostate biopsy in the subject. In some embodiments, a biopsy-naive subject receiving a score of ≧7.6% means that there is a high risk or a high likelihood that Grade Group ≧2 prostate cancer will be detected from a prostate biopsy in the subject. In some embodiments, a subject with a previous negative prostate biopsy result receiving a score of ≧5.5% has a high risk or a high likelihood that Grade Group ≧2 prostate cancer will be detected from a prostate biopsy in the subject.

[0140] In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of one or more given markers) into data of predictive value for a clinician, subject, or subject's healthcare provider. The clinician, subject, or subject's healthcare provider can access the raw data using any suitable means. Thus, in some embodiments, the present disclosure provides the additional benefit that a clinician, subject, or subject's healthcare provider, who may not be trained in genetics or molecular biology, does not need to understand the raw data. The data can be presented directly to the clinician, subject, or subject's healthcare provider in its most useful form. This allows the clinician or healthcare provider to immediately utilize the information to optimize the subject's care.

[0141] The information can be received, processed, or transmitted to or from one or more laboratories performing the assay, information providers, medical professionals, or subjects using any suitable method. For example, in some embodiments of the present disclosure, a sample (e.g., a biopsy or serum or urine sample) is obtained from a subject and submitted to a profiling service (e.g., a clinical laboratory at a medical facility, a genomic profiling business, etc.) located anywhere in the world (e.g., in a country different from the country where the subject lives or where the information will ultimately be used) to generate raw data. If the sample includes tissue or other biological samples, the subject can visit a medical center to obtain the sample and send it to the profiling center, or the subject themselves can collect the sample (e.g., a urine sample) and send it directly to the profiling center. If the sample includes previously determined biological information, the information can be sent directly by the subject to the profiling service (e.g., an information card containing the information can be scanned by a computer and the data can be transmitted to the profiling center's computer using an electronic communication system). Once received by the profiling service, the sample may be processed to produce a profile (ie, expression data) useful for diagnostic or prognostic information desired for the subject.

[0142] The profile data is then prepared in a format suitable for interpretation by one or more healthcare professionals (e.g., a treating clinician, physician assistant, nurse, or pharmacist). For example, rather than providing raw expression data, the prepared format can represent a subject's diagnosis or risk assessment (e.g., levels of cancer markers described herein) along with a recommendation for a particular treatment option. The data can be displayed to the healthcare professional by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the healthcare professional (e.g., at the point of care) or displayed to the healthcare professional on a computer monitor.

[0143] In some embodiments, information is first analyzed at the point of care or at a local facility. The raw data is then sent to a central processing facility for further analysis and / or to convert the raw data into information useful to medical professionals or subjects. A central processing facility offers the advantages of privacy (all data is stored at the central facility with uniform security protocols), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data after the subject's treatment. For example, using an electronic communication system, the central facility can provide the data to medical professionals, subjects, or researchers.

[0144] In some embodiments, the subject or the subject's healthcare provider can access the data directly using an electronic communication system. The subject can choose further intervention or counseling based on the results.

[0145] In some embodiments, the data is used in research applications, for example, the data can be used to further optimize the inclusion or exclusion of markers as useful indicators of a particular condition or disease stage or as companion diagnostics for determining a therapeutic course of action.

[0146] In some embodiments, the level or amount of expression of one or more genes described herein is used to determine a score. In some embodiments, determining the score comprises executing an algorithm to generate a score. In some embodiments, the score correlates with or indicates the subject's likelihood of having or developing Grade Group ≥ 2 prostate cancer. In some embodiments, the score indicates the likelihood that Grade Group ≥ 2 prostate cancer will be detected in a subject's prostate biopsy. Algorithms for determining a score with acceptable diagnostic accuracy can be derived based on, for example, but not limited to, logistic regression with stepwise feature selection, logistic regression with recursive feature elimination, and regularized logistic regression with elastic net. In some embodiments, executing the algorithm comprises using a processor.

[0147] In some embodiments, the algorithm is Equation 1, 2, 3 or 4 below.

[0148] In some embodiments, the subject has had a previous negative prostate biopsy result, and the step of determining the score comprises executing Equation 1: Equation 1:

[0149]

number

[0150] In some embodiments, the subject is prostate biopsy naive (i.e., has not had a previous prostate biopsy), and determining the score comprises executing Equation 2: Equation 2:

[0151]

number

[0152] In Equations 1 and 2, "reference" refers to a reference gene described herein (e.g., KLK3).

[0153] In some embodiments, the subject has had a previous negative prostate biopsy result, and the step of determining the score comprises executing Equation 3: Equation 3:

[0154]

number

[0155] In some embodiments, the subject is prostate biopsy naive (i.e., has not had a previous prostate biopsy), and determining the score comprises executing Equation 4: Equation 4:

[0156]

number

[0157] In each of Equations 1-4: (i) "CRT" refers to the cycle threshold value identified by the method described herein for determining the amount of gene expression; (ii) "e" is Euler's number; and (iii) "MPS2" is a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a subject's prostate biopsy. In Equations 1 and 3, "family history," "abnormal DRE," and "previous negative biopsy" are binary values ​​of 1 or 0, with 1 = yes and 0 = no. Specifically, if the subject has a family history of prostate cancer, the value is 1; if the subject had an abnormal DRE, the value is 1; if the subject had a previous negative prostate biopsy, the value is 1. In each of Equations 1-4, (a)-(w) are independent coefficients based on the selected model, e.g., logistic regression with stepwise feature selection, logistic regression with recursive feature elimination, or regularized logistic regression with elastic net. For illustrative purposes, coefficients are presented in Table B.

[0158] [Table 2] TIFF2026503720000026.tif42169

[0159] The methods disclosed herein may also include a step of transmitting data / information. For example, data / information derived from target detection and / or quantification may be transmitted to another device and / or apparatus. In some examples, information derived from an algorithm may be transmitted to another device and / or apparatus. Transmitting data / information may include transferring data / information from a first source to a second source. The first and second sources may be in the same approximate location (e.g., in the same room, building, block, campus). Alternatively, the first and second sources may be in multiple locations (e.g., multiple cities, states, countries, continents, etc.).

[0160] The transmission of data / information can include digital or analog transmission. Digital transmission can include the physical transfer of data (digital bit streams) over point-to-point or point-to-multipoint communication channels. Examples of such channels are copper wire, fiber optics, wireless communication channels, and storage media. Data can be represented as electromagnetic signals, such as voltages, radio waves, microwaves, or infrared signals.

[0161] Analog transmission can involve the transfer of continuously varying analog signals. Messages can be represented either by a sequence of pulses using a line code (baseband transmission) or by a limited set of continuously varying waveforms using digital modulation methods (passband transmission). Passband modulation and corresponding demodulation (also known as detection) can be performed by modem equipment. According to the most common definition of a digital signal, both baseband and passband signals representing bit streams are considered digital transmissions, although alternative definitions consider only baseband signals as digital and passband transmission of digital data as a form of digital-to-analog conversion.

[0162] In some embodiments, a report is generated that includes a score. In some embodiments, the score indicates the likelihood that Grade Group ≧2 prostate cancer will be detected from the subject's prostate biopsy. In some embodiments, the report is accessible by or provided to the subject's healthcare provider. In some embodiments, the report is accessible or provided as a digital or paper copy. In some embodiments, the report is delivered to the subject's healthcare provider in a digital format described herein (e.g., via email) or via courier if the report is written on a paper copy.

[0163] In some embodiments, the report includes treatment options. In some embodiments, the report includes treatment options for Grade Group > 2 prostate cancer.

[0164] Diagnostic accuracy The diagnostic accuracy of the methods or kits described herein can be determined by analyzing the area under the curve (AUC) derived from a receiver operating characteristic (ROC) curve. An ROC curve is a graphical plot illustrating the performance of a binary classifier system as its discrimination threshold is varied. An ROC curve is plotted by true positive rate against false positive rate, with true positive rate plotted on the y-axis and false positive rate plotted on the x-axis. The true positive rate, also referred to as sensitivity, is calculated by dividing the number of true positives by the sum of true positives and false negatives. The false positive rate is calculated by either (1) dividing the number of false positives by the sum of true negatives and false positives, or (2) subtracting the specificity from 1, where specificity is calculated by dividing the number of true negatives by the sum of true negatives and false positives. In some embodiments, the ROC curve is generated based on the individual amount of expression of each gene. In some embodiments, the ROC curve is generated based on a combination of the amount of expression of each gene.

