Cancer mutational signatures identification in clinical assays using neural embedding-based representations

Adar Yaacov*, Gil Ben Cohen, Jakob Landau, Tom Hope, Itamar Simon, Shai Rosenberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Original languageEnglish
Article number101608
JournalCell Reports Medicine
Issue number6
StatePublished - 18 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)


  • cancer genomics
  • gene panels
  • machine learning
  • mutational signatures
  • precision oncology


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