Singh, Parminder Pal

127 Background: Bone metastases affect up to 90 % of patients with metastatic hormone-resistant prostate cancer (mHRPC) and cause substantial morbidity from skeletal-related events (SREs) such as fracture, spinal-cord compression, and palliative interventions. Bone-modifying agents (BMAs) including bisphosphonates and denosumab reduce SRE risk and are NCCN-recommended, however, real-world use remains inconsistent. Methods: Patients with mHRPC treated from January 2017-September 2024 across Mayo Clinic sites were identified using SQL-based EHR queries confirmed by manual chart review. Bone Metastases were detected through keyword searchers of radiology reports and confirmed through manual chart review. SRE-prevention dosing was defined as zoledronic acid 4mg IV or denosumab 120mg SC; osteoporosis dosing was excluded. SREs were identified using diagnosis codes and text-pattern searches for radiotherapy, surgery, fracture, and spinal-cord compression. Descriptive statistics summarized BMA utilization and SRE incidence. Categorical variables were compared using Chi-square tests, and a risk ratio (RR) with 95% confidence intervals was calculated to evaluate association between early BMA initiation and SRE risk. Results: Among 778 patients with mHRPC, 685 (88 %) had bone metastases (mean age 70.5 years). Of these, 169 (24.7 %) had an SRE, and 261 (38.1 %) received BMAs at SRE-prevention doses (denosumab n = 157 [60 %]; zoledronic acid n = 104 [40 %]). Based on a chi-square test, combined SRE incidence did not differ significantly (No BMA 23 %, denosumab 23 %, zoledronic 33 %; p = 0.069). Bone pain at baseline was more common in patients who later received a BMA (136 [68 %]) than in those who did not (82 [41 %]; p < 0.001). Among 200 randomly sampled non-BMA patients, 83 had no recorded BMA discussion (66 also lacked bone pain), 38 lacked dental clearance, 32 had uncompleted plans and 18 declined therapy. Early BMA initiation (before first bone metastases) was associated with fewer SREs (16 % vs 29 %; RR 0.56, 95 % CI 0.33–0.96). Conclusions: Less than half of bone-metastatic mHRPC patients received BMAs. Non-initiation was often linked to modifiable factors such as delayed dental clearance or lack of treatment discussions, highlighting the need for system-level strategies to improve timely bone-directed care. SREs were common, affecting one quarter of patients. Small sample size precluded meaningful comparison of the impact of BMAs on SRE incidence however early BMA initiation may lower SREs. Incidence of SREs stratified by no BMA vs BMA treatment. SRE No BMA BMA Overall Pathological Fracture 6.4% (27/424) 4.9% (13/261) 5.8% (40/685) Radiotherapy to Bone 13.9% (59/424) 18.8% (49/261) 15.7% (108/685) Spinal Cord Compression 0.7% (3/424) 3.1% (8/261) 1.6% (11/685) Surgery to Bone 2.1% (9/424) 0.3% (1/261) 1.5% (10/685) Combined 23% (98/424) 27.2% (71/261) 24.7% (169/685)

483 Background: Sarcomatoid dedifferentiation (sRCC) in metastatic clear-cell renal cell carcinoma (mccRCC) confers aggressive disease and poor prognosis. We maintain a living meta-analysis of first-line immune checkpoint inhibitor (ICI)–based combinations in mccRCC (Riaz et al., Eur Urol Oncol 2021), which demonstrated improved survival with ICI-based regimens over sunitinib. Here, we present an updated synthesis focused on the comparative efficacy of ICI combinations in the sarcomatoid subset. Methods: A systematic search of MEDLINE, EMBASE, and CENTRAL through October 2025 was performed as part of our living meta-analysis of first-line mccRCC therapy. Eligible phase III RCTs compared ICI combinations (IO/IO or IO/TKI) versus sunitinib, including CheckMate 214, CheckMate 9ER, KEYNOTE-426, CLEAR/KEYNOTE-581, JAVELIN Renal 101, and IMmotion151. Extracted outcomes included overall (OS) and progression-free survival (PFS), and objective (ORR), complete (CR), and partial (PR) response rates. Random-effects meta-analyses generated pooled hazard ratios (HRs) for time-to-event outcomes and risk ratios (RRs) for response rates with 95% confidence intervals. Subgroup interaction analyses compared sarcomatoid versus non-sarcomatoid subgroups, with p < 0.1 considered significant. Results: Six phase III RCTs (n = 5,121; 624 sarcomatoid) were included. Across all ICI-based combinations, ICI therapy improved outcomes