Author Correction: FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes.

Mutations in the transcription factor FOXA1 define a unique subset of prostate cancers but the functional consequences of these mutations and whether they confer gain or loss of function is unknown1-9. Here, by annotating the landscape of FOXA1 mutations from 3,086 human prostate cancers, we define two hotspots in the forkhead domain: Wing2 (around 50% of all mutations) and the highly conserved DNA-contact residue R219 (around 5% of all mutations). Wing2 mutations are detected in adenocarcinomas at all stages, whereas R219 mutations are enriched in metastatic tumours with neuroendocrine histology. Interrogation of the biological properties of wild-type FOXA1 and fourteen FOXA1 mutants reveals gain of function in mouse prostate organoid proliferation assays. Twelve of these mutants, as well as wild-type FOXA1, promoted an exaggerated pro-luminal differentiation program, whereas two different R219 mutants blocked luminal differentiation and activated a mesenchymal and neuroendocrine transcriptional program. Assay for transposase-accessible chromatin using sequencing (ATAC-seq) of wild-type FOXA1 and representative Wing2 and R219 mutants revealed marked, mutant-specific changes in open chromatin at thousands of genomic loci and exposed sites of FOXA1 binding and associated increases in gene expression. Of note, ATAC-seq peaks in cells expressing R219 mutants lacked the canonical core FOXA1-binding motifs (GTAAAC/T) but were enriched for a related, non-canonical motif (GTAAAG/A), which was preferentially activated by R219-mutant FOXA1 in reporter assays. Thus, FOXA1 mutations alter its pioneering function and perturb normal luminal epithelial differentiation programs, providing further support for the role of lineage plasticity in cancer progression.

Quantification and molecular correlates of tertiary lymphoid structures in primary prostate cancer.

OBJECTIVE: To morphologically describe tertiary lymphoid structures (TLS) in prostatectomy specimens and correlate them with clinical and transcriptomic features. METHODOLOGY: A total of 72 consecutive cases of entirely submitted radical prostatectomy (RP) patients tested with the Decipher Genomic Classifier were included in the study. Images were manually annotated using QuPath tools to denote tumor regions and each cluster of TLS. Clusters of lymphocytes that were surrounded on all four sides by tumor were defined as intra-tumor TLS (IT-TLS). Clusters of lymphocytes at the leading edge of carcinoma with either the prostatic pseudocapsule or benign parenchyma at one end were defined as peri-tumor TLS (PT-TLS). A classification algorithm to distinguish lymphocytes from non-lymphocytic cells using a supervised machine learning model was used. The associations between TLS formation and 265 gene expression-based signatures were examined. RESULTS: The magnitude of total TLS correlations with primary tumor gene expression signatures was moderate (~0.35-0.5) with several HLA, T-cell and B-cell Cluster signatures, showing positive correlation with various metrics for quantification of TLS. On the other hand, immune suppressive signatures (Treg, MDSC) were negatively correlated. While signatures for macrophages, NK cells and other immune cell types were uncorrelated for the most part. PT-TLS was associated with MHC signatures while IT TLS correlated with MHC and T-cell signatures. CONCLUSIONS: Clusters of inflammatory cells in the RP specimen can be divided spatially into PT TLS and IT-TLS, each with its unique molecular correlates of tumor immune microenvironment. The presence of TLS is positively correlated with MHC signatures, T- cell and B-cell cluster signatures but, negatively correlated with immune suppressive signatures. A subset of prostate cancer demonstrate a robust inflammatory response, and warrant further characterization in larger cohorts.

F-SHARP: a Phase I/II trial of focal salvage high-dose-rate brachytherapy for Radiorecurrent prostate cancer.

