Analysis of mutational signatures in multiple cancer studies: Recent Bayesian tools

Mutational signatures, defined as characteristic patterns of somatic mutations in cancer genomes, shed light on the mutagenic mechanisms and origins of cancer. Mutational signature analysis [1–4] leverages computational techniques to identify recurring patterns within the spectrum of genetic alterations observed in tumors. Using mutational signature analysis, investigators can identify biological processes responsible for tumorigenesis. For example, Nik-Zainal et al. [5] used mutational signature analysis to identify signatures associated with the activity of APOBEC cytidine deaminases, a family of enzymes that can induce cytosine-to-uracil deamination, leading to characteristic C>T and C>G mutations at TpC dinucleotides. This signature pointed to endogenous APOBEC activity as a significant mutagenic process in the development of breast cancer. For another example, Alexandrov et al. [6] investigated mutational signatures in lung cancers occurring in smokers and found a strong prevalence of a signature characterized by C>A transversions. This signature was experimentally linked to benzo[a]pyrene, a carcinogen present in tobacco smoke, providing evidence that tobacco-induced DNA damage is a driving mutational process in lung tumorigenesis. In addition to their role in the elucidation of cancer etiology, mutational signatures can provide therapeutic and prognostic insights [4,7], as documented in a recent review by Brady 2022 [8]. For example, mutational signatures of homologous recombination deficiency are predictive of PARP inhibitor efficacy, mutational signatures indicating mismatch repair deficiency are directly correlated with response to immune checkpoint blockade, while APOBEC-related signatures are predictive of ATR inhibitor efficacy.