[0165] In some embodiments, the AUC values ​​of the methods or kits described herein are greater than 0.50. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.60. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.70. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.71. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.72. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.73. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.74. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.75. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.76. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.77. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.78. In some embodiments, the AUC values ​​of the methods or kits described herein are at least 0.79. In some embodiments, the AUC value of the methods or kits described herein is at least 0.80. In some embodiments, the AUC value of the methods or kits described herein is at least 0.81. In some embodiments, the AUC value of the methods or kits described herein is at least 0.82. In some embodiments, the AUC value of the methods or kits described herein is at least 0.83. In some embodiments, the AUC value of the methods or kits described herein is at least 0.84. In some embodiments, the AUC value of the methods or kits described herein is at least 0.85. In some embodiments, the AUC value of the methods or kits described herein is at least 0.86. In some embodiments, the AUC value of the methods or kits described herein is at least 0.87.In some embodiments, the AUC value of the methods or kits described herein is at least 0.88. In some embodiments, the AUC value of the methods or kits described herein is at least 0.89. In some embodiments, the AUC value of the methods or kits described herein is at least 0.90.

[0166] The diagnostic accuracy of the expression level of individual genes or the combination of expression levels of specific genes can be maximized by performing a cutoff analysis that takes into account the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) required for clinical utility. The expression level results are analyzed in any of a variety of ways. In some embodiments, the results are analyzed using univariate or single variable analysis (SV). In some embodiments, the results are analyzed using multivariate analysis (MV).

[0167] The generation of ROC curves and analysis of sample populations can be used to establish cutoff values ​​used to distinguish between different subject subgroups. For example, a cutoff value can be used to distinguish between a high likelihood of detecting Grade Group ≥ 2 prostate cancer from a subject's prostate biopsy and a low likelihood of detecting Grade Group ≥ 2 prostate cancer from a subject's prostate biopsy. In some embodiments, the cutoff value can distinguish between these subjects. In some embodiments, the cutoff value can distinguish subjects with non-aggressive cancer from subjects with aggressive cancer.

[0168] In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a subject's prostate biopsy with a diagnostic accuracy of at least 0.70. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a subject's prostate biopsy with a diagnostic accuracy of at least 0.75. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a subject's prostate biopsy with a diagnostic accuracy of at least 0.80.

[0169] In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a prostate biopsy-naive subject with a diagnostic accuracy of at least 0.70. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a prostate biopsy-naive subject with a diagnostic accuracy of at least 0.75. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a prostate biopsy-naive subject with a diagnostic accuracy of at least 0.80.

[0170] In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject with a previous negative prostate biopsy with a diagnostic accuracy of at least 0.70. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject with a previous negative prostate biopsy with a diagnostic accuracy of at least 0.75. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject with a previous negative prostate biopsy with a diagnostic accuracy of at least 0.80. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject with a previous negative prostate biopsy with a diagnostic accuracy of at least 0.81. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject with a previous negative prostate biopsy with a diagnostic accuracy of at least 0.82.

[0171] In some embodiments, each of the referenced diagnostic accuracies is achievable when the urine sample is obtained within 1 hour after a subject's digital rectal examination (DRE). In some embodiments, each of the referenced diagnostic accuracies is achievable when the urine sample is obtained 30 to 60 minutes after a subject's DRE. In some embodiments, the urine sample is obtained 30 to 180 minutes after a subject's DRE. In some embodiments, the urine sample is obtained within 1 hour after a subject's DRE. In some embodiments, the urine sample is obtained within 2 hours after a subject's DRE. In some embodiments, the urine sample is obtained within 3 hours after a subject's DRE. In some embodiments, the urine sample is obtained from a subject who has not undergone a DRE.

[0172] Kits and Devices In some embodiments, the present disclosure provides a kit for analyzing cancer, comprising: (a) a probe set comprising a plurality of probes comprising target-specific sequences complementary to one or more target molecules, wherein the one or more target molecules comprise one or more cancer markers; and (b) a computer model or algorithm for analyzing the expression level and / or expression profile of the one or more target molecules in a sample. The target molecules may comprise one or more or a combination of the target molecules described herein.

[0173] In some embodiments, the present disclosure provides a kit for analyzing cancer, comprising: (a) a probe set comprising a plurality of probes comprising target-specific sequences complementary to one or more target molecules of a biomarker library; and (b) a computer model or algorithm for analyzing the expression level and / or expression profile of one or more target molecules in a sample. Control samples and / or nucleic acids may optionally be provided in the kit. Control samples may include tissue and / or nucleic acids obtained from or representative of tumor samples from healthy subjects, and tissue and / or nucleic acids obtained from or representative of tumor samples from subjects diagnosed with cancer.

[0174] Instructions for using the kit to perform one or more methods of the present disclosure may be provided, and may be provided in any fixed medium. The instructions may be located inside or outside a container or housing and / or printed on the inside or outside of any surface thereof. The kit may be in a multiplexed format for simultaneously detecting and / or quantifying one or more different target polynucleotides representing expressed target molecules.

[0175] In some embodiments, the present disclosure provides a kit comprising a container containing a reagent composition for detecting the expression levels of at least three genes described herein; and instructions for detecting the expression levels. In some embodiments, the reagent composition comprises a polynucleotide reagent for detecting the amount of mRNA expressed by at least three genes. In some embodiments, the reagent composition comprises a polynucleotide reagent for detecting the expression levels of reference genes, and the instructions are further for normalizing the expression levels of the at least three genes to the expression levels of the reference genes. In some embodiments, the instructions are further for generating a report comprising a score determined by the expression levels of the at least three genes, the score indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from the subject's prostate biopsy.

[0176] Devices useful for carrying out the methods of the present disclosure are also provided. The devices can include components for carrying out one or more methods of characterizing the expression level of a target molecule of the present disclosure, such as nucleic acid extraction, amplification, and / or detection. Such components can include one or more of an amplification chamber (e.g., a thermal cycler), a plate reader, a spectrophotometer, a capillary electrophoresis apparatus, a chip reader, and / or a robotic sample handling component. These components can ultimately yield data reflecting the expression level of the target molecule used in the assay being employed.

[0177] The device may include excitation and / or detection means. Any instrument may be used for excitation that is capable of exciting the molecular species of interest and provides a wavelength that is shorter than the emission wavelength(s) to be detected. Commercially available devices may provide suitable detection components along with suitable excitation wavelengths.

[0178] Illustrative excitation sources include broadband UV light sources, such as a deuterium lamp with an appropriate filter, white light sources, such as the output of a xenon or deuterium lamp, continuous wave (cw) gas lasers, solid-state diode lasers, or pulsed lasers, after passing through a monochromator to extract the desired wavelength(s). The emitted light can be detected by any suitable device or technique; many suitable approaches are known in the art. For example, a fluorometer or spectrophotometer can be used to detect whether the test sample emits light at a wavelength characteristic of the label used in the assay.

[0179] The device can include means for linking the results obtained to a given sample with a means for identifying that sample. Such means can include manual labels, bar codes, and other indicia that can be linked to the sample container and / or, for example, when coded particles are added to the sample, can optionally be included within the sample itself. The results can be linked to the sample, for example, in computer memory containing a record of the sample designation and expression levels obtained from the sample. Linking the results to the sample can also include linking to a specific sample receptacle in the device, which receptacle is also linked to the sample identity.

[0180] The device may also include a means for correlating the expression level of the target molecule being studied with a prognosis of disease outcome. Such means may include one or more of a variety of correlation techniques, including lookup tables, algorithms, multivariate models, and linear or non-linear combinations of expression models or algorithms. The expression levels may be converted into one or more likelihood scores that reflect the likelihood that the subject providing the sample will exhibit a particular disease outcome. The model and / or algorithm may be provided in a machine-readable format and may optionally further specify a treatment modality for the subject or class of subjects.

