Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA

Abstract

Clustered somatic mutations are common in cancer genomes and previous analyses reveal several types of clustered single-base substitutions, which include doublet- and multi-base substitutions1–5, diffuse hypermutation termed omikli6, and longer strand-coordinated events termed kataegis3,7–9. Here we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome-sequenced cancers from 30 types of cancer10. Clustered mutations were highly enriched in driver genes and associated with differential gene expression and changes in overall survival. Several distinct mutational processes gave rise to clustered indels, including signatures that were enriched in tobacco smokers and homologous-recombination-deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, whereas most multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, which have previously been attributed to APOBEC3 activity6, accounted for a large proportion of clustered substitutions; however, only 16.2% of omikli matched APOBEC3 patterns. Kataegis was generated by multiple mutational processes, and 76.1% of all kataegic events exhibited mutational patterns that are associated with the activation-induced deaminase (AID) and APOBEC3 family of deaminases. Co-occurrence of APOBEC3 kataegis and extrachromosomal DNA (ecDNA), termed kyklonas (Greek for cyclone), was found in 31% of samples with ecDNA. Multiple distinct kyklonic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kyklonic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fuelling the evolution of ecDNA.

Examining clustered somatic mutations with SigProfilerClusters

“…Elucidating the compendium of clustered somatic mutations in the genome of a sample allows further understanding of the mutational process that give rise to these events and can provide novel insights into disease etiology (Bergstrom, et al, 2022; Mas-Ponte and Supek, 2020; Supek and Lehner, 2017). Previous studies have traditionally interrogated the complete mutational catalogs of cancer genomes, which can lead to the inability to detect processes active at low levels or those which have been transiently activated.…”

“…Some clustered mutations have been implicated in cancer evolution (Bergstrom, et al, 2022;Chen, et al, 2013;Consortium, 2020;Mas-Ponte and Supek, 2020;Supek and Lehner, 2017;Taylor, et al, 2013), while de novo clustered mutations have been identified in the human germline and shown to contribute to developmental disorders (Kaplanis, et al, 2019). In recent years, sets of simultaneously occurring clustered substitutions have been further subclassified into independent events (Bergstrom, et al, 2022;Mas-Ponte and Supek, 2020) Traditional methods separate clustered mutations based upon a predefined inter-mutational distance (IMD) threshold typically between 1 and 2 kilobases (Alexandrov, et al, 2020;Alexandrov, et al, 2013;Chan, et al, 2015;D'Antonio, et al, 2016;Maciejowski, et al, 2020;Nik-Zainal, et al, 2019;Taylor, et al, 2013). Many of these approaches utilize a piece-wise linear regression to segment each chromosome, which, in most cases, is optimized for calling larger strand-coordinated kataegic events (Alexandrov, et al, 2013;Lin, et al, 2021;Mas-Ponte and Supek, 2020).…”

“…Separation and classification of clustered events is required to fully elucidate the mutational processes operating in cancer and normal somatic cells (Bergstrom, et al, 2022; Supek and Lehner, 2017). Here we present SigProfilerClusters, a tool to comprehensively characterize and subclassify clustered mutations from the complete catalog of mutations within the genome of a single sample ( Fig.…”

“…Further, SigProfilerClusters integrates within the larger suite of SigProfiler tools (Bergstrom, et al, 2020; Bergstrom, et al, 2019; Islam, et al, 2020) to facilitate downstream mutational signature analysis of both non-clustered and clustered single-base substitutions and indels, thus, allowing the accurate detection of mutational processes giving rise to even low levels of clustered events ( Fig. 1 d ) (Bergstrom, et al, 2019; Bergstrom, et al, 2022; Islam, et al, 2020).…”

“…Some clustered mutations have been implicated in cancer evolution (Bergstrom, et al, 2022; Chen, et al, 2013; Consortium, 2020; Mas-Ponte and Supek, 2020; Supek and Lehner, 2017; Taylor, et al, 2013), while de novo clustered mutations have been identified in the human germline and shown to contribute to developmental disorders (Kaplanis, et al, 2019; Veltman and Brunner, 2012). In recent years, sets of simultaneously occurring clustered substitutions have been further subclassified into independent events (Bergstrom, et al, 2022; Mas-Ponte and Supek, 2020), including: (i) doublet-base substitutions (DBSs); (ii) multi-base substitutions (MBSs); (iii) diffuse hypermutation termed omikli ; (iv) longer strand-coordinated events termed kataegis ; and (v) recurrent hypermutation of extra-chromosomal DNA (ecDNA) termed kyklonas .…”