The Prostate

Background: Chronic inflammation is the most common histological feature in Benign Prostatic Hyperplasia (BPH), and T cells are a key component of immune infiltrate. Advanced BPH is commonly associated with the formation of nodules, but it remains unclear whether a link exists among T cell infiltration, nodular development, and BPH progression. Using a Toxoplasma gondii (T. gondii) model and human specimens, we characterize the subtypes of T cells present during prostatic hyperplasia and their association with nodular development of the prostate. Methods: Male CBA/j mice were intraperitoneally infected with T. gondii parasites, and flow cytometry was performed on the prostate to quantify the number of CD4+ and CD8+ T cells. Histology was used to score microglandular hyperplasia (MGH), and immunofluorescence was used to quantify and examine the locality of CD4+ and CD8+ T cells and compared that to human BPH tissue. Results: We found that infecting male mice with T. gondii resulted in an increase of both CD4+ and CD8+ T cells in the prostate acutely and that CD8+ cells remained sustained at chronically. We also established the presence of glandular nodule formation at this timepoint through hematoxylin and eosin (H&E) staining. Immunofluorescence revealed that CD8+ cells were found proximal to forming glandular nodules relative to non-nodular glands. We also found more CD8+ cells localized to non-nodular glands in nodular BPH tissue versus non-nodular BPH tissue. Finally, we discovered a higher prevalence of CD8+ cells in T. gondii IgG+ patients than in IgG- patients. All T. gondii IgG+ patients exhibited nodular BPH, whereas all but one IgG- patient exhibited non-nodular BPH. Conclusions: This study is the first to investigate the presence and location of CD4+ and CD8+ T cells within nodular and non-nodular BPH glands. We found an association of the presence of CD8+ T cells with nodular progression. This association held true in human prostate tissue. Translationally, CD8+ T cells may enhance nodular BPH progression, and T. gondii infection may promote this CD8+ T cell-mediated response.

Background: Biochemical recurrence (BCR) occurs in 20-40% of men after radical prostatectomy. Existing postoperative recurrence risk tools based on PSA and pathology are clinically useful but show only moderate and variable discrimination, highlighting the need for biomarkers that improve risk stratification and consequent treatment decisions. We hypothesized that the prostate microenvironment, including both the tumor and non-cancerous adjacent tissue, may contain prognostic features associated with adverse postoperative PSA outcomes. Methods: We assembled a cohort of matched tumor-adjacent benign and tumor prostate tissue from 243 men across three institutions to establish a discovery cohort (n=123; 43 postoperative PSA events, 35%) and validation cohort (n=120; 46 events, 38%). For primary binary analyses, a postoperative PSA event included BCR, defined as two consecutive postoperative PSA values >=0.2 ng/mL, or PSA persistence. We performed RNA sequencing of matched tumor-adjacent benign and tumor tissues, quantified immune signatures, and developed an integrated model combining the adjacent-tissue B-cell signature, preoperative PSA, and radical prostatectomy Gleason score (BRIGADE). CAPRA-S-adjusted Cox analyses excluding recurrence-time-0 cases evaluated time to BCR, and CD19 multiplex immunofluorescence provided tissue-level confirmation (n=10). Results: In prostatectomy specimens, tumors from patients without a postoperative PSA event were enriched for B-cell transcriptional programs, whereas tumors from event-positive patients showed elevated proliferation signatures. B-cell-related transcriptional programs were correlated between tumor and adjacent tissue. Tumor-adjacent benign B-cell scores were higher in no-event cases and discriminated postoperative PSA-event status in PCBN discovery (AUC 0.63) and BM validation (AUC 0.81) cohorts, outperforming numerous other immune-related signatures. In CAPRA-S-adjusted Cox sensitivity analyses excluding recurrence-time-0 cases, higher adjacent-tissue B-cell activity was associated with reduced recurrence risk in PCBN (HR 0.42, 95% CI 0.19-0.94; BH-adjusted p=0.035) and BM (HR 0.54, 95% CI 0.30-0.95; BH-adjusted p=0.034). Tissue-based validation showed that CD19+ B-cell density in adjacent benign tissue was higher in no-event than event-positive patients (median 0.1145 vs 0.0471; p=0.008). BRIGADE achieved an AUC of 0.68 in cross-validation and 0.83 in independent validation, compared to AUCs of 0.54-0.63 and 0.44-0.78 for the tested clinical predictors, respectively. At the fixed classification threshold, the validation-cohort odds ratio for BRIGADE was 2.75. The adjacent B-cell score remained associated with lower odds of a postoperative PSA event after adjustment for PSA and Gleason score. Conclusions: B-cell infiltration in tumor-adjacent benign prostate tissue may complement existing clinicopathologic models for stratifying adverse postoperative PSA outcomes and subsequent BCR after radical prostatectomy. The transcriptomic signal was recapitulated by CD19-based tissue staining, supporting further development of a pathology-based assay.

