Citations

Abstract Background: Chronic inflammation is implicated in ovarian carcinogenesis, but how different inflammation-related exposures individually or jointly affect histotype-specific associations remains unclear. Materials and Methods: We pooled data from 16 case-control studies in the Ovarian Cancer Association Consortium to evaluate associations of eight inflammation-related factors (anti-inflammatory: aspirin use, tubal ligation (TL); pro-inflammatory: endometriosis, obesity, lifetime ovulatory cycles (LOC), smoking, pelvic inflammatory disease (PID), polycystic ovary syndrome (PCOS)) with epithelial ovarian cancer (OvC) by histologic subtype. We examined individual associations and clustering of risk factors across histotypes and computed population attributable risk (PAR) for each factor. We assessed additive and multiplicative interactions for exposure combinations. Results: Associations with OvC risk differed by histotype (e.g., high-grade serous: aspirin: OR=0.90; 95%CI 0.82, 0.99; TL: OR=0.80; 95%CI 0.73, 0.88; overall serous: endometriosis: OR=1.17; 95%CI 1.03, 1.31; high LOC: OR=1.42; 95%CI 1.28, 1.58; obesity (low-grade serous): OR=1.50; 95%CI 1.14, 1.98). Clustering analyses showed highly correlated risk profiles in endometrioid and clear cell (r=0.91). High-grade serous and mucinous profiles were moderately correlated with endometrioid and clear cell (r=0.60) tumors. The profile for low-grade serous (r=0.36) tumors was distinct from other histotypes. PAR estimates suggested modifying aspirin use, TL, and LOCs could substantially reduce burdens of endometrioid, clear cell and mucinous tumors. Out of 28 exposure combinations tested in overall OvC and 189 by histotype, we observed 12 interactions. Not using aspirin regularly showed positive additive interactions with obesity and high LOCs, particularly in endometrioid tumors (obesity relative excess risk due to interaction (RERI)=0.74, 95%CI 0.31, 1.18; Pint=0.001 for; LOCs RERI=0.80, 95%CI 0.03, 1.56; Pint=0.04). Not using aspirin regularly also showed a positive additive interaction with endometriosis in clear cell tumors (RERI=1.77, 95%CI 0.03, 3.52; Pint=0.05). Lack of TL showed positive interactions with obesity in endometrioid (RERI=0.86, 95%CI 0.17, 1.53; Pint=0.01) and mucinous (RERI=1.10, 95%CI 0.23, 1.97; Pint=0.01) tumors, while negative additive interactions were observed for smoking and endometriosis in endometrioid tumors (RERI=-1.12, 95%CI -2.19, -0.05; Pint=0.04). A multiplicative interaction was observed between obesity and endometriosis in mucinous tumors (Pint=0.01). Conclusion: The findings suggest ovarian tumorigenesis is strongly shaped by pro- and anti-inflammatory pathways that act largely independently. Further examining these pathways may clarify the origins of histotype heterogeneity and guide prevention strategies. Citation Format: Maxwell Akonde, Britton Trabert, SHELLEY TWOROGER, Allan Jensen, Kathryn L. Terry, Joshua Sampson, Hoda Anton-Culver, David Bowtell, Elisa V. Bandera, Angela Brooks-Wilson, Andrew Berchuck, Daniel William Cramer, Linda S. Cook, Julie M. Cunningham, Jennifer A. Doherty, Ellen L. Goode, Marc T. Goodman, Holly Ruth Harris, Susanne K. Kjaer, Nhu Le, Alice Wen-Ron Lee, Francesmary Modugno, Kirsten B. Moysich, Celeste Pearce, Malcolm C. Pike, Harvey A. Risch, Mary A. Rossing, Joellen M. Schildkrau, Daniel O. Stram, Rebecca Sutphen, David Van Den Berg, Penelope M. Webb, Anna Wu, Argyrios Ziogas, Nicolas A. Wentzensen. Inflammation-related exposures and histotype- specific ovarian cancer risk in the Ovarian Cancer Association Consortium (OCAC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6261.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.

