Differential DNA mismatch repair underlies mutation rate variation across the human genome

The mutagenic consequences of defective DNA repair

Multiple separate repair mechanisms safeguard the genome against various types of DNA damage, and their failure can increase the rate of spontaneous mutagenesis. The malfunction of distinct repair mechanisms leads to genomic instability through different mutagenic processes. For example, defective mismatch repair causes high base substitution rates and microsatellite instability, whereas homologous recombination deficiency is characteristically associated with deletions and chromosome instability. This review presents a comprehensive collection of all mutagenic phenotypes associated with the loss of each DNA repair mechanism, drawing on data from a variety of model organisms and mutagenesis assays, and placing greatest emphasis on systematic analyses of human cancer datasets. We describe the latest theories on the mechanism of each mutagenic process, often explained by reliance on an alternative repair pathway or the error-prone replication of unrepaired, damaged DNA. Aided by the concept of mutational signatures, the genomic phenotypes can be used in cancer diagnosis to identify defective DNA repair pathways.

Strand-resolved mutagenicity of DNA damage and repair

DNA base damage is a major source of oncogenic mutations1. Such damage can produce strand-phased mutation patterns and multiallelic variation through the process of lesion segregation2. Here we exploited these properties to reveal how strand-asymmetric processes, such as replication and transcription, shape DNA damage and repair. Despite distinct mechanisms of leading and lagging strand replication3,4, we observe identical fidelity and damage tolerance for both strands. For small alkylation adducts of DNA, our results support a model in which the same translesion polymerase is recruited on-the-fly to both replication strands, starkly contrasting the strand asymmetric tolerance of bulky UV-induced adducts5. The accumulation of multiple distinct mutations at the site of persistent lesions provides the means to quantify the relative efficiency of repair processes genome wide and at single-base resolution. At multiple scales, we show DNA damage-induced mutations are largely shaped by the influence of DNA accessibility on repair efficiency, rather than gradients of DNA damage. Finally, we reveal specific genomic conditions that can actively drive oncogenic mutagenesis by corrupting the fidelity of nucleotide excision repair. These results provide insight into how strand-asymmetric mechanisms underlie the formation, tolerance and repair of DNA damage, thereby shaping cancer genome evolution.

H3K4me1 recruits DNA repair proteins in plants

DNA repair proteins can be recruited by their histone reader domains to specific epigenomic features, with consequences on intragenomic mutation rate variation. Here, we investigated H3K4me1-associated hypomutation in plants. We first examined 2 proteins which, in plants, contain Tudor histone reader domains: PRECOCIOUS DISSOCIATION OF SISTERS 5 (PDS5C), involved in homology-directed repair, and MUTS HOMOLOG 6 (MSH6), a mismatch repair protein. The MSH6 Tudor domain of Arabidopsis (Arabidopsis thaliana) binds to H3K4me1 as previously demonstrated for PDS5C, which localizes to H3K4me1-rich gene bodies and essential genes. Mutations revealed by ultradeep sequencing of wild-type and msh6 knockout lines in Arabidopsis show that functional MSH6 is critical for the reduced rate of single-base substitution (SBS) mutations in gene bodies and H3K4me1-rich regions. We explored the breadth of these mechanisms among plants by examining a large rice (Oryza sativa) mutation data set. H3K4me1-associated hypomutation is conserved in rice as are the H3K4me1-binding residues of MSH6 and PDS5C Tudor domains. Recruitment of DNA repair proteins by H3K4me1 in plants reveals convergent, but distinct, epigenome-recruited DNA repair mechanisms from those well described in humans. The emergent model of H3K4me1-recruited repair in plants is consistent with evolutionary theory regarding mutation modifier systems and offers mechanistic insight into intragenomic mutation rate variation in plants.

Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation

Local mutation rates in human are highly heterogeneous, with known variability at the scale of megabase-sized chromosomal domains, and, on the other extreme, at the scale of oligonucleotides. The intermediate, kilobase-scale heterogeneity in mutation risk is less well characterized. Here, by analyzing thousands of somatic genomes, we studied mutation risk gradients along gene bodies, representing a genomic scale spanning roughly 1-10 kb, hypothesizing that different mutational mechanisms are differently distributed across gene segments. The main heterogeneity concerns several kilobases at the transcription start site and further downstream into 5' ends of gene bodies; these are commonly hypomutated with several mutational signatures, most prominently the ubiquitous C > T changes at CpG dinucleotides. The width and shape of this mutational coldspot at 5' gene ends is variable across genes, and corresponds to variable interval of lowered DNA methylation depending on gene activity level and regulation. Such hypomutated loci, at 5' gene ends or elsewhere, correspond to DNA hypomethylation that can associate with various landmarks, including intragenic enhancers, Polycomb-marked regions, or chromatin loop anchor points. Tissue-specific DNA hypomethylation begets tissue-specific local hypomutation. Of note, direction of mutation risk is inverted for AID/APOBEC3 cytosine deaminase activity, whose signatures are enriched in hypomethylated regions.

Emergence of enhancers at late DNA replicating regions

Enhancers are fast-evolving genomic sequences that control spatiotemporal gene expression patterns. By examining enhancer turnover across mammalian species and in multiple tissue types, we uncover a relationship between the emergence of enhancers and genome organization as a function of germline DNA replication time. While enhancers are most abundant in euchromatic regions, enhancers emerge almost twice as often in late compared to early germline replicating regions, independent of transposable elements. Using a deep learning sequence model, we demonstrate that new enhancers are enriched for mutations that alter transcription factor (TF) binding. Recently evolved enhancers appear to be mostly neutrally evolving and enriched in eQTLs. They also show more tissue specificity than conserved enhancers, and the TFs that bind to these elements, as inferred by binding sequences, also show increased tissue-specific gene expression. We find a similar relationship with DNA replication time in cancer, suggesting that these observations may be time-invariant principles of genome evolution. Our work underscores that genome organization has a profound impact in shaping mammalian gene regulation.

Aneuploidy and complex genomic rearrangements in cancer evolution

Mutational processes that alter large genomic regions occur frequently in developing tumors. They range from simple copy number gains and losses to the shattering and reassembly of entire chromosomes. These catastrophic events, such as chromothripsis, chromoplexy and the formation of extrachromosomal DNA, affect the expression of many genes and therefore have a substantial effect on the fitness of the cells in which they arise. In this review, we cover large genomic alterations, the mechanisms that cause them and their effect on tumor development and evolution.

