Abstract 2761: Large language model-derived re-contextualization reveals functional landscapes across cancers.

Abstract The vast heterogeneity of human cancer necessitates a deeper understanding of how fundamental biological pathways are functionally reconfigured across different cancer types in contrast to non-malignant diseases. Conventional pathway analyses based on static, disease-agnostic gene sets often obscure the plasticity and tumor-specific roles of pathways. We address this limitation by leveraging the knowledge integration of large language models (LLMs) to generate and embed context-aware pathway descriptions, enabling a novel framework for quantifying pathway relevance and functional variability across the oncological landscape. We applied this framework to analyze 268 KEGG pathways across 35 cancer types and 7 non-malignant diseases. We performed two complementary analyses of the LLM-generated context-aware pathway descriptions: 1) disease-level analysis by applying unsupervised clustering to the embeddings derived from the context-aware descriptions to map inter-disease functional relationships; and 2) pathway-level analysis that quantifies the functional variability of each pathway across diseases to understand their disease-specific roles. The disease-level analysis revealed a coherent functional atlas of diseases. Within cancers, lineage and cell type drove distinct yet biologically consistent groups, such as hematologic malignancies, gastrointestinal cancers, and hormonal cancers. Non-malignant conditions (e.g., neurodegenerative disorders) formed separate, distinct clusters. Pathway-level analysis showed marked functional variability. Pathways with the lowest variation across diseases mainly involved conserved cellular functions (e.g., mitophagy, AMPK signaling), while highly dispersed pathways captured context-specific programs (e.g., viral carcinogenesis, transcriptional misregulation in cancer). Independent validation using a literature-derived pathway relevance score across diseases confirmed that the top 20 most dispersed pathways had significantly higher relevance scores than the bottom 20 (p 0.01). Furthermore, the continuous level of dispersion correlated positively with pathway relevance (p 0.01). These observations support that the semantic heterogeneity captured by LLMs across diseases truly reflects biological specificity and functional relevance. In summary, LLM-generated context-aware pathway descriptions and their corresponding embeddings successfully capture disease-specific functional organization and reveal mechanistic coherence across cancers. This represents a significant methodological advance over traditional static approaches, providing a dynamic, biologically relevant map for understanding heterogeneous cancer mechanisms and identifying novel therapeutic strategies. Citation Format: Yibing Guo, Yanhao Tan, Chien-Hung Shih, Li-Ju Wang, Yu-Chiao Chiu, . Large language model-derived re-contextualization reveals functional landscapes across cancers 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 2761.