Abstract 1119: SoMatt: A foundation model for the tumor genome

Abstract Somatic mutations in cancer cells can lead to diverse phenotypic consequences due to variations in mutation types and locations within the same gene. Interactions between mutations across different genes further contribute to tumor heterogeneity, affecting pathology, phenotype, and clinical outcomes. Current computational methods often treat all mutations within a gene as functionally equivalent and overlook combinatorial effects between genes. Additionally, the diversity of sequencing platforms and gene panels poses significant challenges for integrative genomic analysis across datasets. Here, we introduce Somatic Attention (SoMatt), a foundation model for tumor genomes that leverages self-attention mechanisms to model interactions among mutations within a tumor. Trained on 167, 335 tumors from 149, 831 unique cancer patients from AACR Project GENIE, SoMatt generates comprehensive tumor embeddings suitable for various downstream tasks. The model addresses three critical challenges in tumor genome analysis: (a) distinguishing the effects of different mutations within the same gene, (b) capturing complex interactions among multiple mutations within a tumor, and (c) integrating data from diverse targeted mutation panels without requiring uniformity. We demonstrate that SoMatt accurately predicts patient survival outcomes, achieving a concordance index (C-index) of 0.76 across 90 cancer types, and classifies cancer types with an accuracy of 59%, outperforming baseline models in both tasks. Notably, SoMatt also enables patient stratification beyond traditional subtyping methods by identifying previously unrecognized, molecularly defined patient subgroups with significantly different outcomes, suggesting a novel approach to cancer subtype classification based on tumor molecular profiles. SoMatt produce a robust and scalable framework for comprehensive modeling of tumor genomes, addressing key challenges in cancer genomics and advancing the potential for precision oncology. Citation Format: David Andreas Arredondo, Ala Jararweh, Luis Tafoya, Avinash Sahu. SoMatt: A foundation model for the tumor genome [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 1119.