versus sunitinib in both sarcomatoid and non-sarcomatoid disease. In the pooled analysis, the OS interaction favored sRCC meeting the prespecified significance threshold for subgroup analyses (HR 0.53 vs 0.68; p-interaction = 0.06 < 0.10), while no significant interaction was seen for PFS. Response outcomes showed the strongest differential benefit, particularly with IO/TKI regimens. IO/IO results were directionally consistent but limited by smaller non-sarcomatoid samples. Conclusions: ICI-based combinations significantly improve survival and response outcomes versus sunitinib in mccRCC. Using a prespecified α = 0.10 for interaction testing, the pooled analysis showed a significant OS interaction and higher response rates in sRCC, indicating enhanced ICI sensitivity. These updated meta-analysis data support ICI regimens as the preferred first-line therapy for mRCC with sarcomatoid de-differentiation. Endpoint Regimen Subgroup (S = sarcomatoid; NS = non-sarcomatoid) Effect (95% CI) p-interaction PFS IO/IO S 0.51 (0.40–0.65) – IO/TKI S vs NS 0.48 (0.36–0.64) vs 0.46 (0.39–0.52) 0.81 OS IO/IO S 0.54 (0.07–4.42) – IO/TKI S vs NS 0.51 (0.17–1.48) vs 0.68 (0.30–1.58) 0.24 ORR IO/IO S 2.93 (0.49–17.37) – IO/TKI S vs NS 2.43 (1.84–3.21) vs 1.95 (1.67–2.28) 0.03 CR IO/IO S 3.81 (0.82–17.71) – IO/TKI S vs NS 5.93 (4.73–7.42) vs 2.85 (0.10–83.73) 0.007 PR IO/IO S 3.48 (1.70–7.11) – IO/TKI S vs NS 2.10 (1.78–2.47) vs 1.75 (1.18–2.61) <0.001

405 Background: Clinical trials are critical to advancing prostate cancer treatment, yet patient enrollment remains a major bottleneck. Despite strong interest in AI for trial matching, real-world use remains limited due to reasons such as complex infrastructure requirements. To address this, we developed a lightweight, scalable AI framework leveraging the Google Healthcare Search API—requiring minimal technical expertise—to automate trial eligibility screening. Methods: We used a pre-trained GPT-5 large language model (LLM) to decompose the eligibility criteria of 13 prostate cancer clinical trials into 269 YES/NO questions, such that for an eligible patient of a given trial, the chart review would yield YES for inclusion and NO for exclusion questions. From 348 patients enrolled in one of the trials, we built a dataset linking each eligibility question to each patient, resulting in 4,947 patient–question pairs (2,034 inclusion pairs with YES labels and 2,913 exclusion pairs with NO labels). The task was framed as binary classification. During evaluation, for each patient–question pair, the LLM extracted search phrases from the question to retrieve relevant EHR information via the Google Healthcare Search API, then used the question and retrieved information to predict YES or NO responses. Performance was measured using accuracy, precision, recall, specificity, and F1 score. Results: Across 4,947 question–patient pairs, our framework achieved 0.81 accuracy, 0.85 precision, 0.65 recall, 0.92 specificity and a F1 score of 0.74. Results for different phases of trials is reported in Table 1, which demonstrates the approach’s robustness across trial phases. Predictions for exclusion criteria were highly precise, while inclusion criteria were more challenging. Most errors were observed in criteria involving complex nested logical conditions, interpretable clinical definitions and data not explicitly measurable or recorded in the EHR (e.g., planned treatments or clinician-assessed scores). Conclusions: This work demonstrates a practical AI framework to support clinical trial matching. The system can identify complex disease states (biochemical recurrence vs radiographic metastasis), castration status (sensitive, resistant) and genomics. Future work should focus on developing human-in-the-loop supervision and improving question decomposition to reduce errors in complex eligibility criteria. Screening performance of the proposed framework across different phases of trials. Trial Phase #Patient-Question Pairs #YES/NO labels Accuracy Precision Recall Specificity F1 score I, II, I/II 4,227 Yes: 1,735 No: 2,492 0.80 0.84 0.66 0.92 0.74 III 720 Yes: 299 No: 421 0.82 0.90 0.63 0.95 0.74 All 4,947 Yes: 2,034 No: 2,913 0.81 0.85 0.65 0.92 0.74 #YES/NO labels: The number of patient-question pairs that are labeled YES/NO among total patient-question pairs of the same row.

