Morphologic patterns observed in prostate biopsy cases with discrepant grade group and molecular risk classification.

BACKGROUND: Molecular-based risk classifier tests are increasingly being utilized by urologists and radiation oncologists to guide clinical decision making. The Decipher prostate biopsy test is a 22-gene RNA biomarker assay designed to predict likelihood of high-grade disease at radical prostatectomy and risk of metastasis and mortality. The test provides a risk category of low, intermediate, or high. We investigated histologic features of biopsies in which the Grade Group (GG) and Decipher risk category (molecular risk) were discrepant. METHODS: Our institutional urologic outcomes database was searched for men who underwent prostate biopsies with subsequent Decipher testing from 2016 to 2020. We defined discrepant GG and molecular risk as either GG1-2 with high Decipher risk category or GG ≥ 3 with low Decipher risk category. The biopsy slide on which Decipher testing was performed was re-reviewed for GG and various histologic features, including % Gleason pattern 4, types of Gleason pattern 4 and 5, other "high risk" features (e.g., complex papillary, ductal carcinoma, intraductal carcinoma [IDC]), and other unusual and often "difficult to grade" patterns (e.g., atrophic carcinoma, mucin rupture, pseudohyperplastic carcinoma, collagenous fibroplasia, foamy gland carcinoma, carcinoma with basal cell marker expression, carcinoma with prominent vacuoles, and stromal reaction). Follow-up data was also obtained from the electronic medical record. RESULTS: Of 178 men who underwent prostate biopsies and had Decipher testing performed, 41 (23%) had discrepant GG and molecular risk. Slides were available for review for 33/41 (80%). Of these 33 patients, 23 (70%) had GG1-2 (GG1 n = 5, GG2 n = 18) with high Decipher risk, and 10 (30%) had GG ≥ 3 with low Decipher risk. Of the 5 GG1 cases, one case was considered GG2 on re-review; no other high risk features were identified but each case showed at least one of the following "difficult to grade" patterns: 3 atrophic carcinoma, 1 collagenous fibroplasia, 1 carcinoma with mucin rupture, and 1 carcinoma with basal cell marker expression. Of the 18 GG2 high Decipher risk cases, 2 showed GG3 on re-review, 5 showed large cribriform and/or other high risk features, and 10 showed a "difficult to grade" pattern. Of the 10 GG ≥ 3 low Decipher risk cases, 5 had known high risk features including 2 with large cribriform, 1 with IDC, and 1 with Gleason pattern 5. CONCLUSIONS: In GG1-2 high Decipher risk cases, difficult to grade patterns were frequently seen in the absence of other known high risk morphologic features; whether these constitute true high risk cases requires further study. In the GG ≥ 3 low Decipher risk cases, aggressive histologic patterns such as large cribriform and IDC were observed in half (50%) of cases; therefore, the molecular classifier may not capture all high risk histologic patterns.

Prognostic Significance of Immune Cell Infiltration in Muscle-invasive Bladder Cancer Treated with Definitive Chemoradiation: A Secondary Analysis of RTOG 0524 and RTOG 0712.

Chemoradiation therapy (CRT) is a treatment for muscle-invasive bladder cancer (MIBC). Using a novel transcriptomic profiling panel, we validated prognostic immune biomarkers to CRT using 70 pretreatment tumor samples from prospective trials of MIBC (NRG/RTOG 0524 and 0712). Disease-free survival (DFS) and overall survival (OS) were estimated via the Kaplan-Meier method and stratified by genes correlated with immune cell activation. Cox proportional-hazards models were used to assess group differences. Clustering of gene expression profiles revealed that the cluster with high immune cell content was associated with longer DFS (hazard ratio [HR] 0.53, 95% confidence interval [CI] 0.26-1.10; p = 0.071) and OS (HR 0.48, 95% CI 0.24-0.97; p = 0.040) than the cluster with low immune cell content. Higher expression of T-cell infiltration genes (CD8A and ICOS) was associated with longer DFS (HR 0.40, 95% CI 0.21-0.75; p = 0.005) and OS (HR 0.49, 95% CI 0.25-0.94; p = 0.033). Higher IDO1 expression (IFNγ signature) was also associated with longer DFS (HR 0.44, 95% CI 0.24-0.88; p = 0.021) and OS (HR 0.49, 95% CI 0.24-0.99; p = 0.048). These findings should be validated in prospective CRT trials that include biomarkers, particularly for trials incorporating immunotherapy for MIBC. PATIENT SUMMARY: We analyzed patient samples from two clinical trials (NRG/RTOG 0524 and 0712) of chemoradiation for muscle-invasive bladder cancer using a novel method to assess immune cells in the tumor microenvironment. Higher expression of genes associated with immune activation and high overall immune-cell content were associated with better disease-free survival and overall survival for patients treated with chemoradiation.

Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI.

Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n = 198) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

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 impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable-risk prostate cancer.

BACKGROUND: The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model. METHODS: Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction. RESULTS: The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model. CONCLUSIONS: GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Large cribriform glands (> 0.25 mm diameter) as a predictor of adverse pathology in men with Grade Group 2 prostate cancer.

AIMS: A recent outcome-based, radical prostatectomy study defined > 0.25 mm diameter to distinguish large versus small cribriform glands, with > 0.25 mm associated with worse recurrence-free survival. This study evaluates whether identification of > 0.25 mm cribriform glands in Grade Group 2 patients at biopsy is associated with adverse pathology at radical prostatectomy. METHODS AND RESULTS: Tumours containing biopsy slides for 133 patients with Grade Group 2 prostate cancer with subsequent radical prostatectomy were re-reviewed for large cribriform glands (diameter > 0.25 mm). The primary outcome was adverse pathology (Grade Groups 3-5; stage pT3a or greater, or pN1). The secondary outcome was recurrence-free survival. Cribriform pattern was present in 52 of 133 (39%) patients; of these, 16 of 52 (31%) had large cribriform glands and 36 of 52 (69%) had only small cribriform glands. Patients with large cribriform glands had significantly more adverse pathology at radical prostatectomy compared to patients with small cribriform glands and no cribriform glands (large = 11 of 16, 69%; small = 12 of 36, 33%; no cribriform = 25 of 81, 31%; χ2 P-value 0.01). On multivariate analysis, large cribriform glands were also associated with adverse pathology, independent of age, prostate-specific antigen (PSA)/PSA density at diagnosis, year of diagnosis and biopsy cores percentage positive (global P-value 0.02). Large cribriform glands were also associated with increased CAPRA-S surgical risk score (Kruskal-Wallis P-value 0.02). CONCLUSIONS: Large cribriform glands using a diameter > 0.25 mm definition in Grade Group 2 patients on biopsy are associated with increased risk of adverse pathology at radical prostatectomy. The presence of large cribriform histology should be considered when offering active surveillance for those with Grade Group 2 disease.

First-in-human Evaluation of a Prostate-specific Membrane Antigen-targeted Near-infrared Fluorescent Small Molecule for Fluorescence-based Identification of Prostate Cancer in Patients with High-risk Prostate Cancer Undergoing Robotic-assisted Prostatectomy.

BACKGROUND: Men with high-risk prostate cancer undergoing surgery likely recur due to failure to completely excise regional and/or local disease. OBJECTIVE: The first-in-human evaluation of safety, pharmacokinetics, and exploratory efficacy of IS-002, a novel near-infrared prostate-specific membrane antigen (PSMA)-targeted fluorescence imaging agent, designed for intraoperative prostate cancer visualization. DESIGN, SETTING, AND PARTICIPANTS: A phase 1, single-center, dose-escalation study was conducted in 24 men with high-risk prostate cancer scheduled for robotic-assisted radical prostatectomy with (extended) pelvic lymph node dissection using the da Vinci surgical system. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Adverse events (AEs), vital signs, complete blood count, complete metabolic panel, urinalysis, and electrocardiogram were assessed over a 14-d period and compared with baseline. The pharmacokinetic profile of IS-002 was determined. Diagnostic accuracy was assessed for exploratory efficacy. RESULTS AND LIMITATIONS: AEs predominantly included discoloration of urine (n = 22/24; expected, related, grade 1). There were no grade ≥2 AEs. IS-002 Cmax and area under the curve increased with increasing dose. Plasma concentrations declined rapidly in a biphasic manner, with the median terminal half-lives ranging from 5.0 to 7.6 h, independent of dose and renal function. At 25 µg/kg, the exploratory efficacy readouts for the negative and positive predictive values were, 97% and 45% for lymph nodes, and 100% and 80% for residual/locoregional disease detection, respectively. CONCLUSIONS: IS-002 is safe and well tolerated, and has the potential to enable intraoperative tumor detection that could not be identified using standard imaging. PATIENT SUMMARY: IS-002 is a new imaging agent that specifically targets the prostate-specific membrane antigen receptor. In this study, we tested IS-002 for the first time in men with high-risk prostate cancer undergoing surgery and found that IS-002 is safe, is cleared from the body quickly, and potentially allows identification of prostate cancer in areas that would not be identified by conventional white light imaging.

