AI and Cancer Research (Top Journals) ── Cancer biology ── 2026-10-03
Cancer molecular biology and AI
Abstract Although genetic sequencing is routine in cancer care, translating a tumor’s complex mutation profile into actionable treatment decisions remains a central challenge. MutationProjector is pretrained on a large corpus of genomic alterations across more than 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. When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts. It identifies unexpected biomarkers, including KMT2D 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. Significance: This work describes MutationProjector, a tumor genomic foundation model pretrained on more than 30,000 tumors. It maps mutations to biological states for diagnosis and treatment, achieving state-of-the-art performance in predicting therapy resistance and identifying biomarkers such as KMT2D (sensitivity) and SMARCA4/STK11 coalterations (resistance).
Kong J, Lee I, Boecher D, Singhal A, Kelly MR, Moon J, et al. A Foundation Model of Cancer Genotype Enables Precise Predictions of Therapeutic Response. Cancer Discov. 2026 May 26;16(10):2021-36. doi:10.1158/2159-8290.cd-25-1735. PMID: 42188872; PMCID: PMC13367315. ［Abstract only］