Abstract Background: Prostate cancer remains one of the most common malignancies in men, with significant heterogeneity in clinical outcomes. Early and accurate diagnosis and risk stratification is crucial for effective treatment and improved outcomes. Although Gleason score is an established risk stratification tool, it does not fully explain outcome variability. We present a multimodal deep-learning framework that integrates attention-based whole-slide image (WSI) features with a clinical variable (Age) to predict survival in patients with prostate adenocarcinoma. Design: Clinical data and WSI for 500 patients with prostate adenocarcinoma were obtained from TCGA-PRAD. Clinical data were parsed for recurrence or progression (RoP) events and age. RoP was used as the outcome label, and age was used as a patient feature. Diagnostic WSI tumor regions were annotated and tiled at 224×224 pixels using QuPath (Bankhead Sci Rep 2017). Tile embeddings were extracted with UNI2-h (Mahmood Nat Med 2025). An end-to-end machine learning pipeline was developed to aggregate tile embeddings into slide-level embeddings using Clustering-Constrained Attention Multiple Instance Learning (CLAM) and predict RoP (Lu Nat Biomed 2021). A second pipeline used a Gleason score-trained binary cross entropy (BCE) model to predict RoP in the same patients. Three models were evaluated: WSI-only CLAM model, WSI+age CLAM model, and Gleason-only BCE model. All models were trained and evaluated using 5-fold stratified cross-validation. Each fold was repeated 3 times with independent random initialization. AUROC was averaged across all folds and runs per model. Standard deviation was also computed. Model performance was compared using Friedman’s test, with post hoc pairwise comparisons by Wilcoxon signed-rank test. Results: The Gleason BCE model underperformed (AUROC 0.67 ± 0.07). Both WSI-based models outperformed the Gleason BCE model, achieving AUROC 0.76 ± 0.02 (without age), and AUROC 0.76 ± 0.01 (with age). AUROCs were significantly different by Friedman’s test (p = 0.0224) and showed a trend toward significance in post-hoc comparison between WSI and Gleason-based approaches (p = 0.06). Conclusion: AI models using WSI-derived features, with or without basic clinical context, outperformed traditional Gleason score for recurrence risk prediction in prostate cancer. These findings support the integration of digital pathology and AI into routine prognostic assessment for prostate cancer. Future work will focus on including additional patients, clinical variables, and multi-class prognostic labels to expand prognostic prediction beyond RoP. Citation Format: Justin Johnson, Kingsley Ebare. Whole-slide image and clinical feature integration for superior prostate cancer risk stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1441.

Abstract 5-Fluorouracil (5-FU) is a widely prescribed chemotherapy for colorectal, breast, gastric, and head and neck cancers. The drug inhibits thymidylate synthase, blocking DNA synthesis and causing cytotoxic stress that halts tumor growth. However, patients carrying deleterious variants in DPYD, the gene encoding the rate-limiting enzyme in 5-FU metabolism (dihydropyrimidine dehydrogenase, DPD), can experience fatal toxicity due to impaired drug clearance and accumulation of 5-FU metabolites. Current pharmacogenetic screening guidelines in the U.S. recommend testing for few of the >2,000 nonsynonymous variants that have been reported for DPYD, leaving patients with rare, uncharacterized mutations at risk. To interpret expanded testing, however, a means to classify DPYD variants of unknown significance is needed. To address this, we developed MPACT-DPD, a random-forest-based classifier that accurately predicts the functional impact of DPYD missense variants. Our model was trained on in-vitro activity of 156 variants and leveraged a feature set of biochemical, evolutionary, and AlphaFold3-derived structural features. We optimized hyperparameters using ten-fold stratified cross-validation and evaluated model performance with Matthews correlation coefficient (MCC) to account for moderate class imbalance (7:3 neutral to deleterious). It achieved exceptional performance, with a Matthews correlation coeffient (MCC) of 0.906 and an accuracy of 95.1% on an independent validation set (n=43). Furthermore, a SHAP (SHapley Additive exPlanations)-based interpretability analysis revealed cofactor proximity and residue conservation as the key drivers of predictions. MPACT-DPD showed superior performance at variant classification against other tools, including a DPYD gene-specific variant classifier, and has the potential to expand pre-treatment genetic screening to improve the safety of personalized 5-FU-based chemotherapy. Citation Format: Lulu Jiang, Ryan Jonathan Swartz, Lauryn Allyn Hahn, Brianna Bembenek, Hannah Marie Krause, Kelly Bouchonville, Steven M. Offer. MPACT-DPD: An interpretable machine learning classifier for predicting the functional impact of DPYD missense variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2712.