We present PRECISE (PRostate Expert-annotated Contiguous IHC-H\&E Serial sEctions), a hybrid histopathology dataset of paired hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) whole-slide images (WSIs), comprising 37 prostate core needle biopsies from 25 patients, each with matched H\&E and CKAPM+racemase staining. To the best of our knowledge, this is the first publicly available dataset offering spatially harmonized, pixel-level expert annotations across both staining modalities in prostate biopsy WSIs - directly mirroring the two-stage (H\&E-then-IHC) clinical diagnostic workflow used to resolve morphological uncertainty, restricted to cases in which that workflow reached diagnostic consensus. The dataset contains 24,387 annotations spanning seven diagnostically critical classes: malignant glands, benign glands, stromal tissue, intraductal carcinoma (IDC-P), high-grade prostatic intraepithelial neoplasia (HGPIN), atypical intraductal proliferation (AIP), and tissue artifacts. Unlike existing resources, which focus on binary tumor classification or lack IHC pairing, this dataset captures the full morphological spectrum encountered in routine prostate pathology, including rare precursor lesions and confounding entities underrepresented in current benchmarks. Annotations were validated through a structured three-stage consensus by two expert uropathologists, with IHC serving as biological ground truth for boundary definition. PRECISE is designed as a robust benchmark for multimodal semantic segmentation and self-supervised learning, and is openly released to promote reproducible research and accelerate AI-assisted diagnosis in prostate cancer.

Objective: To develop and evaluate an Observational Medical Outcomes Partnership (OMOP) standardized prostate cancer database from the University of Texas Medical Branch (UTMB) Epic Electronic Health Record (EHR) and improve data quality using natural language processing (NLP) and prostate-specific antigen (PSA) based algorithms. Materials and Methods: We built a data pipeline to transform UTMB Epic EHR data from 2010 to 2021 into OMOP Common Data Model (CDM) v5.4. Data quality was assessed by comparing the OMOP-standardized data with Galveston Cancer Registry data using availability agreement, Cohen's kappa, and Intraclass Correlation Coefficient. NLP was used to extract PSA, Gleason score, and cancer stage from clinical text, and PSA-based algorithms were used to identify missing treatment and biochemical recurrence. Results: We extracted 815 analytic cases from UTMB EHR. Among them, 700, or 85.9%, were complete and concordant with the cancer registry. PSA showed excellent value agreement. Structured Gleason score and stage data were sparse, with fewer than 20 cases, but NLP greatly improved capture. Treatment agreement was good compared with the cancer registry and improved slightly for radical prostatectomy after applying a PSA-based algorithm. Using PSA trajectories, we identified 60 cases of biochemical recurrence. Discussion: The OMOP-standardized data from UTMB showed good agreement with the cancer registry. However, structured EHR fields incompletely captured diagnosis, pathology, and treatment details. NLP and PSA-based algorithms substantially improved data capture. Manual review also revealed errors in registry data, showing that OMOP-standardized EHR data can complement and help improve cancer registry quality. Conclusion: OMOP standardization combined with NLP and PSA-based algorithms improved prostate cancer data quality and research readiness.

