Local Determinants of the Mutational Landscape of the Human Genome

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.

The Germline and Somatic Origins of Prostate Cancer Heterogeneity

SIGNIFICANCE

This study uncovered 223 recurrently mutated driver regions using the largest cohort of prostate tumors to date. It reveals associations between germline SNPs, somatic drivers, and tumor aggression, offering significant insights into how prostate tumor evolution is shaped by germline factors and the timing of somatic mutations.

DNA replication initiation drives focal mutagenesis and rearrangements in human cancers

The rate and pattern of mutagenesis in cancer genomes is significantly influenced by DNA accessibility and active biological processes. Here we show that efficient sites of replication initiation drive and modulate specific mutational processes in cancer. Sites of replication initiation impede nucleotide excision repair in melanoma and are off-targets for activation-induced deaminase (AICDA) activity in lymphomas. Using ductal pancreatic adenocarcinoma as a cancer model, we demonstrate that the initiation of DNA synthesis is error-prone at G-quadruplex-forming sequences in tumours displaying markers of replication stress, resulting in a previously recognised but uncharacterised mutational signature. Finally, we demonstrate that replication origins serve as hotspots for genomic rearrangements, including structural and copy number variations. These findings reveal replication origins as functional determinants of tumour biology and demonstrate that replication initiation both passively and actively drives focal mutagenesis in cancer genomes.

Methods and applications of genome-wide profiling of DNA damage and rare mutations

DNA damage is a threat to genome integrity and can be a cause of many human diseases, owing to either changes in the chemical structure of DNA or conversion of the damage into a mutation, that is, a permanent change in DNA sequence. Determining the exact positions of DNA damage and ensuing mutations in the genome are important for identifying mechanisms of disease aetiology when characteristic mutations are prevalent and probably causative in a particular disease. However, this approach is challenging particularly when levels of DNA damage are low, for example, as a result of chronic exposure to environmental agents or certain endogenous processes, such as the generation of reactive oxygen species. Over the past few years, a comprehensive toolbox of genome-wide methods has been developed for the detection of DNA damage and rare mutations at single-nucleotide resolution in mammalian cells. Here, we review and compare these methods, describe their current applications and discuss future research questions that can now be addressed.

Case Report: Concurrent de novo pathogenic variants in the LMNA gene as a cause of sporadic partial lipodystrophy

Introduction

Inherited lipodystrophies are a group of rare diseases defined by severe reduction in adipose tissue mass and classified as generalized or partial. We report a non-familial (sporadic) case of partial lipodystrophy caused by a novel genetic mechanism involving closely linked de novo pathogenic variants in the LMNA gene.

Methods

A female adult with partial lipodystrophy and her parents were evaluated for gene variants across the exome under different mendelian inheritance models (autosomal dominant, recessive, compound heterozygous, and X-linked) to find pathogenic variants. Body composition was assessed via dual-energy X-ray absorptiometry (DXA).

Results

The patient showed absence of adipose tissue in the limbs; preservation of adiposity in the face, neck, and trunk; muscular hypertrophy, hypertriglyceridemia and insulin resistance. DXA revealed a fat mass of 15.4%, with android-to-gynoid ratio, trunk/limb, and trunk/leg ratios exceeding the published upper limits of 90% reference intervals. Two heterozygous missense de novo pathogenic variants in cis within the LMNA gene were found in the proband: p.Y481H and p.K486N (NP_733821.1). These variants have functional effects and were reported in inherited Emery-Dreifuss muscular dystrophy 2 (p.Y481H) and familial partial lipodystrophy type 2 (p.K486N). Molecular modeling analyses provided additional insights into the protein instability conferred by these variants in the lamin A/C Ig-like domain.

Conclusion

In a case of sporadic partial lipodystrophy, we describe two concurrent de novo pathogenic variants within the same gene (LMNA) as a novel pathogenic mechanism. This finding expands the genetic and phenotypic spectrum of partial lipodystrophy and laminopathy syndromes.

Human DNA polymerase ε is a source of C>T mutations at CpG dinucleotides

C-to-T transitions in CpG dinucleotides are the most prevalent mutations in human cancers and genetic diseases. These mutations have been attributed to deamination of 5-methylcytosine (5mC), an epigenetic modification found on CpGs. We recently linked CpG>TpG mutations to replication and hypothesized that errors introduced by polymerase ε (Pol ε) may represent an alternative source of mutations. Here we present a new method called polymerase error rate sequencing (PER-seq) to measure the error spectrum of DNA polymerases in isolation. We find that the most common human cancer-associated Pol ε mutant (P286R) produces an excess of CpG>TpG errors, phenocopying the mutation spectrum of tumors carrying this mutation and deficiencies in mismatch repair. Notably, we also discover that wild-type Pol ε has a sevenfold higher error rate when replicating 5mCpG compared to C in other contexts. Together, our results from PER-seq and human cancers demonstrate that replication errors are a major contributor to CpG>TpG mutagenesis in replicating cells, fundamentally changing our understanding of this important disease-causing mutational mechanism.