[0181] The device may also include an output means for outputting the disease state, prognosis, and / or treatment modality. Such output means may take any form of transmitting results to the subject and / or healthcare provider and may include a monitor, a printed format, or both. The device may use a computer system to perform one or more of the steps provided.

[0182] II. Prognosis, diagnosis or treatment The methods, compositions, and kits disclosed herein are useful for prognosing, diagnosing, predicating, monitoring, and / or treating cancer (e.g., prostate cancer, and in some embodiments, Grade Group ≥ 2 prostate cancer) in a subject. In some embodiments, predicting and / or monitoring cancer status or outcome includes assessing the presence or risk of high-grade prostate cancer (i.e., Grade Group ≥ 2 prostate cancer). In some embodiments, predicting and / or monitoring cancer status or outcome includes determining the efficacy of a treatment. In some embodiments, the methods and kits disclosed herein are useful for indicating the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in a subject.

[0183] In some embodiments, the method includes determining, recommending, or administering a therapeutic regimen. In some embodiments, the therapeutic regimen is an anti-cancer therapy. In some embodiments, the method includes modifying the therapeutic regimen. Modifying the therapeutic regimen can include increasing the therapeutic dosage, decreasing the therapeutic dosage, or terminating the therapeutic regimen.

[0184] For example, in some embodiments, the methods described herein are useful for identifying subjects who have high-grade prostate cancer. In some embodiments, the methods described herein are useful for identifying subjects who have a high likelihood of having high-grade prostate cancer detectable in a prostate biopsy. Such subjects may be administered a prostate cancer therapy (e.g., one or more of surgery, radiation therapy, hormone therapy, targeted therapy, chemotherapy, immunotherapy, radiopharmaceuticals, or bone-modifying drugs).

[0185] Conversely, in some embodiments, a subject identified as having low-grade prostate cancer or a low likelihood of having high-grade prostate cancer, for example, based on the level of expression of the described markers, may be given the option to avoid biopsy or treatment and opt for watchful waiting or minimal treatment.

[0186] In some embodiments, the prostate cancer therapy comprises the administration of a chemotherapeutic agent. Examples of chemotherapeutic agents include alkylating agents, antimetabolites, plant alkaloids and terpenoids, vinca alkaloids, podophyllotoxins, taxanes, topoisomerase inhibitors, and cytotoxic antibiotics. Cisplatin, carboplatin, and oxaliplatin are examples of alkylating agents. Other alkylating agents include mechlorethamine, cyclophosphamide, chlorambucil, and ifosfamide. Alkylating agents can impair cellular function by forming covalent bonds with amino, carboxyl, sulfhydryl, and phosphate groups in biologically important molecules. Alternatively, alkylating agents can chemically modify cellular DNA.

[0187] Biological therapies (sometimes called immunotherapy, biotherapy, or biological response modifier (BRM) therapy) directly or indirectly use the body's immune system to fight cancer or reduce the side effects that can be caused by some cancer treatments. Biological therapies include interferons, interleukins, colony-stimulating factors, monoclonal antibodies, vaccines, gene therapy, and nonspecific immunomodulators.

[0188] In some embodiments, the biologic therapy is an immune checkpoint therapy. Immune checkpoint inhibitors target CTLA-4, PD-1, or PD-L1. Examples include, but are not limited to, ipilimumab, nivolumab, cemiplimab, avelumab, durvalumab, tremelimumab, dostarlimab, pembrolizumab, spartalizumab, and atezolizumab.

[0189] In some embodiments, the prostate cancer therapy is FDA approved for treating prostate cancer. ... tetraxetan, Lynparza, mitoxantrone hydrochloride, nilandron, nilutamide, Nuvequo, olaparib, Orgovyx, Pluvicto, Provenge, radium-223 dichloride, relugolix, rubraca, rucaparib camsylate, sipuleucel-t, taxotere, xofigo, xtandi, yonsa, zoladex, xytiga, or any combination thereof. [Example]

[0190] experiment The following examples are provided to demonstrate and further illustrate certain embodiments and aspects of the present disclosure and should not be construed as limiting the scope thereof.

[0191] [Example 1] method Early genetic screening We used RNA-seq data from the Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) cohort to select potential grade-associated genes (The Cancer Genome Atlas Research Network. The Molecular Taxonomy of Primary Prostate Cancer. Cell. 2015;163(4):1011-25). Differential analyses between high-grade (Gleason >6) and low-grade (Gleason = 6) and between high-grade and benign tumors were performed according to the limma+voom procedure (Law CW et al., voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29). Candidate genes were manually selected by assessing the logFC and p-values ​​of both comparisons. Several genes known to be prostate cancer biomarkers were also included (Table 5). A total of 53 genes and one gene fusion (TMPRSS2-ERG) were selected (Figure 5).

[0192] Patient cohort Information about the training (University of Michigan) and validation (NCI-EDRN) cohorts is shown in Figure 1B and Table 1 .

[0193] Of 815 participants, qPCR yielded valid results in 761 (93%). Median age was 63 years (IQR 58-68), median PSA was 5.6 ng / mL (IQR 4.6-7.2), and 163 patients (21%) had a previous negative biopsy (Table 1). 293 men (39%) had GG≥2 cancer in the study biopsy. The accuracy of each candidate gene was quantified using an elastic net mathematical model (Table 4).

[0194] The final MPS2 model included standard clinical variables and the 17 most informative markers from the discovery analysis, including 13 (four high-grade-specific [APOC1, B3GNT6, NKAIN1, SCHLAP1] and nine prostate cancer-specific [PCGEM1, SPON2, TRGV9, PCA3, OR51E2, CAMKK2, TFF3, PCAT14, TMSB15A]), four curated markers (HOXC6, ERG, TMPRSS2:ERG, KLK4), and the reference gene KLK3. Model coefficients were determined in the overall cohort (Table 6). Calibration and internal cross-validation were performed (Figures 2C and 3C-D), and the MPS2 model was locked for external validation.

[0195] Urine RNA extraction and cDNA synthesis RNA isolation for MPS2 analysis was performed using the MagMAX® mirVana Total RNA Isolation Kit (ThermoFisher Scientific®). Briefly, 500 μL of a 1:1 mixture of urine and Hologic urine transport medium was mixed using Lysis Binding Mix (a component of the ThermoFisher Scientific® MagMAX® mirVana Total RNA Isolation Kit). Next, Binding Bead Mix (a component of the ThermoFisher Scientific® MagMAX® mirVana Total RNA Isolation Kit) was added to concentrate nucleic acids from the urine sample, followed by TURBO DNase® digestion and cleanup. Finally, RNA was eluted. For high-throughput urine RNA extraction, urine samples were processed using the semi-automated KingFisher Flex System® (ThermoFisher Scientific®). After RNA extraction, 16 μL of RNA was used to synthesize cDNA using SuperScript IV VILO® Master Mix (ThermoFisher Scientific®), followed by pre-amplification using TaqMan® PreAmp® Master Mix (ThermoFisher Scientific®).

[0196] OpenArray® Profiling OpenArray® technology (ThermoFisher Scientific®) is a high-throughput real-time PCR genotyping method that allows for the rapid screening of several TaqMan® assays in several samples. This real-time method involves the use of an array composed of 3072 through-holes run on a QuantStudio® 12K Flex real-time PCR system equipped with an OpenArray® block.

[0197] For each sample, 2.5 μL of pre-amplified cDNA and 2.5 μL of 2× TaqMan® OpenArray® master mix were manually mixed and loaded into a 384-well plate according to the manufacturer's instructions (ThermoFisher Scientific®). The QuantStudio® 12K Flex OpenArray® AccuFill® System transferred the previously generated mix to the TaqMan® OpenArray plate. Amplification was performed using a QuantStudio® 12K Flex Real-Time PCR System (ThermoFisher Scientific®) instrument, and expression was analyzed using the ΔΔCt method with QuantStudio 12K Flex software (ThermoFisher Scientific®).

