Abstract LB443: A foundation model of cancer genotype enables precise predictions of therapeutic response

Abstract While genetic sequencing is routine in cancer care, translating a tumor’s complex mutation profile into actionable treatment decisions remains a central challenge. Here we introduce MutationProjector, an AI foundation model that projects a tumor genotype into unified coordinates representing its biological state, enabling broad applications in diagnosis and therapy selection. MutationProjector is pre-trained from a large corpus of genomic alterations across 30, 000+ tumors, integrated with extensive molecular knowledge. The resulting projection reveals a tumor’s altered molecular pathways, facilitating model interpretation, and it accurately reconstructs held-out mutations, demonstrating model generalization. The projection also stratified squamous cell carcinomas, human papilloma virus (HPV) infection status and expression-based molecular subtypes (i. e. basal versus luminal bladder and breast cancer subtypes), despite not explicitly trained on these tasks. When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves best-in-class performance in all contexts. For instance, in a non-small-cell lung cancer cohort treated with anti-PD1/PD-L1, patients predicted to be sensitive had a one-year progression free survival rate of 39%, compared to 16% for those predicted to be resistant. Furthermore, it identifies unexpected biomarkers, including KMT2A mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance. These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes. Citation Format: JungHo Kong, Ingoo Lee, Dean Boecher, Akshat Singhal, Marcus Kelly, Jimin Moon, Chang Ho Ahn, Chan-Young Ock, Dexter Pratt, Tannavee Kumar, Timothy Sears, David Laub, Sarah Wright, Patrick Wall, Hannah Carter, Zhen Wang, Trey Ideker. A foundation model of cancer genotype enables precise predictions of therapeutic response abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (8Suppl): Abstract nr LB443.