Abstract 1052: Integrating geographical and socioeconomic data with genomic information for enhanced prediction of cancer risk using large language models and knowledge graphs

Cancer remains a leading cause of mortality worldwide, with risk and survival outcomes influenced by a complex interplay of genetic, environmental, and socioeconomic factors. Traditional cancer risk prediction models often focus on genetic, lifestyle, and family history factors but frequently overlook the critical roles of geographical location and social determinants of health (SDoH). This study introduces a novel approach that integrates diverse multimodal datasets—including environmental exposures, healthcare access, socioeconomic conditions, and genomic data—within a geographic framework to develop more comprehensive and spatially contextualized cancer risk prediction and survival models. First, we developed a data model to integrate the various data modalities. We leverage our internal Sylvester Data Portal Informatics Platform to manage and map the various data types and metadata. Using the GPT-4o Large Language Model, we identified entities and their relationships from semi-structured and unstructured data sources, and used them to construct a multimodal knowledge graph (KG). Our KG comprises 700,000+ nodes spanning 32 entity types (e.g., genes, 5 cancer types) and 2.134 million edges/links across 107 relationship types (e.g., has_genotype, from_race, from_sex). Graph Neural Networks (GNNs) were then trained on this KG to predict cancer risk and survival outcomes based on demographic factors, location, and tumor genetics. Querying our KG using Graph Retrieval Augmented Generation (GraphRAG) and predicting links using our trained GNN, we uncovered novel insights into the relationships among genetics, demographics, environmental exposures, and cancer statistics, highlighting potential complex gene-environment interactions that contribute to cancer risk and survival disparities, and suggesting directions for future research to reduce cancer risk and improve health outcomes. Citation Format: Ofek Mendel, Michael S. Sinclair, Kathleen M. Jagodnik, Till Krenz, Jeronimo Pissinis, Vasileios Stathias, Alon Bartal, Stephan Schürer. Integrating geographical and socioeconomic data with genomic information for enhanced prediction of cancer risk using large language models and knowledge graphs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1052.