Abstract Introduction: Src Family Kinases (SFKs) have a crucial role in tumor proliferation, survival, and metastasis. NXP900 is a highly selective SFK inhibitor with a novel mechanism of action that locks SFKs in a closed, inhibited conformation, providing sustained suppression of catalytic and non-catalytic functions, which is distinct from other SFK inhibitors like dasatinib. A phase I clinical trial of NXP900 in patients with advanced solid tumors is ongoing. Our preclinical studies showed therapeutic efficacy of NXP900 in cholangiocarcinoma (CCA) through decreased YAP, RAF-MAP and PI3K-AKT signaling. Multiomic profiling (RNA-seq, proteomics, phosphoproteomics) of CCA patient-derived xenografts implicated AKT pathway activation in primary NXP900 resistance. NXP900-resistant CCA cell lines generated via prolonged sublethal dose escalation showed a strong IL13RA2-AKT signature on multiomic analysis. Thus, we hypothesized that IL13RA2-AKT signaling was a mechanism of NXP900 resistance. We aimed to define this mechanism and demonstrate that the combinatorial targeting of IL13RA2 or AKT resensitizes to NXP900 treatment. Methods: HuCCT1 or HuCCT1-Resisant (HuCCT1-R) cells were injected bilaterally into the flanks of NOD/SCID mice and were allowed to grow for 3 months prior to tumor collection and analysis. Tumor engraftment and growth between HuCCT1 and HuCCT1-R groups were compared. To delineate the mechanisms for acquired NXP900 resistance, in-vitro treatment studies were performed using siRNAs targeting IL13RA2 as well as combination treatment with capivasertib, a pan-AKT inhibitor. Results: The HuCCT1-R cell line exhibited a higher rate of tumor engraftment and formed significantly larger tumors compared to the parental HuCCT1 cell line, indicating enhanced tumorigenic potential associated with acquired resistance. Silencing of IL13RA2 in HuCCT1-R cells resulted in pronounced reduction of cell proliferation and partially restored sensitivity to NXP900 treatment. Immunoblot analysis demonstrated a corresponding reduction in phosphorylated AKT (Thr308), consistent with suppression of downstream PI3K-AKT signaling. Furthermore, combination treatment of HuCCT1-R cells with the AKT inhibitor capivasertib, and NXP900 led to a significant increase in cell death and a greater inhibition in cell proliferation compared to either agent alone, indicating enhanced responsiveness and improved therapeutic efficacy. Conclusion: These findings confirm the central role of IL13RA2 signaling and the PI3K-AKT pathway in mediating acquired resistance to NXP900, consistent with our previous multiomics analysis. Importantly, this study identifies combination strategies that may be able to overcome NXP900 resistance, supporting the development of combinatorial approaches to restore therapeutic efficacy in resistant tumors. Citation Format: Shelby K. Yee, Hendrien Kuipers, Danielle M. Carlson, Jack W. Sample, Enis Ozmert, Hidemi Nishi, Erik Jessen, Dong-Gi Mun, Aushinie M. Abeynayake, Jennifer L. Tomlinson, Amro M. Abdelrahman, Nathan W. Werneburg, Binbin Li, Mitesh J. Borad, Mark J. Truty, Sumera I. Ilyas, Gregory J. Gores, Rory L. Smoot. Resistance to the SFK inhibitor NXP900 in cholangiocarcinoma is characterized by IL13RA2-AKT signaling and can be overcome by combination therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7124.

