Abstract 1252: AI-derived predictions improve identification of real-world cancer driver mutations
Authors: Thinh N. Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Gregory Riely, Debyani Chakravarty, Nikolaus Schultz, Justin Jee
Journal: Cancer Research (2024)

Abstract AI-based variant effect predictors (VEPs), such as AlphaMissense, which is based on the protein structure prediction method AlphaFold, have gained significant attention for their potential to predict pathogenic effects of mutations. However, the utility of these methods for identifying pathogenic mutations in cancer remains unknown. We sought to quantify the prevalence of variants of unknown significance (VUSs) across the pan-cancer, multi-institution AACR GENIE cohort (N=160,965 samples) and test the utility of VEPs (SIFT, PolyPhen, MutationAssessor, REVEL, CADD, and AlphaMissense), using publicly available mutation annotations from each method, for 1) Annotating known pathogenic somatic cancer variants across the GENIE cohort, using known benign single nucleotide polymorphisms as negative controls 2) Identifying VUSs associated with overall survival (OS) in two cohorts of patients with non-small cell cancer (NSCLC), the MSK-IMPACT NCSLC (N=8,690) and non-MSK AACR GENIE Biopharma Collaborative NSCLC (N=977) cohorts, and 3) Identifying VUSs associated with known genomic patterns of mutual exclusivity in the MSK-IMPACT NSCLC cohort. In the GENIE cohort, 79% of mutations identified were VUSs according to an FDA-recognized molecular knowledge database, with a wide range of VUS frequencies across genes. Among VEPs trained without prior human-annotated knowledge, CADD and AlphaMissense had the highest AUROCs (0.940, 95%CI 0.937-0.943 and 0.917, 95%CI 0.913-0.922 respectively) for predicting known cancer drivers, demonstrating the power of protein structure modeling or functional genomic data for enhancing cancer drivers prediction. All VEPs identified known driver mutations in tumor suppressor genes more accurately than oncogenes, highlighting the challenge in identifying gain-of-function mutations. We tested whether VUSs reclassified as pathogenic by VEPs are associated with prognosis using inverse probability of treatment-weighted Cox’s propensity hazard models controlling for tumor mutational burden, treatment history, and other factors. VUSs reclassified as pathogenic by AlphaMissense and other VEPs in KEAP1 and SMARCA4 correlated with worse OS, comparable to that of known oncogenic mutations in those genes. Patients with tumors containing VUSs annotated as benign had similar OS to patients without analogous driver mutations. Finally, VUSs reclassified as oncogenic by AlphaMissense in genes within the RTK/RAS and NRF2 pathways followed expected patterns of mutual exclusivity, further suggesting biological validity. Despite not being trained to predict somatic effects in cancer, AI-derived mutation annotations can broaden the subset of annotated pathogenic variants in cancer and contribute to a more complete understanding of cancer genetics. Real-world outcomes and alteration patterns are additional benchmarks to consider when assessing the utility of VEPs in cancer. Citation Format: Thinh N. Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Gregory Riely, Debyani Chakravarty, Nikolaus Schultz, Justin Jee. AI-derived predictions improve identification of real-world cancer driver mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1252.