PT  - JOURNAL ARTICLE
AU  - Xiaoqing Huang
AU  - Itay Sason
AU  - Damian Wojtowicz
AU  - Yoo-Ah Kim
AU  - Mark D.M. Leiserson
AU  - Teresa M. Przytycka
AU  - Roded Sharan
TI  - Hidden Markov Models Lead to Higher Resolution Maps of Mutation Signature Activity in Cancer
AID  - 10.1101/392639
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 392639
4099  - http://biorxiv.org/content/early/2018/08/16/392639.short
4100  - http://biorxiv.org/content/early/2018/08/16/392639.full
AB  - Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to uncover the characteristic signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome and are biased by factors such as the genome’s chromatin structure or replication origins. These factors may lead to statistical dependencies among neighboring mutations, calling for modeling approaches that can account for such dependencies to better estimate mutational process activities.Here we develop the first sequence-dependent models for mutation signatures. We apply these models to characterize genomic and other factors that influence the activity of previously validated mutation signatures in breast cancer. We find that our tool, SigMa, can accurately assign genomic mutations to mutation signatures, yielding assignments that are of higher likelihood than those obtained with models that assume independence between signatures and align better with current biological knowledge. Our analysis resolves a controversy related to the dependency of APOBEC signatures on replication time and links Signatures 18 and 30 to oxidative damage.Modeling the sequential dependencies of mutation signatures leads to improved estimates of mutation signature activity both at the tumor-level and within specific genomic regions, yielding higher resolution maps of mutation signature activity in cancer.