Transcriptomic Heterogeneity of Expansile Cribriform and Other Gleason Pattern 4 Prostate Cancer Subtypes.

BACKGROUND: Prostate cancers featuring an expansile cribriform (EC) pattern are associated with worse clinical outcomes following radical prostatectomy (RP). However, studies of the genomic characteristics of Gleason pattern 4 subtypes are limited. OBJECTIVE: To explore transcriptomic characteristics and heterogeneity within Gleason pattern 4 subtypes (fused/poorly formed, glomeruloid, small cribriform, EC/intraductal carcinoma [IDC]) and the association with biochemical recurrence (BCR)-free survival. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study including 165 men with grade group 2-4 prostate cancer who underwent RP at a single academic institution (2016-2020) and Decipher testing of the RP specimen. Patients with Gleason pattern 5 were excluded. IDC and EC patterns were grouped. Median follow-up was 2.5 yr after RP for patients without BCR. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Prompted by heterogeneity within pattern 4 subtypes identified via exploratory analyses, we investigated transcriptomic consensus clusters using partitioning around medoids and hallmark gene set scores. The primary clinical outcome was BCR, defined as two consecutive prostate-specific antigen measurements >0.2 ng/ml at least 8 wk after RP, or any additional treatment. Multivariable Cox proportional-hazards models were used to determine factors associated with BCR-free survival. RESULTS AND LIMITATIONS: In this cohort, 99/165 patients (60%) had EC and 67 experienced BCR. Exploratory analyses and clustering demonstrated transcriptomic heterogeneity within each Gleason pattern 4 subtype. In the multivariable model controlled for pattern 4 subtype, margin status, Cancer of the Prostate Risk Assessment Post-Surgical score, and Decipher score, a newly identified steroid hormone-driven cluster (hazard ratio 2.35 95% confidence interval 1.01-5.47) was associated with worse BCR-free survival. The study is limited by intermediate follow-up, no validation cohort, and lack of accounting for intratumoral and intraprostatic heterogeneity. CONCLUSIONS: Transcriptomic heterogeneity was present within and across each Gleason pattern 4 subtype, demonstrating there is additional biologic diversity not captured by histologic subtypes. This heterogeneity can be used to develop novel signatures and to classify transcriptomic subtypes, which may help in refining risk stratification following RP to further guide decision-making on adjuvant and salvage treatments. PATIENT SUMMARY: We studied prostatectomy specimens and found that tumors with similar microscopic appearance can have genetic differences that may help to predict outcomes after prostatectomy for prostate cancer. Our results demonstrate that further gene expression analysis of prostate cancer subtypes may improve risk stratification after prostatectomy. Future studies are needed to develop novel gene expression signatures and validate these findings in independent sets of patients.

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.

Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial.

PURPOSE: Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up. METHODS AND MATERIALS: After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors. RESULTS: Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04). CONCLUSIONS: This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Renaming Grade Group 1 Prostate "Cancer" From a Pathology Perspective: A Call for Multidisciplinary Discussion.

