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Abstract Background and Objective Management of von Hippel–Lindau (VHL) disease is challenging due to multisystem involvement and limited systemic treatment options. Belzutifan has shown promising efficacy in clinical trials; however, real-world data remain limited. We aimed to evaluate its effectiveness and safety in a real-world cohort. Methods We conducted a retrospective study of 59 patients with VHL disease treated with belzutifan between August 2021 and January 2026. Objective response rates (ORR) for renal cell carcinoma (RCC), central nervous system hemangioblastomas (CNS HB), and pancreatic neuroendocrine tumors (pNET) were assessed by an independent radiologist. Retinal hemangioblastoma outcomes were evaluated through ophthalmology records by chart review. Results Median follow-up was 29 months. ORR was 27.6%, 39.6%, and 85.7% for RCC, CNS HB, and pNET, respectively. Among 13 patients with retinal hemangioblastomas (20 eyes), 17 eyes (85%) showed improvement. At data cutoff, 47 patients (79.6%) remained on treatment, while 12 (20.3%) discontinued (7 due to adverse events, 3 to disease progression, and 2 to other reasons). The most common adverse event was anemia (86.4%). Grade ≥3 events occurred in 10 patients (16.9%). The procedure rate per patient-year decreased from 0.35 in the 3 years prior to treatment to 0.08 during follow-up (Incidence Rate Ratio, 0.23; 95% CI, 0.12–0.47; P < 0.001) Conclusions Belzutifan demonstrated efficacy across VHL-associated lesions, with favorable safety profile. Most adverse events were grade 1–2. A reduction in procedure rate was observed after treatment initiation.

Background: Noninvasive tests are well validated in metabolic dysfunction–associated steatotic liver disease, but evidence in metabolic dysfunction and alcohol-associated liver disease (MetALD) and alcohol-associated liver disease (ALD) is limited. We evaluated noninvasive test–based clinical care pathways for fibrosis risk stratification in MetALD and ALD. Methods: This is a multinational, multicenter, cross-sectional study across 35 centers in 16 countries (2013–2025), including adults with MetALD or ALD. Fibrosis was assessed by liver biopsy and/or vibration-controlled transient elastography (VCTE). In biopsy-proven participants, we analyzed the sequential pathway (Fibrosis-4 [FIB-4] first, followed by VCTE for indeterminate FIB-4) to identify advanced fibrosis (≥F3). Diagnostic accuracy was assessed using the AUC. Results: Among 893 participants (41.3% MetALD and 58.7% ALD), the median age was 52 [43.0–61.0] years, and 79.4% were male. The estimated prevalence of advanced fibrosis was 45% (32.2% in MetALD and 54.0% in ALD). Participants with MetALD had a more dysmetabolic profile but lower fibrosis stages than ALD. Among biopsy-proven participants, FIB-4 AUC was 0.720, and VCTE AUC was 0.833. FIB-4 performed better in MetALD than ALD (AUC 0.773 vs. 0.540), while VCTE accuracy was similar across groups. In the sequential pathways, the false-negative rate for advanced fibrosis (misclassified as low risk) was 6.8% (4.9% MetALD and 11.3% ALD). Conclusions: In biopsy-proven patients, FIB-4 performance was acceptable in MetALD but substantially weaker in ALD. The standard 2-step pathway performed adequately in MetALD but missed a clinically meaningful proportion of advanced fibrosis in ALD.

