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This cohort study examines patterns of bone-modifying agent use from 2013 to 2024 among men with metastatic castration-resistant prostate cancer.

21 Background: Large language models (LLMs) are becoming an increasing source of medical information for both patients and providers. Clinical decision making is occasionally complex and may use factors outside of traditional guidelines. We conducted a comparative performance evaluation of frontier models on standardized genitourinary (GU) oncology knowledge assessments derived from the American Society of Clinical Oncology Self-Evaluation Program 2025 (ASCO SEP 2025) in addition to challenging questions developed by GU oncology experts. Methods: Three frontier LLMs were selected: ChatGPT-5.2 (OpenAI, San Francisco, CA, USA), Claude Opus 4.6 (Anthropic, San Francisco, CA, USA), and Gemini 3 Pro (Google DeepMind, Mountain View, CA, USA). Model selection was based on publicly reported performance on “Humanity’s Last Exam,” a benchmark of advanced scientific reasoning (Scale AI leaderboard Jan-Feb 2026). The OpenEvidence (OpenEvidence Inc., MA, USA) search engine was also selected for evaluation. All models were asked 145 ASCO SEP 2025 GU oncology questions and 12 real world challenging scenarios using an uniform prompt. Subject matter experts peer reviewed LLM generated responses to challenging questions and credit was given only for correct rationale and option selection. Results: All models demonstrated high and comparable accuracy on Standardized knowledge assessment, with Gemini 3 Pro leading numerically at 97.24% (95% CI, 93.1% – 99.0%), OpenEvidence followed with an accuracy of 94.48% (95% CI, 89.5% – 97.3%), while Chat GPT -5.2 and Claude OPUS 4.6 scored 92.41% (95%, CI 86.9% – 95.7%) and 79.31% (95% CI, 71.9% – 85.1%) respectively. On challenging real world scenarios, ChatGPT-5.2 demonstrated the highest numerical accuracy at 100% (95% CI, 75.8% – 100%), Gemini 3 Pro and Open Evidence scored 75.0% (95% CI, 46.8%–91.1%) each, followed by Claude Opus 4.6 at 66.7% (39.1–86.3). Conclusions: All current frontier AI models appear to provide accurate medical information and rationale on a large majority of guideline based medical knowledge assessments for GU oncology. Frontier AI models also appear to have emerging competence in highly challenging real-world scenarios. AI based tools may become a valuable and reliable tool for patients and providers. Comparative LLM performance. Model ASCO SEP 2025 % (95% CI) Real World Scenarios % (95% CI) Gemini 3 Pro 97.24% (93.1–99.0) 75.0% (46.8–91.1) OpenEvidence 94.48% (89.5–97.3) 75.0% (46.8–91.1) ChatGPT-5.2 92.41% (86.9–95.7) 100% (75.8–100) Claude Opus 4.6 79.31% (71.9–85.1) 66.67% (39.1–86.3)

57 Background: Cardiovascular disease (CVD) is the leading non-cancer cause of death among men with prostate cancer (PC), particularly during androgen-deprivation therapy. While CVD risk-prediction models are well established in the general population, barriers to integration within oncology workflows remain poorly defined. Guided by the PRISM and RE-AIM frameworks, this study evaluated multilevel determinants influencing adoption, feasibility, and sustainability of CVD risk-assessment tools in PC care. Methods: A national, cross-sectional survey of 45 physicians (medical oncology 49%, urology 38%, radiation oncology 13%) from 18 U.S. states was conducted. Respondents represented early- (27%), mid- (36%), and late-career (38%) clinicians across academic (87%) and community (13%) settings. Quantitative items mapped to PRISM/RE-AIM domains assessed adaptability, cost/resource feasibility, effectiveness, equity, and sustainability; open-ended responses explored contextual barriers and facilitators. Descriptive statistics summarized quantitative data. Results: Physicians reported moderate-to-high readiness to adopt a CVD risk-assessment tool, emphasizing workflow alignment and EHR integration over algorithmic complexity. Table 1 summarizes domain-specific findings. Ninety-three percent of respondents favored lab-independent models that minimize workflow disruption. Conclusions: Pre-implementation analysis highlighted that structural barriers, rather than algorithmic or statistical limitations, remain the primary obstacles to adopting CVD risk-assessment tools in prostate-cancer care. Physicians emphasized the need for EHR-integrated, non-laboratory-based, and time-efficient tools that align with routine oncology workflows and minimize cross-specialty friction. Addressing these practical and organizational determinants may be pivotal to achieving equitable, scalable, and sustainable cardio-oncology implementation across diverse practice settings. PRISM and RE-AIM constructs evaluation results. Construct Likely/Very Likely (%) Interpretation Perceived Effectiveness 87% Broad confidence in clinical utility Equity in Patient Reach 75% Strong perceived equity in reach Equity in Effectiveness 73% Expected to perform consistently across populations Adaptability to Workflow 71% High compatibility with existing clinic workflows Cost/Resource Feasibility 65% Feasible for resource-constrained settings Likelihood of Adoption 60% Moderate readiness for implementation Equity in Adoption 58% Equitable uptake possible despite resource differences Reach to Target Population 57% Moderate likelihood of reaching most patients Sustainability Over Time 51% Moderate long-term feasibility Consistency of Implementation 42% Need for standardized protocols

