The Prostate

ObjectiveTo demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance. MethodsWe assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci ("interfocal stroma") were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence. ResultsWe identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-countingmethod (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR. ConclusionsWe were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

ObjectivesBenign Prostatic Hyperplasia (BPH) is the non-cancerous enlargement of the prostate accompanied by lower urinary tract symptoms, affecting 50% of men by the age of 501,2. Advanced highly symptomatic BPH exhibits large epithelial glandular nodules with microglandular/atypical adenomatous hyperplasia, but how these features form is unknown3. Our lab has reported that the common parasite Toxoplasma gondii can infect the prostate and induce glandular nodule formation in mice3. The objective of this study is to determine if T. gondii exposure in humans correlates to BPH and nodule formation and if it induces urinary dysfunction concurrent in the mouse model. MethodsWe assessed Toxoplasma exposure by serum ELISA in patients with BPH and non-BPH donor controls, and compared seropositivity rates between the groups. We further assessed the histopathology of these patients for the presence of inflammation and epithelial glandular nodule formation and compared Toxoplasma positive and negative samples. We determined voiding function in Toxoplasma-infected mice between 14 and 60 days of infection with void spot with Void Whizzard software. ResultsMen diagnosed with BPH are more likely to be seropositive for Toxoplasma than age-matched undiagnosed donor controls. In addition, BPH patients that are seropositive for Toxoplasma are more likely to exhibit glandular nodule formation with microglandular / adenomous hyperplasia than seronegative BPH patients. In animal studies, Toxoplasma infection results in abnormal void patterns concurrent with microglandular hyperplasia and nodule formation. ConclusionsThese results suggest that Toxoplasma may be contributing to BPH pathology and lower urinary tract dysfunction in both humans and mice, opening new insights into the development of this important disease. The results also serve to further characterize this model of prostatic hyperplasia and define it as a potential urinary dysfunction model.

Aims Active surveillance (AS) allows selected men with localized prostate cancer to defer curative therapy and reduce treatment morbidity. Conversion from AS to treatment is commonly triggered by Gleason grade group (GGG) upgrading on confirmatory biopsy. We developed and validated a digital pathology artificial intelligence (DPAI) biomarker to predict GGG upgrading in AS-eligible patients. Materials & Methods The DPAI model was trained using histopathology image features from diagnostic biopsies of 998 patients and validated in an independent cohort of 296 patients meeting criteria for AS. Logistic regression estimated the probability of confirmatory-biopsy GGG increase, and feature selection identified the most predictive variables. Results AI-GUR (Artificial Intelligence-Gleason Upgrade Risk) predicted GGG reclassification at confirmatory biopsy (OR 1.60; p=0.0003), and provided information beyond conventional stratification (risk group, CAPRA) and cribriform morphology (all p<0.01). Predicted risks were similar across time from diagnosis (~10-15% to ~85% at 1, 1.5, or 2 years; p for time=0.50), consistent with initial biopsy mischaracterization rather than time-dependent progression. Conclusions AI-GUR provides individualized estimates of confirmatory-biopsy GGG upgrading for AS candidates. Using DPAI may improve shared decision-making by complementing standard clinicopathologic tools and molecular testing using the same biopsy specimen, while informing the likelihood of grade upgrade at confirmation.

