Correlation-based and feature-driven mutation signature analyses to identify genetic features associated with DNA mutagenic processes in cancer genomes

Abstract

Mutation signatures represent unique sequence footprints of somatic mutations resulting from specific DNA mutagenic and repair processes; however, their causal associations and potential utility for genome research remain largely unknown. In this study, we performed PanCancer-scale correlative analyses to identify the genomic features associated with tumor mutation burdens (TMB) and individual mutation signatures. We observed that TMB was correlated with tumor purity, ploidy, and the level of aneuploidy, as well as with the expression of cell proliferation-related genes representing genomic covariates in evaluating TMB. Correlative analyses of mutation signature levels with genes belonging to DNA damage-repair processes revealed that deficiencies of NHEJ1 and ALKBH3 may elevate TMB levels in cancer genomes accompanying APOBEC overactivity and DNA mismatch repair deficiency, respectively. We further employed a strategy to identify feature-driven, de novo mutation signatures and demonstrated they can be reconstructed using known causal features such as APOBEC overexpression, MLH1 underexpression, POLE mutations, and the level of homologous recombination deficiency. We further demonstrated, that tumor hypoxia-related mutation signatures are similar to those associated with APOBEC suggesting that APOBEC-related mutagenic activity mediates hypoxia-related mutational consequences in cancer genomes, and also, that mutation signatures can be further used to predict hypoxic tumors. Taken together, our study advances mutation signature-level mechanistic insights in cancer genomes, extending categories of cancer-relevant mutation signatures and their potential biological implications. Author summaryMutation signature analysis is powerful in deciphering the causative mutagenic events and their contributions in individual cancer genomes, but the causal relationship of individual mutation signatures are still largely unknown. PanCancer-scaled correlative analysis revealed mutation resource candidates in cancer genomes such as NHEJ1 and ALKBH3 deficiencies that may facilitate the accumulation of mutations in the setting of APOBEC overactivity and DNA mismatch repair deficiency, respectively. A feature-driven mutation discovery approach was employed to identify the mutation signatures representing homologous recombination deficiency and tumor hypoxia, the extent of which may serve as mutation-based phenotypic measures, previously estimated by DNA copy number alterations and mRNA expression signatures, respectively. Our study advances our understanding into the mechanistic insights of mutation signatures and proposes a method to utilize somatic mutations as a molecular proxy in terms of mutation signatures.