Contemporary Diagnosis of Very Low-risk Prostate Cancer in a Multihospital Health Care System.

The National Comprehensive Cancer Network (NCCN) very low risk (VLR) category for prostate cancer (PCa) represents clinically insignificant disease, and detection of VLR PCa contributes to overdiagnosis. Greater use of magnetic resonance imaging (MRI) and biomarkers before patient selection for prostate biopsy (PBx) reduces unnecessary biopsies and may reduce the diagnosis of clinically insignificant PCa. We tested a hypothesis that the proportion of VLR diagnoses has decreased with greater use of MRI-informed PBx using data from our 11-hospital system. From 2018 to 2023, 351/3197 (11%) men diagnosed with PCa met the NCCN VLR criteria. The proportion of VLR diagnoses did not change from 2018 to 2023 (p = 0.8) despite an increase in the use of MRI-informed PBx (from 49% to 82%; p < 0.001). Of patients who underwent combined systematic and targeted PBx and were diagnosed with VLR disease, cancer was found in systematic PBx regions in 79% of cases and in targeted PBx regions in 31% of cases. When performing both systematic and targeted PBx, prebiopsy MRI-based risk calculators could limit VLR diagnosis by 41% using a risk threshold of >5% for Gleason grade group ≥3 PCa to recommend biopsy; the reduction would be 77% if performing targeted PBx only. These findings suggest that VLR disease continues to account for a significant minority of PCa diagnoses and could be limited by targeted PBx and risk stratification calculators. PATIENT SUMMARY: We looked at recent trends for the diagnosis of very low-risk (VLR) prostate cancer. We found that VLR cancer still seems to be frequently diagnosed despite the use of MRI (magnetic resonance imaging) scans before biopsy. The use of risk calculators to identify men who could avoid biopsy and/or biopsy only for lesions that are visible on MRI could reduce the overdiagnosis of VLR prostate cancer.

Transcriptome-Based Prognostic and Predictive Biomarker Analysis of ENACT: A Randomized Controlled Trial of Enzalutamide in Men Undergoing Active Surveillance.

PURPOSE: Few studies have explored the potential for pharmacological interventions to delay disease progression in patients undergoing active surveillance (AS). This preplanned transcriptomic analysis of patient samples from the ENACT trial aims to identify biomarkers in patients on AS who are at increased risk for disease progression or who may derive the greatest benefit from enzalutamide treatment. PATIENTS AND METHODS: In the phase II ENACT (ClinicalTrials.gov identifier: NCT02799745) trial, patients on AS were randomly assigned 1:1 to 160 mg orally once daily enzalutamide monotherapy or continued AS for 1 year. Transcriptional analyses were conducted on biopsies collected at trial screening, year 1, and year 2. Three gene expression signatures were evaluated in samples collected at screening and in available samples from patients on AS at any time during surveillance (expanded cohort): Decipher genomic classifier, androgen receptor activity (AR-A) score, and Prediction Analysis of Microarray 50 (PAM50) cell subtype signature. RESULTS: The Decipher genomic classifier score was prognostic; higher scores were associated with disease progression in the expanded cohort and AS arm of the expanded cohort. Patients with higher Decipher scores had greater positive treatment effect from enzalutamide as measured by time to secondary rise in prostate-specific antigen >25% above baseline. In patients treated with enzalutamide, higher AR-A scores and PAM50 luminal subtypes were associated with a greater likelihood of negative biopsy incidence at year 2. CONCLUSION: This analysis suggests that the Decipher genomic classifier may be prognostic for disease progression in AS patients with low- to intermediate-risk prostate cancer. Higher Decipher and AR-A scores, as well as PAM50 luminal subtypes, may also serve as biomarkers for treatment response.

Prostate Cancer, Version 3.2024.

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk assessment after a prostate cancer diagnosis and for the care of patients with localized, regional, recurrent, and metastatic disease. These NCCN Guidelines Insights summarize the panel's discussions for the 2024 update to the guidelines with regard to initial risk stratification, initial management of very-low-risk disease, and the treatment of nonmetastatic recurrence.

Ultrasensitive PSA: rethinking post-surgical management for node positive prostate cancer.