Abstract Introduction: Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods: We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results: Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion: This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Citation Format: Keli S. Santos-Parker, Jessica R. Santos-Parker, W. Fraser Symmans, Laura J. Esserman, Christina Yau, Angie DeMichele, Laura van't Veer, Doug Yee, Fabien Reyal, Helena Earl, Jean Abraham, David Cameron, Peter Hall, Judy Boughey, Matthew Goetz, Gabe Sonke, Miguel Martín, Sara López-Tarruella, Priyanka Sharma, Rachel Freiberg, Jane Perlmutter, Aditya Bardia, Martin Eklund, Rachel Freiberg, Lajos Pusztai. A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1401.

Abstract Background: Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, develops through multiple pathways; however, the extent to which HBV, HCV, and non-viral etiologies shape tumor biology and influence treatment responses remain incompletely understood. The availability of viral detection methods from sequencing data and multiomics workflows give us a new opportunities to study how etiology influences the molecular landscape of HCC. Here, we analyze TCGA's LIHC samples categorized into HBV-, HCV-, and non-viral tumors to compare genomic, transcriptomic and methylation profile of these HCC tumors to identify biological pathways specific to each group. Methods: Whole-exome, RNA-seq, and clinical data from TCGA-LIHC (n=325) were analyzed. Viral status was assigned using Exogene (≥5 HBV/HCV reads; mixed infections excluded; non-viral = 0 reads). Mutation and clinical comparisons were performed using cBioPortal. RNA-seq data were processed with edgeR for 3-way differential expression (median count ≥25; |log2FC|>2; FDR<0.05). Pathway enrichment used ShinyGO. Findings were validated using the GSE62232 microarray cohort analyzed with limma. Results: Demographic patterns reflected known risk profiles: HBV patients presented younger (mean 53.9 years) and were predominantly male (78.8%) and Asian (92.9%), while HCV and non-viral groups were older and primarily White. Non-viral HCC showed the highest proportion of females (42.7%) and nearly half lacked significant fibrosis, consistent with metabolic disease. In contrast, HCV had the highest cirrhosis burden (47.2%), and HBV tumors showed the greatest proportion of poorly differentiated histology (52.2%).The comparison of the expressed genes between the 3 tumor subtypes revealed distinct etiology-associated pathways. Metabolic and mitochondrial including fatty acid β-oxidation differential expressed in Non-viral HCC. Activation of antiviral and interferon signaling, including type I IFN response and OAS-mediated defense pathways are observed in HCV-HCC. Finally, Cell-cycle and proliferation pathways are differentially expressed in HBV-positive, a finding consistent with effects of viral integration. Clustering analysis separated HBV and non-viral tumors into distinct groups. These signatures were reproduced in a validation cohort. Conclusion: Our analyses highlight that HBV-, HCV-, and non-viral HCC harbor distinct transcriptional and signaling pathways. These etiology-specific signatures provide a framework for developing more precise biomarkers and for tailoring therapeutic strategies to the molecular biology of each etiology. Acknowledgement: We acknowledge the late Dr. Sean Cleary, MD, for his invaluable contributions to this project. Disclosure: AI has been used to trim sentences in order to fit in the word limit. Citation Format: Amerti Guta, Daniel O'Brien, Aditya V. Bhagwate, Jean-Pierre A. Kocher, Lewis R. Roberts. Multiomic dissection of HBV-, HCV-, and non-viral HCC reveals distinct proliferation, interferon, and metabolic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2682.