Cell cycle gene alterations associate with a redistribution of mutation risk across chromosomal domains in human cancers

Mutations in human cells exhibit increased burden in heterochromatic, late DNA replication time (RT) chromosomal domains, with variation in mutation rates between tissues mirroring variation in heterochromatin and RT. We observed that regional mutation risk further varies between individual tumors in a manner independent of cell type, identifying three signatures of domain-scale mutagenesis in >4,000 tumor genomes. The major signature reflects remodeling of heterochromatin and of the RT program domains seen across tumors, tissues and cultured cells, and is robustly linked with higher expression of cell proliferation genes. Regional mutagenesis is associated with loss of activity of the tumor-suppressor genes RB1 and TP53, consistent with their roles in cell cycle control, with distinct mutational patterns generated by the two genes. Loss of regional heterogeneity in mutagenesis is associated with deficiencies in various DNA repair pathways. These mutation risk redistribution processes modify the mutation supply towards important genes, diverting the course of somatic evolution.

Tissue-Predisposition to Cancer Driver Mutations

Driver mutations are considered the cornerstone of cancer initiation. They are defined as mutations that convey a competitive fitness advantage, and hence, their mutation frequency in premalignant tissue is expected to exceed the basal mutation rate. In old terms, that translates to "the survival of the fittest" and implies that a selective process underlies the frequency of cancer driver mutations. In that sense, each tissue is its own niche that creates a molecular selective pressure that may favor the propagation of a mutation or not. At the heart of this stands one of the biggest riddles in cancer biology: the tissue-predisposition to cancer driver mutations. The frequency of cancer driver mutations among tissues is non-uniform: for instance, mutations in APC are particularly frequent in colorectal cancer, and 99% of chronic myeloid leukemia patients harbor the driver BCR-ABL1 fusion mutation, which is rarely found in solid tumors. Here, we provide a mechanistic framework that aims to explain how tissue-specific features, ranging from epigenetic underpinnings to the expression of viral transposable elements, establish a molecular basis for selecting cancer driver mutations in a tissue-specific manner.

Genomic and demographic processes differentially influence genetic variation across the human X chromosome

Many forces influence genetic variation across the genome including mutation, recombination, selection, and demography. Increased mutation and recombination both lead to increases in genetic diversity in a region-specific manner, while complex demographic patterns shape patterns of diversity on a more global scale. While these processes act across the entire genome, the X chromosome is particularly interesting because it contains several distinct regions that are subject to different combinations and strengths of these forces: the pseudoautosomal regions (PARs) and the X-transposed region (XTR). The X chromosome thus can serve as a unique model for studying how genetic and demographic forces act in different contexts to shape patterns of observed variation. We therefore sought to explore diversity, divergence, and linkage disequilibrium in each region of the X chromosome using genomic data from 26 human populations. Across populations, we find that both diversity and substitution rate are consistently elevated in PAR1 and the XTR compared to the rest of the X chromosome. In contrast, linkage disequilibrium is lowest in PAR1, consistent with the high recombination rate in this region, and highest in the region of the X chromosome that does not recombine in males. However, linkage disequilibrium in the XTR is intermediate between PAR1 and the autosomes, and much lower than the non-recombining X. Finally, in addition to these global patterns, we also observed variation in ratios of X versus autosomal diversity consistent with population-specific evolutionary history as well. While our results were generally consistent with previous work, two unexpected observations emerged. First, our results suggest that the XTR does not behave like the rest of the recombining X and may need to be evaluated separately in future studies. Second, the different regions of the X chromosome appear to exhibit unique patterns of linked selection across different human populations. Together, our results highlight profound regional differences across the X chromosome, simultaneously making it an ideal system for exploring the action of evolutionary forces as well as necessitating its careful consideration and treatment in genomic analyses.

Two independent DNA repair pathways cause mutagenesis in template switching deficient Saccharomyces cerevisiae

Upon DNA replication stress, cells utilize the postreplication repair pathway to repair single-stranded DNA and maintain genome integrity. Postreplication repair is divided into 2 branches: error-prone translesion synthesis, signaled by proliferating cell nuclear antigen (PCNA) monoubiquitination, and error-free template switching, signaled by PCNA polyubiquitination. In Saccharomyces cerevisiae, Rad5 is involved in both branches of repair during DNA replication stress. When the PCNA polyubiquitination function of Rad5 s disrupted, Rad5 recruits translesion synthesis polymerases to stalled replication forks, resulting in mutagenic repair. Details of how mutagenic repair is carried out, as well as the relationship between Rad5-mediated mutagenic repair and the canonical PCNA-mediated mutagenic repair, remain to be understood. We find that Rad5-mediated mutagenic repair requires the translesion synthesis polymerase ζ but does not require other yeast translesion polymerase activities. Furthermore, we show that Rad5-mediated mutagenic repair is independent of PCNA binding by Rev1 and so is separable from canonical mutagenic repair. In the absence of error-free template switching, both modes of mutagenic repair contribute additively to replication stress response in a replication timing-independent manner. Cellular contexts where error-free template switching is compromised are not simply laboratory phenomena, as we find that a natural variant in RAD5 is defective in PCNA polyubiquitination and therefore defective in error-free repair, resulting in Rad5- and PCNA-mediated mutagenic repair. Our results highlight the importance of Rad5 in regulating spontaneous mutagenesis and genetic diversity in S. cerevisiae through different modes of postreplication repair.

Landscape of somatic mutated genes and inherited susceptibility genes in gynecological cancer

Traditionally, gynecological cancers have been classified based on histology. Since remarkable advancements in next-generation sequencing technology have enabled the exploration of somatic mutations in various cancer types, comprehensive sequencing efforts have revealed the genomic landscapes of some common forms of human cancer. The genomic features of various gynecological malignancies have been reported by several studies of large-scale genomic cohorts, including The Cancer Genome Atlas. Although recent comprehensive genomic profiling tests, which can detect hundreds of genetic mutations at a time from cancer tissues or blood samples, have been increasingly used as diagnostic clinical biomarkers and in therapeutic management decisions, germline pathogenic variants associated with hereditary cancers can also be detected using this test. Gynecological cancers are closely related to genetic factors, with approximately 5% of endometrial cancer cases and 20% of ovarian cancer cases being caused by germline pathogenic variants. Hereditary breast and ovarian cancer syndrome and Lynch syndrome are the two major cancer susceptibility syndromes among gynecological cancers. In addition, several other hereditary syndromes have been reported to be associated with gynecological cancers. In this review, we highlight the genes for somatic mutation and germline pathogenic variants commonly seen in gynecological cancers. We first describe the relationship between clinicopathological attributes and somatic mutated genes. Subsequently, we discuss the characteristics and clinical management of inherited cancer syndromes resulting from pathogenic germline variants in gynecological malignancies.

Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

Cryoelectron tomography reveals the multiplex anatomy of condensed native chromatin and its unfolding by histone citrullination

Nucleosome chains fold and self-associate to form higher-order structures whose internal organization is unknown. Here, cryoelectron tomography (cryo-ET) of native human chromatin reveals intrinsic folding motifs such as (1) non-uniform nucleosome stacking, (2) intermittent parallel and perpendicular orientations of adjacent nucleosome planes, and (3) a regressive nucleosome chain path, which deviates from the direct zigzag topology seen in reconstituted nucleosomal arrays. By examining the self-associated structures, we observed prominent nucleosome stacking in cis and anti-parallel nucleosome interactions, which are consistent with partial nucleosome interdigitation in trans. Histone citrullination strongly inhibits nucleosome stacking and self-association with a modest effect on chromatin folding, whereas the reconstituted arrays undergo a dramatic unfolding into open zigzag chains induced by histone citrullination. This study sheds light on the internal structure of compact chromatin nanoparticles and suggests a mechanism for how epigenetic changes in chromatin folding are retained across both open and condensed forms.

Genome-wide probing of eukaryotic nascent RNA structure elucidates cotranscriptional folding and its antimutagenic effect

The transcriptional intermediates of RNAs fold into secondary structures with multiple regulatory roles, yet the details of such cotranscriptional RNA folding are largely unresolved in eukaryotes. Here, we present eSPET-seq (Structural Probing of Elongating Transcripts in eukaryotes), a method to assess the cotranscriptional RNA folding in Saccharomyces cerevisiae. Our study reveals pervasive structural transitions during cotranscriptional folding and overall structural similarities between nascent and mature RNAs. Furthermore, a combined analysis with genome-wide R-loop and mutation rate approximations provides quantitative evidence for the antimutator effect of nascent RNA folding through competitive inhibition of the R-loops, known to facilitate transcription-associated mutagenesis. Taken together, we present an experimental evaluation of cotranscriptional folding in eukaryotes and demonstrate the antimutator effect of nascent RNA folding. These results suggest genome-wide coupling between the processing and transmission of genetic information through RNA folding.

Topography of mutational signatures in human cancer

The somatic mutations found in a cancer genome are imprinted by different mutational processes. Each process exhibits a characteristic mutational signature, which can be affected by the genome architecture. However, the interplay between mutational signatures and topographical genomic features has not been extensively explored. Here, we integrate mutations from 5,120 whole-genome-sequenced tumors from 40 cancer types with 516 topographical features from ENCODE to evaluate the effect of nucleosome occupancy, histone modifications, CTCF binding, replication timing, and transcription/replication strand asymmetries on the cancer-specific accumulation of mutations from distinct mutagenic processes. Most mutational signatures are affected by topographical features, with signatures of related etiologies being similarly affected. Certain signatures exhibit periodic behaviors or cancer-type-specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via the COSMIC (Catalog of Somatic Mutations in Cancer) signatures database, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer.

Both cell autonomous and non-autonomous processes modulate the association between replication timing and mutation rate

Cancer somatic mutations are the product of multiple mutational and repair processes, some of which are tightly associated with DNA replication. Mutation rates (MR) are known to be higher in late replication timing (RT) regions, but different processes can affect this association. Systematic analysis of the mutational landscape of 2787 tumors from 32 tumor types revealed that approximately one third of the tumor samples show weak association between replication timing and mutation rate. Further analyses revealed that those samples have unique mutational signatures and are enriched with mutations in genes involved in DNA replication, DNA repair and chromatin structure. Surprisingly, analysis of differentially expressed genes between weak and strong RT-MR association groups revealed that tumors with weak association are enriched with genes associated with cell-cell communication and the immune system, suggesting a non-autonomous response to DNA damage.

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

Genomic diversity landscapes in outcrossing and selfing Caenorhabditis nematodes

Caenorhabditis nematodes form an excellent model for studying how the mode of reproduction affects genetic diversity, as some species reproduce via outcrossing whereas others can self-fertilize. Currently, chromosome-level patterns of diversity and recombination are only available for self-reproducing Caenorhabditis, making the generality of genomic patterns across the genus unclear given the profound potential influence of reproductive mode. Here we present a whole-genome diversity landscape, coupled with a new genetic map, for the outcrossing nematode C. remanei. We demonstrate that the genomic distribution of recombination in C. remanei, like the model nematode C. elegans, shows high recombination rates on chromosome arms and low rates toward the central regions. Patterns of genetic variation across the genome are also similar between these species, but differ dramatically in scale, being tenfold greater for C. remanei. Historical reconstructions of variation in effective population size over the past million generations echo this difference in polymorphism. Evolutionary simulations demonstrate how selection, recombination, mutation, and selfing shape variation along the genome, and that multiple drivers can produce patterns similar to those observed in natural populations. The results illustrate how genome organization and selection play a crucial role in shaping the genomic pattern of diversity whereas demographic processes scale the level of diversity across the genome as a whole.

Proton and alpha radiation-induced mutational profiles in human cells

Ionizing radiation is known to be DNA damaging and mutagenic, however less is known about which mutational footprints result from exposures of human cells to different types of radiation. We were interested in the mutagenic effects of particle radiation exposures on genomes of various human cell types, in order to gauge the genotoxic risks of galactic cosmic radiation, and of certain types of tumor radiotherapy. To this end, we exposed cultured cell lines from the human blood, breast and lung to fractionated proton and alpha particle (helium nuclei) beams at doses sufficient to considerably affect cell viability. Whole-genome sequencing revealed that mutation rates were not overall markedly increased upon proton and alpha exposures. However, there were modest changes in mutation spectra and distributions, such as the increases in clustered mutations and of certain types of indels and structural variants. The spectrum of mutagenic effects of particle beams may be cell-type and/or genetic background specific. Overall, the mutational effects of repeated exposures to proton and alpha radiation on human cells in culture appear subtle, however further work is warranted to understand effects of long-term exposures on various human tissues.

The landscape of somatic mutations in lymphoblastoid cell lines

Somatic mutations have important biological ramifications while exerting substantial rate, type, and genomic location heterogeneity. Yet, their sporadic occurrence makes them difficult to study at scale and across individuals. Lymphoblastoid cell lines (LCLs), a model system for human population and functional genomics, harbor large numbers of somatic mutations and have been extensively genotyped. By comparing 1,662 LCLs, we report that the mutational landscape of the genome varies across individuals in terms of the number of mutations, their genomic locations, and their spectra; this variation may itself be modulated by somatic trans-acting mutations. Mutations attributed to the translesion DNA polymerase η follow two different modes of formation, with one mode accounting for the hypermutability of the inactive X chromosome. Nonetheless, the distribution of mutations along the inactive X chromosome appears to follow an epigenetic memory of the active form.

