A mixture model for signature discovery from sparse mutation data

Itay Sason, Yuexi Chen, Mark D.M. Leiserson, Roded Sharan

Research output: Contribution to journalArticlepeer-review


Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM.

Original languageEnglish
Article number173
JournalGenome Medicine
Issue number1
StatePublished - Dec 2021


  • Gene panel sequencing
  • Mutational signatures
  • Probabilistic modeling

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)


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