352 Background: Glucagon-like peptide 1 (GLP1) receptor expression in prostate cancer tissue has raised concerns that GLP1 receptor agonists (GLP1A) may influence tumor growth or progression. We compared prostate cancer severity at diagnosis among patients receiving GLP1A versus other glucose lowering therapies. Methods: A retrospective chart review identified patients who received GLP1A or alternative second-line glucose lowering agents (DPP4/SGLT2 inhibitors) and were subsequently diagnosed with prostate cancer. Patients receiving both drug classes were excluded. Baseline demographics (race, ethnicity, age), comorbidities (diabetes, myocardial infarction, peripheral vascular disease) concomitant medications (insulin, metformin, statins), appointments (within past year) and laboratory values (HbA1c, eGFR) at therapy initiation were recorded. Primary outcomes included Gleason grade group and metastatic presentation within three months of diagnosis. Propensity score matching balanced baseline characteristics between groups. Mann-Whitney U tests compared grade groups. Chi-square tests compared metastatic presentation, reporting odds ratios (OR) with 95% confidence intervals (CI). Multivariable logistic regression identified predictors of grade group (low-grade: 1-2; high-grade: ≥3) and metastatic presentation, reporting OR with 95% CI. A p value <0.05 indicated a significant association. Results: Of 260 propensity-matched patients (GLP1A: 130, comparison: 130), 87% were White and 95% were Non-Hispanic/Non-Latino in each group. Median HbA1c was 7% (IQR: 6.4-8) in GLP1A group versus 7.5% (6.2-8.5) in comparison group. Median eGFR was 75mL/min/1.73m 2 (IQR: 61.5-90.0) in GLP1A group versus 60.0 (52.0-80.5) in comparison group. High-grade disease occurred in 33 (25%) GLP1A patients and 37 (29%) comparison patients. Among patients with available imaging (GLP1A: 40; comparison: 32), metastatic presentation occurred in 7 (18%) GLP1A group and 7 (22%) comparison group patients. No significant differences were observed in grade group (GLP1A median: 2; IQR: 1-2 vs. comparison median: 2; 1-3; p=0.44) or metastatic presentation (OR: 1.32; 95%CI: 0.41-4.25; p=0.64) between groups. In multivariable analysis, higher HbA1c was associated with high-grade disease in GLP1A group (OR: 1.83; 95%CI: 1.07-3.35; p=0.03) while higher eGFR was associated with low-grade disease in the comparison group (0.97; 0.93-0.99; p=0.04). Conclusions: GLP1A use was not associated with prostate cancer grade or metastatic presentation at diagnosis compared to alternative glucose-lowering agents. However, the association between elevated HbA1c and high-grade disease in GLP1A patients suggests a potential impact of poor glycemic control on tumor biology. Prospective studies are needed to elucidate the relationship between metabolic dysregulation and prostate cancer severity.

Abstract Background Clival metastases are a rare and clinically aggressive manifestation of advanced prostate cancer, associated with cranial nerve palsy and poor survival. The molecular features of prostate cancers giving rise to clivus metastases remain unknown. Patients and methods We performed a multi-center retrospective study across six institutions, identifying prostate cancer patients with radiographically confirmed clival metastases and available next-generation sequencing (NGS) data. Baseline characteristics and clinical outcomes were collected. Genomic alterations from tissue- and/or blood-based assays were aggregated at the patient level and compared with a publicly available metastatic castration-resistant prostate cancer (mCRPC) cohort (SU2C/PCF). Results Fifty-nine patients with clival metastases contributed 87 molecular assays. More than half of patients had Gleason grade group 5 cancer and presented with de novo metastatic (M1) disease. The median interval from initial prostate cancer diagnosis to clival metastasis was 71.4 months (95% CI, 42.0-101.7), while median overall survival following clival involvement was only 15.3 months (95% CI, 6.9-22.8). Compared with the SU2C/PCF mCRPC cohort, clival metastases showed significant enrichment of BRAF and CHEK2 alterations as well as homologous recombination repair (HRR) with relative depletion of AR-related, PI3K pathway, and G2–M pathway alterations. Conclusion Prostate cancers giving rise to clival metastases exhibit a distinct molecular profile enriched for DNA damage–repair and RAF kinase alterations, suggesting unique metastatic biology and potential therapeutic vulnerabilities.