Abstract Amyotrophic lateral sclerosis (ALS) affects approximately 1.5 per 100,000 person-years in the United States, with a point prevalence of 3.8-5.6 per 100,000. ALS arises from the intersection of diverse pathogenic mechanisms spanning genetic mutations (C9orf72, SOD1, FUS, TARDBP), protein misfolding, RNA-metabolism defects, nucleocytoplasmic transport failure, prion-like propagation, mitochondrial dysfunction, and neuroinflammation. Increasing evidence from plasma proteomics and machine-learning studies indicates that these perturbations emerge years prior to clinical onset, highlighting an urgent need for mechanistically grounded patient stratification. At the same time, epidemiologic and molecular studies suggest unexpected parallels between ALS and cancer—including dysregulated DNA-damage responses, cell-cycle control, metabolic rewiring, and aberrant immune signaling—yet these cross-disease links remain fragmented and poorly mapped. We developed a multi-scale ALS-cancer knowledge graph integrating genetic, transcriptomic, proteomic, neuropathological, and longitudinal clinical datasets. Knowledge-graph completion approaches, including link prediction, graph embeddings, and embedding-based reasoning, were applied to infer missing edges and reveal latent modules connecting ALS-associated genetic lesions to oncogenic pathways implicated in genome instability, altered proliferation, metabolic remodeling, and immune dysregulation. This approach enables systematic interrogation of shared molecular programs across ALS and cancer, exposing under-characterized immune, metabolic, and axonal-transport circuits whose dual roles have been suggested but never comprehensively mapped. Predicted associations were evaluated using enrichment for established ALS genes, overlap with curated oncogenic and neurodegenerative pathways, and their ability to stratify patients by site of onset, progression rate, and biomarker signatures. These analyses generated patient-specific pathway fingerprints that (i) resolve dominant biological mechanisms across clinical subgroups, (ii) illuminate mechanistic intersections between neurodegeneration and malignancy, and (iii) prioritize repurposed oncology targets with potential relevance to ALS. This knowledge-graph framework provides a scalable and mechanistically informed strategy for classifying ALS patients, uncovering ALS-cancer pathway convergence, and supporting targeted therapeutic discovery across traditionally siloed disease domains. Citation Format: Vasileios Alevizos, Sabrina Edralin, Clark Xu, George A. Papakostas, Zongliang Yue. Mapping convergent neurodegenerative and oncogenic pathways in ALS using a multi-scale knowledge-graph framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6875.

Abstract Chimeric antigen receptor (CAR) T-cell therapy achieves high response rates in relapsed/refractory aggressive B-cell non-Hodgkin lymphoma, yet only 40% of patients achieve durable remission. Identifying biological programs linked to long-term response remains critical. As metabolic fitness underlies T-cell persistence and effector function, we profiled metabolic pathways associated with clinical outcomes following commercial CD19 CAR T-cell therapy. Single-cell RNA sequencing of peripheral blood mononuclear cells collected at baseline (BL), peak CAR-T expansion (PK), and one-month post-infusion (M1) was analyzed using CellRanger v7.0.1, immunopipe. Seurat v4.3.0 was applied for unsupervised clustering to delineate cell subsets based on top differentially expressed genes. Patients were categorized as durable complete remission (CR ≥6 months; n = 16), primary refractory (PD1; n = 4), or relapse after initial response (PD2; n = 12). Gene-set enrichment analysis defined metabolic pathway activity across T-cell, monocyte, dendritic cell (DC), and natural killer (NK) subsets. Oxidative phosphorylation (OXPHOS) emerged as the dominant metabolic program distinguishing clinical outcomes. At BL and PK, OXPHOS was consistently enriched in PD1 relative to CR across T-cell, monocyte, and DC subsets, suggesting early oxidative activation in non-responders. By M1, this pattern inverted, with higher OXPHOS activity in CR. PD2 largely paralleled PD1, but several subsets including CD8 T central memory, classical monocytes TGFβ, intermediate monocytes CD38, monocytic myeloid-derived suppressor cell (mMDSC) HIF1A, and mMDSC SIRPA, showed OXPHOS enrichment in CR at BL and/or PK that persisted through M1. In contrast, DC and NK subsets exhibited the opposite pattern: OXPHOS was enriched in CR at PK (conventional DC 2 (cDC2), plasmacytoid DC (pDC), NK, proliferating NK), but shifted toward enrichment in PD2 at M1 (cDC2, NK, NK CD56bright). Glycolysis (GLY) followed a similar trajectory in monocytes and NK cells, with enrichment in PD1/PD2 at BL and PK, followed by enrichment in CR at M1. In T-cells, PD1 maintained GLY enrichment from BL through PK, with no significant differences observed at M1. The inositol-phosphate metabolism pathway showed a more static pattern and was consistently enriched in multiple PD2 effector and memory T-cell subsets without reversal at M1. Taken together, these data reveal distinct, lineage-specific metabolic signatures that differentiate durable remission from early and late progression. Dynamic OXPHOS and GLY programming, characterized by lower activity early and enhanced activity at M1 in CR, may reflect adaptive metabolic programming that supports sustained antitumor immunity. Immune-metabolic profiling may therefore serve as a biomarker of response and highlight actionable metabolic pathways to enhance CAR T-cell durability. Citation Format: Melinda S.Y. Tan, Panwen Wang, Patrizia Mondello, Jacqueline Turner, Andre de Menezes Silva Corraes, Chen Wu, Zuoyi Shao, Kevin Regan, Ma Audrey, Arushi Khurana, Nora N. Benanni, Yucai Wang, Paul Hampel, Jonas Paludo, Saad J. Kenderian, Urshila Durani, Patrick B. Johnston, Jose Caetano Villasboas, Stephen M. Ansell, Ying Li, Haidong Dong, Hu Zeng, Yi Lin. Metabolic pathway signatures defining response to CD19 CAR T-Cell therapy in aggressive B-cell non-hodgkin lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3278.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy due in part to metabolic rewiring and a high rate of metastasis. Pancreatic tumors are particularly dependent upon glutamine (Gln) as a nutrient source for multiple metabolic pathways. To adapt to low levels of glutamine, PDAC cells upregulate the Rac1 dependent process of macropinocytosis, which internalizes extracellular macromolecules to scavenge them as a glutamine source. The proto oncogene Vav1 is an activator of the potent Rac/Cdc42 signaling cascades that regulate actin dynamics. While Vav1 is normally restricted to hematopoietic cells, Vav1 expression is significantly increased in PDAC, with high Vav1 correlating with worse overall survival. Here we uncover a novel Vav1 mediated regulation of glutamine metabolism in PDAC cells. While Vav1 is a potent activator of Rac1, surprisingly, Vav1 expression inhibited macropinocytosis in PDAC cells. Intriguingly, genomic and metabolomic analysis revealed that high Vav1 expression correlates with increased dependence on glutamine and glutamate (Glu) metabolism. Indeed, Vav1 dependent macropinocytosis was rescued by cell permeable glutamate, confirming disrupted Gln/Glu balance in Vav1 knockdown cells. RPPA screening indicated a Vav1 dependent effect on glutaminase 1 (GLS1, KGA isoform), which converts glutamine to glutamate. Mechanistically, we discovered that Vav1 modulates GLS1 localization to the mitochondria through regulation of GLS1 posttranslational modification. Disrupting GLS1 localization by deleting its mitochondrial targeting motif phenocopied loss of Vav1 in reducing the invasive potential of tumor cells. Investigation of Vav1 dependent metabolic consequences showed that knockdown of Vav1 alters 13C Gln derived metabolic flux into the TCA cycle, as well as a deficiency in the level of the antioxidant glutathione (GSH), which utilizes glutamate for its synthesis. Consequently, targeting Vav1 resulted in increased levels of cellular and mitochondrial reactive oxygen species (ROS) and increased sensitivity to lipid peroxidation. Notably, dual targeting of both glucose and glutamine pathways showed significant synergistic effects on cellular viability in Vav1 expressing cells. Here we established a new tumor promoting role for Vav1 via Gln/Glu regulation and uncover a novel subcellular regulation of mitochondrial glutamine metabolism. Our findings establish Vav1 as a critical regulator of metabolic rewiring in PDAC and orchestrates glutamine metabolism by controlling the KGA isoform of GLS1 mitochondrial localization. Importantly, Vav1 functions as a signaling adapter in glutamine metabolism, providing a potential therapeutic vulnerabilities for PDAC patients with high Vav1 expression. Citation Format: Mustafa Emre Gedik, Omar L. Gutierrez-Ruiz, Katherine M. Johnson, Ella Rose D. Chianis, Jing Chen, Alex Bittner, Adam Zahm, Ankit Chhoda, Taro Hitosugi, Mark A. McNiven, Gina Razidlo. Vav1 promotes pancreatic tumor cell invasion via GLS1-dependent glutamine metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7322.