Introduction: Clinicians may offer patients with positive lymph nodes (pN1) and undetectable PSA following surgery for prostate cancer either observation or adjuvant therapy based on AUA, EAU, and NCCN guidelines considering standard PSA detection thresholds of <0.1ng/ml. Here we sought to investigate the outcomes of pN1 patients in the era of ultrasensitive PSA testing. Methods: We queried the Northwestern Electronic Data Warehouse for patients with prostate cancer who were pN1 at radical prostatectomy and followed with ultrasensitive PSA. Patients receiving neoadjuvant treatment were excluded. We compared clinical characteristics including age, race, pre-operative PSA, Gleason grade, tumor stage, surgical margins, and nodal specimens to identify factors associated with achievement and maintenance of an undetectable PSA (defined as <0.01 ng/mL). Statistics were performed using t-test, Mann-Whitney U test, chi-squared analysis, and logistic regression with significance defined as p<0.05. Results: From 2018-2023, 188 patients were included. Subsequently, 39 (20.7%) had a PSA decline to undetectable levels (<0.01 ng/mL) post-operatively at a median time of 63 days. Seven percent of these men (3/39) were treated with adjuvant RT + ADT with undetectable PSA levels. 13/39 (33.3%) had eventual rises in PSA to ≥0.01 ng/mL for which they underwent salvage RT with ADT. Overall, 23/39 (59%) patients achieved and maintained undetectable PSA levels without subsequent therapy at median follow-up of 24.2 mo. Compared to patients with PSA persistence after surgery or elevations to detectable levels (≥0.01 ng/mL), patients who achieved and maintained undetectable levels had lower Gleason grades (p=0.03), lower tumor stage (p<0.001), fewer positive margins (p=0.02), and fewer involved lymph nodes (p=0.02). On multivariable analysis, only primary tumor (pT) stage was associated with achieving and maintaining an undetectable PSA; pT3b disease was associated with a 6.6-fold increased chance of developing a detectable PSA (p=0.03). Conclusion: Ultrasensitive PSA can aid initiation of early salvage therapy for lymph node positive patients after radical prostatectomy while avoiding overtreatment in a significant subset. 20% of patients achieved an undetectable PSA and over half of this subset remained undetectable after 2 years.

Prostate Cancer Detection Rate of Transperineal Prostate Biopsy: Cognitive vs Software Fusion, A Multicenter Analysis.

OBJECTIVE: To compare clinically significant prostate cancer detection with TP-TBx utilizing software vs cognitive fusion. It is established that MRI prior to prostate biopsy improves detection of clinically significant cancer (csPCa, Grade Group ≥2). MRI/US fusion targeted biopsy via a transperineal approach (TP-TBx) is increasing in utilization due to the clean percutaneous approach that greatly reduces postbiopsy infection. However, the comparative effectiveness of formal software fusion over cognitive fusion remains under studied. MATERIALS AND METHODS: We performed a retrospective multicenter study from June 2020 to July 2022 including age, race, prostate-specific antigen (PSA), prostate volume, PI-RADS, lesion size(s), number of cores sampled, indication (elevated PSA, prior negative, active surveillance) and anesthesia type. Surgeon preference determined use of cognitive (PrecisionPoint) vs software fusion techniques. Multivariable logistic regression determined factors associated with TP-TBx detection of csPCa. RESULTS: We identified 490 patients (201 cognitive, 289 software fusion) who underwent TP-TBx. Patient age, PSA, number of targets, and PI-RADS were similar (all P > .05). Software fusion TP-TBx had 4 [95% confidence interval (CI) 3-5] more (estimated median difference) systematic cores sampled. csPCa was detected in 44% of all patients. In adjusted analysis, cognitive vs software fusion was similar in detection of csPCa (odds ratio 1.46, 95% CI 0.82-2.58). CONCLUSION: Cognitive vs software fusion TP-TBx has similar csPCa detection, despite fewer systematic cores taken with cognitive fusion. The expense, additional time requirement, and similar outcomes of software fusion platforms confers higher value to cognitive TP-Bx.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.

