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

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.

journal_name

Elife

journal_title

eLife

authors

Afsari B,Kuo A,Zhang Y,Li L,Lahouel K,Danilova L,Favorov A,Rosenquist TA,Grollman AP,Kinzler KW,Cope L,Vogelstein B,Tomasetti C

doi

10.7554/eLife.61082

subject

Has Abstract

pub_date

2021-01-25 00:00:00

issn

2050-084X

pii

61082

journal_volume

10

pub_type

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