Background: The T-cell receptor {beta} (TCR{beta}) repertoire reflects antigen-driven adaptive immune responses and provides insight into tumor-immune interaction. In prostate cancer (PCa), the immunosuppressive tumor microenvironment limits effective T-cell activation, and the antigenic drivers shaping intratumoral TCR repertoires remains poorly defined. This study aimed to characterize matched tumor and peripheral TCR{beta} repertoires from treatment-naive PCa patients and to identify shared clonotypes and antigenic specificities associated with disease severity. Methods: Next-generation sequencing was used to profile TCR{beta} repertoires from matched tumor biopsies and peripheral blood mononuclear cells obtained from treatment-naive PCa patients. Repertoires clonality, diversity, and was assessed using established metrics. Antigenic convergence was evaluated using GLIPH2 to identify shared CDR3{beta} motifs and predicted tumor-associated antigen (TAA) recognition, followed by functional validation using IFN-{gamma} ELISpot and T-cell expansion assays. Results: Tumor-derived TCR{beta} repertoires displayed reduced richness and increased clonality compared with peripheral blood mononuclear cells, consistent with local antigen-driven expansion. High-grade tumors demonstrated greater interpatient clonotype sharing and motif-level convergence, indicative of recognition of common TAAs. GLIPH2 analysis associated expanded clonotypes with epitopes derived from prostate-specific G-protein coupled receptor (PSGR), prostate-specific membrane antigen (PSMA), and prostate-specific antigen (PSA). Functional validation confirmed that peptide pools containing PSGR- and PSMA-derived epitopes induced IFN-{gamma} production and antigen-specific T-cell proliferation in vitro. Conclusions: These findings reveal an oligoclonal, antigen-driven intratumoral TCR{beta} landscape and identify PSGR and PSMA as immunogenic, potentially actionable targets. Integration of TCR profiling with antigen discovery pipelines may support the development of TCR-based biomarkers and precision immunotherapeutic strategies in prostate cancer.

Single-cell RNA sequencing (scRNA-seq) effectively captures the differences in transcriptomic landscape of cell types and cell states between benign and cancer tissues. Pooling publicly available datasets distributed across independent studies enables increased sample representation and cross-study comparisons. Here we present a harmonized scRNA-seq atlas of the human prostate constructed by integrating 17 available studies, comprising 163 samples from 106 donors. The dataset contains benign tissue, primary tumors, and metastatic disease profiles. Raw sequencing FASTQ data files were uniformly reprocessed to minimize technical variability. Study metadata were curated and standardized using a unified schema capturing donor identity, tissue site, disease context, and histologic grade. Post quality control, the integrated dataset contains 754,000 high-quality cells. Harmonized cell type annotations were generated using a pseudobulk correlation framework informed by multiple reference resources. The workflow identified 17 distinct cell types representing epithelial, mesenchymal, and immune compartments of the prostate. The processed expression matrices, standardized metadata, and analysis workflows are publicly available to support reproducible analysis and enable exploration of heterogeneity across prostate disease states.

Suppressor of cytokine signaling 1 (SOCS1) negative regulates inflammatory cytokine production and attenuates oncogenic growth factor signaling pathways. Reduced SOCS1 protein expression in human prostate cancer correlates with greater disease severity. To define the physiological functions of SOCS1 functions in the prostate, we conditionally ablated Socs1 in prostate epithelial cells of C57BL/6 mice. These Socs1{Delta}PE mice exhibited normal prostate development, maturation and lobular architecture. However, adult Socs1{Delta}PEmice developed progressive epithelial hyperplasia and inflammatory cell infiltration that were temporally and spatially distinct. SOCS1-deficient prostate showed increased epithelial cell proliferation and elevated oxidative stress markers, and prostate organoids recapitulated this hyperplasia phenotype. Diet-induced obesity exacerbated both hyperplasia and inflammation in SOCS1-deficient prostate. Upon transurethral infection with uropathogenic Escherichia coli UPEC1677 expressing the genotoxin colibactin, Socs1{Delta}PE mice developed invasive prostate cancer with complete loss of lobular architecture, whereas control mice developed hyperplasia and pre-neoplastic lesions. In vitro, SOCS1-deficient prostate organoid-derived epithelial cells exhibited increased DNA damage following exposure to UPEC1677. Deletion of the colibactin biosynthetic gene clbP in UPEC1677 abolished its ability to induce DNA damage in SOCS1-deficient cells and to drive prostate cancer in vivo. Proteomic analysis of prostate organoids revealed dysregulation of basal and luminal epithelial lineage markers and signaling pathway proteins that could promote neoplasia in SOCS1-deficient cells. Collectively, these findings establish an essential, epithelial cell-intrinsic role for SOCS1 in maintaining prostate tissue homeostasis by restraining proliferation, regulating lineage plasticity, limiting inflammation and oxidative stress, and conferring protection against genotoxic injury and neoplastic transformation.

