Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We performed a comprehensive gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a FDR of 1%. We associated rare inherited deleterious variants in novel genes such as MSH3, EXO1, SETD2, and MTOR with two different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, and WRN with deficiency in homologous recombination repair. In addition, we identified associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the novel genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the newly-identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

Genomic analyses have revealed mutational signatures that are associated with DNA maintenance gone awry, a common occurrence in tumors. Because cancer therapeutics often target synthesis of DNA building blocks, DNA replication or DNA repair, we hypothesized that mutational signatures would make useful markers of drug sensitivity. We rigorously tested this hypothesis by a global analysis of various drug screening and genetic screening data sets, derived from cancer cell line panels. We introduce a novel computational method that detects mutational signatures in cell lines by stringently adjusting for the confounding germline mutational processes, which are difficult to remove when healthy samples from the same individuals are not available. This revealed many associations between diverse mutational signatures and drug activity in cancer cell lines, which are comparably or more numerous than associations with classical genetic features such as cancer driver mutations or copy number alterations. Validation across independent drug screening data and across genetic screens involving drug target genes revealed hundreds of robustly supported associations, which are provided as a resource for drug repurposing guided by mutational signature markers. We suggest that cancer cells bearing genomic signatures of deficiencies in certain DNA repair pathways may be vulnerable to particular types of therapeutics, such as epigenetic drugs.

The propensity to acquire mutations depends on the oligonucleotide context of a DNA locus. In turn, this differential mutability of oligonucleotides varies across individuals due to exposure to mutagenic agents or due to variable efficiency of DNA repair pathways. Such variability is captured by mutational signatures, mathematical constructs resulting from a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation frequency data that results from genome sequencing, and additionally to facilitate insight into underlying biological mechanisms. In cancer genomics, novel approaches to analyze somatic mutation patterns may help explain the etiology of various tumor types, as well as provide a more accurate baseline to infer positive and negative selection on somatic changes that drive tumor evolution. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens, including radiation, chemical exposure and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutational spectra of >1,000 tumors originating from diverse human tissues. Overall, we suggest that the power of DNA sequence-based mutational signature analysis can be enhanced by drawing on DNA shape features.