Abstract Background Prostate cancer (PCa) diagnosis remains challenged by the limited specificity of prostate-specific antigen (PSA) testing, which cannot reliably distinguish malignancy from benign prostatic hyperplasia (BPH). MicroRNAs (miRNAs) are emerging candidates for liquid biopsy-based diagnostics, but most studies assess expression in isolation within a single compartment (biological source - Tissue, blood, serum, urine etc.), overlooking both compartment-specific behavior and the coordinated relationships among miRNAs. Methods We profiled four candidate miRNAs --- miR-19b-3p, miR-21-5p, miR-101-3p and miR-375-3p, across four biological compartments (prostate tumor tissue, urine, serum, and blood) in 179 patients undergoing prostate biopsy for clinical suspicion of PCa (104 PCa, 75 BPH) using qRT-PCR. Urinary exosomal RNA was isolated with a commercial exosome isolation kit so from here onwards this compartment will be referred to as urine. Differential expression was quantified using Cohen's d; inter-miRNA coordination was assessed via Spearman correlation and differential correlation ({delta} r) analysis; and a compartment-level network rewiring score was derived as the sum of {delta} r| across miRNA pairs. Cross-compartment structural alignment was evaluated by comparing correlation patterns at the population level. Diagnostic models combining PSA, age, and urinary exosomal-miRNA features were evaluated using Logistic Regression, Elastic Net Logistic Regression and Naive Bayes classifiers under leave-one-out cross-validation (LOOCV). Results Effect sizes were largest and most consistent in urine, with miR-101-3p showing the strongest separation between PCa and BPH (d = -1.01), followed by miR-21-5p (d {approx}-0.72$) and miR-19b-3p (d {approx}-0.64). Two markers (miR-19b-3p, miR-375-3p) showed directional reversals across compartments, indicating that disease-associated signals are compartment-specific rather than uniformly conserved. In tumor tissue, PCa was associated with substantial reorganization of inter-miRNA coordination (network rewiring score = 2.46), including the emergence of a strong miR-21-5p--miR-375-3p co-regulatory axis ({delta} r = +0.87$) and decoupling of the miR-21-5p--miR-19b-3p relationship ({delta}r = -0.64$). Urine showed a structurally distinct coordination pattern (rewiring score = 1.77), dominated by a miR-101-3p--miR-19b-3p axis (r = +0.56) absent from tissue; cross-compartment comparison showed concordance in only 1 of 5 miRNA pairs, indicating that urine's architecture is largely independent of tissue's. For diagnostic translation, the conventional PSA cutoff (4 ng/mL) achieved 100% sensitivity but only 23.5% specificity. In urine, miR-101-3p performs better than other miRNAs, with AUC of 0.77 (95% CI: 0.62--0.90). Adding PSA and age to the urinary miR-101-3p further improved discrimination to an AUC of 0.91 (95% CI: 0.82--0.99), with 70% specificity at 92% sensitivity; this pattern was consistent across Elastic Net and Logistic Regression classifiers. Expanding the model to include all urinary miRNAs, age, and pair-derived coordination features did not improve on this result (AUC = 0.88), indicating that population-level coordination changes did not translate into additional individual-level diagnostic value in this cohort. Conclusions miRNA signals in extracellular compartments do not represent direct surrogates of tumor-level molecular architecture; each compartment harbors a distinct, transformed coordination structure reflecting its biological context. While these coordination-level changes are mechanistically informative, the most direct translational gain in this study came from a parsimonious model combining PSA, age with a single urinary marker, miR-101-3p, which improved AUC from 0.77 to 0.91, with specificity 70.5% at 90% sensitivity criteria. This combination represents a promising, interpretable candidate for reducing unnecessary prostate biopsies, pending validation in larger, independent cohorts. Keywords: MicroRNA, Compartment-Specific Biomarkers, Urinary Exosomes, Differential Correlation, Liquid Biopsy, Machine learning, PSA, Early diagnosis