Cell-type specificity of the human mutation landscape with respect to DNA replication dynamics

The patterns of genomic mutations are associated with various genomic features, most notably late replication timing, yet it remains contested which mutation types and signatures relate to DNA replication dynamics and to what extent. Here, we perform high-resolution comparisons of mutational landscapes between lymphoblastoid cell lines, chronic lymphocytic leukemia tumors, and three colon adenocarcinoma cell lines, including two with mismatch repair deficiency. Using cell-type-matched replication timing profiles, we demonstrate that mutation rates exhibit heterogeneous replication timing associations among cell types. This cell-type heterogeneity extends to the underlying mutational pathways, as mutational signatures show inconsistent replication timing bias between cell types. Moreover, replicative strand asymmetries exhibit similar cell-type specificity, albeit with different relationships to replication timing than mutation rates. Overall, we reveal an underappreciated complexity and cell-type specificity of mutational pathways and their relationship to replication timing.

Mutational signatures association with replication timing in normal cells reveals similarities and differences with matched cancer tissues

Mutational signatures' association with replication timing (RT) has been studied in cancer samples, but the RT distribution of somatic mutations in non-cancerous cells was only minimally explored. Here, we performed comprehensive analyses of mutational signatures in 2.9 million somatic mutations across multiple non-cancerous tissues, stratified by early and late RT regions. We found that many mutational processes are active mainly or solely in early RT, such as SBS16 in hepatocytes and SBS88 in the colon, or in late RT, such as SBS4 in lung and hepatocytes, and SBS18 across many tissues. The two ubiquitous signatures, SBS1 and SBS5, showed late and early bias, respectively, across multiple tissues and in mutations representing germ cells. We also performed a direct comparison with cancer samples in 4 matched tissue-cancer types. Unexpectedly, while for most signatures the RT bias was consistent in normal tissue and in cancer, we found that SBS1's late RT bias is lost in cancer.

Strand Asymmetries Across Genomic Processes

Across biological systems, a number of genomic processes, including transcription, replication, DNA repair, and transcription factor binding, display intrinsic directionalities. These directionalities are reflected in the asymmetric distribution of nucleotides, motifs, genes, transposon integration sites, and other functional elements across the two complementary strands. Strand asymmetries, including GC skews and mutational biases, have shaped the nucleotide composition of diverse organisms. The investigation of strand asymmetries often serves as a method to understand underlying biological mechanisms, including protein binding preferences, transcription factor interactions, retrotransposition, DNA damage and repair preferences, transcription-replication collisions, and mutagenesis mechanisms. Research into this subject also enables the identification of functional genomic sites, such as replication origins and transcription start sites. Improvements in our ability to detect and quantify DNA strand asymmetries will provide insights into diverse functionalities of the genome, the contribution of different mutational mechanisms in germline and somatic mutagenesis, and our knowledge of genome instability and evolution, which all have significant clinical implications in human disease, including cancer. In this review, we describe key developments that have been made across the field of genomic strand asymmetries, as well as the discovery of associated mechanisms.

Proteomic profiling reveals distinct phases to the restoration of chromatin following DNA replication

Chromatin organization must be maintained during cell proliferation to preserve cellular identity and genome integrity. However, DNA replication results in transient displacement of DNA-bound proteins, and it is unclear how they regain access to newly replicated DNA. Using quantitative proteomics coupled to Nascent Chromatin Capture or isolation of Proteins on Nascent DNA, we provide time-resolved binding kinetics for thousands of proteins behind replisomes within euchromatin and heterochromatin in human cells. This shows that most proteins regain access within minutes to newly replicated DNA. In contrast, 25% of the identified proteins do not, and this delay cannot be inferred from their known function or nuclear abundance. Instead, chromatin organization and G1 phase entry affect their reassociation. Finally, DNA replication not only disrupts but also promotes recruitment of transcription factors and chromatin remodelers, providing a significant advance in understanding how DNA replication could contribute to programmed changes of cell memory.

Locally advanced rectal cancer patients with mismatch repair protein deficiency can obtain better pathological response after regional chemoembolization

Background and objective

Preoperative transcatheter rectal arterial chemoembolization (TRACE) can enhance the pathological response rate in some patients with locally advanced rectal cancer (LARC). However, how to accurately identify patients who can benefit from this neoadjuvant modality therapy remains to be further studied. Deficient mismatch repair (dMMR) protein plays a crucial role in maintaining genome stability. A proportion of patients with rectal cancer are caused by the loss of mismatch repair (MMR) protein. Given the role of MMR in guiding the efficacy in patients with colorectal carcinoma (CRC), this study is designed to evaluate the effect of dMMR status on the response to neoadjuvant therapy through a retrospective analysis.

Methods

We launched a retrospective study. First, we selected patients with LARC from the database, and these patients had received preoperative TRACE combined with concurrent chemoradiotherapy. Then, the tumor tissue biopsied by colonoscopy before intervention was taken for immunohistochemistry. According to the expression of MLH-1, MSH-2, MSH-6 and PMS-2, these patients were divided into dMMR protein group and proficient MMR (pMMR) protein group. All patients underwent pathological examination at the end of neoadjuvant therapy, either surgically excised tissue or colonoscopically biopsied tissue. The end point was the pathologic complete response (pCR) after TRACE combined with concurrent chemoradiotherapy.

Results

From January 2013 to January 2021, a total of 82 patients with LARC received preoperative TRACE combined with concurrent chemoradiotherapy, and the treatment was well tolerated. Among 82 patients, there were 42 patients in the pMMR group and 40 patients in the dMMR group. 69 patients returned to the hospital for radical resection. In 8 patients, the colonoscopy showed good tumor regression grade after 4 weeks of interventional therapy and refused surgery. The remaining five patients were neither surgically treated nor reexamined by colonoscopy. 77 patients were eventually enrolled in the study. Individually, the pCR rates of these two groups (10%, 4/40 vs. 43%, 16/37) showed significant difference (P < 0.05). Biomarker analysis indicated that patients with dMMR protein had a better propensity for pCR.

Conclusion

In patients with LARC, preoperative TRACE combined with concurrent chemoradiotherapy showed good pCR rates, especially in patients with dMMR. Patients with MMR protein defects have a better propensity for pCR.

Primary refractory plasmablastic lymphoma: A precision oncology approach

Introduction

Hematologic malignancies are currently underrepresented in multidisciplinary molecular-tumor-boards (MTB). This study assesses the potential of precision-oncology in primary-refractory plasmablastic-lymphoma (prPBL), a highly lethal blood cancer.