[0198] Data Preprocessing To remove obvious outliers from the three technical replicates of OpenArray® QPCR and address undetected data points, the following data preprocessing steps were established: 1) If Ct = "indetermined" or amplification status = "indeterminate / no amplification," Ct was set to 35; 2) The standard deviation (SD) of the three replicates was calculated; 3) If SD >= 1, the replicate with the greatest difference from the mean was removed; if SD < 1, all three replicates were retained; 4) The Ct mean was calculated from the remaining two or three replicates. All Ct means were normalized by KLK3 using the following formula: - [Ct mean of gene X - Ct mean of KLK3]. The normalized Ct was used for downstream model building. Because no powers were applied, the normalized data were expressed on a logarithmic scale. Reasoning that samples with low KLK3 may indicate invalid DRE and be unreliable, a 95th percentile cutoff was set to remove samples with high KLK3 Ct means.

[0199] Mathematical Model Building To avoid multicollinearity in the regression model, highly correlated variables were identified and removed in a stepwise procedure. Specifically, the variance inflation factor (VIF) was calculated for all variables (gene expression from 54 probes plus clinical variables, including PSA density and prostate volume), and the variable with the highest VIF was removed; the VIF was recalculated for the remaining variables, and this step was repeated until no more variables had a VIF > 5. Nine probes (including probes targeting COL9A2, PLA2G7, HPN, CYB561A3, PDLIM5, MYO6, GAPDH, GDF15, and one of the two PCA3 probes) and PSA density were removed in the prefiltering step. To select significant genes, we evaluated three model-building strategies, including logistic regression with stepwise feature selection (James DA et al., Modern applied statistics with S-PLUS. Technometrics. 1996;38(1):77), logistic regression with recursive feature elimination, and regularized logistic regression with elastic net (Friedman J et al., Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33(1):1-22). The model-building steps were performed using the glmStepAIC (in both forward and reverse directions), rfe, and glmnet functions in the R package "caret" (Kuhn M et al., caret: Classification and Regression Training. R package version 6.0-86. Astrophysics Source Code). Elastic net is considered a mathematical model with built-in feature selection, since non-significant variables are assigned zero importance. We evaluated mathematical model performance using repeated cross-validation (10 folds, 3 replicates). We up-sampled the minor class (high grade, 39%, Table 1) to create balanced classes in training.For stepwise and RFE, feature selection was considered part of the mathematical model construction and was encapsulated inside each fold (Figure 2A).

[0200] Elastic Net was chosen for constructing the final mathematical model because it showed the best performance in terms of median AUC across a total of 30 resamplings. To select a robust gene panel, a final mathematical model was developed using an ensemble approach by integrating information from multiple Elastic Net regression models constructed from the resamplings (Figure 2B). Specifically, the training data was first randomly divided into four folds, and this step was repeated four times to generate a total of 40 resamplings. An Elastic Net regression model was fitted to the 40 subsamples of the training data; then, the frequency and importance of each gene across all resamplings were summed together. Genes were then ranked by selection frequency and summed importance, and the top 17 genes were selected for inclusion in the final model. The number of genes was determined based on preliminary analysis of optimal feature size using RFE and the optimal design of the OpenArray® plate. The coefficients of each of the 17 genes were estimated by fitting the entire training data with an Elastic Net regression model (referred to as "MPS2"). Additionally, an enhanced model was constructed by incorporating 17 genetic and clinical variables (age, race, family history, abnormal DRE, and previous negative biopsy) (termed "MPS2c"). In addition to the variables mentioned above, prostate volume was added to construct a third model (termed "MPS2cv" or "MPS2+") that can be used when this information is available.

[0201] Mathematical Model Calibration A calibration curve was used to assess the agreement between the predicted probability and the observed prevalence in each bin. Logistic regression is considered a well-calibrated classifier. However, calibration is necessary when there is a distribution shift between the training and validation populations. In this study, the classes were balanced in the training, while the validation cohort was a continuous cohort, and its 20% high-grade prevalence reflected the unbalanced true distribution. Without calibration, the calibration curve (Figure 7) showed an overall overestimation of risk. Therefore, it was important to calibrate the model to make predictions for each individual patient that reflected the actual risk in the true population. Two calibration techniques were tested: recalibration on a large scale (reestimation of the model intercept) and recalibration on a resampled UM cohort to match the 20% high grades in the validation cohort (reestimation of the intercept and slope) (Vergouwe Y et al., A closed testing procedure to select an appropriate method for updating prediction models. Stat Med. 2017;36(28):4529-39). Calibration curves were generated using original and calibrated probabilities. Recalibration performed better in the high probability bins, and all subsequent calibrations were performed using this approach.

[0202] Model Validation Raw data from the validation cohort were preprocessed in the same manner as the training cohort. Using the normalized Ct values ​​from the validation cohort, predictions from the three models (MPS2, MPS2c, and MPS2cv) were generated using the "predict" function in caret (Kuhn M et al., caret: Classification and Regression Training. R package version 6.0-86. Astrophysics Source Code.). Calibration of the model predictions was then performed using the intercept and slope for each model separately, estimated as described in the "Model Calibration" section above.

[0203] statistical analysis Statistical analysis was performed using R version 4.1 (R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013 2013). The Kruskal-Wallis test was used for group comparisons, with a p-value of <0.05 considered statistically significant. Regularized logistic regression with elastic nets was used to build a model for predicting high-grade prostate cancer, implemented by a wrapper function for "glmnet" provided by caret (Kuhn M et al., caret: Classification and Regression Training. R package version 6.0-86. Astrophysics Source Code.). Diagnostic potential was visualized by receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC) using the R package pROC (Robin X et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77). Calibration analysis was performed with the calibrate function in caret by setting cut = 8 (number of bins). Decision curve analysis (DCA) was performed using the dca function in dcurves (Sjoberg DD. Dcurves: decision curve analysis for model evaluation, 2021).

[0204] Across clinically relevant threshold values ​​ranging from approximately 4% to 20%, the MPS2 model provided the highest net clinical benefit across all tests (Figure 10A). The threshold probability (x-axis) reflects how much patients and clinicians value potential clinical outcomes. For example, a threshold probability of approximately 4% applies to patients who choose to proceed with biopsy if their risk of clinically significant prostate cancer is approximately 4% or greater. For clinically significant prostate cancer, a threshold probability of approximately 4% represents a risk-averse population, such as younger men with a long life expectancy. At a practical level, this implies that clinicians are willing to perform as many as 20 biopsies to detect additional clinically significant prostate cancer. At the other end of the range, a threshold probability of 20% applies to patients who choose to proceed with biopsy only if their risk of clinically significant prostate cancer is ≥ 20%. Such a population strongly values ​​avoiding biopsy and is willing to accept a higher risk of delayed detection of clinically significant prostate cancer. The units of net benefit (y-axis) are true positives. A net benefit of 0.15 is equivalent to an approach in which 15 patients per 100 would be referred for biopsy based on the use of the test, and all 15 patients would be found to have clinically significant prostate cancer. Plots are generated with ggplot2 (Wickham H. Springer; New York: 2009. Ggplot2: elegant graphics for data analysis).

[0205] result Results include urinary transcript markers of high-grade prostate cancer.

[0206] Additional cancer- and high-grade prostate cancer-specific transcripts were identified and added to the MPS test (Tomlins SA et al., Urine TMPRSS2:ERG fusion transcript stratifies prostate cancer risk in men with elevated serum PSA. Sci Transl Med. 2011;3(94):94ra72; Tomlins SA et al., Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. 2016;70(1):45-53). For transcript 4 nomination, RNA-seq data from the Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) cohort were analyzed (see Methods above). Briefly, biomarker discovery was performed using RNA sequencing (RNA-seq) data from 220 benign prostate, 71 GG1, and 484 GG≥2 cancers available through The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) portal, and the University of Michigan (UM). 44 transcripts meeting predefined nomination criteria were supplemented with 10 curated cancer-associated genes. This analysis resulted in a 54-marker panel, including two reference genes and the PCA3 and T2:ERG gene fusions derived from the original MPS assay (Table 5). The expression of each of these genes in association with increasing Gleason score is shown in Figure 4A–D.