BACKGROUND: Intraprostatic local radiorecurrence (LRR) after definitive radiation is being increasingly identified due to the implementation of molecular positron emission tomography (PET)/computed tomography (CT) imaging for the evaluation of biochemical recurrence. Salvage high-dose rate (HDR) brachytherapy offers a promising local therapy option, with encouraging toxicity and efficacy based on early series. Furthermore, the incorporation of advanced imaging allows for focal HDR to further reduce toxicity to maximise the therapeutic ratio. The objectives of the 'focal salvage HDR brachytherapy for locally recurrent prostate cancer in patients treated with prior radiotherapy' (F-SHARP) trial are to determine the acute and late toxicity and efficacy outcomes of focal salvage HDR brachytherapy for LRR prostate cancer. STUDY DESIGN: The F-SHARP is a multi-institutional two-stage Phase I/II clinical trial of salvage focal HDR brachytherapy for LRR prostate cancer enrolling patients at three centres. ENDPOINTS: The primary endpoint is the acute radiation-related Grade ≥3 Common Terminology Criteria for Adverse Events (CTCAE, version 4.03) genitourinary (GU) and gastrointestinal (GI) toxicity rate, defined as within 3 months of brachytherapy. Secondary endpoints include acute and late CTCAE toxicity, biochemical failure, patterns of clinical progression, disease-specific and overall survival, and health-related quality of life, as measured by the International Prostate Symptom Score and 26-item Expanded Prostate Cancer Index Composite instruments. PATIENTS AND METHODS: Key eligibility criteria include: biopsy confirmed LRR prostate adenocarcinoma after prior definitive radiation therapy using any radiotherapeutic modality, no evidence of regional or distant metastasis, and cT1-3a Nx or N0 prostate cancer at initial treatment. All patients will have multiparametric magnetic resonance imaging and molecular PET/CT imaging if possible. In Stage 1, seven patients will be accrued. If there are two or more GI or GU Grade ≥3 toxicities, the study will be stopped. Otherwise, 17 additional patients will be accrued (total of 24 patients). For Stage 2, the cohort will expand to 62 subjects to study the efficacy outcomes, long-term toxicity profile, quality of life, and compare single- vs multi-fraction HDR. Transcriptomic analysis of recurrence biopsies will be performed to identify potential prognostic and predictive biomarkers.

A novel prostate cancer subtyping classifier based on luminal and basal phenotypes.

BACKGROUND: Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought. METHODS: Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts. RESULTS: Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01-0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09-0.51). CONCLUSIONS: With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features. PLAIN LANGUAGE SUMMARY: Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors-the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.

Pathological Effects of Apalutamide in Lower-risk Prostate Cancer: Results From a Phase II Clinical Trial.

PURPOSE: Active surveillance is a safe and effective strategy for men with lower-risk prostate cancer who want to avoid local therapy; however, many patients on active surveillance progress to active treatment (eg, prostatectomy or radiation). We hypothesized that apalutamide would decrease active surveillance attrition rates through downstaging low-grade tumors. MATERIALS AND METHODS: This was an open-label, single-arm, phase II study testing 90 days of oral apalutamide 240 mg daily in men with low- to intermediate-risk prostate cancer on active surveillance. The primary objective was to determine the percentage of patients with a negative biopsy immediately following treatment. Secondary objectives were to assess long-term clinical outcomes, quality of life, safety, and biomarkers of response/resistance. RESULTS: Twenty-three patients enrolled and 22 completed 90 days of apalutamide with post-treatment biopsy. Fifteen (65%) had Grade Group 1 disease, and all others had Grade Group 2 disease. Seven (30%) had favorable- to intermediate-risk disease. Of 22 evaluable patients, 13 (59%) had no residual cancer on post-treatment biopsy. The median time to first positive biopsy was 364 days (95% CI: 91-742 days). The impact of apalutamide on quality of life was minimal and transient. Decipher risk classifier revealed a greater number of negative post-treatment biopsies in those with higher baseline genomic risk score (P = .01). CONCLUSIONS: The negative repeat biopsy rate following 90 days of apalutamide was high in men with prostate cancer followed on active surveillance. Apalutamide was safe, well tolerated, and had minimal impact on quality of life. Randomized studies evaluating the effects of apalutamide in men enrolled on active surveillance are warranted.