For over a century, the goal of estimating clinical outcome from tumor biopsies has been based on histomorphology of 2D tissue slices that represent a small fraction of collected samples. Its power derives from histologys 1) unbiased representation of cell types, 2) subcellular resolution that allows the characterization of health and disease states across cell types, and 3) multi-millimeter fields of view that allow assessment of tumor heterogeneity. Histologys dependence upon physical slices, however, limits assessment of 3-dimensional cellular volumes and tissue architecture. Here, we used propagation-based phase-contrast micro-CT (PBCT) to create 3D histological images of residual formalin-fixed, paraffin-embedded (FFPE) prostate needle biopsies. The resulting isotropic, grey-scale, 0.5 micron voxel matrices were used to explore the potential of for the 3D virtual histology to distinguish diagnostic categories including benign prostatic tissue and prostatic adenocarcinoma of Gleason patterns 3, 4, and 5. Maximum intensity projections of stacks of digital slices totaling 5 microns "slices" allowed the study of virtual sections corresponding to actual serial H&E-stained sections of tissue cut after micro-CT imaging. Like histology, our PBCT reconstructions allowed us to distinguish between non-infiltrative and undulating glands of benign prostatic tissue, infiltrative round glands of Gleason pattern 3, cribriform structures of Gleason pattern 4, and comedonecrosis of Gleason pattern 5. Unlike histology, micro-CT allowed us to further probe 3D tissue architecture in volumetric context. User-friendly exploration of sample volumes was achieved using a customized Neuroglancer multiplanar and 3D rendering interface. Sparsely trained cycleGAN produced plausible virtual H&E staining from the unstained micro-CT reconstructions. Unlike tissue-section based histology, micro-CT-based virtual histology yields nondestructive 3D characterization of cancer cell and tissue architecture, including glandular spaces, without the undersampling or cutting artifacts of histology. These findings demonstrate the feasibility of PBCT-based 3D virtual histology of prostate cancer and suggest the exploration of derived quantitative analyses of tumor properties for potential contributions to patient care.

Circulating prostate-specific antigen (PSA) discriminates poorly in the diagnostic gray zone (3.0-9.99 ng/mL), where ~75% of biopsies yield no clinically significant prostate cancer (PCa). We evaluated whether urinary creatine riboside (CR), a tumor-derived metabolite excreted through the prostatic urethra, complements PSA for gray-zone detection and independently predicts prostate-cancer-specific mortality (PCSM). In the NCI-Maryland PCa Case-Control Study (951 cases, 962 controls; 47.6% African American men; median follow-up 11.5 years), urinary CR was quantified by UPLC-MS/MS. Within the PSA gray zone (n = 668), urinary CR was complementary to PSA, with markedly higher single-marker discrimination than PSA (AUC 0.93, 95% CI 0.88-0.98 vs 0.77, 0.66-0.89) and additive when combined ({Delta}AUC +0.17, p < 0.001; 91.4% sensitivity at 80% specificity). After adjustment for 11 clinical and sociodemographic covariates, urinary CR independently predicted PCSM complementary to PSA (Fine-Gray SHR 1.72, 1.35-2.19 for CR; 1.35, 1.08-1.68 for PSA; Harrell's C 0.85 for CR + PSA vs 0.77 for PSA alone), with strongest signal in African American men (SHR 2.43, 1.57-3.75 for CR). We conclude that urinary CR is a candidate non-invasive biomarker complementary to PSA - improving gray-zone triage and predicting PCSM; prospective validation in biopsy-referred cohorts is warranted.

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

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.