[0207] Next, a custom ThermoFisher® OpenArray® QPCR-based platform (see Methods) was developed for the detection of these transcripts in urine samples collected from patients immediately after a digital rectal examination (DRE). The patient training cohort (Figure 1B, Table 1) consisted of men undergoing prostate biopsy at the University of Michigan (UM). Of the initial 921 patients included in the UM training cohort, 761 men had available Prostate Cancer Prevention Trial (PCPT) clinical variables, a PSA <10 ng / mL, available prostate volume, and a threshold cycle (Ct) <27 for KLK (PSA) transcripts in their urine biospecimens (Figure 1B). Of these 761 patients, 293 (38.5%) were found to have GG≥2 prostate cancer on biopsy (Table 1).

[0208] MPS2 model development was initiated after QPCR analysis of 54 markers in urine samples from the training cohort (Figure 2A). An important aspect of model development is dimensionality reduction to reduce model complexity and avoid overfitting. After pre-filtering with a variance inflation factor (VIF) >5 to remove redundant variables, 46 genes remained. Three different model-building algorithms with encapsulated feature selection, including logistic regression (forward and backward) with stepwise feature selection, logistic regression with recursive feature selection (RFE), and regularized logistic regression with elastic net, were first evaluated using repeated cross-validation (CV) on the training cohort. Elastic net has built-in feature selection to provide feature importance, which can be used for this purpose. The median area under the curve (AUC) of the elastic net regression approach was the highest (Figure 6); therefore, it was selected to build the final MPS2 mathematical model. Using an ensemble approach that integrates data from multiple mathematical models across resampling, the development set was randomly divided into four folds, and the model producing the highest AUC was identified for each fold. This approach was repeated 10 times with different random seeds, resulting in a total of 40 elastic net models. The frequency of model inclusion and its importance for clinically significant prostate cancer detection were tabulated across models. Based on an analysis of the optimal feature size and technical features of the OpenArray™ platform, 17 biomarkers providing optimal discrimination accuracy for GG≧2 prostate cancer were included in the MPS2 and MPS2+ (plus prostate volume) models along with standard clinical variables and the normalization gene KLK3. Prior to external validation, the models were calibrated and internally cross-validated (see Figure 1B). Model performance, starting with the 54 MPS2 genes and MPS2 genes plus clinical variables (with and without prostate volume), was assessed using iterative CV. Prediction of high-grade prostate cancer improved with the inclusion of more genes compared to PCA3 and T2:ERG alone in terms of AUC (0.784 vs. 0.731) (Figures 3A and 3B).Incorporation of clinical variables (not including prostate volume) increased the AUC to 0.802 (Fig. 3C), and inclusion of prostate volume raised the AUC to 0.820 (Fig. 3D).

[0209] We observed that certain genes were frequently selected in different CV folds, while the selection of other genes was more random. To select a robust gene set, the training data containing all genes was divided into four subsamples, and the data division was repeated 10 times using different random seeds, resulting in a total of 40 subsamples (Figure 2B). Each subsample was trained with an elastic net, and the top 17 genes were selected for the final mathematical model based on importance and frequency (Table 3). The final MPS2 model was constructed using 17 genes from the entire training cohort, and the MPS2c and MPS2cv models were constructed by adding clinical variables for those without and those with prostate volume, respectively. Calibration curves after applying class imbalance correction showed that the predicted risk closely matched the observed risk (Figure 3E). The performance of the locked 17-transcript MPS2 model was next examined in a validation cohort consisting of the blinded, multicenter National Cancer Institute-Early Detection Research Network (NCI-EDRN) prostate biopsy cohort (Figure 1B, Table 1). Of the 743 final patients included in the validation cohort, 20.3% had GG≥2 prostate cancer on biopsy (Table 1). For the MPS2 model, logistic regression is considered a well-calibrated classifier; however, calibration is necessary if there is a distribution shift between the training and validation populations. While the classes in this study were balanced in training, the validation cohort was a continuous cohort, and its 20% high-grade prevalence reflected the true distribution, which was unbalanced. Without calibration, the MPS2 calibration curve (Figure 7) showed an overall overestimation of risk. Therefore, as detailed in the Methods section above, it was important to calibrate the model to generate predictions for each individual patient that reflected the actual risk in the true population (see also Tables 6, 7, and 8).

[0210] As shown in Figure 4A, the final MPS2 model outperformed the original MPS model, with values ​​similar to those obtained in the training cohort. MPS2, MPS2c, and MPS2cv had AUC values ​​of 0.750, 0.807, and 0.818, respectively, compared to 0.730 for the original MPS assay. The final calibration curves for each model showed that the predicted risk closely matched the observed risk in the validation cohort (Figure 4B). Decision curve analysis also demonstrated the net benefit of the MPS2 model across different probability thresholds versus "treat all" or "not treat all" (Figure 4C), and also calculated the intervention (biopsy) avoided across different probability thresholds by each model (Figure 4D, Table 4).

[0211] Of the 859 men participating in the PCA3 trial, 46 (5.4%) were ineligible for the current analysis due to inadequate urine volume or unavailable clinical data. Of the 813 validated patients (Figure 11), qPCR was successful in 743 (91%). The median PSA level was 5.6 ng / mL (IQR 4.1–8.0), and 247 men (33%) had a previous negative biopsy (Table 1). 151 men (20%) had GG≥2 PCa (prostate cancer) in the study biopsy. The median MPS2 value was significantly higher in men with GG≥2 prostate cancer than in men with negative biopsies and men with GG1 prostate cancer (0.44 vs. 0.08 and 0.20, respectively; both p<0.001) (Table 1, Figure 8A). Similarly, the median MPS2+ score was significantly higher in men with GG≥2 cancers compared with negative or GG1 biopsies (0.54 vs. 0.08 and 0.25, respectively; p<0.001, Figure 8B). The AUCs for GG≥2 cancers were 0.60 for PSA, 0.66 for PCPTrc, 0.77 for PHI, 0.76 for dmx2, 0.72 for dmx3, and 0.74 for MPS, compared with 0.81 for MPS2 and 0.82 for MPS2+ (Figure 9). The observed prevalence of GG≥2 cancers closely approximated the predicted probabilities of MPS2 and MPS2+ (Figure 2C), reflecting excellent calibration. Critically, the MPS2 model was particularly well calibrated for predicted probabilities <30%.

[0212] Using a test threshold of detecting 95% of GG≧2 prostate cancers (i.e., 95% sensitivity), the proportion of unnecessary biopsies that would have been avoided using each test was 11% for PSA, 20% for PCPTrc, 26% for PHI, 27% for dmx2, 17% for dmx3, and 23% for MPS, compared with 37% for MPS2 and 41% for MPS2+. Full performance measures and unnecessary biopsies avoided are listed in Table 2. Crucially, MPS2 and MPS2+ provided 99% sensitivity and 99% NPV for GG≧3 prostate cancer.

[0213] The initial biopsy population included 496 patients with a median PSA of 5.0 ng / mL (IQR 3.8-6.6) (Table 7). In the study biopsies, 133 patients (27%) had GG≥2 cancer. Using a 95% sensitivity threshold, the proportion of unnecessary biopsies avoided was 15% for PSA, 27% for PCPT, 30% for PHI, 30% for dmx2, 17% for dmx3, and 27% for MPS, compared with 35% for MPS2 (Table 2). Although initial biopsy patients often did not have available prostate volume, use of MPS2+ would have avoided 42% of unnecessary biopsies.

[0214] The repeat biopsy population included 247 men with a median PSA of 7.2 ng / mL (IQR 5.5–9.8), of whom 18 (7.3%) were found to have GG≥2 prostate cancer (Table 7). At 95% sensitivity for GG≥2 cancer, the proportion of unnecessary biopsies avoided was 15% for PSA, 8.7% for PHI, 14% for dmx2, 16% for dmx3, and 15% for MPS, compared with 46% for MPS2 and 51% for MPS2+ (Table 2). Thus, the MPS2 test should have avoided approximately half of unnecessary biopsies while maintaining 95% detection of GG≥2 prostate cancer. The performance of the MPS2 model with and without clinical factors is provided by subgroups (Tables 8–9). Across clinically relevant threshold values ​​ranging from 5% to 20%, the MPS2 model provided the highest net clinical benefit across all tests (Figure 10A). When benefit is expressed as a net reduction in unnecessary biopsies, MPS2 provided the greatest net reduction in unnecessary biopsies without missing a single patient with GG≧2 prostate cancer (FIG. 10B).

