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The international Intermediate Clinical Endpoints in Cancer of the Prostate working group has established metastasis-free survival as a surrogate for overall survival in localised prostate cancer based on the findings of 19 predominantly radiotherapy-based trials. We sought to comprehensively assess aggregate trial-level performance of commonly reported intermediate clinical endpoints across all randomised trials in localised prostate cancer. For this meta-analysis, we searched PubMed for all trials in localised or biochemically recurrent prostate cancer published between Jan 1, 1970, and Jan 15, 2020. Eligible trials had to be randomised, therapeutic, reporting overall survival and at least one intermediate clinical endpoint, and with a sample size of at least 70 participants. Trials of metastatic disease were excluded. Intermediate clinical endpoints included biochemical failure, local failure, distant metastases, biochemical failure-free survival, progression-free survival, and metastasis-free survival. Candidacy for surrogacy was assessed using the second condition of the meta-analytical approach (ie, correlation of the treatment effect of the intermediate clinical endpoint and overall survival), using R2 weighted by the inverse variance of the log intermediate clinical endpoint hazard ratio. The intermediate clinical endpoint was deemed to be a surrogate for overall survival if R2 was 0·7 or greater. 75 trials (53 631 patients) were included in our analysis. Median follow-up was 9·1 years (IQR 5·7–10·6). Biochemical failure (R2 0·38 [95% CI 0·11–0·64]), biochemical failure-free survival (R2 0·12 [0·0030–0·33]), biochemical failure and clinical failure (R2 0·28 [0·0045–0·65]), and local failure (R2 0·085 [0·00–0·37]) correlated poorly with overall survival. Progression-free survival (R2 0·46 [95% CI 0·22–0·67]) showed moderate correlation with overall survival, and metastasis-free survival (R2 0·78 [0·59–0·89]) correlated strongly. Intermediate clinical endpoints based on biochemical and local failure did not meet the second condition of the meta-analytical approach and are not surrogate endpoints for overall survival in localised prostate cancer. Our findings validate metastasis-free survival as the only identified surrogate endpoint for overall survival to date. Prostate Cancer Foundation and National Institutes of Health.

Background TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification. Methods Objective We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone. Design Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch. Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model. Results and Limitations All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively. Conclusions A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.

Introduction Metastatic castration‐resistant prostate cancer (mCRPC) is a heterogeneous disease with prognoses varying from months to years at time of castration‐resistant diagnosis. Optimal first‐line therapy for those with different prognoses is unknown. Methods We conducted a retrospective cohort study of men in a national healthcare delivery system receiving first‐line therapy for mCRPC (abiraterone, enzalutamide, docetaxel, or ketoconazole) from 2010 to 2017, with follow‐up through 2019. Using commonly drawn prognostic labs at start of mCRPC therapy (hemoglobin, albumin, and alkaline phosphatase), we categorized men into favorable, intermediate, or poor prognostic groups depending on whether they had none, one to two, or all three laboratory values worse than designated laboratory cutoffs. We used Kaplan–Meier methods to examine prostate specific antigen (PSA) progression‐free and overall survival (OS) according to prognostic group and first‐line therapy, and multivariable cox regression to determine variables associated with survival outcomes. Results Among 4135 patients, median PSA progression‐free survival (PFS) was 6.9 months (95% confidence interval [CI] 6.6–7.3), and median OS 18.8 months (95% CI 18.0–19.6), ranging from 5.7 months (95% CI 4.8–7.0) in the poor prognosis group to 31.3 months (95% CI 29.7–32.9) in the favorable group. OS was similar regardless of initial treatment received for favorable and intermediate groups, but worse for those in the poor prognostic group who received ketoconazole (adjusted hazard ratio 2.07, 95% CI 1.2–3.6). PSA PFS was worse for those who received ketoconazole compared to abiraterone across all prognostic groups (favorable HR 1.76, 95% CI 1.34–2.31; intermediate HR 1.78, 95% CI 1.41–2.25; poor HR 8.01, 95% CI 2.93–21.9). Conclusion Commonly drawn labs at mCRPC treatment start may aid in predicting survival and response to therapies, potentially informing discussions with care teams. First‐line treatment selection impacts disease progression for all men with mCRPC regardless of prognostic group, but impacted OS only for men with poor prognosis at treatment start. Hemoglobin, albumin, and alkaline phosphatase, used to categorize patients with metastatic castration resistant prostate cancer at start of new treatment into prognostic groups, was strongly predictive of survival, but survival did not vary substantially based on which treatment patients received within prognostic groups.