BACKGROUND: Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial. OBJECTIVE: To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial. DESIGN, SETTING, AND PARTICIPANTS: Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models. RESULTS AND LIMITATIONS: The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm. CONCLUSIONS: We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care. PATIENT SUMMARY: This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.

The association between frailty, hypogonadism, and postoperative outcomes among men undergoing radical cystectomy.

INTRODUCTION: Hypogonadism is associated with frailty, lower health-related quality of life, decreased muscle mass, and premature mortality, which may predispose patients to poor postoperative outcomes. We aimed to determine the prevalence of hypogonadism in men undergoing radical cystectomy (RC) and whether hypogonadism and frailty are associated with adverse postoperative outcomes. MATERIALS AND METHODS: The IBM MarketScan database was used to identify men who underwent RC between 2012 and 2021. Frailty was determined using published Hospital Frailty Risk Score ranges. Patients were considered to have hypogonadism if diagnosed within 5 years prior to RC. Length of stay (LOS), complications, emergency department (ED) visits and inpatient readmissions were compared. Sub-group analysis of men with hypogonadism was performed to determine the effect of testosterone replacement therapy (TRT) on clinical outcomes. RESULTS: Among 3,727 men who underwent RC, 226 (6.1%) had a diagnosis of hypogonadism. Overall, 565 (15.2%) men were low-risk frailty, 2,214 (59.4%) intermediate-risk frailty, and 948 (25.4%) were high-risk frailty, and men with hypogonadism were significantly more frail compared to men without hypogonadism (P = 0.027). There was no significant difference in LOS, complications, or rate of ED visits and inpatient readmissions between cohorts (P > 0.05). However, high-risk frailty was associated with an increased risk of 90-day ED visit (HR 1.19, 95%CI 1.00-1.41, P = 0.049) and 90-day readmission (HR 1.60, 95%CI 1.29-1.97, P < 0.001) after RC. Among men with hypogonadism, 58 (25.7%) were on TRT. There was no significant difference in frailty, LOS, complications, or 90-day ED visits or 90-day inpatient readmissions between patient with hypogonadism prescribed TRT and those without TRT. CONCLUSIONS: These findings suggest that hypogonadism and preoperative frailty may be important to evaluate prior to undergoing RC.

Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

IMPLICATIONS: Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Transperineal Versus Transrectal Magnetic Resonance Imaging-targeted and Systematic Prostate Biopsy to Prevent Infectious Complications: The PREVENT Randomized Trial.

BACKGROUND AND OBJECTIVE: The transrectal biopsy approach is traditionally used to detect prostate cancer. An alternative transperineal approach is historically performed under general anesthesia, but recent advances enable transperineal biopsy to be performed under local anesthesia. We sought to compare infectious complications of transperineal biopsy without antibiotic prophylaxis versus transrectal biopsy with targeted prophylaxis. METHODS: We assigned biopsy-naïve participants to undergo transperineal biopsy without antibiotic prophylaxis versus transrectal biopsy with targeted prophylaxis (rectal culture screening for fluoroquinolone-resistant bacteria and antibiotic targeting to culture and sensitivity results) through a multicenter, randomized trial. The primary outcome was post-biopsy infection captured by a prospective medical review and patient report on a 7-d survey. The secondary outcomes included cancer detection, noninfectious complications, and a numerical rating scale (0-10) for biopsy-related pain and discomfort during and 7-d after biopsy. KEY FINDINGS AND LIMITATIONS: A total of 658 participants were randomized, with zero transperineal versus four (1.4%) transrectal biopsy infections (difference -1.4%; 95% confidence interval [CI] -3.2%, 0.3%; p = 0.059). The rates of other complications were very low and similar. Importantly, detection of clinically significant cancer was similar (53% transperineal vs 50% transrectal, adjusted difference 2.0%; 95% CI -6.0, 10). Participants in the transperineal arm experienced worse periprocedural pain (0.6 adjusted difference [0-10 scale], 95% CI 0.2, 0.9), but the effect was small and resolved by 7-d. CONCLUSIONS AND CLINICAL IMPLICATIONS: Office-based transperineal biopsy is tolerable, does not compromise cancer detection, and did not result in infectious complications. Transrectal biopsy with targeted prophylaxis achieved similar infection rates, but requires rectal cultures and careful attention to antibiotic selection and administration. Consideration of these factors and antibiotic stewardship should guide clinical decision-making. PATIENT SUMMARY: In this multicenter randomized trial, we compare prostate biopsy infectious complications for the transperineal versus transrectal approach. The absence of infectious complications with transperineal biopsy without the use of preventative antibiotics is noteworthy, but not significantly different from transrectal biopsy with targeted antibiotic prophylaxis.

