A role for somatic mutations in carcinogenesis is well accepted, but the degree to which mutation rates influence cancer initiation and development is under continuous debate. Recently accumulated genomic data has revealed that thousands of tumor samples are riddled by hypermutation, broadening support that cancers acquire a mutator phenotype. This major expansion of cancer mutation data sets has provided unprecedented statistical power for the analysis of mutation spectra, which has confirmed several classical sources of mutation in cancer, highlighted new prominent mutation sources and empowered the search for cancer drivers. The philosophy and statistical approaches for extracting useful information from catalogues of mutations in cancer genomes are overall analogous to the analysis of mutation spectra obtained in experiments with mutation reporters – the classical approach in molecular genetics. Apparent “irregularities� in distribution of mutation types and position as compared to the null hypothesis of random mutation spectrum are matched against mechanistic knowledge about the chemistry of a mutagenic factor and genetic systems expected to repair the resulting DNA lesions. The confluence of agnostic signature deconvolution and knowledge-based analysis capitalizing on mechanistic insight provides great promise for understanding the basic development of cancer through mutations. I will present the example of such a merger developed in the course of analysis of APOBEC-signature mutagenesis in cancers, which aided in identification of mutagenic enzymes, and highlighted important correlation with clinical features as well as sample-specific mechanistic details of mutagenesis.