Metastatic prostate cancer (PCa) continues to be a major cause of death in males, despite advances in treatment. Most treatment focuses on targeting the Androgen Receptor (AR), the main oncogene responsible for driving most prostate tumors. Despite these therapies targeting AR, the majority of patients still succumb to AR-driven disease. Therefore, there is a critical need for understanding how AR functions to promote prostate cancer growth and identify alternative therapeutic targets in AR-driven PCa. One avenue garnering attention is targeting epigenetic regulators that promote AR-activity; however, the importance of epitranscriptomic regulators, like those that modify mRNAs, is not well understood. Here, we identify a new role for the key catalytic subunit of the RNA N6-methyladenosine (m6A) transferase complex, METTL3, as an AR-coregulator. METTL3 is overexpressed in prostate tumors compared to normal tissue, and METTL3 protein is elevated in AR-expressing cell lines. Depletion of METTL3 significantly reduces proliferation of cancer cells and has no effect on the growth of non-transformed prostate epithelial cells, despite decreasing global m6A levels on mRNA. The catalytic activity of METTL3 is dispensable for the growth of both non-transformed and PCa cell lines, as pharmacologic inhibition of METTL3 does not inhibit proliferation, despite the reduction of global m6A on mRNA. Overexpression of both wild-type and catalytically inactive METTL3 mutants enhances cell viability and rescues cells in which METTL3 is knocked down. Finally, we report on direct interaction between AR and METTL3, their co-localization on chromatin, and reduced AR-cistromic occupancy within cells with METTL3 knockdown. Together, these findings identify a non-enzymatic role for METTL3 in supporting AR-driven transcriptional programs and PCa proliferation.

Background: Biochemical recurrence (BCR) occurs in up to 40% of men following radical prostatectomy (RP). Current risk models rely primarily on clinicopathologic variables and may not fully capture the biological heterogeneity associated with recurrence. The Decipher Genomic Classifier (DGC), prostate-specific membrane antigen positron emission tomography (PSMA-PET), and multiparametric magnetic resonance imaging (mpMRI) provide complementary prognostic information that may improve prediction. Objective: To develop and evaluate machine learning (ML) models integrating DGC, PSMA-PET, and mpMRI for preoperative prediction of BCR following RP. Methods: This retrospective study included patients with available preoperative DGC, PSMA-PET, mpMRI, and clinicopathologic data. Logistic regression (LR), random forest (RF), and XGBoost models were developed using single- and multimodality feature combinations. Early- and intermediate-fusion strategies were evaluated. Performance was assessed using an area under the receiver operating characteristic curve (AUC) and accuracy. Clinical utility was evaluated using decision curve analysis. Results: XGBoost consistently outperformed LR and RF. DGC achieved the highest single-modality performance (AUC 0.94, accuracy 86.7%). Among multimodal models, DGC combined with PSMA-PET using intermediate fusion achieved the best overall performance (AUC 0.93, accuracy 87.0%). Addition of mpMRI reduced performance (AUC 0.85, accuracy 83.0%). Decision curve analysis demonstrated positive net benefit across clinically relevant thresholds. Conclusion: XGBoost-based multimodal fusion improved preoperative BCR prediction following RP. DGC was the strongest individual predictor, while integration with PSMA-PET provided the best overall performance, supporting the potential of radiogenomic ML models for personalized risk stratification.

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

Background: A 2019 report from our institution described a multilayer artificial neural network (ANN) for predicting prostate cancer at biopsy in 334 patients, trained with TensorFlow 1.x and evaluated at three fixed step counts without separating hyperparameter selection from test evaluation. We re-analyzed an expanded cohort from the same institution using contemporary machine-learning practice. Methods: We pooled all available biopsy episodes from the same institutional database (n = 526; 524 after excluding one non-binary outcome code and one record with missing digital rectal examination [DRE] data), retaining the same seven predictors used in the original report (age, prior biopsy history, PSA, prostate volume, DRE, and MRI diffusion-weighted imaging findings in the peripheral and transition zones). Because 27 patients contributed more than one biopsy episode, we used patient-ID-grouped, stratified k-fold cross-validation (StratifiedGroupKFold; scikit-learn 1.8.0) with 3 and 5 folds, repeated over 10 random partitions, to avoid leakage between folds. Four classifiers were compared: L2-regularized logistic regression, gradient boosting, random forest, and a shallow (single hidden layer) multilayer perceptron. Two outcomes were modeled: detection of any prostate cancer, and detection of clinically significant prostate cancer (Gleason score [≥] 7). Results: Any-cancer prevalence was 55.7% (292/524) and Gleason score [≥] 7 prevalence was 39.7% (208/524). With repeated 5-fold cross-validation, gradient boosting gave the highest discrimination for any prostate cancer (mean AUC 0.826, 95% CI 0.823-0.830) and for Gleason score [≥] 7 (mean AUC 0.855, 95% CI 0.852-0.859), closely followed by random forest and logistic regression (AUC 0.81-0.85). The shallow multilayer perceptron performed worse and less consistently than the other three models (any-cancer AUC 0.671; Gleason score [≥] 7 AUC 0.742) and than the deeper five-hidden-layer ANN reported in 2019. Results with 3-fold cross-validation were essentially unchanged. Conclusions: In an expanded cohort, regularized logistic regression, gradient boosting, and random forest all discriminated prostate cancer at biopsy at least as well as the previously reported multilayer ANN, using far simpler models and a methodology that separates hyperparameter tuning from performance estimation. A shallow neural network offered no advantage over these simpler alternatives in this sample size. This is a preprint; the study has not undergone external peer review.

