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

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.

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Publication Details

Journal
Nature Communications
Published
2025-09-26
DOI
https://doi.org/10.1038/s41467-025-63461-8
Citations
1
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
1.82
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AI cancer driver mutation predictions are valid in real-world data

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

Debyani Chakravarty, Thinh Ngoc Tran, Anisha Luthra, Nikolaus Schultz, Michael F. Berger, Chris Fong, Marc Ladanyi, Ronglai Shen, Justin Jee, Gregory J. Riely, Karl Pichotta, Ino de Bruijn, Michele Waters, Xiang Li, Susie Kim, Yuan Chen
article en
1 citations

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.

Nature CommunicationsVol. 16(1)
Memorial Sloan Kettering Cancer Center (US)
Openalex Percentile: Top 12%
Genomics and Rare Diseases
1.82
DOIOpen Access PDFOpenAlex
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