Genomic biomarkers to guide precision radiotherapy in prostate cancer.

Our ability to prognosticate the clinical course of patients with cancer has historically been limited to clinical, histopathological, and radiographic features. It has long been clear however, that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. The advent of efficient genomic sequencing has led to a revolution in cancer care as we try to understand and personalize treatment specific to patient clinico-genomic phenotypes. Within prostate cancer, emerging evidence suggests that tumor genomics (e.g., DNA, RNA, and epigenetics) can be utilized to inform clinical decision making. In addition to providing discriminatory information about prognosis, it is likely tumor genomics also hold a key in predicting response to oncologic therapies which could be used to further tailor treatment recommendations. Herein we review select literature surrounding the use of tumor genomics within the management of prostate cancer, specifically leaning toward analytically validated and clinically tested genomic biomarkers utilized in radiotherapy and/or adjunctive therapies given with radiotherapy.

Does Perioperative Testosterone Predict Post-Prostatectomy Genomic Risk Score?

PURPOSE: The role of endogenous testosterone in de novo prostate cancer pathogenesis in humans remains unclear. The effect of testosterone on the tumor genome is not explored. We sought to explore the correlation between perioperative testosterone level and genomic risk score in a cohort of men who underwent radical prostatectomy. MATERIALS AND METHODS: We included patients who underwent radical prostatectomy (2013-2018) and had adverse pathological features in their final surgical specimens (positive margin, and/or pT3a or higher). The outcome of interest was the genomic risk score: low (<0.45), intermediate (0.45-0.6) and high (>0.6). The associations between serum testosterone level and 188 gene expression-based signatures were examined. Secondary outcomes of interest included biochemical recurrence and receipt of secondary treatment. RESULTS: The median genomic risk score was lower in the low testosterone group compared to the intermediate and normal testosterone groups (0.38 vs 0.52 vs 0.53, respectively; p=0.049). There was no difference in biochemical recurrence-free survival between the 3 testosterone groups (p=0.9). Patients with low testosterone levels had higher odds of receiving secondary treatment (OR: 2.27; 95% CI: 1.14-4.50; p=0.02) than those with normal levels. A total of 43 (of 188) gene expression signatures were associated with testosterone level (p <0.05). In total, 33 signatures were positively associated with serum testosterone levels, including 12 signatures involved in DNA repair pathways. CONCLUSIONS: This is the first study to assess the correlation of preoperative testosterone level on the tumor transcriptome and showed no clinical correlation between pre-defined genomic risk score groups and testosterone groups. This study adds to the notion of the limited role of endogenous testosterone on the development of de novo high-risk localized prostate cancer.

Patients with Muscle-Invasive Bladder Cancer with Nonluminal Subtype Derive Greatest Benefit from Platinum Based Neoadjuvant Chemotherapy.

PURPOSE: Neoadjuvant chemotherapy (NAC) prior to radical cystectomy (RC) in patients with nonmetastatic muscle-invasive bladder cancer (MIBC) confers an absolute survival benefit of 5%-10%. There is evidence that molecular differences between tumors may impact response to therapy, highlighting a need for clinically validated biomarkers to predict response to NAC. MATERIALS AND METHODS: Four bladder cancer cohorts were included. Inverse probability weighting was used to make baseline characteristics (age, sex and clinical tumor stage) between NAC-treated and untreated groups more comparable. Molecular subtypes were determined using a commercial genomic subtyping classifier. Survival rates were estimated using weighted Kaplan-Meier curves. Cox proportional hazards models were used to evaluate the primary and secondary study end points of overall survival (OS) and cancer-specific survival, respectively. RESULTS: A total of 601 patients with MIBC were included, of whom 247 had been treated with NAC and RC, and 354 underwent RC without NAC. With NAC, the overall net benefit to OS and cancer-specific survival at 3 years was 7% and 5%, respectively. After controlling for clinicopathological variables, nonluminal tumors had greatest benefit from NAC, with 10% greater OS at 3 years (71% vs 61%), while luminal tumors had minimal benefit (63% vs 65%) for NAC vs non-NAC. CONCLUSIONS: In patients with MIBC, a commercially available molecular subtyping assay revealed nonluminal tumors received the greatest benefit from NAC, while patients with luminal tumors experienced a minimal survival benefit. A genomic classifier may help identify patients with MIBC who would benefit most from NAC.