A subset of untreated primary prostate cancer (PCa) contain substantial focal T-cell infiltrates, but whether these reflect antitumor responses that could potentially be enhanced by immune checkpoint blockade (ICB) remains unclear. We used immunohistochemistry, immunofluorescence, whole-slide spatial analysis, bulk RNA sequencing, and immune-cell deconvolution to characterize immune infiltrates in untreated primary PCa. Absolute CD8 T-cell density generally increased with total CD3 T-cell density, but the CD8/CD3 ratio decreased as overall T-cell density increased, indicating a preferential increase in CD4 T cells. Highly infiltrated tumors also had lower GZMB abundance relative to CD8 T-cell abundance. Multiplex analysis showed trends toward greater TIM3 and LAG3 expression among PD1CD8 T cells and increased regulatory T-cell features in highly infiltrated tumors. TIGIT cell density and the TIGIT/CD3 ratio increased with T-cell infiltration, whereas PD1/CD3 was not associated with overall CD3 T-cell density. Both TIGIT/CD3 and PD1/CD3 ratios were enriched within lymphoid aggregates compared with matched tumor and benign regions, consistent with these structures being checkpoint-rich immune niches. Transcriptomic analyses supported a shift in relative immune composition toward CD4 T cells and selective increases in immune checkpoints. Together these findings suggest that effective immune responses in a subset of primary PCa with increased T-cell infiltration are being repressed by several mechanisms and may respond to therapies targeting specific immunosuppressive mechanisms.

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [≤]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [≤]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [≤]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.

Recurrent urinary tract infection represents a substantial unmet public health in women. Local administration of estradiol has been shown to reduce recurrence, however in vitro models of the female urinary tract remain limited and the mechanisms underlying the effects of estradiol are incompletely understood. Here, we describe a novel iPSC organoid differentiation protocol and its application to establish a multilayered transwell barrier culture model. Estradiol treatment resulted in reduced expression of innate antimicrobial peptides and cytokines, together with increased expression of demannosylation pathways. Treatment of transwell cultures with a combination of female sex hormones reduced endogenous CXCL8 signaling, independently of a 24-hour uropathogenic Escherichia coli (UPEC) challenge. To our knowledge, this is the first iPSC organoid-derived model of the urinary tract, which provides a platform for investigating interactions between the urothelium, urobiome and hormonal environment.

