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

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.

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

“…While the expression correlation patterns of all other APOBEC signatures are consistent with such understanding, Signature 2D exposure level has slightly negative correlation with immune response (2A, aC6). This is consistent with our previous observation that there is no positive correlation between Signature 2D and APOBEC expression [9]. In addition, Signature 2 exposure level is either not correlated (2D) or has a weak correlation (2C) with the cluster enriched with translesion synthesis (2, aC7 and mC4) whereas both Signature 13C and 13D show strong positive correlation.…”

“…In this study we consider mutational signatures in cancer patients and attempt to identify genes and pathways whose expression and/or genetic alterations are potentially causative of differences in mutational signature strength. Since recent studies revealed that mutations occurring in close proximity to each other, referred to here as cloud mutations, have distinct properties from dispersed mutations [35,9], we additionally subdivided all mutations (and subsequently their attributed signatures) into two groups -close-by Cloud mutations and Dispersed mutations. We used only sufficiently abundant mutational signatures for cloud or dispersed mutations in the cohort of 560 breast cancer genomes [23], as inferred by a recently developed HMM based approach -SIGMA [9].…”

“…Since recent studies revealed that mutations occurring in close proximity to each other, referred to here as cloud mutations, have distinct properties from dispersed mutations [35,9], we additionally subdivided all mutations (and subsequently their attributed signatures) into two groups -close-by Cloud mutations and Dispersed mutations. We used only sufficiently abundant mutational signatures for cloud or dispersed mutations in the cohort of 560 breast cancer genomes [23], as inferred by a recently developed HMM based approach -SIGMA [9]. Consequently, we considered the 10 different phenotype profiles for Signatures 1D, 2C/D, 3C/D, 5D, 8C/D and 13C/D, where the numbering refers to the COSMIC signature index and C/D denotes close-by cloud and dispersed signatures.…”

“…The positive correlation of Signature 1 with the expression of cell cycle genes and lack of such correlation for Signature 5 may explain the stronger association of Signature 5 with the age of patients than Signature 1 in breast cancer [9,24] because cancer related cell division might obscure the association with a patient's age.…”

“…For example, the presence of specific signatures associated with homologous recombination repair deficiency (HRD) can help identify patients who can benefit from PARP inhibitor treatment [2]. With the increased interest in the information on mutagenic processes acting on cancer genomes, several computational approaches have been developed to define mutational signatures in cancer [1,3,4,5,6,7], to identify patients whose genome contains given signatures [6,7,8], and to map patient mutations to these signatures [9].…”