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.

e17155 Background: Localized PCa is classified into six NCCN risk categories, each with specific treatment recommendations. However, treatment concordance across these categories and its variation by race in clinical practice remain unexplored. Therefore, we aim to describe patterns of guideline-concordant treatment receipt for localized PCa across risk groups and race. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2018–2020) was queried to identify patients with localized PCa from 17 registries. Data regarding clinical T stage, Gleason patterns, PSA level, biopsy cores and initial treatment received (radiation, radical prostatectomy, systemic therapy) was obtained. Each patient was then stratified into one of six NCCN-based risk categories using a rule-based algorithm. Using the stratified risk category of each patient, treatment recommendations were obtained from the NCCN guidelines. Concordance was assessed by comparing the received treatment and guideline-recommended treatment. A focused bi-variate logistic regression analysis was performed adjusting for risk groups, and race. Odds ratio (OR) with 95% confidence intervals (CI) were computed to quantify the magnitude of guideline-discordant treatment receipt. Results: This analysis included 69,584 patients with localized PCa (White: 53,875 [77.4%]; Black: 11,265 [16.2%]; Asian/Pacific Islander: 4,133 [5.9%]; American Indian/Alaska Native: 311 0.5%]). In terms of risk groups, patients in higher risk categories had increased odds of discordance compared to the lower risk groups (Table). In terms of race, Black men had significantly higher odds of receiving guideline-discordant therapy (OR:1.67, 95% CI:1.58–1.76), while no statistically significant difference was observed with American Indian/Alaska Native men (OR:1.21, 95% CI: 0.92–1.61) and Asian/Pacific Islander men (OR:1.05, 95% CI:0.97–1.14) compared to White men. Conclusions: Population level data, based on limited sample size in some groups, suggests that treatment concordance with NCCN guidelines varies by risk group and race in localized PCa. Black patients, and those with higher risk groups were more likely to receive guideline-discordant therapy. These findings highlight the need for strategies and prospective studies at individual patient level to evaluate and enhance guideline adherence to promote personalized care for these patients. Variable Discordant Treatment (%) OR (95% CI) White 22168 (41.1) REF Black 5653 (47.5) 1.67 (1.58-1.76) * Asian/Pacific Islander 1822 (44.1) 1.05 (0.97-1.14) American Indian/Alaska Native 191 (61.4) 1.22 (0.92- 1.61) Very Low Risk 770 (9) REF Low Risk 146 (2) 0.16 (0.13-0.19) * Favorable Risk 35 (0.4) 0.03 (0.02-0.04) * Unfavorable Risk 15125 (83.2) 36.30 (33.72-39.25) * High/Very High Risk 13441 (50) 7.32 (6.83-7.86) * * p <0.05.

261 Background: Clinicians use next-generation sequencing (NGS) to tailor targeted treatments for PCa patients. However, the results are often available in unstructured formats and are saturated with technical jargon, making them inaccessible for patients. Therefore, we aimed to assess the efficacy of LLMs to accurately extract genomic characteristics from unstructured reports and generate patient-friendly summaries for ease of variant interpretation in PCa patients. Methods: This retrospective study included patients with PCa who underwent NGS from 2023-2024. The GPT4 model was utilized using structured zero-shot prompts to extract genomic characteristics from unstructured reports in a non-decomposed manner. Prompt development was conducted iteratively using a 2% random sample of the dataset. Genomic characteristics ascertained by LLM were assessed against held out vendor-curated test dataset for evaluation. Performance was assessed using weighted evaluation metrics (precision, recall, and F1-score). Additionally, the extracted variables were organized using rule-based criterion and presented as a separate prompt to GPT4 to generate summaries for interpretation at the level of each biologically relevant variant. Mean Flesch-Kincaid scores and Vocd-D statistic with standard deviations were computed to assess readability and lexical diversity of LLM-generated variant interpretation summaries. Results: A total of 331 patients (370 NGS reports) were