Methods

We evaluated clinicopathological and molecular-genetic data of 14 clinically annotated prPBL-patients from initial diagnosis. For this proof-of-concept study, we employed our certified institutional MTB-pipeline (University-Cancer-Center-Schleswig-Holstein, UCCSH) to annotate a comprehensive dataset within the scope of a virtual MTB-setting, ultimately recommending molecularly stratified therapies. Evidence-levels for MTB-recommendations were defined in accordance with the NCT/DKTK and ESCAT criteria.

Results

Median age in the cohort was 76.5 years (range 56-91), 78.6% of patients were male, 50% were HIV-positive and clinical outcome was dismal. Comprehensive genomic/transcriptomic analysis revealed potential recommendations of a molecularly stratified treatment option with evidence-levels according to NCT/DKTK of at least m2B/ESCAT of at least IIIA were detected for all 14 prPBL-cases. In addition, immunohistochemical-assessment (CD19/CD30/CD38/CD79B) revealed targeted treatment-recommendations in all 14 cases. Genetic alterations were classified by treatment-baskets proposed by Horak et al. Hereby, we identified tyrosine-kinases (TK; n=4), PI3K-MTOR-AKT-pathway (PAM; n=3), cell-cycle-alterations (CC; n=2), RAF-MEK-ERK-cascade (RME; n=2), immune-evasion (IE; n=2), B-cell-targets (BCT; n=25) and others (OTH; n=4) for targeted treatment-recommendations. The minimum requirement for consideration of a drug within the scope of the study was FDA-fast-track development.

Discussion

The presented proof-of-concept study demonstrates the clinical potential of precision-oncology, even in prPBL-patients. Due to the aggressive course of the disease, there is an urgent medical-need for personalized treatment approaches, and this population should be considered for MTB inclusion at the earliest time.

Regional mutational signature activities in cancer genomes

Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development.

Identification of a signature of evolutionarily conserved stress-induced mutagenesis in cancer

The clustering of mutations observed in cancer cells is reminiscent of the stress-induced mutagenesis (SIM) response in bacteria. Bacteria deploy SIM when faced with DNA double-strand breaks in the presence of conditions that elicit an SOS response. SIM employs DinB, the evolutionary precursor to human trans-lesion synthesis (TLS) error-prone polymerases, and results in mutations concentrated around DNA double-strand breaks with an abundance that decays with distance. We performed a quantitative study on single nucleotide variant calls for whole-genome sequencing data from 1950 tumors, non-inherited mutations from 129 normal samples, and acquired mutations in 3 cell line models of stress-induced adaptive mutation. We introduce statistical methods to identify mutational clusters, quantify their shapes and tease out the potential mechanism that produced them. Our results show that mutations in both normal and cancer samples are indeed clustered and have shapes indicative of SIM. Clusters in normal samples occur more often in the same genomic location across samples than in cancer suggesting loss of regulation over the mutational process during carcinogenesis. Additionally, the signatures of TLS contribute the most to mutational cluster formation in both patient samples as well as experimental models of SIM. Furthermore, a measure of cluster shape heterogeneity was associated with cancer patient survival with a hazard ratio of 5.744 (Cox Proportional Hazard Regression, 95% CI: 1.824–18.09). Our results support the conclusion that the ancient and evolutionary-conserved adaptive mutation response found in bacteria is a source of genomic instability in cancer. Biological adaptation through SIM might explain the ability of tumors to evolve in the face of strong selective pressures such as treatment and suggests that the conventional ‘hit it hard’ approaches to therapy could prove themselves counterproductive.

Chromatin accessibility of primary human cancers ties regional mutational processes and signatures with tissues of origin

Somatic mutations in cancer genomes are associated with DNA replication timing (RT) and chromatin accessibility (CA), however these observations are based on normal tissues and cell lines while primary cancer epigenomes remain uncharacterised. Here we use machine learning to model megabase-scale mutation burden in 2,500 whole cancer genomes and 17 cancer types via a compendium of 900 CA and RT profiles covering primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than those of normal tissues, are most predictive of regional mutagenesis in most cancer types. Feature prioritisation shows that the epigenomes of matching cancer types and organ systems are often the strongest predictors of regional mutation burden, highlighting disease-specific associations of mutational processes. The genomic distributions of mutational signatures are also shaped by the epigenomes of matched cancer and tissue types, with SBS5/40, carcinogenic and unknown signatures most accurately predicted by our models. In contrast, fewer associations of RT and regional mutagenesis are found. Lastly, the models highlight genomic regions with overrepresented mutations that dramatically exceed epigenome-derived expectations and show a pan-cancer convergence to genes and pathways involved in development and oncogenesis, indicating the potential of this approach for coding and non-coding driver discovery. The association of regional mutational processes with the epigenomes of primary cancers suggests that the landscape of passenger mutations is predominantly shaped by the epigenomes of cancer cells after oncogenic transformation.

TP53-dependent toxicity of CRISPR/Cas9 cuts is differential across genomic loci and can confound genetic screening

CRISPR/Cas9 gene editing can inactivate genes in a precise manner. This process involves DNA double-strand breaks (DSB), which may incur a loss of cell fitness. We hypothesize that DSB toxicity may be variable depending on the chromatin environment in the targeted locus. Here, by analyzing isogenic cell line pair CRISPR experiments jointly with previous screening data from across ~900 cell lines, we show that TP53-associated break toxicity is higher in genomic regions that harbor active chromatin, such as gene regulatory elements or transcription elongation histone marks. DSB repair pathway choice and DNA sequence context also associate with toxicity. We also show that, due to noise introduced by differential toxicity of sgRNA-targeted sites, the power of genetic screens to detect conditional essentiality is reduced in TP53 wild-type cells. Understanding the determinants of Cas9 cut toxicity will help improve design of CRISPR reagents to avoid incidental selection of TP53-deficient and/or DNA repair deficient cells.

Toxicity of CRISPR/Cas9 induced DNA breaks depends on their repair mechanism, and on the chromatin environment at the cut site. Here the authors show that edits in active genes or regulatory elements can incur a higher toxicity via a TP53-dependent mechanism.