[0215] Translating sequencing-based discoveries into the scalable qPCR platform provided herein is a test incorporating 17 markers of PCA and markers specifically overexpressed by high-grade cancers. Three MPS2 models were developed, incorporating the 17 biomarkers alone or in combination with clinical data (MPS2c) and prostate volume (MPS2cv). Validation of the MPS2 models in a blinded external cohort demonstrated that the models improved the diagnostic accuracy of the original MPS2 model while increasing specificity. The MPS2 test, with 95% sensitivity for GG≥2 cancers, provided 95–99% NPV and 35–51% specificity across subgroups. For individual patients, NPV approaching 100% provides clear guidance for confident decision-making. For clinicians, uniform use of MPS2 could avoid up to half of unnecessary biopsies while maintaining the immediate detection of 95% of GG≥2 cancers diagnosed under a "biopsy all" approach. Crucially, MPS2 provided 99% sensitivity and 99% NPV for GG≧3 cancers, meaning that the rare false-negative MPS2 results were almost uniformly the more favorable GG2 cancers, which are least likely to metastasize.

[0216] Collectively, the findings indicate that MPS2 testing can improve the detection of clinically significant prostate cancer and may be useful in identifying prostate cancer patients who would benefit most from more aggressive treatment.

[0217] [Table 3]

[0218] [Table 4]

[0219] [Table 5]

[0220] [Table 6]

[0221] [Table 7]

[0222] [Table 8]

[0223] [Table 9]

[0224] [Table 10]

[0225] [Table 11]

[0226] [Table 12]

[0227] [Example 2] Sample collection and mRNA detection Urine samples are collected from subjects suspected of having or at risk of developing prostate cancer to determine the likelihood that Grade Group ≥ 2 prostate cancer will be detected from a prostate biopsy in the subject. The subject undergoes a digital rectal examination (DRE), and a urine sample is collected within approximately one hour after the DRE. The urine is placed in a collection tube containing stabilizing buffer at a buffer:sample ratio of approximately 2:5 by volume.

[0228] Positive and negative controls are prepared: the negative control is a sample from a previously reported subject in the "low risk category," and the positive control is a sample from a previously reported subject in the "elevated risk category."

[0229] RNA is isolated from the test sample, negative control, and positive control using a commercially available RNA isolation kit. The extracted RNA is subjected to RT-PCR to generate cDNA, preamplification, and qPCR to determine the amount of mRNA expressed from each of the following genes: TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6. The amount of mRNA expressed from a reference gene, such as KLK3, is also determined using RT-PCR, preamplification, and qPCR. Target-specific primers are used to amplify the cDNA, and fluorophore-emitting gene-specific probes are used to accurately and quantitatively measure the expression levels of the target genes listed above.

[0230] Analysis and MPS2 score generation Based on the qPCR performed above, the average Crt value (cycle threshold value) is determined for each target gene and normalized to the average Crt value of the reference gene (e.g., KLK3) using the following equation: Crt average (target) - Crt average (reference). The normalized Crt is multiplied by a gene-specific coefficient. Exemplary gene-specific coefficients are presented in the table below. Normalized Crt × sum of coefficients = logit value. The logit value is recalibrated using the intercept and slope. The logit value is converted to a score using the logit equation. The gene-specific coefficient, slope, and intercept are different for biopsy-naive subjects or subjects with a previous negative prostate biopsy.

[0231] [Table 13] TIFF2026503720000038.tif30163

[0232] An illustrative calculation for biopsy naive subjects is shown below: 1. Mean Crt values ​​for each of the 17 targets normalized to KLK3 = (mean Crt values ​​for target gene - mean Crt values ​​for KLK3) 2. Normalized value * coefficient of the specified target 3. (sum of (2) for all targets) + intercept 4.(Intercept+(3))*(Slope)=logit 5. MPS2 Probability = (Exp(4)) / Exp(4)+1)

[0233] An illustrative calculation for a subject with a previous negative prostate biopsy is shown below: 1. Average Crt value for each of the 17 targets normalized to KLK3 = (average Crt value for target gene - average Crt value for KLK3) 2. Add the following clinical variables and prostate volume to (1): Age b. African American (binary) c. Negative biopsy = 1 d.Abnormal DRE (binary) e. Family history (binary) f. Serum PSA g PSA volume 3. Normalized value * coefficient of the specified target + sum of (2a-2g) 4. (sum of (3) for all targets) + intercept 5.(Intercept+(3))*(Slope)=logit 6. MPS2 Probability = (Exp(4)) / Exp(4)+1)

[0234] The thresholds for determining low or elevated risk differ for biopsy-naive subjects and subjects with a previous negative prostate biopsy:

[0235] [Table 14]

[0236] Based on the above results, a report is generated with a score and risk category and provided to the subject's healthcare provider (e.g., the subject's urologist) who requested the test.

[0237] All publications, patents, patent applications, and accession numbers mentioned in the above specification are incorporated herein by reference in their entirety. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications and variations of the described compositions and methods of the invention will be apparent to those skilled in the art and are intended to be within the scope of the following claims.

Claims

1. 1. A method of treating prostate cancer, comprising: a) assaying the level of expression of one or more genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject diagnosed with prostate cancer; and b) administering a prostate cancer treatment to subjects identified as having an altered level of expression of said gene relative to subjects free of prostate cancer or subjects with low-grade prostate cancer. A method comprising:

2. 1. A method for characterizing, prognosing, or recommending a treatment for prostate cancer, comprising: a) assaying the level of expression of one or more genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6 in a sample from a subject diagnosed with prostate cancer; and b) identifying the subject as having high-grade prostate cancer if the subject is identified as having an altered level of expression of the gene compared to subjects without prostate cancer or subjects with low-grade prostate cancer. A method comprising:

3. 1. A method for informing prostate cancer survival outcomes, comprising: a) detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6, wherein the amount of expression is present in urine from the subject; b) determining a score based on the amount of expression, the score correlating with or informing the subject's likelihood of having or developing Grade Group ≧2 prostate cancer; A method comprising:

4. 1. A method for identifying a subject who has or has a high likelihood of developing Grade Group ≥ 2 prostate cancer, comprising detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6, wherein the amount of expression is present in the urine of the subject and indicates whether the subject has a high likelihood of having Grade Group ≥ 2 prostate cancer with a diagnostic accuracy (AUC) of ≥ 0.

75.

5. 1. A method for identifying the likelihood of detecting Grade Group ≥ 2 prostate cancer from a prostate biopsy in a subject, comprising detecting the amount of expression of at least three genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6, wherein the amount of expression is present in the subject's urine and indicates the likelihood of detecting Grade Group ≥ 2 prostate cancer from the prostate biopsy in the subject with a diagnostic accuracy (AUC) of ≥ 0.

75.

6. 6. The method of any one of claims 2 to 5, further comprising administering to the subject a prostate cancer treatment.

7. 10. The method of claim 1, wherein the subject has high-grade prostate cancer.

8. 8. The method of claim 7, wherein the high-grade prostate cancer is Grade Group ≥ 2 prostate cancer.

9. 9. The method of any one of claims 1 to 8, wherein the method further comprises determining a score based on the level or amount of expression, the score indicating the likelihood of the subject having or developing Grade Group > 2 prostate cancer.

10. The method of claim 9 further comprising generating a report including the score.

11. 11. The method of claim 9 or 10, wherein Grade Group ≥ 2 prostate cancer is determined by prostate biopsy of the subject.

12. 12. The method of any one of claims 3, 9, 10 or 11, wherein the score has a diagnostic accuracy (AUC) of > 0.

75.

13. The method of any one of claims 9 to 12, further comprising sending a report to the subject or the subject's healthcare provider.

14. The method according to any one of claims 1 or 8 to 14, wherein the one or more genes are two or more genes.

15. The method according to any one of claims 1 to 14, wherein the one or more genes or the at least three genes are five or more genes.

