AI cancer driver mutation predictions are valid in real-world data

Thinh N. Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Xiang Li, Ino Bruijn, Gregory Riely, Michael F. Berger, Marc Ladanyi, Debyani Chakravarty, Nikolaus Schultz () and Justin Jee ()
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Thinh N. Tran: Memorial Sloan Kettering Cancer Center
Chris Fong: Memorial Sloan Kettering Cancer Center
Karl Pichotta: Memorial Sloan Kettering Cancer Center
Anisha Luthra: Memorial Sloan Kettering Cancer Center
Ronglai Shen: Memorial Sloan Kettering Cancer Center
Yuan Chen: Memorial Sloan Kettering Cancer Center
Michele Waters: Memorial Sloan Kettering Cancer Center
Susie Kim: Memorial Sloan Kettering Cancer Center
Xiang Li: Memorial Sloan Kettering Cancer Center
Ino Bruijn: Memorial Sloan Kettering Cancer Center
Gregory Riely: Memorial Sloan Kettering Cancer Center
Michael F. Berger: Memorial Sloan Kettering Cancer Center
Marc Ladanyi: Memorial Sloan Kettering Cancer Center
Debyani Chakravarty: Memorial Sloan Kettering Cancer Center
Nikolaus Schultz: Memorial Sloan Kettering Cancer Center
Justin Jee: Memorial Sloan Kettering Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Characterizing and validating which mutations influence development of cancer is challenging. Artificial intelligence (AI) has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluate multiple computational methods for identifying cancer driver mutations. For re-identifying known drivers, methods incorporating protein structure or functional genomic data outperform methods trained only on evolutionary data. We validate variants of unknown significance (VUSs) annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N = 7965 and 977). VUSs identified as pathogenic drivers by AI in KEAP1 and SMARCA4 are associated with worse survival, unlike “benign” VUSs. “Pathogenic” VUSs also exhibit mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. AI predictions thus contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.

Date: 2025
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DOI: 10.1038/s41467-025-63461-8

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