Citations

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.

Abstract Background: The cellular balance between saturated and monounsaturated fatty acids is a defining feature of tumor lipid metabolism, with SCD1 functioning as a key modulator through the production of MUFAs. Elevated SCD1 activity and MUFA accumulation have been documented in hepatocellular carcinomas, although the consequences of restricting MUFA synthesis have not been fully clarified. Methods: A selective SCD1 inhibitor, SSI-4, was evaluated across established human HCC cell lines and xenograft models. Measurements included cell viability, lipid compositional analyses, and characterization of stress response pathways at both molecular and biochemical levels. The effects of MUFA depletion were studied in vitro and in vivo, with attention to ER stress, apoptosis, ferroptosis markers, and autophagy induction. Results: SSI-4 treatment led to marked reduction in cellular MUFA content and an increase in saturated fatty acid pools. The resulting metabolic stress correlated with activation of unfolded protein response components, caspase-mediated cell death, and the upregulation of ferroptosis-associated genes. Triglyceride and MUFA levels declined precipitously, as confirmed by mass spectrometry-based lipidomics. Tumor growth was suppressed in PDX models and evidence of autophagic flux was apparent in sensitive cell lines. Variability in response highlights metabolic heterogeneity in HCC. Conclusions: Targeted disruption of MUFA biosynthesis inflicts metabolic vulnerability on HCC cells, precipitating membrane alterations and activation of multiple cell death programs. These findings recommend further investigation into metabolic interventions exploiting MUFA depletion, with consideration for stratifying patients according to lipid metabolic phenotypes. Funding: This work is supported by Mayo Clinic Comprehensive Cancer Center and the Jay and Deanie Stein Career Development Award for Cancer Research at Mayo Clinic Jacksonville and the Mayo Clinic Translational Hepatobiliary Cancer SPORE P50CA210964 Career Enhancement Program (CEP) and Florida Department of Health/Florida Cancer Innovation Fund C25C23. Citation Format: Justyna J. Gleba, Victoria N. Rios, Brian M. Necela, Skyeler M. Klinge, Matt L. Pawlush, Mike J. Redig, Aylin Alasonyalilar-Demirer, Han W. Tun, John A. Copland III, . Disrupting tumor lipid homeostasis by MUFA depletion drives cell death in HCC [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 7333.

Abstract Background: Digital access, health literacy, and confidence in obtaining cancer information are well-known factors that influence cancer beliefs and behaviors. However, less is understood about whether these information-related factors vary across regions within a cancer center's catchment area and how they collectively relate to cancer beliefs in large community samples. This study investigated regional differences in digital access, health literacy, and confidence in seeking information, and examined the connections between these factors and cancer risk perceptions, fatalistic beliefs, prevention confusion, and willingness to participate in cancer clinical trials. Methods: Data (N=2,334) from the 2023 Mayo Clinic Comprehensive Cancer Center’s Cancer-Focused Needs Assessment, conducted across catchment areas in Arizona, Florida, and the Midwest (Minnesota, Wisconsin, Iowa). Regional differences in digital access (Internet use and device ownership), health literacy, and confidence in obtaining cancer information were analyzed using multivariable ANCOVAs and logistic regressions, adjusting for sociodemographic covariates. Follow-up models examined whether these information precursors are associated with perceived cancer risk, fatalistic beliefs, confusion about recommendations, and willingness to participate in cancer clinical trials, adjusting for the same covariates. Results: Regions showed minimal differences in digital access and health literacy. Health literacy was instead associated with ethnicity, age, gender, and education (all p<.001). Confidence in obtaining cancer information followed similar trends, with significant influences of ethnicity and gender (p<.01). Internet use was strongly associated with sociodemographic factors (all p<.01). Additionally, the information precursors consistently correlated with cancer beliefs across the full sample: Higher health literacy and greater confidence in obtaining cancer information were associated with increased perceived cancer risk, reduced cancer fatalism, and less confusion regarding prevention guidelines (ps < .001). Internet use and device access were significantly related to a greater willingness to participate in clinical trials (ps < .01). These relationships persisted even after controlling for sociodemographic covariates. Conclusions: Regional differences in digital access and health literacy were minimal, indicating a consistent information environment. Within this context, individual sociodemographic and information-related factors played a larger role, strongly shaping cancer beliefs. These findings underscore technology access and information empowerment as key targets for enhancing cancer prevention knowledge and research engagement, and indicate that the effects of information empowerment are primarily driven at the individual rather than the geographic level. Citation Format: Tingyu Zou, Monica L. Albertie, Emelina Asto-Flores, Manisha Salinas, Farhia Omar, Adeline Abbenyi, Noreen Stephenson, Nuwanthi Heendeniya, James R. Cerhan, Kathleen J. Yost, Folakemi T. Odedina. Digital access, health literacy, and cancer beliefs: Findings from a multi-region community survey [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 7553.

Abstract Background: Symmetry principles, a long foundational framework in physics and chemistry, have rarely been applied to understand biological phenotypes especially in cancers. Here we examine whether symmetric relationships in gene expression can characterize and differentiate healthy from disease conditions. Method: To test this concept, we built a hybrid machine-learning framework, Learning-Based Invariant Feature Engineering (LIFE), that applies two symmetric invariant feature functions, IFF1 and IFF2, to all possible gene pairs in bulk transcriptomic datasets to identify invariant feature genes (IFGs) - gene pairs whose transformed expression values by either IFF1 or IFF2 produce quasi-constant single-value outputs within a phenotype despite inter-individual variability. Results: Using bulk transcriptomes from 25 normal organs (GTEx) and 25 cancer types (TCGA), we computed IFF values for all normally distributed gene pairs, selected the 1000 most stable pairs per phenotype, and evaluated their performance in multiclass classification with five-fold cross-validation and independent hold-out testing. IFGs generated >70% accuracy across organs and cancers, establishing the existence of robust phenotype-specific symmetry “fingerprints.” Mapping approved and experimental drug targets onto networks construction from IFGs (IF-Nets) showed strong enrichment of hubs in cancer networks and highlighted the use of IF-Nets as drug discovery platforms in cancer treatment. Conclusion: Our findings demonstrate that gene-expression symmetry as a unifying organizing principle for phenotype definition and illustrate how IFGs and IF-Nets can guide biomarker design and symmetry-aware pharmacological intervention via “symmetry breaking.” Citation Format: Cristina Correia, Choong-Yong Ung, Cheng Zhang, Shizhen Zhu, Hu Li, . Learning-based invariant feature engineering reveals symmetry-encoded fingerprints of cancers that facilitate drug discovery [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 5516.