Citations

ABSTRACT Background Symptom management in myeloproliferative neoplasms (MPN) remains challenging despite advancements in disease‐directed therapies. This study assessed the impact of demographic, clinical, laboratory and treatment‐related variables on total symptom scores (TSS) and individual symptom scores in patients with polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF). Methods A cohort of 553 patients (PV: 200; ET: 190; MF: 163) was identified through a retrospective chart review. Symptom scores, demographic information and clinical variables were captured at the time of the first TSS assessment. Laboratory results within 1 month and treatment information within 90 days prior to the symptom assessment were captured. Univariable linear regression, followed by multivariable regression analyses using a robust variance estimator were performed, with a p value < 0.05 indicating a significant association. Results MF patients experienced the highest symptom burden and fatigue was the most reported symptom across all MPN subtypes. In PV, depression ( β : 10.53; p = 0.001) was associated with a higher TSS whereas older age (−0.17; 0.01) and higher hemoglobin (−1.24; 0.003) were associated with a lower TSS. In ET, depression ( β : 14.19; p < 0.001) was associated with a higher TSS, whereas in MF, depression (12.28; < 0.001) and higher WBC count (0.22; 0.02) were associated with a higher TSS. Depression and non‐White race in PV, depression in ET, and depression, low hemoglobin, and higher WBC count in MF were associated with multiple symptoms. Conclusions Integrating depression screening and management and optimizing hematologic parameters alongside disease‐directed therapy is crucial to improving patient outcomes.

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.

560 Background: Early diagnosis remains a critical challenge in oncology, particularly for the five deadliest cancers: lung, breast, prostate, colorectal, and pancreatic. These cancers often present with nonspecific symptoms, leading to repeated healthcare visits without a timely suspicion of malignancy. Artificial intelligence (AI) offers an opportunity to augment clinical decision-making and support earlier identification of cancer. C the Signs , an AI cancer prediction platform, has been widely adopted across the UK with substantial evidence supporting its ability to identify cancer at earlier stages. Methods: This retrospective study assessed the performance of C the Signs in intercepting cancer earlier than physician diagnosis. De-identified electronic medical records were accessed via the Mayo Data Platform, spanning a 20-year period from January 1, 2002 to December 31, 2021. The study cohort included 55,375 patients diagnosed with lung, breast, prostate, colorectal or pancreatic cancer. The primary outcome was the proportion of cases identified earlier by the AI model, measured in annual increments up to five years prior to physician diagnosis. Additional analyses were conducted across the five target cancer types. Results: The C the Signs platform identified 14,528 patients (26.2%) with cancer earlier than the original physician diagnosis. Early identification occurred across multiple time points: 8.9% in year 1, 4.7% in year 2, 4.4% in year 3, 4.3% in year 4, and 3.9% in year 5. Across cancer types, the highest early interception rate was observed in prostate cancer (31.7%), while breast cancer showed the lowest impact (21.0%). Conclusions: Use of the C the Signs AI cancer prediction platform demonstrated meaningful improvements in early cancer interception, particularly for lung and prostate cancers. These findings highlight the potential of AI tools to enhance early diagnosis and support timely clinical interventions, offering a scalable solution to improve cancer outcomes across populations.

