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

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.

Automated summary

Determining the etiologic basis of cancer mutations is a key challenge in modern cancer research. Different mutational processes yield distinct DNA mutations, known as mutational signatures, which have provided insights into cancer etiology. Ultimately, the use of supervised machine-learning techniques has led to the identification of more predictive signatures, called SuperSigs, associated with factors such as aging, environmental exposures, and lifestyle factors like obesity.