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<p>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 (<i>N</i> = 7965 and 977). VUSs identified as pathogenic drivers by AI in <i>KEAP1</i> and <i>SMARCA4</i> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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

摘要

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.