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: Changes in the extracellular matrix (ECM) are a recognised feature of aggressive prostate cancer, but they are not exploited in clinical decision-making. We aimed to develop automated quantitative ECM parameters to facilitate risk stratification for localised prostate cancer. Methods: 378 quantitative ECM parameters were derived from picrosirius red-stained diagnostic prostate biopsies in a cohort of 422 patients, matched 1:1 for recurrence, recruited to the CHHiP (Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy in Prostate Cancer) trial of radiotherapy fractionation for localised prostate cancer. These ECM parameters comprehensively described fibre architecture, gaps and ECM texture. Machine learning models at the level of both individual image tiles and patients defined how ECM parameters related to tumour versus normal prostate, Gleason grade group and recurrence. Shapley analysis was used to interpret ECM feature importance and develop signatures associated with recurrence. Results: Specific ECM patterns identified tumour versus normal prostate, Gleason pattern 4 versus 3 and recurrence. ECM patterns associated with recurrence were enriched in Gleason 4+3 patients, versus Gleason 3+4 patients. Shapley analysis revealed that biopsies from patients with recurrence had smaller more elongated gaps between fibres, with finer grained ECM texture and lower ECM homogeneity than less recurrent regions. Interpretation: Quantitative automated analysis of ECM architecture can inform probability of prostate cancer recurrence after radiotherapy; Features relating to ECM gap size and texture are of particular relevance.

Objectives. To determine whether medical machine olfaction via tracking the activation of mammalian G-Protein Coupled odorant Receptors (GPCR) stabilized by proprietary co-polymers on a photonic MZI chip can be used to diagnose prostate cancer (PCa) via urine scent. Specifically, scent character is compared against the current diagnostic PCa screening gold-standard in the US: the serum level of prostate specific antigen (PSA). The device is an artificial nose sensor built on a commercial photonic platform that reads interchangeable Mach-Zehnder interferometer (MZI) chips. These chips were functionalized with a stabilised panel of mammalian olfactory G-protein-coupled receptors (GPCRs). These samples had been characterized into POSITIVE or CONTROL for PCa six to eight years prior by standard hospital diagnostic procedures and by trained medical detection dogs, then stored at -80 Celcius. A subset of 80 patients urine samples was subsequently thawed and used for training and testing the medical machine olfaction system of RealNose as an initial validation of the novel technology and methodological approach. We posed two primary research questions: (a) whether the cancer-associated odor profile would remain detectable by machine-based systems following long-term storage and with what accuracy could it be used to cluster (YES and AUC 0.79 from scent character alone), and (b) what technical and procedural requirements would be necessary to translate such a signal into a clinically useful diagnostic assay (more training samples (500 predicted to yield 0.93) and increased breadth of receptors per chip and/or more chips per device in next iteration seen as helpful). Design, setting, participants. Retrospective diagnostic-accuracy feasibility study on 80 biobanked urine samples (40 PCa, 40 non-cancer; 368 sensor runs; a subset of unknown Gleason grade) from a single UK NHS urology service, the same collection used to train canine detectors. Main outcome measures: Patient-level Receiver Operating Characteristic (ROC) area under the curve (AUC) under patient-grouped cross-validation with a fold-honest pooled-control reference (reconstructed from training-partition controls only); sensitivity, specificity and predictive values at pre-specified operating points; 1000-fold whole-procedure label-permutation significance; patient bootstrap 95% CIs; and leave-one-day-out / leave-one-chip-out generalisation. Results. An L2-regularised linear classifier when allowed to see between three and six chips outcome on a patient sample extracted within-instrument AUC 0.79 (95% CI 0.69 to 0.88; 1000-permutation p = 0.001) from urine scent alone, exceeding this cohort own serum prostate-specific antigen (PSA) discrimination (AUC 0.645; itself within the population range for PSA 0.67) and obtained without a blood draw (at the Youden point, sensitivity 0.75, specificity 0.78, PPV 0.77, NPV 0.76). Upon allowing PSA the total AUC rose to 0.82. This was not a plateau: AUC rose from chance at 30 training samples, passed the serum-PSA range at 40, and reached 0.79 at 80 patients (0.82 if PSA was included), with an inverse-power fit projecting 0.93 by n = 500 and 0.96 by n = 1000. The discriminant was a genuine multivariate receptor pattern, independent of patient age (Spearman 0.09; the cohort is not age-matched). So at least for these data, neither age, nor collection day, ambient humidity/temperature, or overall signal amplitude (sometimes thought of as intensity of smell) were predictive of prostate cancer status, yet the scent character was. Transfer to a new sensor chip fell to AUC 0.57 without calibration, meaning the remaining obstacles are hardware portability rather than signal existence: much as a detection dog acclimatizes to a new setting, the system improves with on-site calibration prior to use. Conclusions: A genuine, confound-controlled olfactory PCa signature is recoverable from 80 samples, surpasses this cohort serum PSA (0.645) and exceeds the population PSA range, and improves monotonically with training-set size. We present this as a small-sample feasibility benchmark, not yet a validated diagnostic; the dominant remaining factor is training-set size, and the path to clinical-utility and improved AUC is clearly found to be a larger, multi-site, age-matched, and ideally prospective training cohort. A transferable small-sample lesson is also reported: adaptive feature searches (evolutionary and self-calibrating-protocol handle search) artificially inflate cross-validation and collapse under whole-procedure permutation, whereas non-adaptive averaging survives, giving a robust scent signal obtainable from the headspace of urine samples and recordable by the RealNose device that keeps improving with expanding sample training set.