16. The method according to any one of claims 1 to 15, wherein the one or more genes or the at least three genes are 10 or more genes.

17. The method of any one of claims 1 to 16, wherein the one or more genes or the at least three genes are TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

18. The method of any one of claims 1 to 17, comprising assaying the level of expression of 1 to 20 additional genes.

19. The method of any one of claims 1 to 18, wherein the subject has not had a previous prostate biopsy.

20. The method of any one of claims 1 to 19, wherein the subject has had a previous negative prostate biopsy result.

21. 21. The method of any one of claims 9 to 20, wherein one or more clinical variables are associated with the subject, and the method further comprises identifying at least one of the one or more clinical variables and determining a score based on the at least one of the one or more clinical variables.

22. 22. The method of claim 21, wherein at least one of the one or more clinical variables is the subject's age, race, family history of prostate cancer, digital rectal examination (DRE) results, prostate biopsy results, prostate-specific antigen (PSA) expression levels based on a serum sample, multi-perimetric MRI (mpMRI) results, or any combination thereof.

23. 23. The method of claim 22, wherein the subject's DRE or prostate biopsy is performed within 30 days before the urine sample is obtained.

24. The subject has had a previous negative prostate biopsy result, and the step of determining the score comprises: a) Equation 1 [Equation 1] x = intercept + slope ((-1) ((a) (CRT mean APOC1 - CRT mean reference) + (b) (CRT mean B3GNT6 - CRT mean reference) + (c) (CRT mean CAMKK2 - CRT mean reference) + (d) (CRT mean ERG - CRT mean reference) + (e) (CRT mean HOXC6 - CRT mean reference) + (f) (CRT mean KLK4 - CRT mean reference) + (g) (CRT mean NKAIN1 - CRT mean reference) + (h) (CRT mean OR51E2 - CRT mean reference) + (i) (CRT mean PCA3 - CRT mean reference) + (j) (CRT mean PCAT14 - C RT average) + (k) (CRT average PCGEM1 - CRT average) + (l) (CRT average SCHLAP1 - CRT average) + (m) (CRT average SPON2 - CRT average) + (n) (CRT average TFF3 - CRT average) + (o) (CRT average T2:ERG - CRT average) + (p) (CRT average TMSB15A - CRT average) + (q) (CRT average TRGV9 - CRT average)) + ((r) (age) + (s) (family history) + (t) (abnormal DRE) + (u) (previous negative biopsy) + (v) (PSA) + (w) (prostate volume)); or b) Equation 3 [Equation 2] x = intercept + slope ((-1) ((a) (CRT mean APOC1 - CRT mean KLK3) + (b) (CRT mean B3GNT6 - CRT mean KLK3) + (c) (CRT mean CAMKK2 - CRT mean KLK3) + (d) (CRT mean ERG - CRT mean KLK3) + (e) (CRT mean HOXC6 - CRT mean KLK3) + (f) (CRT mean KLK4 - CRT mean KLK3) + (g) (CRT mean NKAIN1 - CRT mean KLK3) + (h) (CRT mean OR51E2 - CRT mean KLK3) + (i) (CRT mean average PCA3 - CRT average KLK3) + (j) (CRT average PCAT14 - CRT average KLK3) + (k) (CRT average PCGEM1 - CRT average KLK3) + (l) (CRT average SCHLAP1 - CRT average KLK3) + (m) (CRT average SPON2 - C RT average KLK3) + (n) (CRT average TFF3 - CRT average KLK3) + (o) (CRT average T2: ERG - CRT average KLK3) + (p) (CRT average TMSB15A - CRT average KLK3) + (q) (CRT average TRGV9 - CRT average KLK3)) The method of any one of claims 21 to 23, comprising performing:

25. The subject is prostate biopsy naive, and the step of determining the score comprises: a) Equation 2 [Equation 3] x = intercept + slope ((-1) ((a) (CRT mean APOC1 - CRT mean reference) + (b) (CRT mean B3GNT6 - CRT mean reference) + (c) (CRT mean CAMKK2 - CRT mean reference) + (d) (CRT mean ERG - CRT mean reference) + (e) (CRT mean HOXC6 - CRT mean reference) + (f) (CRT mean KLK4 - CRT mean reference) + (g) (CRT mean NKAIN1 - CRT mean reference) + (h) (CRT mean OR51E2 - CRT mean reference) + (i) (CRT mean (PCA3 - CRT mean) + (j) (CRT mean PCAT14 - CRT mean) + (k) (CRT mean PCGEM1 - CRT mean) + (l) (CRT mean SCHLAP1 - CRT mean) + (m) (CRT mean SPON2 - CRT mean) + (n) (CRT mean TFF3 - CRT mean) + (o) (CRT mean T2:ERG - CRT mean) + (p) (CRT mean TMSB15A - CRT mean) + (q) (CRT mean TRGV9 - CRT mean)); or b) Equation 4 [Equation 4] x = intercept + slope ((-1) ((a) (CRT mean APOC1 - CRT mean KLK3) + (b) (CRT mean B3GNT6 - CRT mean KLK3) + (c) (CRT mean CAMKK2 - CRT mean KLK3) + (d) (CRT mean ERG - CRT mean KLK3) + (e) (CRT mean HOXC6 - CRT mean KLK3) + (f) (CRT mean KLK4 - CRT mean KLK3) + (g) (CRT mean NKAIN1 - CRT mean KLK3) + (h) (CRT mean OR51E2 - CRT mean KLK3) + (i) (CRT mean PCA3 - CRT mean KLK3) + (j) (CRT mean PCAT14 -CRT mean KLK3) + (k) (CRT mean PCGEM1 - CRT mean KLK3) + (l) (CRT mean SCHLAP1 - CRT mean KLK3) + (m) (CRT mean SPON2 - CRT mean KLK3) + (n) (CRT mean TFF3 - CRT mean KLK3) + (o) (CRT mean T2:ERG - CRT mean KLK3) + (p) (CRT mean TMSB15A - CRT mean KLK3) + (q) (CRT mean TRGV9 - CRT mean KLK3)) + ((r) (age) + (s) (family history) + (t) (abnormal DRE) + (u) (previous negative biopsy) + (v) (PSA) + (w) (prostate volume)). The method of any one of claims 21 to 23, comprising performing:

26. 26. The method of claim 24 or 25, wherein executing comprises using a processor.

27. 27. The method of any one of claims 1 to 26, wherein the score has a diagnostic accuracy (AUC) of ≧0.

80.

28. 28. The method of any one of claims 1 or 6-27, wherein the prostate cancer treatment is one or more of surgery, radiation therapy, hormone therapy, targeted therapy, chemotherapy, immunotherapy, radiopharmaceuticals, and bone modifying drugs.

29. 29. The method of any one of claims 1 to 28, wherein the level or amount of expression is the amount of mRNA or protein expressed by the gene.

30. 30. The method of any one of claims 1-2 or 7-29, wherein the sample is selected from tissue, blood, plasma, serum, urine, prostate secretions, and prostate cancer cells.

31. 31. The method of claim 30, wherein the sample is urine, and the urine is obtained within 30 minutes of the subject's DRE.

32. The method of any of claims 9 to 31, further comprising determining a prostate volume of the subject and determining a score based on the subject's prostate volume.

33. 33. The method of claim 32, wherein the score has a diagnostic accuracy (AUC) of ≧0.

81.

34. 33. The method of claim 32, wherein the score has a diagnostic accuracy 1% to 10% higher than a score determined by the amount of expression of PCA3 and TMPRSS2-ERG alone.

35. 35. The method of any one of claims 1 to 34, wherein the step of detecting the level or amount of expression of the gene comprises detecting the amount of mRNA expression of the gene.

36. 36. The method of claim 35, wherein detecting the level or amount of mRNA expression comprises reacting the sample or urine with a reagent composition comprising a polynucleotide reagent.

37. 36. The method of claim 35, wherein detecting the level or amount of mRNA expression comprises synthesizing cDNA complementary to the mRNA expressed by the gene, amplifying the cDNA, and detecting the cDNA.