Abstract BACKGROUND Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome characterized by development of multiple neoplasms, including central nervous system (CNS) hemangioblastomas, renal cell carcinoma (RCC), pancreatic neuroendocrine tumors (pNETs), and retinal hemangioblastomas. Belzutifan, a hypoxia-inducible factor 2-alpha (HIF-2α) inhibitor, has demonstrated efficacy in treating VHL-associated tumors. This study evaluates our institutional experience with belzutifan in patients with VHL-related tumors. MATERIAL AND METHODS We conducted a retrospective cohort study across three Mayo Clinic sites, including patients aged ≥18 years with genetically confirmed VHL disease who received belzutifan between October 2021 and March 2025. Clinical/radiological data, and adverse events (AEs) were extracted from electronic health records. Tumor responses were assessed using RECIST v1.1 criteria for RCC, pNET, CNS hemangioblastoma. Retinal hemangioblastoma responses were evaluated through ophthalmologic assessments. RESULTS Forty-five patients were included (median age 44 58% female). Sixty-seven percent had RCC, 91% had CNS hemangioblastomas, 53% had pNETs, and 62% had retinal hemangioblastomas. The median duration of belzutifan therapy was 22.6 months (range 1.0-41.4 months). Among patients with RCC (n=26), overall response rate (ORR) was 19% and median time to best response (MTBR) was 7.4 months. Five patients had partial response (PR), 15 had stable disease (SD), and 1 had progressive disease (PD). For CNS hemangioblastomas (n=41), ORR was 12%, MTBR was 6.2 months. 5 patients had PR, 30 had SD, and none had PD. For pNET (n=17), ORR was 41%, MTBR was 8.2 months. One patient had CR, 6 patients had PR, 6 had SD, none had PD. For retinal hemangioblastomas (n=28), 6 (21%) showed improvement, 9 (32%) remained stable, and 3 (11%) showed progression. MTBR was 6.7 months. Treatment-related AEs occurred in 98% of patients. Common AEs included anemia (89%), fatigue (80%), hypoxia (18%), and headache (13%). Grade 3 AEs were observed in 20% of patients, including anemia (7%), hypoxia (9%), fatigue (4%), pneumonitis (2%), loss of consciousness (2%). One patient experienced a grade 4 event (status epilepticus). Anemia management involved iron supplementation (n=11), RBC transfusion (n=3), dose reduction (n=2), treatment holiday (n=8), permanent discontinuation (n=1). Hypoxia management included dose reduction (n=1), treatment holiday (n=3), discontinuation (n=1). CONCLUSION Belzutifan shows clinical activity across various VHL-associated neoplasms in real-world practice, with a manageable safety profile. The lower ORR compared to the phase III trial may reflect differences in baseline disease, including lower tumor burden in our cohort. These findings support its continued use in VHL disease, with attention to monitoring and managing treatment-related AEs.

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.

Background: Longitudinal genomic profiling through serial circulating tumor DNA (ctDNA) testing offers a noninvasive method to monitor clonal evolution in advanced prostate cancer. This study evaluated the frequency and nature of newly emergent and potentially actionable genomic alterations detected through serial testing in a real-world setting. Methods: We conducted a retrospective analysis of advanced prostate cancer patients who underwent multiple Guardant360 ctDNA tests between October 2020 and March 2023. The study focused on identifying new genomic alterations absent in the initial test, with particular attention to alterations relevant for on-label therapies, therapies approved in other oncologic indications (i.e., off-label), or a clinical trial. Results: Among 479 patients with at least two ctDNA tests, the median interval between the first and second evaluable tests was 207 days. New and potentially actionable alterations emerged in 57.8% of patients, including potential targets for on-label therapies (16.7%), off-label therapies (16.5%), and clinical trials (55.7%). Tumor mutational burden (TMB) increased from “low” to “high” in 11% of patients, although none had microsatellite instability or mismatch repair deficiency. In a Mayo Clinic subset, ten patients received olaparib based on treatment-emergent alterations, but none achieved a prostate-specific antigen (PSA) response. Two patients who transitioned from low to high TMB received pembrolizumab, both with progressive disease as best response. Conclusions: In a large real-world cohort, serial ctDNA testing frequently identified new alterations that were not detected at baseline and are potentially actionable therapeutic targets, highlighting the value of serial genomic profiling for capturing clonal dynamics. Additional research is needed to better establish a framework for retesting and to clarify how these results should influence subsequent treatment decisions.

Abstract Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI‐driven tools are being developed to integrate diverse data sources, including multi‐omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end‐of‐life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up‐to‐date evidence, and captures critical patient‐reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.

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.