Heterogeneity in Genomic Risk Assessment from Tissue Based Prognostic Signatures Used in the Biopsy Setting and the Impact of Magnetic Resonance Imaging Targeted Biopsy.

PURPOSE: Genomic prognostic signatures are used on prostate biopsy tissue for cancer risk assessment, but tumor heterogeneity and multifocality may be an issue. We evaluated the variability in genomic risk assessment from different biopsy cores within the prostate using 3 prognostic signatures (Decipher, CCP, GPS). MATERIALS AND METHODS: Men in this study came from 2 prospective prostate cancer trials of patients undergoing multiparametric magnetic resonance imaging and magnetic resonance imaging targeted biopsy with genomic profiling of positive biopsy cores. We explored the relationship among tumor grade, magnetic resonance imaging risk and genomic risk for each signature. We evaluated the variability in genomic risk assessment between different biopsy cores and assessed how often magnetic resonance imaging targeted biopsy or the current standard of care (profiling the core with the highest grade) resulted in the highest genomic risk level. RESULTS: In all, 224 positive biopsy cores from 78 men with prostate cancer were profiled. For each signature, higher biopsy grade (p <0.001) and magnetic resonance imaging risk level (p <0.001) were associated with higher genomic scores. Genomic scores from different biopsy cores varied with risk categories changing by 21% to 62% depending on which core or signature was used. Magnetic resonance imaging targeted biopsy and profiling the core with the highest grade resulted in the highest genomic risk level in 72% to 84% and 75% to 87% of cases, respectively, depending on the signature used. CONCLUSIONS: There is variation in genomic risk assessment from different biopsy cores regardless of the signature used. Magnetic resonance imaging directed biopsy or profiling the highest grade core resulted in the highest genomic risk level in most cases.

Identifying and treating ROBO1-ve /DOCK1+ve prostate cancer: An aggressive cancer subtype prevalent in African American patients.

BACKGROUND: There is a need to develop novel therapies which could be beneficial to patients with prostate cancer (CaP) including those who are predisposed to poor outcome, such as African-Americans. This study investigates the role of ROBO1-pathway in predicting outcome and race-based disparity in patients with CaP. METHODS AND RESULTS: Aided by RNA sequencing-based DECIPHER-testing and immunohistochemical (IHC) analysis of tumors we show that ROBO1 is lost during the progressive stages of CaP, a prevalent feature in African-Americans. We show that the loss of ROBO1 predicts high-risk of recurrence, metastasis and poor outcome of androgen-deprivation therapy in radical prostatectomy-treated patients. These data identified an aggressive ROBO1deficient /DOCK1+ve sub-class of CaP. Combined genetic and IHC data showed that ROBO1 loss is accompanied by DOCK1/Rac1 elevation in grade-III/IV primary-tumors and Mets. We observed that the hypermethylation of ROBO1-promoter contributes to loss of expression that is highly prevalent in African-Americans. Because of limitations in restoring ROBO1 function, we asked if targeting the DOCK1 could be an ideal strategy to inhibit progression or treat ROBO1deficient metastatic-CaP. We tested the pharmacological efficacy of CPYPP, a selective inhibitor of DOCK1 under in vitro and in vivo conditions. Using ROBO1-ve and ROBO1+ve CaP models, we determined the median effective concentration of CPYPP for growth. DOCK1-inhibitor treatment significantly decreased the (a) Rac1-GTP/ß-catenin activity, (b) transmigration of ROBO1deficient cells across endothelial lining, and (c) metastatic spread of ROBO1deficient cells through the vasculature of transgenicfl Zebrafish model. CONCLUSION: We suggest that ROBO1 status forms as predictive biomarker of outcome in high-risk populations such as African-Americans and DOCK1-targeting therapy has a clinical potential for treating metastatic-CaP.

