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 A Rosenquist,
Arthur P Grollman,
Ken W Kinzler,
Leslie Cope,
Bert Vogelstein,
Cristian Tomasetti
Affiliations
Bahman Afsari
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States; Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow, Russian Federation
Alexander Favorov
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States; Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow, Russian Federation
Thomas A Rosenquist
State University of New York at Stony Brook, Stony Brook, United States
Arthur P Grollman
State University of New York at Stony Brook, Stony Brook, United States
Ken W Kinzler
Ludwig Center & Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center, Baltimore, United States
Leslie Cope
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States
Bert Vogelstein
Ludwig Center & Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center, Baltimore, United States
Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, United States; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
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.