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure (HF) cases and remains diagnostically challenging due to nonspecific symptoms and a lack of accessible noninvasive screening tools, leading to widespread underdiagnosis and delayed treatment. The electrocardiogram (ECG) is a low-cost, widely available tool that reflects myocardial electrical remodeling. We previously developed and externally validated an ECG-based artificial intelligence (ECG-AI) model capable of classifying ECGs into four categories: reduced ejection fraction (rEF), mid-range ejection fraction (mEF), HFpEF, and controls. In this study, we evaluate the ability of this ECG-AI model to predict future HFpEF diagnosis using real-world data from a large integrated health system. Methods: We applied the validated ECG-AI model to an independent cohort of 7713 patients from Wake Forest Baptist Health (WFBH), using one ECG per patient recorded before the clinical diagnosis of HFpEF. Model discrimination was evaluated across multiple prediction windows from six months up to ten years before diagnosis using the area under the receiver operating characteristic curve (AUC), with comparisons performed using DeLong’s test. Performance was also compared head-to-head with the H2FPEF score, a validated clinical score for HFpEF, in the subset of patients with complete data for score calculation. ECG-AI outputs were stratified into quartiles and evaluated using Kaplan–Meier survival analysis and multivariable Cox proportional hazards regression adjusted for demographics and major comorbidities. Results: Of the 7713 patients, 283 (3.7%) were subsequently diagnosed with HFpEF. The ECG-AI model achieved an AUC of 0.79 (95% CI, 0.72–0.86) using ECGs recorded within six months before diagnosis. Discrimination remained stable across prediction windows extending up to ten years before diagnosis (AUC range, 0.78–0.80; all DeLong p > 0.01 vs. the six-month window). Compared with the H2FPEF score, ECG-AI demonstrated superior discrimination, improving the AUC by 0.06–0.07 across the evaluated prediction windows. Patients in the highest ECG-AI risk quartile had an unadjusted hazard ratio (HR) of 11.18 (95% CI, 7.27–17.21; C-index, 0.75) and an adjusted HR of 6.60 (95% CI, 4.24–10.28; C-index, 0.82). Adding clinical covariates to ECG-AI improved the C-index by 0.07 and 0.09 for the five- and ten-year prediction windows, respectively. Conclusions: An independently validated ECG-AI model predicted incident HFpEF up to ten years before clinical diagnosis and outperformed the H2FPEF score using ECG alone, indicating that ECG-derived signatures precede clinical recognition of the syndrome. ECG-AI shows promise as a prognostic, risk-stratification tool to prioritize further evaluation; prospective validation and single-lead assessment are needed before screening or wearable deployment.

Glioblastoma (GBM) is a devastating cancer with a dismal prognosis. Current treatment includes temozolomide (TMZ), which is more effective in about 50% of GBMs that have O 6 - methylguanine DNA methyltransferase ( MGMT ) promoter methylation. MGMT is a DNA repair protein that reverses TMZ-induced DNA damage. EGFR is a prime oncogene in GBM. Here, we report that EGFR inhibition induced the down-regulation of MGMT in GBM cells, revealing a previously unidentified link between EGFR signaling and response to TMZ. EGFR inhibition led to activation of two transcription factors, activator protein-1 (AP-1), which repressed MGMT transcription, and nuclear factor κB (NF-κB), which up-regulated MGMT transcription. EGFR inhibition also induced AP-1–mediated transcription of miR-616. miR-616 inhibited both MGMT translation and NF-κB activation. Thus, the overall effect of EGFR inhibition was down-regulation of MGMT expression. In addition, we provided an explanation for the prior failure of clinical trials that used concomitant EGFR tyrosine kinase inhibitors (TKIs) and TMZ. TMZ up-regulated MGMT, and concomitant treatment with EGFR TKIs and TMZ failed to down-regulate MGMT. However, pretreatment with EGFR TKIs followed by TMZ efficiently down-regulated MGMT and enhanced TMZ sensitivity in experimental models. Posttreatment tumor tissues from two clinical trials were used to validate these findings. We demonstrated that EGFR inhibitors induced down-regulation of MGMT in posttreatment resected tumor tissues from patients with GBM and the failure of EGFR inhibition to down-regulate MGMT if TMZ was used concomitantly. These data support using EGFR TKIs before TMZ treatment as a therapeutic approach in MGMT unmethylated GBM.

Abstract: Cardiovascular disease (CVD) remains the leading cause of death worldwide. While advances in treatment have improved outcomes, the greatest gains in public health will come from primary prevention. Risk stratification is the foundation of CVD prevention. Traditional tools, such as pooled cohort equations (PCEs) and SCORE2, do not capture non-traditional risk factors and individual variability, driving efforts to refine existing approaches. We critically discuss emerging risk determinants for primary CVD prevention. Coronary artery calcium scoring can reclassify CVD risk but has limitations related to cost, access, and unvalidated improvements in clinical endpoints. Biomarkers such as apolipoprotein B and lipoprotein(a) may identify residual risk beyond LDL-C, particularly in patients with lipid discordance, but remain underused due to uncertain thresholds and the lack of prospective triallevel outcome data. Trials of colchicine in primary prevention have yielded mixed results, making the role of targeting inflammation unclear in CVD prevention. Polygenic risk scores can stratify genetic risk but face challenges in sensitivity, specificity, and generalizability. Multiomics approaches, while offering the promise of deeper phenotyping, lack established clinical applications and consensus on when their use is appropriate. Artificial intelligence may allow better prediction across cohorts but requires broader validation. Clonal hematopoiesis of indeterminate potential, a marker of inflammation and age-related CVD risk, lacks evidence supporting routine screening or intervention in younger, asymptomatic individuals. The proliferation of new risk stratification tools poses a key challenge: determining when additional data meaningfully alter clinical decisions. Until robust outcome-based evidence emerges, risk enhancers should guide, but not dictate, therapy in selected patients.