Genomic and clinical characterization of stromal infiltration markers in prostate cancer.

BACKGROUND: The progression of prostate cancer is a complex, multistep process that involves molecular alterations in cells of the tumor and the microenvironment, with associated interactions between the stroma and epithelium. Genomic expression analyses of stromal infiltration markers were performed to determine the significance thereof in prostate cancer. METHODS: Genome-wide expression profiles of formalin-fixed, paraffin-embedded radical prostatectomy samples were evaluated from a prospective registry cohort (n = 5239) and 3 retrospective institutional cohorts (n = 1135). Two independent stromal gene expression signatures implied stromal infiltration. Cox proportional hazards regression defined the association between stromal infiltration expression and metastasis-free survival (MFS). RESULTS: Stromal expression scores were correlated with stromal signature genes and with other key stromal markers (CAV1, VIM, and TAGLN), basal activity, and CD3 and CD4 immune biomarkers (r > 0.5 for all). The top decile of stromal expression was associated with high genomic risk scores (Decipher ≥ 0.6) , high Cancer of the Prostate Risk Assessment-Postsurgical scores, Gleason 9 to 10 disease, and a higher risk for metastasis (hazard ratio, 2.35; 95% CI, 1.37-4.02; P = .001). A higher stromal infiltration score was also associated with decreased expression of DNA repair genes and higher radiation sensitivity genomic scores. Postoperative radiation therapy (RT) was associated with an MFS benefit for patients with high stromal scores, but not for patients with low stromal scores (Pinteraction  = .02). CONCLUSIONS: Expression of stromal infiltration markers is correlated with prostate cancer aggressiveness/progression and may be predictive of a response to RT. Stromal infiltration markers should be studied and considered for incorporation into clinical prognostication and decision making.

A Genomic Classifier for Predicting Clinically Aggressive Luminal Bladder Tumors with Higher Rates of Pathological Up Staging.

PURPOSE: Urothelial carcinoma of the luminal molecular subtype is associated with lower rates of pathological up staging from clinical stage T1-T2 to nonorgan confined (pT3 or greater and/or pN+) disease at radical cystectomy. However, approximately a third of luminal urothelial carcinoma cases were up staged to nonorgan confined disease, and these may be under treated if neoadjuvant chemotherapy is withheld. In this study we trained a genomic classifier to predict luminal nonorgan confined disease in patients diagnosed with clinically organ confined (cT1/T2) disease. MATERIALS AND METHODS: Specimens from transurethral resected high grade cT1-T2N0M0 urothelial carcinoma of the bladder that belonged to the luminal subtype (Seiler 2017) were randomly split into training (75) and testing (25) sets for the development of a single sample luminal up staging classifier using lasso/ridge-penalized logistic regression. All patients underwent radical cystectomy without neoadjuvant chemotherapy and the primary end point was up staging to nonorgan confined disease. A radical cystectomy cohort and a platinum treated neoadjuvant chemotherapy cohort were used to evaluate the luminal up staging classifier. RESULTS: Up staging to nonorgan confined disease occurred in 34% of luminal cases. The luminal up staging classifier predicted up staging in 32 of 34 cases, with 6 false-positives (AUC 0.96). The sensitivity for detection of luminal pN+ disease was 95% (20 of 21). Patients with predicted nonorgan confined luminal tumors had worse survival than those with organ confined luminal tumors (p=0.001). On multivariable analysis the luminal up staging classifier was a significant predictor of overall survival after adjusting for clinical variables available at transurethral resection. The luminal up staging classifier also predicted overall survival for aggressive luminal TCGA (The Cancer Genome Atlas) cases (n=83, p=0.043). In the neoadjuvant chemotherapy cohort the luminal up staging classifier predicted 9 up staging cases, all of which had excellent prognosis. CONCLUSIONS: A luminal up staging classifier was developed that distinguishes a subset of cT1-T2N0M0 luminal urothelial carcinoma cases at high risk for up staging to nonorgan confined disease at radical cystectomy and of death. Validation of this model in an independent, large patient cohort is necessary to determine how molecular stratification of luminal tumors could be used to guide treatment of these patients.