Diverse mutational landscapes in human lymphocytes

The lymphocyte genome is prone to many threats, including programmed mutation during differentiation1, antigen-driven proliferation and residency in diverse microenvironments. Here, after developing protocols for expansion of single-cell lymphocyte cultures, we sequenced whole genomes from 717 normal naive and memory B and T cells and haematopoietic stem cells. All lymphocyte subsets carried more point mutations and structural variants than haematopoietic stem cells, with higher burdens in memory cells than in naive cells, and with T cells accumulating mutations at a higher rate throughout life. Off-target effects of immunological diversification accounted for approximately half of the additional differentiation-associated mutations in lymphocytes. Memory B cells acquired, on average, 18 off-target mutations genome-wide for every on-target IGHV mutation during the germinal centre reaction. Structural variation was 16-fold higher in lymphocytes than in stem cells, with around 15% of deletions being attributable to off-target recombinase-activating gene activity. DNA damage from ultraviolet light exposure and other sporadic mutational processes generated hundreds to thousands of mutations in some memory cells. The mutation burden and signatures of normal B cells were broadly similar to those seen in many B-cell cancers, suggesting that malignant transformation of lymphocytes arises from the same mutational processes that are active across normal ontogeny. The mutational landscape of normal lymphocytes chronicles the off-target effects of programmed genome engineering during immunological diversification and the consequences of differentiation, proliferation and residency in diverse microenvironments.

The shaping of cancer genomes with the regional impact of mutation processes

Mutation signature analysis has been used to infer the contributions of various DNA mutagenic-repair events in individual cancer genomes. Here, we build a statistical framework using a multinomial distribution to assign individual mutations to their cognate mutation signatures. We applied it to 47 million somatic mutations in 1925 publicly available cancer genomes to obtain a mutation signature map at the resolution of individual somatic mutations. Based on mutation signature-level genetic-epigenetic correlative analyses, mutations with transcriptional and replicative strand asymmetries show different enrichment patterns across genomes, and “transcribed” chromatin states and gene boundaries are particularly vulnerable to transcription-coupled repair activities. While causative processes of cancer-driving mutations can be diverse, as shown for converging effects of multiple mutational processes on TP53 mutations, the substantial fraction of recurrently mutated amino acids points to specific mutational processes, e.g., age-related C-to-T transition for KRAS p.G12 mutations. Our investigation of evolutionary trajectories with respect to mutation signatures further revealed that candidate pairs of early- vs. late-operative mutation processes in cancer genomes represent evolutionary dynamics of multiple mutational processes in the shaping of cancer genomes. We also observed that the local mutation clusters of kataegis often include mutations arising from multiple mutational processes, suggestive of a locally synchronous impact of multiple mutational processes on cancer genomes. Taken together, our examination of the genome-wide landscape of mutation signatures at the resolution of individual somatic mutations shows the spatially and temporally distinct mutagenesis-repair-replication histories of various mutational processes and their effects on shaping cancer genomes.

A statistical model that assigns non-hereditary DNA alterations known as somatic mutations to mutation “signatures” (groups of mutations arising from a specific biological process) on cancer genomes provides novel insights into disease evolution. Somatic mutations result from exposure to factors often linked to cancer development, such as tobacco or ultraviolet radiation. However, assigning a somatic mutation to a particular mutation “signature” remains challenging. The model created by Ruibin Xi (Peking University, China) and Tae-Min Kim (Catholic University of Korea, Seoul, South Korea) and co-workers grouped 47 million somatic mutations in 1925 cancer genomes into localized clusters before connecting them with mutation signatures. This strategy highlights the spatial and temporal patterns related to the origins of mutations, how the DNA strands are repaired and replicated, and how this influences the emerging cancer genome.

Single-Cell Mononucleotide Microsatellite Analysis Reveals Differential Insertion-Deletion Dynamics in Mouse T Cells

Microsatellite sequences are particularly prone to slippage during DNA replication, forming insertion-deletion loops that, if left unrepaired, result in de novo mutations (expansions or contractions of the repeat array). Mismatch repair (MMR) is a critical DNA repair mechanism that corrects these insertion-deletion loops, thereby maintaining microsatellite stability. MMR deficiency gives rise to the molecular phenotype known as microsatellite instability (MSI). By sequencing MMR-proficient and -deficient (Mlh1+/+ and Mlh1−/−) single-cell exomes from mouse T cells, we reveal here several previously unrecognized features of in vivo MSI. Specifically, mutational dynamics of insertions and deletions were different on multiple levels. Factors that associated with propensity of mononucleotide microsatellites to insertions versus deletions were: microsatellite length, nucleotide composition of the mononucleotide tract, gene length and transcriptional status, as well replication timing. Here, we show on a single-cell level that deletions — the predominant MSI type in MMR-deficient cells — are preferentially associated with longer A/T tracts, long or transcribed genes and later-replicating genes.

The impact of rare germline variants on human somatic mutation processes

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 perform a 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 false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

Alterations in transcriptional networks in cancer: the role of noncoding somatic driver mutations

Aberrant gene expression is a cancer hallmark and it is known that almost every tumor acquires somatic mutations in transcription factors, chromatin regulators, or the DNA regulatory elements that are critical for transcriptional control and cell phenotype. While the role of transcription factors and chromatin regulators has been widely studied, relatively few noncoding driver mutations have been identified and functionally characterized to date. Here, we review the current understanding of somatic variants in noncoding regions of the cancer genome and their impact on chromatin architecture and transcriptional networks. We also discuss approaches and ongoing challenges for noncoding driver discovery, and highlight insights gained from recent studies exploring the nature and impact of noncoding drivers on tumor formation.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Strand asymmetry influences mismatch resolution during a single-strand annealing

Background

Biases of DNA repair can shape the nucleotide landscape of genomes at evolutionary timescales. The molecular mechanisms of those biases are still poorly understood because it is difficult to isolate the contributions of DNA repair from those of DNA damage.

Results

Here, we develop a genome-wide assay whereby the same DNA lesion is repaired in different genomic contexts. We insert thousands of barcoded transposons carrying a reporter of DNA mismatch repair in the genome of mouse embryonic stem cells. Upon inducing a double-strand break between tandem repeats, a mismatch is generated if the break is repaired through single-strand annealing. The resolution of the mismatch showed a 60–80% bias in favor of the strand with the longest 3′ flap. The location of the lesion in the genome and the type of mismatch had little influence on the bias. Instead, we observe a complete reversal of the bias when the longest 3′ flap is moved to the opposite strand by changing the position of the double-strand break in the reporter.

Conclusions

These results suggest that the processing of the double-strand break has a major influence on the repair of mismatches during a single-strand annealing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02665-3.