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)

e17027 Background: Guidelines recommend active surveillance for low-risk prostate cancer and definitive therapy for high-risk disease. We evaluated national patterns of risk-misaligned care and the survival consequences of undertreatment. Methods: National Cancer Database prostate cancer cases diagnosed from 2010–2022 were analyzed. Low-risk disease was defined as clinical T1–T2, Grade Group 1, and PSA < 10; overtreatment was definitive treatment vs active surveillance or no treatment. High-risk disease was defined as Grade Group ≥4, PSA ≥20, or clinical T3–T4; undertreatment was no definitive local therapy or incomplete definitive therapy. Multivariable logistic regression evaluated factors associated with misalignment. Overall survival (OS) among high-risk patients was assessed using multivariable Cox regression. Results: Among low-risk patients (N = 124,562), overtreatment occurred in 40% of cases. Overtreatment was more common among young men (adjusted odds ratio [aOR] 0.982; p < 0.001) and less common at academic centers (aOR 0.414; p = 0.015). Compared with White men, odds of overtreatment were lower among Black men (aOR 0.946; p = 0.002) and Asian men (aOR 0.827; p < 0.001), with additional variation by education and region (all p < 0.001). Among high-risk patients (N = 318,532), undertreatment affected 27.3% of patients and was more common among older men (aOR 1.061; p < 0.001). Compared with White men, undertreatment was higher among Black men (aOR 1.479; p < 0.001) and lower among Asian patients (aOR 0.863; p < 0.001) (overall race p < 0.001). Compared with private insurance, undertreatment was higher in Medicaid (aOR 2.038; p < 0.001) and uninsured patients (aOR 2.649; p < 0.001), and slightly lower with Medicare (aOR 0.931; p < 0.001) (overall insurance p < 0.001). Multiple comorbidities were associated with higher undertreatment (≥2 vs 0: aOR 1.252; p < 0.001). Facility type was not independently associated (p = 0.490), while region remained significant (overall p < 0.001). In the survival cohort (high risk) (N = 338,919), undertreatment was associated with substantially worse OS (HR 2.745, 95% CI 2.706–2.784; p < 0.001), adjusted for age (HR 1.056; p < 0.001), race (p = 0.001), insurance (p < 0.001), comorbidity (p < 0.001), income (overall p < 0.001), education (overall p < 0.001), and facility region (overall p < 0.001). Conclusions: Low-risk overtreatment remains common nationwide and varies across patient and health-system factors. In high-risk disease, undertreatment affects over one-quarter of patients and disproportionately impacts Black men and those with Medicaid or no insurance. Undertreatment confers a nearly threefold mortality hazard (adjusted HR 2.75; p < 0.001), highlighting a major national quality gap and the urgent need for system-level interventions to ensure equitable, guideline-concordant delivery of definitive therapy.

e13611 Background: Decentralized cancer care models may reduce treatment burden for patients with advanced prostate cancer (CaP), particularly those facing mobility, transportation, of scheduling challenges. However, prospective patient-reported experience and quality-of-life data comparing home- versus clinic-based oncology care remain limited. Mayo Clinic Cancer CARE Beyond Walls (CCBW) is a patient-centered program designed to deliver selected standard-of-care cancer therapies in the home with virtual clinical oversight. This observational study evaluated patient-reported experience, feasibility, safety, and quality of life among men with advanced or metastatic CaP receiving care at home. Methods: Men with advanced/metastatic CaP receiving active standard-of-care therapy including hormone-based treatments, oral anticancer therapies, anti-bone resorptive agents, or intravenous chemotherapy who were participating in a CCBW randomized trial were enrolled into a companion observational study. After confirmation of treatment tolerance in clinic, patients received home-based care for 24 weeks (Arm A) or in-clinic care for 8 weeks followed by home-based care for 16 weeks (Arm B). Patient-reported outcomes were collected at baseline and Weeks 8, 16, and 24, using CAHPS-based measures and CCBW-specific surveys assessing overall cancer care experience, patient preference for site of care, comfort with home infusions, functional/symptom assessment and quality of life. Descriptive analyses were performed. Results: Eighteen participants were analyzed (Arm A n = 10; Arm B n = 8). Participants were medically stable, and predominantly insured by Medicare. Survey completion was 100% at baseline and 83% at all follow-up time points. At Weeks 16 and 24 when all respondents had received home-based care, 100% and 92% reported being “quite a bit” or “very much” comfortable receiving treatment at home. Communication with at-home and remote nursing teams was rated highly, and most respondents reported transition, scheduling, and equipment use easy or very easy. At Weeks 16 and 24, 45% and 40% of respondents reported improved quality of life from receiving home-based care, with no reports of worsening. No emergency department visits, hospitalizations, or urgent care visits occurred in either arm. Conclusions: In this prospective, observational study, home-based oncology care delivered through CCBW was feasible, safe, and associated with high patient satisfaction among men with advanced CaP receiving diverse systemic therapies. These findings support further evaluation and expansion of decentralized care integrating home-based treatment with virtual clinical oversight.