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance.

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate-selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal-specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal-specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R2 of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer.

BACKGROUND: Prostate cancer (PCa) is the most common malignant neoplasm among men in many countries. Since most precancerous and cancerous tissues show signs of inflammation, chronic bacterial prostatitis has been hypothesized to be a possible etiology. However, establishing a causal relationship between microbial inflammation and PCa requires a comprehensive analysis of the prostate microbiome. The aim of this study was to characterize the microbiome in prostate tissue of PCa patients and investigate its association with tumour clinical characteristics as well as host expression profiles. RESULTS: The metagenome and metatranscriptome of tumour and the adjacent benign tissues were assessed in 65 Chinese radical prostatectomy specimens. Escherichia, Propionibacterium, Acinetobacter and Pseudomonas were abundant in both metagenome and metatranscriptome, thus constituting the core of the prostate microbiome. The biodiversity of the microbiomes could not be differentiated between the matched tumour/benign specimens or between the tumour specimens of low and high Gleason Scores. The expression profile of ten Pseudomonas genes was strongly correlated with that of eight host small RNA genes; three of the RNA genes may negatively associate with metastasis. Few viruses could be identified from the prostate microbiomes. CONCLUSIONS: This is the first study of the human prostate microbiome employing an integrated metagenomics and metatranscriptomics approach. In this Chinese cohort, both metagenome and metatranscriptome analyses showed a non-sterile microenvironment in the prostate of PCa patients, but we did not find links between the microbiome and local progression of PCa. However, the correlated expression of Pseudomonas genes and human small RNA genes may provide tantalizing preliminary evidence that Pseudomonas infection may impede metastasis.

Characterization of transcriptomic signature of primary prostate cancer analogous to prostatic small cell neuroendocrine carcinoma.

Prostatic small cell neuroendocrine carcinoma (SC/NE) is well studied in metastatic castration-resistant prostate cancer; however, it is not well characterized in the primary setting. Herein, we used gene expression profiling of SC/NE prostate cancer (PCa) to develop a 212 gene signature to identify treatment-naïve primary prostatic tumors that are molecularly analogous to SC/NE (SC/NE-like PCa). The 212 gene signature was tested in several cohorts confirming similar molecular profile between prostatic SC/NE and small cell lung carcinoma. The signature was then translated into a genomic score (SCGScore) using modularized logistic regression modeling and validated in four independent cohorts achieving an average AUC >0.95. The signature was evaluated in more than 25,000 primary adenocarcinomas to characterize the biology, prognosis and potential therapeutic response of predicted SC/NE-like tumors. Assessing SCGScore in a prospective cohort of 17,967 RP and 6,697 biopsy treatment-naïve primary tumors from the Decipher Genomic Resource Information Database registry, approximately 1% of the patients were found to have a SC/NE-like transcriptional profile, whereas 0.5 and 3% of GG1 and GG5 patients respectively showed to be SC/NE-like. More than 80% of these patients are genomically high-risk based on Decipher score. Interrogating in vitro drug sensitivity analyses, SC/NE-like prostatic tumors showed higher response to PARP and HDAC inhibitors.