included in the study. Evaluation on the held-out test dataset (Table) showed that the weighted average precision ranged from 80% for extracting tumor mutational burden to 100% for extracting the CtDNA fraction from unstructured reports. The weighted average recall ranged from 85% for extracting CtDNA tumor fraction to 99% for ascertaining variant/alteration. The weighted average F1-scores varied from 0.83 for extracting tumor mutational burden to 1.0 for extracting specimen site from NGS reports. The mean Flesch-Kincaid score was 8.29 ± 1 indicating average/standard readability ease. The mean Vocd-D statistic was 7.55 ± 1 indicating focused summaries, however, with limited lexical diversity. Conclusions: This study suggests that large language models can effectively extract genomic characteristics from unstructured reports and can potentially improve genomic literacy among prostate cancer patients by providing easy-to-interpret variant summaries. However, targeted prompting may be required to increase lexical diversity for improved engagement. Performance across different genomic characteristics. Category Precision Recall F1-Score Altered gene 0.86 0.97 0.89 Variant/alteration type 0.98 0.99 0.98 Variant allele fraction 0.96 0.96 0.96 Test name/type 1 0.99 1 Specimen site 1 0.99 1 Tumor mutational burden 0.8 0.93 0.83 CtDNA tumor fraction 1 0.85 0.92 MSI status 0.99 0.97 0.98

454 Background: Limited data exist on outcomes in mRCC patients with major venous involvement (MVI). Assessment of impact of a given variable on prognosis in cancer patients is challenging given competing causes of death (PMID: 25417239). This analysis aims to estimate cancer prognosis in mRCC patients with MVI, considering death from competing causes. Methods: Data from the Surveillance Epidemiology and End Results (SEER) database (2010-2020) were analyzed for survival and mortality in mRCC patients with MVI. 5-year (y) overall survival (OS) and 5-y probability of cancer death were calculated using the actuarial method in SEER*Stat 8.4.4, reported as percentages with 95% confidence intervals (CI). Results were based on the site of MVI (renal vein, inferior vena cava [IVC]) and stratified by age (<65, >65 years), gender (male, female), and race/ethnicity (Non-Hispanic [NH] White, NH Black, NH Asian/Pacific Islander [API], Hispanic). Results: A total of 89047 mRCC patients were included in our analysis of whom 10785 (12%) had major venous involvement (MVI). Overall, patients who experienced MVI had worse prognosis as compared to those who did not have MVI (5-y OS: 53.2% vs 84.4% and 5-y cancer death: 38.6% vs 8.2% respectively). Among cohort with MVI, prognosis was poor with IVC involvement as compared to involvement of renal vein (5-y OS: 36% vs 57%; 5-y cancer death: 56% vs 35% respectively). Patients who had the worst prognosis with MVI included older adults (>65 y) (5-y OS: 47.8%; 5-y cancer death: 40.8%) and NH Black (5-y OS: 41.5%; 5-y cancer death: 49.8%) (Table). Conclusions: Prognosis in mRCC with MVI, especially with IVC involvement, was poor, particularly for older adults (>65 y) and NH Black patients. Further research is needed for better prognostication and risk stratification to improve outcomes for high-risk patients. Subgroup No MVI MVI N 5-year OS (%), 95% CI 5-year Cancer death (%), 95% CI N 5-year OS (%), 95% CI 5-year Cancer death (%), 95% CI Overall population 78,262 84.4 (84.1-84.7) 8.2 (8.0-8.4) 10,785 53.2 (52.0-54.3) 38.6 (37.5-39.7) Age <65 y 49,217 89.0 (88.6-89.2) 6.5 (6.3-6.8) 5,897 57.5 (56.0-59.0) 36.8 (35.4-38.3) >65 y 29,045 77.0 (76.1-77.2) 10.9 (10.5-11.4) 4,888 47.8 (46.0-49.5) 40.8 (39.2-42.4) Gender Male 49,221 83.0 (82.7-83.5) 8.9 (8.6-9.2) 7,503 53.2 (51.8-54.6) 38.3 (37.0-39.6) Female 29,041 86.5 (86.0-86.9) 7.0 (6.7-7.4) 3,282 53.0 (51.0-55.0) 39.4 (37.4-41.3) Race/ethnicity NH White 49,520 84.0 (83.7-84.4) 8.3 (8.0-8.5) 7,182 53.5 (52.1-54.8) 38.0 (36.7-39.3) NH Black 8,878 82.0 (81.0-82.9) 8.1 (7.5-8.8) 685 41.5 (37.1-45.9) 49.8 (45.4-54.1) NH API 4,747 86.0 (85.1-87.4) 8.5 (7.6-9.4) 713 52.9 (48.2-57.3) 39.9 (35.5-44.1) Hispanic (All Races) 13,826 86.5 (85.8-87.2) 7.9 (7.4-8.4) 2,014 56.2 (53.4-58.8) 36.7 (34.1-39.2) N: number of cases; OS: overall survival; CI: confidence interval; NH: Non-Hispanic; API: Asian/Pacific Islander; MVI: major venous involvement.