Abstract Background Socioeconomic position (SEP) is a fundamental social determinant of health (SDoH) contributing to observed disparities in cancer and cardiovascular (CV) care among individuals with prostate cancer (PC). Understanding the influence of SEP on CV health is essential for addressing outcome inequities within this population. Methods We conducted a retrospective study utilizing Surveillance, Epidemiology, and End Results (SEER)-Medicare to evaluate CV outcomes in patients ≥65 years with PC from 2009 to 2017. We studied the impact of SEP on cardiovascular events (CVEs), cardiovascular mortality (CVm), PC-specific mortality (PCsm), and all-cause mortality. CVE, CVm, and PCsm were analyzed using competing risk models and all-cause mortality with Cox proportional hazards models. A subgroup analysis was performed in Non-Hispanic Black and Non-Hispanic White individuals. Results We included 141 242 patients of whom 76 844 were in high SEP areas (45.6%) and 64 398 in low SEP ones (54.4%). Both groups had a mean age of 72 years; patients in low SEP areas were 67.5% Non-Hispanic White, 34.4% having diabetes, 73.3% hypertension, and 67.7% dyslipidemia versus the high SEP group with 85.6% Non-Hispanic White, 30.4% diabetes, 69.9% hypertension, and 70.7% dyslipidemia. Patients in low SEP areas had higher risks of CVm (subdistribution hazard ratio [sHR] = 1.18, 95% CI = 1.12 to 1.25), PCsm (sHR = 1.12, 95% CI = 1.06 to 1.17), CVE (sHR = 1.07, 95% CI = 1.04 to 1.09), and all-cause mortality (HR 1.16, 95% CI = 1.13 to 1.20). Accentuated results were shown in the Non-Hispanic Black subgroup. Conclusion Adverse SEP plays a significant role in shaping outcomes among older patients with PC, contributing to elevated risks of adverse CV and survival outcomes.

ImportancePatients with metastatic castration–resistant prostate cancer (mCRPC) harboring BRCA1/2 alterations are eligible to receive poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors as single agents or in combination with an androgen receptor pathway inhibitor after these agents showed survival improvement in their landmark clinical trials. However, data are limited regarding the uptake of PARP inhibitors in these patients.ObjectiveTo investigate the use of PARP inhibitors in patients with mCRPC harboring BRCA1/2 alterations.Design, Setting, and ParticipantsThis retrospective cohort study used the deidentified Flatiron-Health electronic health record–derived database of US community and academic practices to extract patient-level data. The data cutoff date was May 31, 2024. Patients with mCRPC with evidence of harboring BRCA1/2 alterations and alive after August 15, 2020 (ie, 3 months after the approval of the first PARP inhibitor, rucaparib, in mCRPC) were included. Statistical analysis was performed from September 2024 to May 2025.ExposuresAge, race and ethnicity, insurance status, and practice type at the time of mCRPC diagnosis.Main Outcomes and MeasuresThe receipt of PARP inhibitors. Multivariable logistic regression was conducted to assess the association between the exposures and the main outcome.ResultsOf 24 105 patients with metastatic prostate cancer, 443 male patients (median [IQR] age, 72 [65-79] years) had mCRPC with BRCA1/2 alterations and were eligible and included in our analysis. Of these patients, 227 (51.2%) received a PARP inhibitor, whereas 216 (48.8%) did not. Compared with patients covered by a commercial health plan, those covered by Medicare were significantly more likely to receive a PARP inhibitor (odds ratio, 1.91; 95% CI, 1.02-3.66; P = .047). The odds of receiving a PARP inhibitor were not significantly higher in patients treated in community practice compared with those treated in academic centers (odds ratio, 1.64; 95% CI, 1.00-2.70; P = .05).Conclusions and RelevanceThis retrospective cohort study of patients with mCRPC and evidence of BRCA1/2 alterations found that approximately half of patients did not receive PARP inhibitors despite evidence of survival improvement in this population. These findings highlight the need to increase awareness of the survival data and access to life-prolonging therapies in patients with mCRPC.