AbstractNeuroendocrine prostate cancer (NEPC) is a highly aggressive and therapy-resistant subtype that arises from adenocarcinoma through lineage plasticity; however, the molecular mechanisms driving this transition remain incompletely defined. Insulinoma-associated protein 1 (INSM1), a zinc-finger transcription factor and established neuroendocrine lineage marker, has been implicated in a variety of neuroendocrine malignancies, yet its functional contribution to NEPC progression is not well understood. In this study, we demonstrate that INSM1 is consistently upregulated across NEPC patient tumors and experimental models, including both ASCL1 and NEUROD1 molecular subtypes, as revealed by integrated bulk and single-cell transcriptomic analyses. Functional studies revealed that INSM1 is sufficient to induce and necessary to maintain neuroendocrine lineage programs in prostate cancer, as overexpression promoted and depletion suppressed neuroendocrine-associated transcriptional networks. Mechanistically, pro-neural transcription factors, including ASCL1, NEUROD1, NEUROG3, and MYCN, directly or indirectly activate INSM1 expression, positioning it as a critical downstream effector of neuroendocrine lineage specification. Therapeutically, we identify homo-harringtonine (HHT), an FDA-approved protein synthesis inhibitor, as a potent suppressor of INSM1. HHT selectively reduces viability of INSM1-high NEPC cells at nanomolar concentrations, promotes ubiquitin-mediated degradation of INSM1, and significantly inhibits tumor growth in vivo. Notably, INSM1 depletion further enhances cellular sensitivity to HHT treatment. Collectively, our findings establish INSM1 as a key regulator of neuroendocrine plasticity and a promising therapeutic vulnerability in NEPC, providing a rationale for targeting INSM1 to suppress tumor progression.

Bipolar androgen therapy (BAT) exploits the paradoxical vulnerability of castration-resistant prostate cancer (CRPC) cells to rapid cycling between castrate and supraphysiologic androgen concentrations, but clinical BAT uses testosterone, which can also activate wild-type androgen receptor (AR) in androgen-responsive tissues, causing systemic side effects. 5{beta}-dihydrotestosterone (5{beta}-DHT) is a naturally occurring testosterone metabolite generally considered androgenically inactive because it binds wild-type AR weakly, yet its activity against clinically relevant AR mutants has not been systematically evaluated. Here, we tested whether 5{beta}-DHT and related 5{beta}-reduced testosterone metabolites activate AR signaling and growth programs in prostate cancer models that carry AR mutations. In C4-2 cells, 5{beta}-DHT and 3{beta}-etiocholanediol (3{beta}-ecdiol) increased canonical AR target genes, including KLK3 and TMPRSS2, with weaker activity than testosterone, whereas other 5{beta} metabolites showed limited activity. In androgen-responsive LNCaP and C4-2 models, 5{beta}-DHT and 3{beta}-ecdiol promoted cell growth under androgen-depleted conditions, and this effect was suppressed by enzalutamide, supporting AR dependence. RNA-seq confirmed that 5{beta}-DHT and 3{beta}-ecdiol induced androgen-response gene sets substantially overlapping with testosterone, albeit at lower transcriptional magnitude. Further, we found that 5{beta}-DHT, but not 3{beta}-ecdiol, suppresses cell proliferation of LNCaP, C4-2, and PC-3 cells stably expressing the clinically relevant AR gain-of-function mutants W742C and H875Y through activating AR-induced senescence-like features after high-dose exposure, consistent with the therapeutic logic of BAT. These findings identify 5{beta}-DHT as an overlooked mutant-AR agonist capable of BAT-like tumor suppression and propose it as a testosterone surrogate in BAT with potentially reduced systemic androgenic side effects. HighlightsO_LI5{beta}-DHT and 3{beta}-ecdiol promote AR-dependent prostate cancer cell growth C_LIO_LIBoth are weaker AR agonists than testosterone by RNA-seq and qPCR C_LIO_LISupraphysiologic 5{beta}-DHT suppresses growth via AR-mediated senescence C_LIO_LIGrowth suppression extends to AR mutants W742C and H875Y C_LIO_LI5{beta}-DHT may be a lower-androgenicity testosterone surrogate for BAT C_LI

