Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer

Bahman Afsari, Albert Kuo, Yifan Zhang, Lu Li, Kamel Lahouel, Ludmila Danilova, Alexander Favorov, Thomas Rosenquist, Arthur P. Grollman, Ken W. Kinzler, Leslie Cope, Bert Vogelstein, Cristian Tomasetti

Research output: Contribution to journalArticlepeer-review

Abstract

Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Different mutational processes induce different types of DNA mutations, providing “mutational signatures” that have led to key insights into cancer etiology. The most widely used signatures for assessing genomic data are based on unsupervised patterns that are then retrospectively correlated with certain features of cancer. We show here that supervised machine-learning techniques can identify signatures, called SuperSigs, that are more predictive than those currently available. Surprisingly, we found that aging yields different SuperSigs in different tissues, and the same is true for environmental exposures. We were able to discover SuperSigs associated with obesity, the most important lifestyle factor contributing to cancer in Western populations.

Original languageEnglish (US)
Pages (from-to)1-71
Number of pages71
JournaleLife
Volume10
DOIs
StatePublished - Jan 2021

ASJC Scopus subject areas

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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