5108 Background: We previously proposed a hybrid framework combining LLMs and rule based algorithm (RBA) for automating risk stratification in locPCa. Herein, we aim to validate the risk stratification agent (RSA) in a prospective cohort, develop & evaluate the treatment recommendation agent (TRA) based on NCCN guidelines, and develop an interactive interface to facilitate clinicians for accurate risk stratification and treatment recommendations at the point of care. Methods: This study included pts with locPCa (2004-2024) presenting at Mayo Clinic with at least 1 positive prostate biopsy and MRI report available. For RSA prospective validation, GPT4 extracted key phenotypic variables (PSA, T stage, prostate volume, number of cores, Gleason patterns, grade group) from unstructured MRI and biopsy reports using a zeroshot prompt. An RBA then classified pts into NCCN risk groups. The agent performance was compared with the treating clinician's documentation and evaluated against gold-standard labels manually annotated by two independent clinicians. For development of TRA, two experiments were performed using GPT4 with and without retrieval-augmented generation (RAG) to generate treatment plans. Generated treatment plans were evaluated using NCCN guideline-informed treatment decision tree based algorithm (DTA). Evaluation metrics, weighted – accuracy (acc) and F1, were computed. A clinician facing interface (lisr.org/risk) was developed to provide accurate risk stratification and treatment plans. Results: A total of 858 pts were included (500 for prospective validation, 358 for treatment recommendations). Prospective validation for RSA demonstrated a higher F1 score of 0.89 compared to the treating clinician (F1: 0.58). Treatment recommendation experiments showed that GPT4 with RAG achieved higher acc (64% full – all correct treatment options and 36% partial – at least 1 correct treatment option) compared to GPT4 alone (35% and 65%, respectively). Sensitivity analysis using DTA-informed GPT4 note generation achieved 94% full acc and 6% partial acc. GPT4 alone generated hallucinated treatment options in 71% of cases, while GPT4 with RAG reduced this to 32%. Conclusions: The multi-agent framework based on LLM and RBA achieves high accuracy in risk stratification and treatment recommendation for locPCa. A multi-agent framework with an interactive interface holds high promise to enable efficient, accurate decision-making, and improve locPCa management at the point of care Performance evaluation. RSA(acc; F1) % TRA(full acc; partial acc) % Clinician RSA* GPT4 RAG+GPT4 DTA+GPT4 Overall 60; 58 89; 89 29; 71 64; 36 94; 6 V. High 94; 33 96; 96 55; 45 69; 31 93; 7 High 46; 52 92; 89 - - - Int. Unf 80; 61 91; 90 5; 95 38; 62 93; 7 Int. Fav 57; 64 84; 87 0; 100 98; 2 100; 0 Low 57; 65 87; 80 100; 0 95; 5 90; 10 V. Low 8; 13 50; 67^ - - - *Statistical significance (p<0.01) except for ^V. Low.

TPS5126 Background: The treatment of advanced prostate cancer (PC) is challenging, with undesirable side effects that impact patient quality of life. Radioimmunotherapy (RIT) can localize therapy to specific tumor cells in multiple organs to reduce or eliminate damage to normal tissue. The cell surface glycoprotein prostate-specific membrane antigen (PSMA) is an ideal therapeutic target as it is highly expressed by malignant prostate cells. There is a strong rationale for further investigation of the 177 Lu-labeled, chelator-conjugated antibody, 1 77 Lu-rosopatamab, as a potential first-line RIT candidate for the treatment of PC. Methods: This multinational, multicenter, prospective, randomized, open label phase 3 study will have 2 parts: a dosimetry and safety lead-in (n=30) and a randomized treatment expansion (n=490). In Part 1, patients will be divided into 3 groups (n=10 each) to receive 2 single intravenous (IV) injections of 76 millicuries (mCi) each, 14 days apart, of 177 Lu-rosopatamab with best standard of care (SoC) combinations with abiraterone, enzalutamide, or docetaxel to fully characterize biodistribution and safety profiles of 177 Lu-DOTA-rosopatamab + SoC. SoC received will be determined prior to treatment with 177 Lu-rosopatamab. In Part 2, patients will be enrolled in a 2:1 ratio to receive either the best SoC or 2 single IV injections of 76 mCi each of 177 Lu-rosopatamab, given 14 days apart, plus best SoC. SoC will be determined prior to randomization. Eligible patients must have PSMA-expressing metastatic castration-resistant PC (mCRPC) that have progressed despite prior therapy with either enzalutamide or abiraterone plus prednisone, and 1 line of prior taxane therapy or have refused or are ineligible for taxanes. Patients must have adequate organ function including at least 150x10 9 /L platelets, hemoglobin 10 g/dL, and have PSMA-positive disease on 68 Ga-PSMA-11 PET/CT imaging as confirmed by a central reader. Key exclusion criteria include small cell histology, increased risk of hemorrhage or bleeding, known brain or hepatic metastases, or history of stroke, seizure, or treatment with radioisotopes within 6 months prior to randomization. The primary endpoint is radiographic progression-free survival (rPFS). Key secondary endpoint is OS. Additional secondary endpoints include 5-year overall survival, tumor objective response rate, time to symptomatic skeletal event, and health-related quality of life. An alpha control and 95% confidence intervals will be used; patients will be substratified between TLX591 + 2 nd ARPI or TLX591 + docetaxel. This study is currently enrolling. Clinical trial information: NCT06520345 .

