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Diffsig: Associating Risk Factors With Mutational Signatures.

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  • 1UNC-Chapel Hill.

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Identifying the causes of cancer mutations is difficult. The new Diffsig model and R package link risk factors to mutational signatures, aiding cancer research and therapy development.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Computational Biology

Background:

  • Somatic mutational signatures offer insights into cancer's molecular vulnerabilities and potential therapeutic targets.
  • Identifying the etiological origins of these signatures is statistically challenging due to small sample sizes and algorithm variability.
  • This limitation hinders the strong association of specific mutational signatures with particular risk factors.

Approach:

  • Introduced Diffsig, a Bayesian Dirichlet-multinomial hierarchical model designed to estimate associations between risk factors and mutational signatures.
  • The Diffsig model facilitates the suggestion of etiologies for pre-defined mutational signatures.
  • It enables the testing of diverse risk factors while accounting for uncertainty in low-observation samples.

Key Points:

  • Diffsig accurately estimates associations between risk factors and mutational signatures, as validated by simulations.
  • The model was applied to breast cancer data, analyzing relationships between five known signatures and etiologic variables.
  • Results confirmed established mechanisms of cancer development, demonstrating the tool's practical utility.

Conclusions:

  • Diffsig provides a robust statistical framework for uncovering the etiologies of mutational signatures.
  • This approach enhances our understanding of cancer development and can inform therapeutic strategies.
  • The Diffsig R package is publicly available, promoting further research in cancer genomics.