ABSTRACT
Cancer cells accumulate somatic mutations as result of DNA damage and inaccurate repair mechanisms. Different genetic instability processes result in distinct non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.
Footnotes
Funding J.J.M. is supported by grant BX003692. D.F. and J.J.M. are supported by a grant from the John P. Hanson Foundation for Cancer Research