5064 Background: FoundationOneCDx (F1CDx) supports two FDA-approved biomarkers to guide treatment decisions for ICI for patients with mCRPC: MSI status and TMB. MSI-H and TMB-H (10+ mut/MB) have strongly overlapping prevalence. We sought to better characterize ICI outcome associations of TMB-H / non-MSI-H population and relative effectiveness of taxanes & ICI among patients who received these agents in sequence. Methods: Following a prespecified analysis plan, this study used the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine mCRPC clinico-genomic database (FH-FMI CGDB), with data originating from ~280 US cancer clinics (~800 sites of care). Inclusion criteria included patients with mCRPC treated with single-agent anti-PD(L)1 therapy in the FH network between 1/1/2011 - 3/30/2024. This study used the MSI and TMB algorithms from the tissue based F1CDx. Time to next treatment (TTNT) and OS were assessed with Kaplan-Meier plots and in multivariable Cox models adjusted for ECOG performance score, socioeconomic status, prior treatment history, and baseline PSA. Among patients who received taxanes in a prior line of therapy, the effectiveness (TTNT1 vs. TTNT2) of taxane and ICI were compared. Results: Among 2995 prostate cancer tissue specimens in the database, 95 (3.1%) were MSI-H and 142 (4.7%) were TMB-H. 94 (3.1%) were MSI-H & TMB-H, 1 was MSI-H & TMB-L, and 48 (1.6%) were TMB-H & not MSI-H. Among these, 84 patients with mCRPC were treated with ICI and met inclusion criteria, including MSI-H & TMB-H (n = 30), non-MSI-H & TMB-H (n = 8), and non-MSI-H and TMB-L (n = 46). The respective median TTNT on ICI was 8.0 vs. 9.6 vs. 3 months. The respective median OS from initiation of ICI was 10.9 vs. not reached vs. 4.4 months. In multivariable models evaluating ICI only, compared to non-MSI-H & TMB-L, the MSI-H & TMB-H group had more favorable TTNT (HR: 0.20, 95%CI: 0.10 – 0.41, p < 0.001) and OS (HR: 0.33, 95%CI: 0.16 – 0.70, p = 0.004), and the non-MSI-H & TMB-H group also had more favorable TTNT (HR: 0.13, 95%CI: 0.04 – 0.45, p = 0.002) and OS (HR: 0.20, 95%CI: 0.05 – 0.75, p = 0.017). 50 of the 84 (60%) patients treated with ICI had prior mCRPC taxane treatment. Better TTNT2 on subsequent ICI vs. prior taxane was observed for MSI-H (HR: 0.49, 95%CI: 0.23 – 1.01, p = 0.051) and TMB-H (0.54, 95%CI: 0.30 – 0.98, p = 0.044), but the opposite was true for non-MSI-H and TMB-L subgroups, with significant treatment interactions for each (p = 0.0018, p = 0.00052). Conclusions: TMB-H (4.7%) is more prevalent than MSI-H (3.1%) by F1CDx in mCRPC. Non-MSI-H / TMB-H (1.6%) in the routine practice cohort have similar outcome associations on ICI to MSI-H. Both MSI-H and TMB-H by F1CDx are predictive of differential benefit for ICI vs. taxanes in later mCRPC treatment lines.