Automated Gleason grading now matches expert pathologists on the cohorts where systems are developed and tuned, but deployment-relevant gaps remain: whether an automated grade, applied without site-specific tuning or pathologist oversight, stratifies outcome comparably to expert grading on slides from unseen institutions and in cross-specimen applications. We tested this for disease-free interval (DFI), a curated recurrence endpoint. A production gland-level prostate diagnostic (PathTools Prostate v11.0) was applied frozen and uncalibrated to 298 diagnostic whole-slide images from 274 TCGA-PRAD radical-prostatectomy patients, a cohort outside its development distribution and needle-core-biopsy training data, contributed by 25 source sites under heterogeneous digitization; tissue was detected automatically with no expert region annotation. From the output we derived an ISUP grade group and continuous high-grade content, and evaluated each grade as a standalone predictor of DFI (24 events) by Harrell's c-index with 95% bootstrap confidence intervals, a paired between-method bootstrap, and Kaplan-Meier curves with the log-rank test. The automated grade reproduced the clinical grade group at quadratic-weighted kappa = 0.62 (95% CI 0.53-0.70; 48% exact, 86% within one group), within the expert inter-observer range. As the sole predictor it stratified recurrence (log-rank p = 0.022; c-index 0.69, 95% CI 0.58-0.79), and the continuous high-grade fraction was robustly prognostic (hazard ratio 1.37 per SD, p = 0.029; c-index 0.71, 0.61-0.81). Standalone discrimination was not statistically separable from the clinical grade (c-index 0.78, 0.69-0.86; paired {triangleup} c-index spanning zero), and in a joint model the automated grade added nothing beyond it, consistent with both measuring a shared morphological axis. From a single out-of-distribution slide with no pathologist oversight, the automated grade provides standalone recurrence stratification not statistically separable from whole-gland expert grading, demonstrating robust generalizability beyond training data; reported as a continuous high-grade fraction, it offers reproducible, expert-free, grade-equivalent risk stratification for harmonizing large archival or genomically-profiled cohorts.

Background: Deep-learning systems for Gleason grading are developed almost entirely on high-end clinical scanners and on cohorts from a small number of Western institutions, yet deployment increasingly involves other devices and other populations. These two distribution shifts, device and population, are rarely tested together on the same physical slides. The PAR dataset, from Erbil, Iraq, digitizes each biopsy on three scanners and provides three distinct pathologist grades, so it permits both tests at once on a Middle Eastern cohort. A concurrent study by the dataset originators validated a task-specific model and two foundation models on PAR; we complement it by testing an inde-pendently developed detect-then-grade pipeline and by separating scanner effects on detection from scanner effects on grading. Methods: We applied one fixed model de-veloped on North American and European material to all 1017 whole-slide images (339 slides from 185 patients, three scanners; 49.6% clinically significant cancer) with no scanner-specific or population-specific tuning. We measured cancer detection (area under the ROC curve of the predicted cancer-tissue fraction), all-slide ISUP agreement of the deployed detect-then-grade pipeline (quadratic-weighted kappa, QWK), and grading agreement on pathologist-confirmed cancers, at the slide level and, because a case carries up to two slides, at the patient level. The reference reader was S.A.; thresholds and operating points were cross-validated leave-one-out; scanners were compared by paired within-biopsy bootstrap and confidence intervals confirmed by patient-cluster bootstrap. Results: Detection was statistically equivalent across scanners (AUC 0.987 to 0.991; paired differences at most 0.003) and transferred to this non-Western cohort with no per-population tuning. At a 95% sensitivity operating point the deployed pipeline reached cross-validated all-slide QWK of 0.86, 0.81, and 0.86 (Grundium, Hamamatsu, Leica), matching the inter-pathologist ceiling of 0.81, against 0.23 to 0.62 for the ungated model. Grading of confirmed cancers was scanner dependent: the compact Grundium (0.63) did not differ from the clinical Leica (0.67; paired difference 0.04, 95% CI -0.03 to 0.11), while both exceeded Hamamatsu (0.44). Results held at the patient level, with grading somewhat lower for two scanners; the two slides of a case disagreed in grade in 43% of cases, and patient clustering did not widen the intervals. Conclusions: Can-cer-tissue fraction is a triage signal robust across scanner and transferable to an un-derrepresented population for detection, while grading is the scanner-sensitive step. Prostate grading models should be deployed as a detect-then-grade pipeline, with grading validated per device and confirmed on the local population.