Despite the innovations made to enhance smarter screening and conservative management for low-grade prostate cancer, overdiagnosis, and overtreatment remains a major health care problem. Driven by the primary goal of reducing harm to the patients, relabeling of nonlethal grade group 1 (GG 1) prostate cancer has been proposed but faced varying degrees of support and objection from clinicians and pathologists. GG 1 tumor exhibits histologic (invasive) and molecular features of cancer but paradoxically, if pure, is unable to metastasize, rarely extends out of the prostate, and if resected, has a cancer-specific survival approaching 100%. Most of the arguments against relabeling GG 1 relate to concerns of missing a higher-grade component through the unsampled area at biopsy. However, the designation of tumor benignity or malignancy should not be based on the shortcomings of a diagnostic procedure and sampling errors. This review explores possible solutions, mainly the feasibility of renaming GG 1 in radical prostatectomy (RP) with ramifications in biopsy diagnosis, acceptable for both pathologists and clinicians. One workable approach is to rename GG 1 in RP with a cautious neutral or nonbenign non-cancer term (eg, acinar neoplasm) using "defined criteria" that will stop the indiscriminate reporting of every GG 1 in biopsy as carcinoma including eventual insignificant microtumors in RPs. Use of a corresponding noncommittal term at biopsy while commenting on the possibility of an undersampled nonindolent cancer, might reduce the pathologist's concerns about upgrading. Dropping the word "carcinoma" in biopsy preempts the negative consequences of labeling the patient with cancer, including unnecessary definitive therapy (the root cause of overtreatment). Renaming should retain the status quo of contemporary grading and risk stratifications for management algorithms while trying to minimize overtreatment. However, the optimal approach to find answers to this issue is through multidisciplinary discussions of key stakeholders with a specific focus on patient-centered concerns and their ramifications in our practices. GG 1 renaming has been brought up in the past and came up again despite the continued counterarguments, and if not addressed more comprehensively will likely continue to reemerge as overdiagnosis, overtreatment, and patient's sufferings persist.

Protocol for a 4-arm randomized controlled trial testing remotely delivered exercise-only, diet-only, and exercise + diet interventions among men with prostate cancer treated with radical prostatectomy (Prostate 8-II).

BACKGROUND: Nutrition and physical activity are associated with prostate cancer recurrence and mortality. Few randomized controlled trials (RCT) have examined the effects of long-term exercise and diet changes on prostate cancer clinical, biological, and patient-reported outcomes. METHODS: Prostate 8-II is a 4-arm RCT among 200 men with prostate cancer who chose radical prostatectomy (RP) as their primary treatment. Men are enrolled prior to RP and randomized to exercise-only, diet-only, exercise + diet, or usual care (50/arm). Participants begin their assigned intervention 0-5 weeks prior to RP and continue for 24-months following surgery. The 3 active intervention arms receive access to a web-portal and text messages, coaching calls, and other intervention resources (e.g., heart rate sensor and resistance bands and/or recipe booklet). Weekly exercise goals for the exercise intervention groups are 150 min moderate or 75 min vigorous aerobic exercise, 2 strength sessions, and 2 flexibility sessions. Diet intervention groups work with a dietitian to customize their goals (e.g., increase cruciferous vegetables, cooked tomatoes, healthy fats, fish; limit processed meats, whole milk). The primary endpoint is biochemical recurrence. Secondary endpoints include change in tumor biomarkers from biopsy to RP as well as patient-reported outcomes (e.g., quality-of-life), blood and urine biomarkers, and anthropometry at 0, 6, 12, and 24 months. CONCLUSION: This 4-arm RCT will examine the impact of change in exercise and diet (alone or in combination) on prostate cancer recurrence, biology, and quality-of-life.

Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard.

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.

Analysis of a Biopsy-Based Genomic Classifier in High-Risk Prostate Cancer: Meta-Analysis of the NRG Oncology/Radiation Therapy Oncology Group 9202, 9413, and 9902 Phase 3 Randomized Trials.

