Correlation-based and feature-driven mutation signature analyses to identify genetic features associated with DNA mutagenic processes in cancer genomes

Mutation signatures represent unique sequence footprints of somatic mutations resulting from specific DNA mutagenic and repair processes. However, their causal associations and the potential utility for genome research remain largely unknown. In this study, we performed PanCancer-scale correlative analyses to identify the genomic features associated with tumor mutation burdens (TMB) and individual mutation signatures. We observed that TMB was correlated with tumor purity, ploidy, and the level of aneuploidy, as well as with the expression of cell proliferation-related genes representing genomic covariates in evaluating TMB. Correlative analyses of mutation signature levels with genes belonging to specific DNA damage-repair processes revealed that deficiencies of NHEJ1 and ALKBH3 may contribute to mutations in the settings of APOBEC cytidine deaminase activation and DNA mismatch repair deficiency, respectively. We further employed a strategy to identify feature-driven, de novo mutation signatures and demonstrated that mutation signatures can be reconstructed using known causal features. Using the strategy, we further identified tumor hypoxia-related mutation signatures similar to the APOBEC-related mutation signatures, suggesting that APOBEC activity mediates hypoxia-related mutational consequences in cancer genomes. Our study advances the mechanistic insights into the TMB and signature-based DNA mutagenic and repair processes in cancer genomes. We also propose that feature-driven mutation signature analysis can further extend the categories of cancer-relevant mutation signatures and their causal relationships.

Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

ATM-dependent formation of a novel chromatin compartment regulates the Response to DNA Double Strand Breaks and the biogenesis of translocations

DNA Double-Strand Breaks (DSBs) repair is essential to safeguard genome integrity but the contribution of chromosome folding into this process remains elusive. Here we unveiled basic principles of chromosome dynamics upon DSBs in mammalian cells, controlled by key kinases from the DNA Damage Response. We report that ATM is responsible for the reinforcement of topologically associating domains (TAD) that experience a DSB. ATM further drives the formation of a new chromatin sub-compartment (″D″ compartment) upon clustering of damaged TADs decorated with γH2AX and 53BP1. ″D″ compartment formation mostly occurs in G1, is independent of cohesin and is enhanced upon DNA-PK pharmacological inhibition. Importantly, a subset of DNA damage responsive genes that are upregulated following DSBs also physically localize in the D sub-compartment and this ensures their optimal activation, providing a function for DSB clustering in activating the DNA Damage Response. However, these DSB-induced changes in genome organization also come at the expense of an increased translocations rate, which we could also detect on cancer genomes. Overall, our work provides a function for DSB-induced compartmentalization in orchestrating the DNA Damage Response and highlights the critical impact of chromosome architecture in genomic instability.

A mixture model for signature discovery from sparse mutation data

Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, . In application to simulated and real gene-panel sequences, is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM.

Structural and functional analysis of somatic coding and UTR indels in breast and lung cancer genomes

Insertions and deletions (Indels) represent one of the major variation types in the human genome and have been implicated in diseases including cancer. To study the features of somatic indels in different cancer genomes, we investigated the indels from two large samples of cancer types: invasive breast carcinoma (BRCA) and lung adenocarcinoma (LUAD). Besides mapping somatic indels in both coding and untranslated regions (UTRs) from the cancer whole exome sequences, we investigated the overlap between these indels and transcription factor binding sites (TFBSs), the key elements for regulation of gene expression that have been found in both coding and non-coding sequences. Compared to the germline indels in healthy genomes, somatic indels contain more coding indels with higher than expected frame-shift (FS) indels in cancer genomes. LUAD has a higher ratio of deletions and higher coding and FS indel rates than BRCA. More importantly, these somatic indels in cancer genomes tend to locate in sequences with important functions, which can affect the core secondary structures of proteins and have a bigger overlap with predicted TFBSs in coding regions than the germline indels. The somatic CDS indels are also enriched in highly conserved nucleotides when compared with germline CDS indels.

Unravelling roles of error-prone DNA polymerases in shaping cancer genomes

Mutagenesis is a key hallmark and enabling characteristic of cancer cells, yet the diverse underlying mutagenic mechanisms that shape cancer genomes are not understood. This review will consider the emerging challenge of determining how DNA damage response pathways—both tolerance and repair—act upon specific forms of DNA damage to generate mutations characteristic of tumors. DNA polymerases are typically the ultimate mutagenic effectors of DNA repair pathways. Therefore, understanding the contributions of DNA polymerases is critical to develop a more comprehensive picture of mutagenic mechanisms in tumors. Selection of an appropriate DNA polymerase—whether error-free or error-prone—for a particular DNA template is critical to the maintenance of genome stability. We review different modes of DNA polymerase dysregulation including mutation, polymorphism, and over-expression of the polymerases themselves or their associated activators. Based upon recent findings connecting DNA polymerases with specific mechanisms of mutagenesis, we propose that compensation for DNA repair defects by error-prone polymerases may be a general paradigm molding the mutational landscape of cancer cells. Notably, we demonstrate that correlation of error-prone polymerase expression with mutation burden in a subset of patient tumors from The Cancer Genome Atlas can identify mechanistic hypotheses for further testing. We contrast experimental approaches from broad, genome-wide strategies to approaches with a narrower focus on a few hundred base pairs of DNA. In addition, we consider recent developments in computational annotation of patient tumor data to identify patterns of mutagenesis. Finally, we discuss the innovations and future experiments that will develop a more comprehensive portrait of mutagenic mechanisms in human tumors.

Non-cancer-related pathogenic germline variants and expression consequences in ten-thousand cancer genomes

Background DNA sequencing is increasingly incorporated into the routine care of cancer patients, many of whom also carry inherited, moderate/high-penetrance variants associated with other diseases. Yet, the prevalence and consequence of such variants remain unclear. Methods We analyzed the germline genomes of 10,389 adult cancer cases in the TCGA cohort, identifying pathogenic/likely pathogenic variants in autosomal-dominant genes, autosomal-recessive genes, and 59 medically actionable genes curated by the American College of Molecular Genetics (i.e., the ACMG 59 genes). We also analyzed variant- and gene-level expression consequences in carriers. Results The affected genes exhibited varying pan-ancestry and population-specific patterns, and overall, the European population showed the highest frequency of pathogenic/likely pathogenic variants. We further identified genes showing expression consequence supporting variant functionality, including altered gene expression, allelic specific expression, and mis-splicing determined by a massively parallel splicing assay. Conclusions Our results demonstrate that expression-altering variants are found in a substantial fraction of cases and illustrate the yield of genomic risk assessments for a wide range of diseases across diverse populations.