A four-gene transcript score to predict metastatic-lethal progression in men treated for localized prostate cancer: Development and validation studies.

BACKGROUND: Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed. METHODS: Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance. RESULTS: Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10-11 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery). CONCLUSION: Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.

Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness.

PURPOSE: We sought to 1) assess the association of radiomics features based on multiparametric magnetic resonance imaging with histopathological Gleason score, gene signatures and gene expression levels in prostate cancer and 2) build machine learning models based on radiomics features to predict adverse histopathological scores and the Decipher® genomics metastasis risk score. MATERIALS AND METHODS: We retrospectively analyzed the records of 64 patients with prostate cancer with a mean age of 64 years (range 41 to 76) who underwent magnetic resonance imaging between January 2016 and January 2017 before radical prostatectomy. A total of 226 magnetic resonance imaging radiomics features, including histogram and texture features in addition to lesion size and the PI-RADS™ (Prostate Imaging Reporting and Data System) score, were extracted from T2-weighted, apparent diffusion coefficient and diffusion kurtosis imaging maps. Radiomics features were correlated with the pathological Gleason score, 40 gene expression signatures, including Decipher, and 698 prostate cancer related gene expression levels. Cross-validated, lasso regularized, logistic regression machine learning models based on radiomics features were built and evaluated for the prediction of Gleason score 8 or greater and Decipher score 0.6 or greater. RESULTS: A total of 14 radiomics features significantly correlated with the Gleason score (highest correlation r = 0.39, p = 0.001). A total of 31 texture and histogram features significantly correlated with 19 gene signatures, particularly with the PORTOS (Post-Operative Radiation Therapy Outcomes Score) signature (strongest correlation r = -0.481, p = 0.002). A total of 40 diffusion-weighted imaging features correlated significantly with 132 gene expression levels. Machine learning prediction models showed fair performance to predict a Gleason score of 8 or greater (AUC 0.72) and excellent performance to predict a Decipher score of 0.6 or greater (AUC 0.84). CONCLUSIONS: Magnetic resonance imaging radiomics features are promising markers of prostate cancer aggressiveness on the histopathological and genomics levels.

African American Specific Gene Panel Predictive of Poor Prostate Cancer Outcome.

PURPOSE: Most prostate cancer in African American men lacks the ETS (E26 transforming specific) family fusion event (ETS-). We aimed to establish clinically relevant biomarkers in African American men by studying ETS dependent gene expression patterns to identified race specific genes predictive of outcomes. MATERIALS AND METHODS: Two multicenter cohorts of a total of 1,427 men were used for the discovery and validation (635 and 792 men, respectively) of race specific predictive biomarkers. We used false discovery rate adjusted q values to identify race and ETS dependent genes which were differentially expressed in African American men who experienced biochemical recurrence within 5 years. Principal component modeling along with survival analysis was done to assess the accuracy of the gene panel in predicting recurrence. RESULTS: We identified 3,047 genes which were differentially expressed based on ETS status. Of these genes 362 were differentially expressed in a race specific manner (false discovery rate 0.025 or less). A total of 81 genes were race specific and over expressed in African American men who experienced biochemical recurrence. The final gene panel included APOD, BCL6, EMP1, MYADM, SRGN and TIMP3. These genes were associated with 5-year biochemical recurrence (HR 1.97, 95% CI 1.27-3.06, p = 0.002) and they improved the predictive accuracy of clinicopathological variables only in African American men (60-month time dependent AUC 0.72). CONCLUSIONS: In an effort to elucidate biological features associated with prostate cancer aggressiveness in African American men we identified ETS dependent biomarkers predicting early onset biochemical recurrence only in African American men. Thus, these ETS dependent biomarkers representing ideal candidates for biomarkers of aggressive disease in this patient population.

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.

OBJECTIVES: We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases). METHODS: This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions. RESULTS: MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018). CONCLUSIONS: MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI. KEY POINTS: • MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.