The vagina undergoes physiologic changes across the menstrual cycle, pregnancy, birth and menopause. Most women will experience vaginal dysfunction at some point during their lives and treatment options are limited. The cyclic regeneration of the vaginal epithelium during each menstrual cycle suggests it is a stem-cell based tissue; however, human vaginal epithelial stem cells (veSCs) have not been identified. We utilized in vitro colony forming and organoid assays to confirm that cells in the human vaginal epithelium have self-renewal and differentiation potential. Specifically, we determined that stem cell activity resides in the ITGA6+ and NGFR+ fractions of the basal epithelium. We performed single-cell RNA sequencing to identify the distinct cellular compartments of the full thickness human vagina, including spatially distinct populations comprising layers of the stratified vaginal epithelium. Markers of these cells within the vaginal epithelium were validated by immunohistochemistry. CD9+ITGA6+NGFR+ and CD9+ITGA6+NGFR- cells were capable of efficient colony formation but only the CD9+ITGA6+NGFR+ fraction produced organoids containing basal, intermediate and superficial layers of the vaginal epithelium. We characterized the premenopausal human vagina at single cell resolution, validated markers and assays to test veSC developmental potential, and identified putative stem cells that may open new avenues for treating vaginal dysfunction.

Objectives: Natural language processing (NLP) can enable scalable extraction of clinically relevant information from unstructured radiology reports retrieved from electronic healthcare data warehouses, but reliance on externally hosted models may pose cost, privacy, and deployment challenges. We compared self-hosted discriminative and generative NLP pipelines for automated extraction of Prostate Imaging and Reporting Data System (PIRADS) scores from multiparametric magnetic resonance imaging (mpMRI) reports used in prostate cancer risk assessment. Materials and Methods: We identified 44,511 mpMRI reports across 68 Veterans Affairs (VA) healthcare systems. A stratified random sample of 1,973 reports was used to train, test, and evaluate multiple pipeline configurations combining Named Entity Recognition (NER) models and large language models (LLMs). Performance was assessed by accuracy of maximum PI-RADS extraction and processing speed using self-hosted implementations of spaCy NER, Transformers NER, and generative LLMs Llama 3, Qwen3, and Gemma3. Results: Across the top 10 pipeline configurations, accuracy for maximum PI-RADS extraction ranged from 89.3% to 95.5%, with processing times spanning 150 milliseconds to 70 seconds per report. Generative LLM pipelines achieved the highest accuracy (up to 95.5%) but were substantially slower (2 to 70 seconds), whereas NER based pipelines demonstrated lower accuracy (88.5%) with faster performance (50 to 150 milliseconds). Discussion: Discriminative NER pipelines achieved high accuracy while offering advantages in speed and potential scalability. Accuracy gains from LLMs were accompanied by significantly higher computational cost, potentially limiting feasibility in high-volume clinical environments. Conclusion: Discriminative methods were more efficient than generative models in annotating PIRADS from mpMRI report text, providing insights into configurations for optimal clinical deployment when volume is a limiting factor. However, generative AI offered improved accuracy with less upfront development.

Objective: To determine whether the most cited and the most publicly discussed benign prostatic hyperplasia (BPH) literature describe the same body of work, and whether clinicians and patients read different evidence. Methods: Four Boolean Web of Science searches and four matched Altmetric Explorer searches were run in February 2024, restricted to literature indexed with urologic terminology, and screened in duplicate. Two arms were assembled: the 50 most-cited articles (citation census May 2024) and the 50 with the highest Altmetric Attention Scores. Funding source and intervention focus were hand-coded from the full text of all 100 articles; open-access status came from Unpaywall. Arms were compared with Mann-Whitney U and Fisher exact tests Results: Eight of 50 articles (16%) appeared in both arms. Citation selected articles were older (median 2008 versus 2018, p<0.001), more often randomized trials (58% versus 22%, p<0.001), and more often published in urology-specific journals (96% versus 66%, p<0.001). Industry funded 50% versus 18% of articles (p=0.001) and non-industry sources 8% versus 36% (p=0.001). Only 10% of citation-selected articles were open access versus 56% (p<0.001). Attention data were recoverable for only 13 citation-selected articles (median score 9 versus 17). The two arms were cited in the 2026 AUA BPH Guideline at indistinguishable rates (22% versus 20%, p=1.00). Conclusions: These are largely distinct bodies of work, separated most decisively by whether they can be read without a subscription, yet both inform guideline development equally. Patients arrive with evidence systematically different from, and no less guideline-relevant than, that underpinning their urologist's training.