Background Rapid (“warm”) autopsies of patients with advanced metastatic cancer provide important insight into the natural history, pathobiology and histomorphology of disease in treatment-resistant tumors. Plasmacytoid urothelial carcinoma (PUC) is a rare variant of urothelial carcinoma characterized by neoplastic cells morphologically resembling plasma cells. PUC is typically aggressive, high-stage at presentation, and associated with poor outcomes. Recurrence is common in PUC, with the majority of recurrences occurring in the peritoneum. Case presentation Here, we report rapid autopsy findings from a patient with recurrent PUC. The patient had persistent pain after cystoprostatectomy, although initial post-operative imaging showed no evidence of disease. Imaging obtained shortly before his death showed only subtle growth along vascular tissue planes; however, extensive disease was seen on autopsy. Plasmacytoid tumor cells formed sheets involving many serosal surfaces. Molecular interrogation confirmed a mutation in CDH1 exon 12 leading to early truncation of the CDH1 protein in the tumor cells. Conclusions The sheet-like growth pattern of PUC makes early phases of disease spread much more difficult to capture on cross-sectional imaging. Alternative forms of surveillance may be required for detection of recurrent PUC, and providers may need to treat based on symptoms and clinical suspicion.

Abstract Background 177Lu-PSMA-617 is approved for patients with metastatic castration-resistant prostate cancer (mCRPC). Although treatment is associated with improved outcomes, not all patients benefit and response is heterogeneous. We aim to characterize genomic alterations associated with benefit to 177Lu-PSMA-617. Materials and Methods This study used the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) clinical-genomic database (n = 2445). The primary endpoint was ≥50% PSA decline (PSA50) from baseline with 177Lu-PSMA-617 in molecular subgroups. Secondary endpoints included 90% PSA decline (PSA90). Associations were assessed using Fisher’s exact test and Cox regression in multivariable analysis. Results We identified 183 mCRPC patients treated with 177Lu-PSMA-617. Median number of prior lines of mCRPC therapy was 3. Overall, PSA50 was 49%, median progression-free survival was 7.6 months, and median overall survival was 13.9 months. NF1 (n = 8) and FOXA1 alterations (n = 5) were associated with increased PSA50 (88% vs 47%, P = .03 for NF1; 100% vs 47%, P = .03 for FOXA1). Among CRPC sequenced tumors (n = 119), androgen receptor (AR) alterations (n = 58) were associated with lower PSA50 (38% vs 60%, P = .03). While any tumor suppressor genes (TSG) (PTEN, TP53, RB1) (n = 109) or TP53 (n = 83) alteration were associated with lower PSA90 (P = .02 for both), NF1 (n = 8), and FOXA1 alterations were associated with higher PSA90 (P = .03 and P = .003, respectively). Conclusions This analysis identifies potential genomic predictors of response to 177Lu-PSMA-617, with NF1 and FOXA1 alterations associated with favorable outcomes and AR and TSG alterations with diminished response. These hypothesis-generating findings suggest genomic profiling may inform selection for PSMA-targeted therapy and warrant prospective validation in larger cohorts.

Background: [sup.177]Lu-PSMA-617 is approved for patients with metastatic castration-resistant prostate cancer (mCRPC). Although treatment is associated with improved outcomes, not all patients benefit and response is heterogeneous. We aim to characterize genomic alterations associated with benefit to [sup.177]Lu-PSMA-617. Materials and Methods: This study used the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) clinical-genomic database (n = 2445). The primary endpoint was [greater than or equal to] 50% PSA decline (PS[A.sub.50]) from baseline with [sup.177]Lu-PSMA-617 in molecular subgroups. Secondary endpoints included 90% PSA decline (PS[A.sub.90]). Associations were assessed using Fisher's exact test and Cox regression in multivariable analysis. Results: We identified 183 mCRPC patients treated with [sup.177]Lu-PSMA-617 Median number of prior lines of mCRPC therapy was 3. Overall, PS[A.sub.50] was 49%, median progression-free survival was 76 months, and median overall survival was 13.9 months. NF1 (n = 8) and FOXA1 alterations (n = 5) were associated with increased PS[A.sub.50] (88% vs 47%, P = .03 for NF1; 100% vs 47%, P = .03 for FOXA1). Among CRPC sequenced tumors (n = 119), androgen receptor (AR) alterations (n = 58) were associated with lower PS[A.sub.50] (38% vs 60%, P = .03). While any tumor suppressor genes (TSG) (PTEN, TP53, RB1) (n = 109) or TP53 (n = 83) alteration were associated with lower PS[A.sub.90] (P= .02 for both), nFi (n = 8), and FOXA1 alterations were associated with higher PS[A.sub.90] (P= .03 and P= .003, respectively). Conclusions: This analysis identifies potential genomic predictors of response to [sup.177]Lu-PSMA-617, with NF1 and FOXA1 alterations associated with favorable outcomes and AR and TSG alterations with diminished response. These hypothesis-generating findings suggest genomic profiling may inform selection for PSMA-targeted therapy and warrant prospective validation in larger cohorts. Key words: genomics; metastatic; prostate; [sup.177]Lu-PSMA-617.