Spectrum of DNA mismatch repair failures viewed through the lens of cancer genomics and implications for therapy

Genome sequencing can be used to detect DNA repair failures in tumors and learn about underlying mechanisms. Here, we synthesize findings from genomic studies that examined deficiencies of the DNA mismatch repair (MMR) pathway. The impairment of MMR results in genome-wide hypermutation and in the ‘microsatellite instability’ (MSI) phenotype—occurrence of indel mutations at short tandem repeat (microsatellite) loci. The MSI status of tumors was traditionally assessed by molecular testing of a selected set of MS loci or by measuring MMR protein expression levels. Today, genomic data can provide a more complete picture of the consequences on genomic instability. Multiple computational studies examined somatic mutation distributions that result from failed DNA repair pathways in tumors. These include analyzing the commonly studied trinucleotide mutational spectra of single-nucleotide variants (SNVs), as well as of other features such as indels, structural variants, mutation clusters and regional mutation rate redistribution. The identified mutation patterns can be used to rigorously measure prevalence of MMR failures across cancer types, and potentially to subcategorize the MMR deficiencies. Diverse data sources, genomic and pre-genomic, from human and from experimental models, suggest there are different ways in which MMR can fail, and/or that the cell-type or genetic background may result in different types of MMR mutational patterns. The spectrum of MMR failures may direct cancer evolution, generating particular sets of driver mutations. Moreover, MMR affects outcomes of therapy by DNA damaging drugs, antimetabolites, nonsense-mediated mRNA decay (NMD) inhibitors, and immunotherapy by promoting either resistance or sensitivity, depending on the type of therapy.

Mutation bias reflects natural selection in Arabidopsis thaliana

Since the first half of the twentieth century, evolutionary theory has been dominated by the idea that mutations occur randomly with respect to their consequences1. Here we test this assumption with large surveys of de novo mutations in the plant Arabidopsis thaliana. In contrast to expectations, we find that mutations occur less often in functionally constrained regions of the genome-mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. With independent genomic mutation datasets, including from the largest Arabidopsis mutation accumulation experiment conducted to date, we demonstrate that epigenomic and physical features explain over 90% of variance in the genome-wide pattern of mutation bias surrounding genes. Observed mutation frequencies around genes in turn accurately predict patterns of genetic polymorphisms in natural Arabidopsis accessions (r = 0.96). That mutation bias is the primary force behind patterns of sequence evolution around genes in natural accessions is supported by analyses of allele frequencies. Finally, we find that genes subject to stronger purifying selection have a lower mutation rate. We conclude that epigenome-associated mutation bias2 reduces the occurrence of deleterious mutations in Arabidopsis, challenging the prevailing paradigm that mutation is a directionless force in evolution.

The Role of RAB GTPases and Its Potential in Predicting Immunotherapy Response and Prognosis in Colorectal Cancer

Background: Colorectal cancer (CRC) is the third most common cancer worldwide, in which aberrant activation of the RAS signaling pathway appears frequently. RAB proteins (RABs) are the largest Ras small GTPases superfamily that regulates intracellular membrane trafficking pathways. The dysregulation of RABs have been found in various diseases including cancers. Compared with other members of Ras families, the roles of RABs in colorectal cancer are less well understood.

Methods: We analyzed the differential expression and clinicopathological association of RABs in CRC using RNA sequencing and genotyping datasets from TCGA samples. Moreover, the biological function of RAB17 and RAB34 were investigated in CRC cell lines and patient samples.

Results: Of the 62 RABs we analyzed in CRC, seven (RAB10, RAB11A, RAB15, RAB17, RAB19, RAB20, and RAB25) were significantly upregulated, while six (RAB6B, RAB9B, RAB12, RAB23, RAB31, and RAB34) were significantly downregulated in tumor tissues as compared to normal. We found that the upregulated-RABs, which were highly expressed in metabolic activated CRC subtype (CMS3), are associated with cell cycle related pathways enrichment and positively correlated with the mismatch repair (MMR) genes in CRC, implying their role in regulating cell metabolism and tumor growth. While, high expression of the downregulated-RABs were significantly associated with poor prognostic CRC mesenchymal subtypes (CMS4), immune checkpoint genes, and tumor infiltrating immune cells, indicating their role in predicting prognosis and immunotherapy efficacy. Interestingly, though RAB34 mRNA is downregulated in CRC, its high expression is significantly associated with poor prognosis. In vitro experiments showed that RAB17 overexpression can promote cell proliferation via cell cycle regulation. While, RAB34 overexpression can promote cell migration and invasion and is associated with PD-L1/PD-L2 expression increase in CRC cells.

Conclusions: Our study showed that RABs may play important roles in regulating cell cycle and immune-related pathways, therefore might be potential biomarkers in predicting prognosis and immunotherapy response in CRC.

ABCC9 Is Downregulated and Prone to Microsatellite Instability on ABCC9tetra in Canine Breast Cancer

Tumorigenesis is associated with metabolic abnormalities and genomic instability. Microsatellite mutations, including microsatellite instability (MSI) and loss of heterozygosity (LOH), are associated with the functional impairment of some tumor-related genes. To investigate the role of MSI and LOH in sporadic breast tumors in canines, 22 tumors DNA samples and their adjacent normal tissues were evaluated using polyacrylamide gel electrophoresis and silver staining for 58 microsatellites. Quantitative real-time polymerase chain reaction, promoter methylation analysis and immunohistochemical staining were used to quantify gene expression. The results revealed that a total of 14 tumors (6 benign tumors and 8 breast cancers) exhibited instability as MSI-Low tumors. Most of the microsatellite loci possessed a single occurrence of mutations. The maximum number of MSI mutations on loci was observed in tumors with a lower degree of differentiation. Among the unstable markers, FH2060 (4/22), ABCC9tetra (4/22) and SCN11A (6/22) were high-frequency mutation sites, whereas FH2060 was a high-frequency LOH site (4/22). The ABCC9tetra locus was mutated only in cancerous tissue, although it was excluded by transcription. The corresponding genes and proteins were significantly downregulated in malignant tissues, particularly in tumors with MSI. Furthermore, the promoter methylation results of the adenosine triphosphate binding cassette subfamily C member 9 (ABCC9) showed that there was a high level of methylation in breast tissues, but only one case showed a significant elevation compared with the control. In conclusion, MSI-Low or MSI-Stable is characteristic of most sporadic mammary tumors. Genes associated with tumorigenesis are more likely to develop MSI. ABCC9 protein and transcription abnormalities may be associated with ABCC9tetra instability.

Universal annotation of the human genome through integration of over a thousand epigenomic datasets

BACKGROUND

Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative "stacked modeling" approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.

RESULTS

Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations.