Background: Prostate cancer is the second most common cancer in men worldwide. The Prostate Specific Antigen (PSA) blood test is widely used for prostate cancer detection but suffers from high false-positive rates (up to 80%). Genetic risk scores (GRS/PRS) have a similar performance to PSA testing in predicting prostate cancer risk. Method: GRS269 for prostate cancer was derived using 269 known risk variants and applied to UK Biobank participants. We assessed whether GRS269 improved power to predict prostate cancer diagnosis on top of age and pre-prostatectomy PSA level among 17,380 cases. Longitudinal PSA measurements were processed as median, first, last (most recent), and random PSA. All models were adjusted for age. Results: Across all PSA measures, the integrated model combining GRS269, PSA, and age consistently outperformed models using GRS269 or PSA alone. The highest predictive performance was observed using the last PSA value combined with GRS269 (AUC = 0.82, 95% CI: 0.81-0.82), compared to GRS269 alone (AUC = 0.70, 95% CI: 0.68-0.72) or PSA alone (AUC = 0.73, 95% CI: 0.70-0.75). Conclusion: Combining genetic risk with PSA and age improves prostate cancer risk prediction in a population setting. These findings highlight the potential clinical implications of integrating GRS will enhance early prostate cancer prediction pathways in primary care.

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.

BackgroundRisk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa. MethodsPRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis. ResultsIn the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation. ConclusionsAmong men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.

The PRESERVE trial (NCT04972097) is a prospective, single-arm pivotal IDE study evaluating focal irreversible electroporation (IRE) using the NanoKnife System for intermediate-risk prostate cancer. Men with Gleason Grade Group 2-3 disease underwent focal IRE and were followed for durability of oncologic control and safety. At 24 months, 68 patients completed follow-up with no new treatment failures identified. PSA levels were below baseline in 97% of patients, and one clinically triggered biopsy was negative for cancer. No new device- or procedure-related adverse events occurred beyond 12 months. These findings demonstrate durable efficacy and sustained safety of focal IRE.