270 Background: Inconsistent reporting of subgroup data across clinical trials makes interpretation and application of the evidence from trials challenging. We conducted a living systematic review to summarize evidence for American Society of Clinical Oncology (ASCO) guidelines for management of metastatic castration resistant prostate cancer (mCRPC). Here, we provide an overview of reporting patterns of subgroup data across mCRPC trials. Methods: MEDLINE and EMBASE were systematically searched from each database's inception through September 20 th , 2024, to identify phase II/III/IV randomized clinical trials assessing the efficacy and safety of treatment options in mCRPC. For outcomes of overall survival (OS) and progression free survival (PFS), data from the subgroup analyses was collected and compared across trials to assess heterogeneity in the reporting of subgroups. Results: This analysis included 141 trials (phase II: 83; phase III: 56; phase IV: 2). OS was reported by 113 (80%) trials; 32 (28%) reported OS by any subgroups with 95 unique subgroup categories. PFS was reported by 110 (78%) trials; 23 (21%) reported PFS by any subgroups with 63 unique subgroup categories. Even among consistently reported subgroups, such as age, ECOG performance status, race, geographical area, and Gleason score, considerable heterogeneity in different subgroup levels/thresholds was observed across trials. Top ten most commonly reported subgroups are summarized (Table). Conclusions: Lack of consistent reporting for subgroup analysis in mCRPC trials preclude meaningful conclusions about clinically relevant subgroup and synthesizing evidence for clinical guidelines. A standardized framework for consistent reporting of subgroup data across trials can help inform clinical practice and clinical practice guidelines. OS PFS Number of trials 113 110 Reporting subgroup analysis – N (%) 32 (28) 23 (21) Commonly reported subgroups – (N) Age (26)ECOG-PS (26)LDH (21)PSA (18)Geographic area (15)Alkaline phosphatase (14)Visceral disease (14)Gleason score (9)Race (9)Brief pain inventory level (8) Age (21) ECOG-PS (19)PSA (18)LDH (14)Geographic area (13)Visceral disease (11)Gleason score (10)Alkaline phosphatase (9)Bone metastasis (9)Disease location (9) Age – (N) <65 (14); ≥65 (9); ≤68 (1); ≥69 (1); <70 (5); ≥70 (5); ≤74 (4); 65-75 (5); ≤ 71 (1); >71 (1); ≥75 (11); >Median (1); <Median (1) <65 (12); 65-74 (6); ≥65 (6); <70 (5); ≥70 (5); <75 (4); ≥75 (10); 76-80 (1); >80 (1) ECOG-PS – (N) 0 (15); 0-1 (11), 1 (11); 1-2 (5); 2 (12) 0 (15); 0-1 (5), 1 (12); 1-2 (2); 2 (6) Race – (N) White (9)Non-white (6)African American/black (2)Asian (2) White (8)Non-white (5)African American/black (2)Asian (4) Geographic Area – (N) Europe (8)North America (14)United States (1)Australia (1)Asia (2) Europe (11)North America (13)United States (1)Australia (1)Asia (2)North and South America (2) Gleason Score – (N) 2-6 (1); 7 (1); 8 (1); <8 (8); ≥ 8 (8); 9-10 (1) <8 (10); ≥ 8 (10)

524 Background: Antiangiogenic tyrosine kinase inhibitors (TKIs) play a key role in treating renal cell carcinoma and other solid organ malignancies, but TKIs cause bothersome side effects that frequently necessitate dose reductions or treatment holds. Olanzapine has been useful for palliating symptoms from classical chemotherapies and advanced cancer, but its effect on TKI-related symptoms is largely unknown. This retrospective study aimed to estimate the efficacy of olanzapine for TKI-induced nausea, vomiting, anorexia, weight loss, and insomnia. Methods: All patients prescribed olanzapine with lenvatinib, cabozantinib, axitinib, or tivozanib at Mayo Clinic between January 1, 2018, and June 30, 2024 were assessed for inclusion. For baseline symptom assessment, clinical care team notes documenting the presence or absence of symptoms and indication(s) for starting olanzapine were reviewed. For response assessment, clinical notes and patient portal messages from the first three months after starting olanzapine were evaluated for qualitative descriptions of change in symptom burden. Relevant data were extracted as direct quotations, which were reviewed by the research team and categorized as “improved,” “worsened,” “stable,” or “missing data.” Each symptom domain was analyzed independently