Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer
- Johns Hopkins University, United States
- Johns Hopkins School of Medicine, United States
- Stony Brook University, United States
- Howard Hughes Medical Institute, Ludwig Center, United States
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.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
Article and author information
Author details
Cristian Tomasetti
Funding
The John Templeton Foundation (#61471)
- Bahman Afsari
- Albert Kuo
- YiFan Zhang
- Lu Li
- Kamel Lahouel
- Ludmila Danilova
- Cristian Tomasetti
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Afsari et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer
eLife 10:e61082.
https://doi.org/10.7554/eLife.61082
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