Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer

Autor:	Damian Wojtowicz, Itay Sason, Xiaoqing Huang, Yoo-Ah Kim, Mark D. M. Leiserson, Teresa M. Przytycka, Roded Sharan
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Mutational process Hidden Markov model Mutation signature Breast cancer Medicine Genetics QH426-470
Zdroj:	Genome Medicine, Vol 11, Iss 1, Pp 1-12 (2019)
Druh dokumentu:	article
ISSN:	1756-994X
DOI:	10.1186/s13073-019-0659-1
Popis:	Abstract Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.
Databáze:	Directory of Open Access Journals
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