PURPOSE: Decipher is a genomic classifier (GC) prospectively validated postprostatectomy. We validated the performance of the GC in pretreatment biopsy samples within the context of 3 randomized phase 3 high-risk definitive radiation therapy trials. METHODS AND MATERIALS: A prespecified analysis plan (NRG-GU-TS006) was approved to obtain formalin-fixed paraffin-embedded tissue from biopsy specimens from the NRG biobank from patients enrolled in the NRG/Radiation Therapy Oncology Group (RTOG) 9202, 9413, and 9902 phase 3 randomized trials. After central review, the highest-grade tumors were profiled on clinical-grade whole-transcriptome arrays and GC scores were obtained. The primary objective was to validate the independent prognostic ability for the GC for distant metastases (DM), and secondary for prostate cancer-specific mortality (PCSM) and overall survival (OS) with Cox univariable and multivariable analyses. RESULTS: GC scores were obtained on 385 samples, of which 265 passed microarray quality control (69%) and had a median follow-up of 11 years (interquartile range, 9-13). In the pooled cohort, on univariable analysis, the GC was shown to be a prognostic factor for DM (per 0.1 unit; subdistribution hazard ratio [sHR], 1.29; 95% confidence interval [CI], 1.18-1.41; P < .001), PCSM (sHR, 1.28; 95% CI, 1.16-1.41; P < .001), and OS (hazard ratio [HR], 1.16; 95% CI, 1.08-1.22; P < .001). On multivariable analyses, the GC (per 0.1 unit) was independently associated with DM (sHR, 1.22; 95% CI, 1.09-1.36), PCSM (sHR, 1.23; 95% CI, 1.09-1.39), and OS (HR, 1.12; 95% CI, 1.05-1.20) after adjusting for age, Prostate Specific Antigen, Gleason score, cT stage, trial, and randomized treatment arm. GC had similar prognostic ability in patients receiving short-term or long-term androgen-deprivation therapy, but the absolute improvement in outcome varied by GC risk. CONCLUSIONS: This is the first validation of a gene expression biomarker on pretreatment prostate cancer biopsy samples from prospective randomized trials and demonstrates an independent association of GC score with DM, PCSM, and OS. High-risk prostate cancer is a heterogeneous disease state, and GC can improve risk stratification to help personalize shared decision making.

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

Predictive Model for Hormone Therapy in Prostate CancerDigital pathology images and clinical data from pretreatment prostate tissue were used to generate a predictive model to determine patients who would benefit from androgen deprivation therapy (ADT). In model-positive patients, ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone.

Prostate Cancer in Male-to-Female Transgender Individuals: Histopathologic Findings and Association With Gender-affirming Hormonal Therapy.

Male-to-female (MtF) transgender individuals are at risk for prostate cancer, although guidelines for screening and management in this population are not well established. We describe a series of 9 MtF transgender patients who underwent prostate tissue sampling and highlight histopathologic features and challenges related to pathologic interpretation of prostate tissue in this patient population. Seven of 9 total patients were diagnosed with prostate cancer and all had elevated prostate-specific antigen at the time of diagnosis. Three of the 7 patients diagnosed with prostate cancer had received different types of hormone therapy for gender affirmation before the diagnosis of prostate cancer, and in all 3 of these patients, there was histologic evidence of hormone therapy effect in both benign prostate tissue and/or the adenocarcinoma. The 2 patients with benign prostate tissue underwent transurethral resection for lower urinary tract symptoms and were previously on hormone therapy for gender affirmation. Both of these specimens showed diffuse glandular atrophy and basal cell hyperplasia, indicative of hormone therapy effect on benign prostatic tissue. In the patients diagnosed with prostate cancer, a spectrum of grades was observed, ranging from Grade Group 1 to Grade Group 5. Four patients underwent radical prostatectomy, with 2 cases showing extraprostatic extension and Grade Group 5 prostatic adenocarcinoma, and 2 showing Grade Group 2 prostatic adenocarcinoma. Three of the 4 patients who underwent radical prostatectomy had received gender-affirming hormone therapy before surgery, and all 3 of these specimens showed hormone therapy effect in non-neoplastic prostate tissue and focal hormone therapy effect in prostatic adenocarcinoma. The presence of areas of viable carcinoma without hormone therapy effect enabled the assignment of a Gleason score and Grade Group in these 3 cases. Hormone therapy administered for gender identity affirmation induces histopathologic changes to both benign prostate tissue (nonkeratinizing squamous metaplasia, diffuse atrophy, basal cell hyperplasia, and stromal dominance with decreased numbers of glands) and prostatic adenocarcinoma (nuclear pyknosis, atrophy, cytoplasmic vacuolization, and architectural patterns that would qualify for Gleason 4 and 5 in the absence of hormone therapy effect) that have been traditionally seen in cis-male prostate cancer patients receiving hormone therapy. In the absence of hormone therapy, the morphology of prostatic adenocarcinoma in transgender patients shows classic morphologic features similar to those seen in cis-male patients not on hormone therapy. Prostate cancer with hormone therapy effect may not only be histologically quite subtle and may be overlooked if not suspected, but also should not be assigned a Gleason score because the Gleason score would substantially overstate its biologic potential. Therefore, similar to cis-male patients who have received androgen deprivation therapy for prostate cancer, transgender patients on hormone therapy for gender affirmation may be at risk for both underrecognition and over-grading of prostate cancer, particularly if the pathologist is not aware of the clinical history.

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.