Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer
Posted on 2019-07-27 - 04:00
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 .
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Wojtowicz, Damian; Sason, Itay; Huang, Xiaoqing; Kim, Yoo-Ah; Leiserson, Mark; Przytycka, Teresa; et al. (2019). Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4591538
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AUTHORS (7)
DW
Damian Wojtowicz
IS
Itay Sason
XH
Xiaoqing Huang
YK
Yoo-Ah Kim
ML
Mark Leiserson
TP
Teresa Przytycka
RS
Roded Sharan