CONCLUSIONS

The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

Cancer Mutational Processes Vary in Their Association with Replication Timing and Chromatin Accessibility

Cancer somatic mutations are the product of multiple mutational and repair processes, both of which are tightly associated with DNA replication. Distinctive patterns of somatic mutation accumulation, termed mutational signatures, are indicative of processes sustained within tumors. However, the association of various mutational processes with replication timing (RT) remains an open question. In this study, we systematically analyzed the mutational landscape of 2,787 tumors from 32 tumor types separately for early and late replicating regions using sequence context normalization and chromatin data to account for sequence and chromatin accessibility differences. To account for sequence differences between various genomic regions, an artificial genome-based approach was developed to expand the signature analyses to doublet base substitutions and small insertions and deletions. The association of mutational processes and RT was signature specific: Some signatures were associated with early or late replication (such as SBS7b and SBS7a, respectively), and others had no association. Most associations existed even after normalizing for genome accessibility. A focused mutational signature identification approach was also developed that uses RT information to improve signature identification; this approach found that SBS16, which is biased toward early replication, is strongly associated with better survival rates in liver cancer. Overall, this novel and comprehensive approach provides a better understanding of the etiology of mutational signatures, which may lead to improved cancer prevention, diagnosis, and treatment. SIGNIFICANCE: Many mutational processes associate with early or late replication timing regions independently of chromatin accessibility, enabling development of a focused identification approach to improve mutational signature detection.

Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs

MOTIVATION

Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference.

RESULTS

Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method.

AVAILABILITY AND IMPLEMENTATION

R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enhanced prime editing systems by manipulating cellular determinants of editing outcomes

While prime editing enables precise sequence changes in DNA, cellular determinants of prime editing remain poorly understood. Using pooled CRISPRi screens, we discovered that DNA mismatch repair (MMR) impedes prime editing and promotes undesired indel byproducts. We developed PE4 and PE5 prime editing systems in which transient expression of an engineered MMR-inhibiting protein enhances the efficiency of substitution, small insertion, and small deletion prime edits by an average 7.7-fold and 2.0-fold compared to PE2 and PE3 systems, respectively, while improving edit/indel ratios by 3.4-fold in MMR-proficient cell types. Strategic installation of silent mutations near the intended edit can enhance prime editing outcomes by evading MMR. Prime editor protein optimization resulted in a PEmax architecture that enhances editing efficacy by 2.8-fold on average in HeLa cells. These findings enrich our understanding of prime editing and establish prime editing systems that show substantial improvement across 191 edits in seven mammalian cell types.

The Mutagenic Impact of Environmental Exposures in Human Cells and Cancer: Imprints Through Time

During life, the DNA of our cells is continuously exposed to external damaging processes. Despite the activity of various repair mechanisms, DNA damage eventually results in the accumulation of mutations in the genomes of our cells. Oncogenic mutations are at the root of carcinogenesis, and carcinogenic agents are often highly mutagenic. Over the past decade, whole genome sequencing data of healthy and tumor tissues have revealed how cells in our body gradually accumulate mutations because of exposure to various mutagenic processes. Dissection of mutation profiles based on the type and context specificities of the altered bases has revealed a variety of signatures that reflect past exposure to environmental mutagens, ranging from chemotherapeutic drugs to genotoxic gut bacteria. In this review, we discuss the latest knowledge on somatic mutation accumulation in human cells, and how environmental mutagenic factors further shape the mutation landscapes of tissues. In addition, not all carcinogenic agents induce mutations, which may point to alternative tumor-promoting mechanisms, such as altered clonal selection dynamics. In short, we provide an overview of how environmental factors induce mutations in the DNA of our healthy cells and how this contributes to carcinogenesis. A better understanding of how environmental mutagens shape the genomes of our cells can help to identify potential preventable causes of cancer.

Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer

Immune evasion is a hallmark of cancer, and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (e.g., those with microsatellite instability (MSI)). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable (MSS) colorectal cancer (CRC) express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in MSI CRC. Using a versatile platform for modulating neoantigen expression in CRC organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross priming and drives immediate T cell dysfunction. Strikingly, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.

The origin of human mutation in light of genomic data

Despite years of active research into the role of DNA repair and replication in mutagenesis, surprisingly little is known about the origin of spontaneous human mutation in the germ line. With the advent of high-throughput sequencing, genome-scale data have revealed statistical properties of mutagenesis in humans. These properties include variation of the mutation rate and spectrum along the genome at different scales in relation to epigenomic features and dependency on parental age. Moreover, mutations originated in mothers are less frequent than mutations originated in fathers and have a distinct genomic distribution. Statistical analyses that interpret these patterns in the context of known biochemistry can provide mechanistic models of mutagenesis in humans.

Gene expression levels modulate germline mutation rates through the compound effects of transcription-coupled repair and damage

Of all mammalian organs, the testis has long been observed to have the most diverse gene expression profile. To account for this widespread gene expression, we have proposed a mechanism termed 'transcriptional scanning', which reduces germline mutation rates through transcription-coupled repair (TCR). Our hypothesis contrasts with an earlier observation that mutation rates are overall positively correlated with gene expression levels in yeast, implying that transcription is mutagenic due to effects dominated by transcription-coupled damage (TCD). Here we report evidence that the compound effects of both TCR and TCD during spermatogenesis modulate human germline mutation rates, with TCR dominating in most genes, thus supporting the transcriptional scanning hypothesis. Our analyses address potentially confounding factors, distinguish the differential mutagenic effects acting on the highly expressed genes and the low-to-moderately expressed genes, and resolve concerns relating to the validation of the results using a de novo mutation dataset. We also discuss the theoretical possibility of transcriptional scanning hypothesis from an evolutionary perspective. Together, these analyses support a model by which the coupling of transcription-coupled repair and damage establishes the pattern of germline mutation rates and provide an evolutionary explanation for widespread gene expression during spermatogenesis.

Balancing DNA repair to prevent ageing and cancer

DNA damage is a constant stressor to the cell. Persistent damage to the DNA over time results in an increased risk of mutation and an accumulation of mutations with age. Loss of efficient DNA damage repair can lead to accelerated ageing phenotypes or an increased cancer risk, and the trade-off between cancer susceptibility and longevity is often driven by the cell's response to DNA damage. High levels of mutations in DNA repair mutants often leads to excessive cell death and stem cell exhaustion which may promote premature ageing. Stem cells themselves have distinct characteristics that enable them to retain low mutation rates. However, when mutations do arise, stem cell clonal expansion can also contribute to age-related tissue dysfunction as well as heightened cancer risk. In this review, we will highlight increasing DNA damage and mutation accumulation as hallmarks common to both ageing and cancer. We will propose that anti-ageing interventions might be cancer preventative and discuss the mechanisms through which they may act.