Sequence dependencies and mutation rates of localized mutational processes in cancer.

BACKGROUND: Cancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show nucleotide sequence and functional region preferences. As a result, some sequence contexts mutate at much higher rates than others, with additional variation found between functional regions. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes. METHODS: We count the 11-mer genomic sequences across the genome, and using the PCAWG set of 2583 pan-cancer whole genomes, we associate 11-mers with mutational signatures, hotspots of single nucleotide variants, and specific genomic regions. We evaluate the mutation rates of individual and combined sets of 11-mers and derive mutational sequence motifs. RESULTS: We show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE deficiency), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate and sequence preference increase further when focusing on certain genomic regions, such as signature 62 in transcribed regions, where the mutation rate is increased up to 9-folds over cancer type and mutational signature average. CONCLUSIONS: We summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.

The landscape and driver potential of site-specific hotspots across cancer genomes.

Large sets of whole cancer genomes make it possible to study mutation hotspots genome-wide. Here we detect, categorize, and characterize site-specific hotspots using 2279 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes project and provide a resource of annotated hotspots genome-wide. We investigate the excess of hotspots in both protein-coding and gene regulatory regions and develop measures of positive selection and functional impact for individual hotspots. Using cancer allele fractions, expression aberrations, mutational signatures, and a variety of genomic features, such as potential gain or loss of transcription factor binding sites, we annotate and prioritize all highly mutated hotspots. Genome-wide we find more high-frequency SNV and indel hotspots than expected given mutational background models. Protein-coding regions are generally enriched for SNV hotspots compared to other regions. Gene regulatory hotspots show enrichment of potential same-patient second-hit missense mutations, consistent with enrichment of hotspot driver mutations compared to singletons. For protein-coding regions, splice-sites, promoters, and enhancers, we see an excess of hotspots associated with cancer genes. Interestingly, missense hotspot mutations in tumor suppressors are associated with elevated expression, suggesting localized amino-acid changes with functional impact. For individual non-coding hotspots, only a small number show clear signs of positive selection, including known sites in the TERT promoter and the 5' UTR of TP53. Most of the new candidates have few mutations and limited driver evidence. However, a hotspot in an enhancer of the oncogene POU2AF1, which may create a transcription factor binding site, presents multiple lines of driver-consistent evidence.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.