38. 38. The method of any one of claims 1 to 37, further comprising detecting the level or amount of expression of a reference gene, and normalizing the amount of expression of said one or more genes or said at least three genes to the amount of expression of the reference gene.

39. 39. The method of claim 38, wherein detecting the level or amount of expression of the reference gene comprises detecting the level or amount of mRNA expressed by the reference gene.

40. 40. The method of claim 38 or 39, wherein the reference gene is KLK3, CYPB561A3, EEF1A2, GAPDH, HPN, KLK2, KLK4, LBH, NUDT8, SPDEF or TRGV9.

41. 41. The method of claim 40, wherein the reference gene is KLK3.

42. 42. The method of any one of claims 1 to 41, wherein the level or amount of expression of the gene is different from the amount of expression of the gene in a subject having or at risk of developing Grade Group <2 prostate cancer or in a subject free of prostate cancer.

43. 43. The method of any one of claims 1 to 42, comprising detecting the level or amount of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4 and HOXC6.

44. The method of any one of claims 1 to 43, comprising the step of detecting the level or amount of expression of 1 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

45. The method of any one of claims 1 to 44, comprising the step of detecting the level or amount of expression of 5 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

46. 46. ​​The method of any one of claims 1 to 45, comprising detecting the level or amount of expression of TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4 and HOXC6.

47. The method of claim 46, wherein the level or amount of expression of each of APOC1, CAMKK2, NKAIN1 and PCGEM1 is lower in subjects at risk of having Grade Group ≧2 prostate cancer than in subjects at risk of having or developing Grade Group <2 prostate cancer or subjects without prostate cancer.

48. 48. The method of claim 46 or 47, wherein the level or amount of expression of each of TMPRSS2-ERG, SCHLAP1, OR51E2, PCAT14, PCA3, B3GNT6, TFF3, SPON2, TRGV9, TMSB15A, ERG, KLK4 and HOXC6 is higher in a subject at risk of having Grade Group ≥ 2 prostate cancer than in a subject having or at risk of developing Grade Group < 2 prostate cancer or a subject free of prostate cancer.

49. 1. A method for screening for the expression levels of at least three genes, comprising: a) reacting a urine sample from a human subject with a reagent for detecting the amount of expression of at least three genes, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; and b) detecting the amount of expression of at least three genes, wherein the amount of expression is present in the sample and the detecting step comprises the use of an in vitro assay. A method comprising:

50. 50. The method of claim 49, wherein the in vitro assay is a nucleic acid amplification assay.

51. 51. The method of claim 50, wherein the nucleic acid amplification assay comprises performing a reverse transcription polymerase chain reaction.

52. 1. A method for detecting the amount of mRNA expressed by at least three genes, comprising: a) synthesizing cDNA from mRNA expressed by at least three genes and present in a sample of urine from a human subject, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; b) amplifying the cDNA to provide amplified cDNA; and c) detecting the amplified cDNA, wherein the amplified cDNA indicates the amount of mRNA expressed by the at least three genes. A method comprising:

53. 1. A method for detecting the amount of mRNA expressed by at least three genes, comprising: a) isolating nucleic acid from a first composition comprising urine from a human subject to provide isolated nucleic acid; b) reacting the isolated nucleic acid with a second composition present in the first composition and comprising reagents for detecting the amount of mRNA expressed by at least three genes, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; and c) detecting the amount of mRNA expressed by at least three genes A method comprising:

54. The method of any one of claims 52 to 53, comprising the step of detecting the amount of expression of 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

55. The method of any one of claims 52 to 54, comprising the step of detecting the expression levels of 3 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

56. The method of any one of claims 53 to 54, comprising the step of detecting the expression level of 5 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

57. 55. The method of any one of claims 53 to 54, comprising detecting the amount of expression of each of TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4 and HOXC6.

58. 58. The method of any one of claims 1-57, further comprising the step of informing the subject or the subject's healthcare provider of treatment options for Grade Group > 2 prostate cancer.

59. 59. The method of any one of claims 10-58, wherein the report comprises treatment options for Grade Group > 2 prostate cancer.

60. 60. The method of any one of claims 1-59, further comprising providing to the subject or the subject's healthcare provider instructions for administering to the subject a treatment for Grade Group > 2 prostate cancer.

61. a container containing a reagent composition for detecting the amount of expression of at least three genes; and instructions for detecting the amount of expression present in urine of a subject, wherein the at least three genes are selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6; Kit including:

62. 62. The kit of claim 61, wherein the reagent composition comprises polynucleotide reagents for detecting the amount of mRNA expressed by at least three genes.

63. 63. The kit of claim 61 or 62, wherein the reagent composition comprises a polynucleotide reagent for detecting the amount of expression of a reference gene, and the instructions are further for normalizing the amount of expression of at least three genes to the amount of expression of the reference gene.

64. 64. The kit of claim 63, wherein the reference gene is KLK3, CYPB561A3, EEF1A2, GAPDH, HPN, KLK2, KLK4, LBH, NUDT8, SPDEF or TRGV9.

65. 65. The kit of claim 64, wherein the reference gene is KLK3.

66. 66. The kit of any one of claims 61-65, wherein the instructions are further for generating a report comprising a score determined by the amount of expression of at least three genes, the score indicating the likelihood of the subject having or developing Grade Group ≥ 2 prostate cancer.

67. 67. The kit of claim 66, wherein the Grade Group ≥ 2 prostate cancer is determined by prostate biopsy of the subject.

68. 68. The kit of any one of claims 61 to 67, wherein the subject has not had a previous prostate biopsy.

69. 69. The kit of any one of claims 61 to 68, wherein the subject has had a previous negative prostate biopsy.

70. 70. The kit of any one of claims 61-69, wherein the one or more clinical variables are associated with a subject, and the instructions are also for determining a score based on at least one of the one or more clinical variables.

71. 71. The kit of claim 70, wherein at least one of the one or more clinical variables is the subject's age, race, family history of prostate cancer, digital rectal examination (DRE) results, prostate biopsy results, prostate-specific antigen (PSA) expression levels based on a serum sample, multi-perimetric MRI (mpMRI) results, or any combination thereof.

72. 72. The kit of any one of claims 61 to 71, wherein the instructions are further for determining a score based on the subject's prostate volume.

73. The kit according to any one of claims 61 to 72, wherein the genes are 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 types of genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

74. The kit according to any one of claims 61 to 72, wherein the genes are 3 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

75. The kit according to any one of claims 61 to 72, wherein the genes are 5 to 10 genes selected from TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

76. The kit according to any one of claims 61 to 72, wherein the genes are TMPRSS2-ERG, SCHLAP1, OR51E2, APOC1, PCAT14, CAMKK2, PCA3, NKAIN1, B3GNT6, TFF3, SPON2, PCGEM1, TRGV9, TMSB15A, ERG, KLK4, and HOXC6.

77. 77. The kit of any one of claims 61 to 76, wherein the instructions are further for informing a subject of treatment options for Grade Group > 2 prostate cancer.

78. 78. The kit of any one of claims 66-77, wherein the report comprises treatment options for Grade Group > 2 prostate cancer.

79. 79. The kit of any one of claims 66-78, wherein the instructions are further for administering to a subject a treatment for Grade Group > 2 prostate cancer.

80. 61. The method of any one of claims 1 to 60, which does not include the step of performing a prostate biopsy in the subject.

81. 92. The method of any one of claims 2-5, 9-10, 12-27, 29-58 or 81, wherein the subject is spared an unnecessary prostate biopsy.

82. 61. The method of any one of claims 1 to 60, further comprising the step of performing a prostate biopsy in the subject.

83. 61. The method of any one of claims 1 to 60, further comprising the step of recommending to the subject or the subject's healthcare provider that the subject undergo a prostate biopsy.

84. 83. The method of claim 81 or 82, wherein a prostate biopsy indicates that the subject has Grade Group > 2 prostate cancer.

85. 83. The method of claim 81 or 82, wherein the prostate biopsy indicates that the subject does not have Grade Group > 2 prostate cancer.

86. 84. The method of any one of claims 2-60 and 80-83, further comprising administering to the subject a treatment for Grade Group > 2 prostate cancer.

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