when olanzapine was prescribed for multiple interrelated symptoms. Results: A total of 60 patients who received olanzapine for the treatment of TKI-related adverse effects were included. The most common documented indication for olanzapine was nausea without vomiting (n=35), followed by anorexia (n=25), nausea with vomiting (n=16), weight loss (n=16), and insomnia (n=11). In 32 cases (53%), olanzapine was prescribed for multiple, simultaneous symptoms. Response rates for each symptom domain are shown in the table. In addition, prior to olanzapine, 34 patients (57%) had documented weight loss. Following olanzapine, 50% (n=17) gained weight (median increase: 6.1 kg; range: 2.0 - 10.7 kg), 26% (n=9) stabilized their weight (±1 kg), and 24% (n=8) continued to lose weight. Clinicians utilized a TKI dose reduction within one week of olanzapine initiation in 5 cases (8.3%), and 3 of these patients could resume the higher dose of TKI after starting olanzapine. Only 4 patients (7%) discontinued olanzapine within 3 months due to perceived side effects. Conclusions: Olanzapine appears to be effective in treating TKI-induced nausea, vomiting, anorexia, insomnia, and weight loss. Prospective randomized placebo-controlled studies are needed to confirm these findings. Improvedn (%) Worsened n (%) Stablen (%) Missing Datan (%) Nausea without emesis 32 (84) 3 (8) 1 (3) 2 (5) Nausea with emesis 13 (93) 0 (0) 0 (0) 1 (7) Anorexia 26 (74) 2 (6) 4 (11) 3 (9) Insomnia 11 (85) 0 (0) 2 (15) 0 (0)

520 Background: The annual incidence of renal cancer is 0.02% among the general United States population. End stage renal disease (ESRD) and exposure to dialysis are thought to be the primary drivers of increased risk of renal cancer in renal transplant patients rather than immunosuppression. This has been evidenced by how the incidence of RCC in heart transplant recipients is no different than the normal population. In this study, we aim to characterize RCC incidence among kidney transplant patients (usually exposed to ESRD/dialysis) versus other solid organ transplant patients (usually unexposed to ESRD/dialysis). Methods: We used CPT codes to search for patients who had received a solid organ transplant (kidney, liver, heart, lung, pancreas) at all Mayo Clinic sites from 1/1/2018 to 1/1/2024. Within this cohort, we identified those who had been diagnosed with kidney cancer after initiating dialysis or receiving a transplant, and retrospectively extracted data related to demographics, cancer diagnosis, and transplant details. Results: We identified 10873 patients who had received a solid organ transplant (Table 1). Of these, 178 (1.76%) were diagnosed with kidney cancer after either starting dialysis or receiving a transplant. These 178 patients were of median age 56 years at time of cancer diagnosis (STD 12), 71% male, 70% white, 20% black, 4.5% Asian, 2.8% American Native. Of 5792 patients who had received a kidney transplant (with or without another solid organ transplant), 172 (2.97%) were diagnosed with kidney cancer after having started dialysis or receiving a kidney transplant. Of these 172, 156 (91%) were exposed to dialysis (107 primarily hemodialysis and 48 peritoneal). Of 4392 patients who had received a solid organ transplant other than kidney (liver, heart, lung, and/or pancreas), 6 (0.14%) were diagnosed with kidney cancer after having received a kidney transplant. Of these 6, none underwent dialysis. Conclusions: Patients with kidney transplants are at increased risk of kidney cancer compared to patients with non-kidney solid organ transplants (2.97% vs 0.14%). This supports prior evidence showing ESRD plays a significant role in the pathogenesis of kidney cancer. Patients who received solid organ transplants at Mayo Clinic from 2018-2024 and were subsequently diagnosed with kidney cancer. n % With RCC after TXP 178 1.7587% Cancer Diagnosis after Kidney HD/TXP 172 2.9696% Cancer Diagnosis after Other TXP 6 0.1386% Patients who received solid organ transplants at Mayo Clinic from 2018-2024 n % Unique Patients 10121 100% Kidney Transplant 5792 57.23% Alone 5088 50.27% &Liver 332 3.28% &Heart 119 1.18% &Lung 8 0.08% &Pancreas 245 2.42% Non-kidney Transplant (Liver, Heart, Lung, or Pancreas) 4329 42.77% TXP: Transplant, HD: Hemodialysis.