Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA

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

Degradation of GSPT1 causes TP53-independent cell death in leukemia whilst sparing normal hematopoietic stem cells

Targeted protein degradation is a rapidly advancing and expanding therapeutic approach. Drugs that degrade GSPT1 via the CRL4CRBN ubiquitin ligase are a new class of cancer therapy in active clinical development with evidence of activity against acute myeloid leukemia in early phase trials. However, other than activation of the integrated stress response, the downstream effects of GSPT1 degradation leading to cell death are largely undefined, and no murine models are available to study these agents. We identified the domains of GSPT1 essential for cell survival and show that GSPT1 degradation leads to impaired translation termination, activation of the integrated stress response pathway, and TP53-independent cell death. CRISPR-Cas9 screens implicated decreased translation initiation as protective to GSPT1 degradation, suggesting that cells with higher levels of translation are more susceptible to GSPT1 degradation. We defined two Crbn amino acids that prevent Gspt1 degradation in mice, generated a knock-in mouse with alteration of these residues, and demonstrated the efficacy of GSPT1-degrading drugs in vivo with relative sparing of numbers and function of long-term hematopoietic stem cells. Our results provide a mechanistic basis for the use of GSPT1 degraders for the treatment of cancer, including TP53-mutant AML

The repertoire of mutational signatures in human cancer.

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer

Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P-Bonferroni <1.06 x 10(-7)). In additional analyses in 7,278 participants,Peer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Computational approaches for utilizing mutational signatures for cancer treatment and cancer prevention

The genome of a cancer cell is replete with somatic mutations imprinted by the activities of different endogenous and exogenous processes. Each mutational process exhibits a characteristic pattern of mutations, termed mutational signature. Prior work has shown that mutational signatures can be deciphered from a set of cancer genomes, thus, providing insight into the mutagenic processes that have been operative throughout the lineage of the cancer cell. Analysis of mutational signatures has had three major applications: (i) leveraging mutational signatures to identify environmental mutagens that cause cancer, thus, providing opportunities for developing cancer prevention strategies; (ii) using mutational signatures to better understand the biological mechanisms of DNA damage and repair processes; (iii) utilizing mutational signatures of failed DNA repair as biomarkers for targeted cancer treatment. However, the universal deployment of mutational signatures has been limited mainly by a reliance on whole-genome sequencing and downstream expert interpretation. In this dissertation, we first develop three novel computational frameworks for exploring mutational signatures from large cohorts of cancer. We apply these approaches in a pan-cancer analysis to elucidate the mutational processes giving rise to clustered mutational events encompassing a plethora of operative endogenous and exogenous processes. Comprehensive characterization of these events reveals an enrichment within known driver genes. Importantly, clustered driver mutations are detectable from standard-of-care diagnostic tests and can serve as prognostic biomarkers for the overall survival of a cancer patient. Further, we introduce a novel form of oncogenesis, termed kyklonas, indicative of a repeated hypermutation of extrachromosomal circular DNA driven by the innate immune system. Lastly, we propose an alternative sequencing-independent and cost-effective method for detecting mutational signatures by applying a deep learning approach to digital images of histopathological cancer slides. We demonstrate both the ability of this novel approach for detecting homologous recombination deficiency within breast and ovarian cancers as well as its clinical utility for predicting sensitivity to platinum treatment in individual cancer patients