9558 Background: Ipilimumab 3 mg/kg plus nivolumab 1 mg/kg (Ipi3+Nivo1) is the standard immunotherapy regimen for metastatic melanoma with brain metastases (MBM) but is associated with significant immune-related toxicity, particularly neurologic toxicity. Ipilimumab 1 mg/kg plus nivolumab 3 mg/kg (Ipi1+Nivo3) has demonstrated improved tolerability in extracranial melanoma, though comparative data in MBM are limited. We compared efficacy and toxicity of these dosing strategies in a multisite real-world cohort. Methods: We retrospectively identified patients with MBM treated with ipilimumab plus nivolumab across three Mayo Clinic sites between 2016 and 2024. Patients were classified by induction dosing as Ipi1+Nivo3 or Ipi3+Nivo1. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method and compared using log-rank tests. Best overall response (BOR) and toxicity outcomes were summarized descriptively. Results: Among 147 patients (Ipi1+Nivo3 n=32; Ipi3+Nivo1 n=115), 144 were evaluable for survival. Median age was higher in the Ipi1+Nivo3 group (69.5 vs 66.0 years); males predominated in both groups (71.9% vs 61.7%), and the cohort was predominantly White (>96%). Most patients had ECOG 0–1 in both groups (84.4% in Ipi1+ Nivo3 and 90.4% in Ipi3+Nivo1). BRAF mutation status was balanced between regimens, with approximately half of patients harboring BRAF-mutant disease (51.6% vs 53.2%). Median OS was 18.5 months (95% CI, 5.7–54.7) with Ipi1+Nivo3 versus 10.6 months (95% CI, 6.5–19.1) with Ipi3+Nivo1 (log-rank p=0.25). Two-year OS was 45.9% and 35.0%, and five-year OS was 24.5% and 19.2%, respectively. Median PFS was 7.3 months (95% CI, 1.8–10.9) versus 2.8 months (95% CI, 2.1–4.4) (log-rank p=0.16). BOR was available for 138 patients; objective response rates were 35.5% and 31.8%, respectively. 3–5 adverse events occurred in 24.1% of Ipi1+Nivo3 versus 30.5% of Ipi3+Nivo1 patients. Hospitalizations were less frequent (16.7% vs 36.1%), and neurologic adverse events occurred only with Ipi3+Nivo1 (11.9% vs 0%). Conclusions: In this multisite real-world MBM cohort, Ipi1+Nivo3 demonstrated comparable efficacy, numerically longer survival, and a more favorable toxicity profile compared with standard Ipi3+Nivo1. Notably, no neurologic toxicity was observed with the Ipi1+Nivo3 regimen. These findings support further prospective evaluation of lower dose ipilimumab strategies. Response and survival outcomes. Outcome Ipi1 + Nivo3 Ipi3 + Nivo1 Complete response (CR) 2 / 31 (6.5%) 8 / 107 (7.5%) Partial response (PR) 9 / 31 (29.0%) 26 / 107 (24.3%) Objective response rate (ORR = CR+PR) 11 / 31 (35.5%) 34 / 107 (31.8%) Disease control rate (DCR = CR+PR+SD) 18 / 31 (58.1%) 51 / 107 (47.7%) Median PFS, months (95% CI) 7.3 (1.8–10.9) 2.8 (2.1–4.4) 2-year PFS 27.6% 22.3% Median OS, months (95% CI) 18.5 (5.7–54.7) 10.6 (6.5–19.1) 2-year OS 45.9% 35.0% 5-year OS 24.5% 19.2%