Influence network model uncovers relations between biological processes and mutational signatures

Abstract

There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal link between mutagens and observed mutation patterns remains not fully understood, limiting the utility of mutational signatures. To gain insights into these relationships, we developed a network-based method, named GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW that constructs a directed network among genes and mutational signatures. The approach leverages a sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Applying GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer related mutagenic processes. Our results are consistent with previous findings such as the impact of homologous recombination deficiency on a clustered APOBEC mutations in breast cancer. The network identified by GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs) and a relation between APOBEC mutations and changes in DNA conformation. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW also exposed a possible link between the SBS8 signature of unknown aetiology and the nucleotide excision repair pathway. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW provides a new and powerful method to reveal the relation between mutational signatures and gene expression. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW is freely available at https://github.com/ncbi/GeneSigNet.