Development and Validation of a Prostate Biopsy Risk Calculator in Black Men.

PURPOSE: We sought to develop and validate a prostate biopsy risk calculator for Black men and compare it with the Prostate Cancer Prevention Trial version 2.0, Prostate Biopsy Collaborative Group, and Kaiser Permanente Prostate Cancer Risk Calculators for the detection of Gleason Grade Group (GG) ≥ 2 prostate cancer (PCa). MATERIALS AND METHODS: We prospectively recruited 2 cohorts of men undergoing prostate biopsy from 5 facilities in Chicago. The first cohort was split into development (70%) and internal validation (30%) groups. The second was used for external validation. Iterative logistic regression was used to develop 3 models for predicting GG ≥ 2 PCa. Models were compared for discrimination using the C statistics, calibration curves, and net benefit curves. The frequency of unnecessary biopsies and missed PCas was compared at 10% and 30% risk thresholds. RESULTS: The 2 cohorts included 393 and 292 Black men, respectively. Our first model, Mistry-Sun 1, used serum PSA and prior negative biopsy. Mistry-Sun 2 added abnormal digital rectal exam (DRE) and an interaction term with abnormal DRE and PSA to Mistry-Sun 1. Mistry-Sun 3 added prostate volume, abnormal DRE, and age to Mistry-Sun 1. The C statistics were 0.74, 0.74, and 0.78, respectively, and were similar to or higher than established calculators. At the 10% and 30% risk thresholds our models had the fewest unnecessary biopsies and an appropriate proportion of missed GG ≥ 2 PCas. CONCLUSIONS: Tailoring a risk calculator to detect clinically significant PCa in Black men may improve biopsy decision-making and outcomes compared to tools developed in non-Black populations.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT.

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.

Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology.

The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions regarding the workup of patients with prostate cancer, risk stratification and management of localized disease, post-treatment monitoring, and treatment of recurrence and advanced disease. The Guidelines sections included in this article focus on the management of metastatic castration-sensitive disease, nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). Androgen deprivation therapy (ADT) with treatment intensification is strongly recommended for patients with metastatic castration-sensitive prostate cancer. For patients with nonmetastatic CRPC, ADT is continued with or without the addition of certain secondary hormone therapies depending on prostate-specific antigen doubling time. In the mCRPC setting, ADT is continued with the sequential addition of certain secondary hormone therapies, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, the presence or absence of visceral disease, symptoms, and potential side effects.

Use of the Decipher genomic classifier among men with prostate cancer in the United States.

BACKGROUND: Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer. METHODS: Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy. RESULTS: A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84). CONCLUSIONS: There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use. Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis. After the model was locked, validation was performed on NRG/RTOG 9408 (n = 1,594) that randomized men to radiotherapy +/- 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and predictive model and within predictive model positive and negative subgroup treatment effects. Results: In the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis (subdistribution hazard ratio [sHR] = 0.64, 95%CI [0.45-0.90], p = 0.01). The predictive model-treatment interaction was significant (p-interaction = 0.01). In predictive model positive patients (n = 543, 34%), ADT significantly reduced the risk of distant metastasis compared to radiotherapy alone (sHR = 0.34, 95%CI [0.19-0.63], p < 0.001). There were no significant differences between treatment arms in the predictive model negative subgroup (n = 1,051, 66%; sHR = 0.92, 95%CI [0.59-1.43], p = 0.71). Conclusions: Our data, derived and validated from completed randomized phase III trials, show that an AI-based predictive model was able to identify prostate cancer patients, with predominately intermediate-risk disease, who are likely to benefit from short-term ADT.

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.