Interobserver variability in pathological assessments is a well-recognized challenge that impacts diagnostic reliability and disease understanding. This variability exists across many subspecialties due to the subjective nature of evaluations. Artificial intelligence (AI) applied to whole slide images has potential to standardize procedures and reduce variability in pathology, but transitioning to these technologies does not guarantee improvement. Establishing reliable ground truth datasets with consensus annotations is crucial for developing robust AI solutions. We introduce SortIT, an open-source web application that facilitates systematic creation and evaluation of ground truth image tile annotations. SortIT enables multiple annotators to independently label tiles, with flexible user permission controls. Annotated data can be exported for statistical analysis of observer variation and for creating ground truth datasets from consensus tiles. We outline protocols using SortIT for several use cases: (1) mitosis segmentation in tumor regions, (2) evaluating AI solutions for prostate cancer grading by comparing to expert consensus, and (3) granuloma classification by annotating discriminative tile-level features. Key strengths of SortIT lies in its ease of deployment, making it accessible and usable for a wide range of users. Overall, SortIT provides a valuable tool to establish high-quality ground truth datasets and comprehensively assess observer variability. Critical evaluation of ground truth quality using systematic annotation methodologies is crucial for developing accurate and generalizable diagnostic AI tools. Its open-source nature facilitates community adoption and further development.

The therapeutic efficacy of oncolytic viruses is often limited by the presence of tumor cells that resist virus-mediated killing. Here, we investigated the molecular mechanisms underlying resistance to Vesicular Stomatitis Virus (VSV) in PC3 cells, an aggressive metastatic prostate cancer (PrCa) cell line, using the VSV-sensitive LNCaP cell line as a comparator. RNA sequencing revealed that, relative to untreated cells, VSV-infected PC3 cells upregulated both pro-apoptotic genes, including BIM, PUMA, and NOXA, and anti-apoptotic and antiviral genes, including A20 and RIG-I. In addition, genes associated with antiviral and pro-survival pathways, including NF{kappa}B and PI3K-Akt signaling, were more highly expressed in PC3 cells than in LNCaP cells. At baseline, PC3 cells also exhibited elevated expression of multiple pro-survival genes, including BCL-xL, MCL1, and CK2, compared with LNCaP cells. Complementary proteomic analyses identified enhanced activation of NF{kappa}B, PI3K-Akt, and MSK1 signaling in VSV-infected PC3 cells relative to infected LNCaP cells. Furthermore, pharmacological inhibition of BCL-2 family proteins or NF{kappa}B signaling restored sensitivity to VSV-induced cell death in PC3 cells. Collectively, these findings identify NF{kappa}B-centered pro-survival signaling networks as key contributors to the resistant phenotype of PC3 cells and suggest that combining oncolytic virotherapy with targeted inhibitors may improve therapeutic efficacy in resistant prostate cancers.

Background: Prostate cancer survival varies by stage at diagnosis, and Black men experience a disproportionate burden of advanced disease. We examined whether neighborhood deprivation, measured by Area Deprivation Index (ADI), contributes to racial differences in metastatic presentation. Methods: We conducted a population-based study of men diagnosed with prostate cancer in the Ohio Cancer Incidence Surveillance System from 1996 to 2016. The primary endpoint was distant-stage disease at diagnosis. Generalized additive models assessed nonlinear associations of ADI and diagnosis year with metastatic risk. Inverse probability of treatment weighting (IPTW) models estimated odds ratios comparing Black with White men after sequential adjustment for diagnosis year, age, insurance, and ADI. Results: Among 135,095 men, 18,690 were Black and 116,405 were White. Distant-stage disease occurred in 7.0% of Black men and 5.0% of White men. Black men had higher median ADI (60.9 vs. 47.3). Medicaid-insured men had the highest unadjusted odds of metastatic presentation (OR, 4.68; 95% CI, 4.13-5.31), exceeding uninsured men (OR, 2.91; 95% CI, 2.54-3.34). In IPTW models without age adjustment, the odds ratio decreased from 1.54 to 1.24 after adding insurance and ADI. In age-adjusted IPTW models, the odds ratio decreased from 1.79 to 1.41 after adding insurance and ADI. Generalized additive models showed increasing metastatic risk at higher ADI values and after 2008. Conclusions: Neighborhood deprivation and insurance-related access explained part, but not all, of the excess odds of metastatic diagnosis among Black men. Impact: Integrating ADI into cancer surveillance may improve identification of populations at risk for late-stage diagnosis.