Glutathione S-transferases (GSTs) metabolize drugs and xenobiotics. Yet despite high protein sequence homology, expression of π-class GSTs, the most abundant of the enzymes, varies significantly between species. In mouse liver, hepatocytes exhibit high mGstp expression, while in human liver, hepatocytes contain little or no hGSTP1 mRNA or hGSTP1 protein. π-class GSTs are known to be critical determinants of liver responses to drugs and toxins: when treated with high doses of acetaminophen, mGstp1/2+/+ mice suffer marked liver damage, while mGstp1/2-/- mice escape liver injury. To more faithfully model the contribution of π-class GSTs to human liver toxicology, we introduced hGSTP1, with its exons, introns, and flanking sequences, into the germline of mice carrying disrupted mGstp genes. In the resultant hGSTP1+mGstp1/2-/- strain, π-class GSTs were regulated differently than in wild-type mice. In the liver, enzyme expression was restricted to bile duct cells, Kupffer cells, macrophages, and endothelial cells, reminiscent of human liver, while in the prostate, enzyme production was limited to basal epithelial cells, reminiscent of human prostate. The human patterns of hGSTP1 transgene regulation were accompanied by human patterns of DNA methylation, with bisulfite genomic sequencing revealing establishment of an unmethylated CpG island sequence encompassing the gene promoter. Unlike wild-type or mGstp1/2-/- mice, when hGSTP1+mGstp1/2-/- mice were overdosed with acetaminophen, liver tissues showed limited centrilobular necrosis, suggesting that π-class GSTs may be critical determinants of toxin-induced hepatocyte injury even when not expressed by hepatocytes. By recapitulating human π-class GST expression, hGSTP1+mGstp1/2-/- mice may better model human drug and xenobiotic toxicology.

Despite early diagnosis and established protocols, a subset of prostate cancer patients will eventually be categorized as castration-resistant prostate cancer. Recently, it has been reported that these multi-modal therapy cases may harbor a special subset of cancer cells termed as polypoidal giant cancer cells (PGCC). These cells are phenotypically described either as possessing highly irregular polylobated nuclei or multiple pleomorphic nuclei. To identify and characterize the distribution of these cells, we created a cohort of 5 randomly selected cases of multi-modal therapy failure prostate cancer (16 selected non-osseous and osseous tumor sites) enrolled in Michigan Legacy Tissue Program. In all cases, specific “regions of interest” or “hot spots” within tumor areas showing an increased proportion of these multi-nucleated/polylobated cells under light microscopy were labeled as PGCC-rich area. On microscopic evaluation, overall mean count of PGCC was 42.4 ± 3.91 with case 2 in the study cohort with the highest number of average PGCC count of 17 ± 4.04. Site wise analysis showed retroperitoneal lymph node as the tissue with highest number of average PGCC number/site (5.0 ± 0.32). On correlating the average number of PGCC recorded with the time elapsed from last dose of chemotherapy administered to autopsy, the spearman correlation value (R) was 0.67, but the result was not statistically significant (p = 0.22). A systematic assessment of PGCC in a large stratified cohort of prostate cancer patients integrated with various histopathological and clinical parameters along with discovery of specific biomarkers for PGCC are the future studies suggested.

The substantial biological heterogeneity of metastatic prostate cancer has hindered the development of personalized therapeutic approaches. Therefore, it is difficult to predict the course of metastatic hormone-sensitive prostate cancer (mHSPC), with some men remaining on first-line androgen deprivation therapy (ADT) for several years while others progress more rapidly. Improving our ability to risk-stratify patients would allow for the optimization of systemic therapies and support the development of stratified prospective clinical trials focused on patients likely to have the greatest potential benefit. Here, we applied a liquid biopsy approach to identify clinically relevant, blood-based prognostic biomarkers in patients with mHSPC. Gene expression indicating the presence of CTCs was greater in CHAARTED high-volume (HV) patients (52% CTChigh) than in low-volume (LV) patients (23% CTChigh; * p = 0.03). HV disease (p = 0.005, q = 0.033) and CTC presence at baseline prior to treatment initiation (p = 0.008, q = 0.033) were found to be independently associated with the risk of nonresponse at 7 months. The pooled gene expression from CTCs of pre-ADT samples found AR, DSG2, KLK3, MDK, and PCA3 as genes predictive of nonresponse. These observations support the utility of liquid biomarker approaches to identify patients with poor initial response. This approach could facilitate more precise treatment intensification in the highest risk patients.

BackgroundTumors with mutations associated with homologous recombination deficiency (HRD) are uncommon in prostate cancer (PCa) and variably responsive to PARP inhibition. To better identify tumors with HRD, we developed a transcriptomic signature for HRD in PCa (HRD-P).MethodsBy using an established mutational signature, we created and validated HRD-P in six independent PCa cohorts (primary PCa, n = 8224; metastatic castration-resistant PCa [mCRPC], n = 328). Molecular and clinical features were compared between HRD-P+ tumors and those with single HR-gene mutations.ResultsHRD-P+ tumors were more common than tumors with single HR-gene mutations in primary (201/491, 41% vs 32/491 6.5%) and mCRPC (126/328, 38% vs 82/328, 25%) cases, and HRD-P+ was more predictive of genomic instability suggestive of HRD. HRD-P+ was associated with a shorter time to recurrence following surgery and shorter overall survival in men with mCRPC. In a prospective trial of mCRPC treated with olaparib (n = 10), all three men with HRD-P+ experienced prolonged (>330 days) PSA progression-free survival.ConclusionThese results suggest transcriptomics can identify more patients that harbor phenotypic HRD than single HR-gene mutations and support further exploration of transcriptionally defined HRD tumors perhaps in conjunction with genomic markers for therapeutic application.