Nucleotide excision repair of aflatoxin-induced DNA damage within the 3D human genome organization

Aflatoxin B1 (AFB1), a potent mycotoxin, is one of the environmental risk factors that cause liver cancer. In the liver, the bioactivated AFB1 intercalates into the DNA double helix to form a bulky DNA adduct which will lead to mutation if left unrepaired. Here, we adapted the tXR-seq method to measure the nucleotide excision repair of AFB1-induced DNA adducts at single-nucleotide resolution on a genome-wide scale, and compared it with repair data obtained from conventional UV-damage XR-seq. Our results showed that transcription-coupled repair plays a major role in the damage removal process. We further analyzed the distribution of nucleotide excision repair sites for AFB1-induced DNA adducts within the 3D human genome organization. Our analysis revealed a heterogeneous AFB1-dG repair across four different organization levels, including chromosome territories, A/B compartments, TADs, and chromatin loops. We found that chromosomes positioned closer to the nuclear center and regions within A compartments have higher levels of nucleotide excision repair. Notably, we observed high repair activity around both TAD boundaries and loop anchors. These findings provide insights into the complex interplay between AFB1-induced DNA damage repair, transcription, and 3D genome organization, shedding light on the mechanisms underlying AFB1-induced mutagenesis.

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

RNase P: Beyond Precursor tRNA Processing

Ribonuclease P (RNase P) was first described in the 1970's as an endoribonuclease acting in the maturation of precursor transfer RNAs (tRNAs). More recent studies, however, have uncovered non-canonical roles for RNase P and its components. Here, we review the recent progress of its involvement in chromatin assembly, DNA damage response, and maintenance of genome stability with implications in tumorigenesis. The possibility of RNase P as a therapeutic target in cancer is also discussed.

Extrinsic and intrinsic drivers of natural killer cell clonality

Clonal expansion of antigen-specific lymphocytes is the fundamental mechanism enabling potent adaptive immune responses and the generation of immune memory. Accompanied by pronounced epigenetic remodeling, the massive proliferation of individual cells generates a critical mass of effectors for the control of acute infections, as well as a pool of memory cells protecting against future pathogen encounters. Classically associated with the adaptive immune system, recent work has demonstrated that innate immune memory to human cytomegalovirus (CMV) infection is stably maintained as large clonal expansions of natural killer (NK) cells, raising questions on the mechanisms for clonal selection and expansion in the absence of re-arranged antigen receptors. Here, we discuss clonal NK cell memory in the context of the mechanisms underlying clonal competition of adaptive lymphocytes and propose alternative selection mechanisms that might decide on the clonal success of their innate counterparts. We propose that the integration of external cues with cell-intrinsic sources of heterogeneity, such as variegated receptor expression, transcriptional states, and somatic variants, compose a bottleneck for clonal selection, contributing to the large size of memory NK cell clones.

DNA Repair in Nucleosomes: Insights from Histone Modifications and Mutants

DNA repair pathways play a critical role in genome stability, but in eukaryotic cells, they must operate to repair DNA lesions in the compact and tangled environment of chromatin. Previous studies have shown that the packaging of DNA into nucleosomes, which form the basic building block of chromatin, has a profound impact on DNA repair. In this review, we discuss the principles and mechanisms governing DNA repair in chromatin. We focus on the role of histone post-translational modifications (PTMs) in repair, as well as the molecular mechanisms by which histone mutants affect cellular sensitivity to DNA damage agents and repair activity in chromatin. Importantly, these mechanisms are thought to significantly impact somatic mutation rates in human cancers and potentially contribute to carcinogenesis and other human diseases. For example, a number of the histone mutants studied primarily in yeast have been identified as candidate oncohistone mutations in different cancers. This review highlights these connections and discusses the potential importance of DNA repair in chromatin to human health.

HJURP is recruited to double-strand break sites and facilitates DNA repair by promoting chromatin reorganization

HJURP is overexpressed in several cancer types and strongly correlates with patient survival. However, the mechanistic basis underlying the association of HJURP with cancer aggressiveness is not well understood. HJURP promotes the loading of the histone H3 variant, CENP-A, at the centromeric chromatin, epigenetically defining the centromeres and supporting proper chromosome segregation. In addition, HJURP is associated with DNA repair but its function in this process is still scarcely explored. Here, we demonstrate that HJURP is recruited to DSBs through a mechanism requiring chromatin PARylation and promotes epigenetic alterations that favor the execution of DNA repair. Incorporation of HJURP at DSBs promotes turnover of H3K9me3 and HP1, facilitating DNA damage signaling and DSB repair. Moreover, HJURP overexpression in glioma cell lines also affected global structure of heterochromatin independently of DNA damage induction, promoting genome-wide reorganization and assisting DNA damage response. HJURP overexpression therefore extensively alters DNA damage signaling and DSB repair, and also increases radioresistance of glioma cells. Importantly, HJURP expression levels in tumors are also associated with poor response of patients to radiation. Thus, our results enlarge the understanding of HJURP involvement in DNA repair and highlight it as a promising target for the development of adjuvant therapies that sensitize tumor cells to irradiation.

Analysis of cytosine deamination events in excision repair sequencing reads reveals mechanisms of incision site selection in NER

Nucleotide excision repair (NER) removes helix-distorting DNA lesions and is therefore critical for genome stability. During NER, DNA is unwound on either side of the lesion and excised, but the rules governing incision site selection, particularly in eukaryotic cells, are unclear. Excision repair-sequencing (XR-seq) sequences excised NER fragments, but analysis has been limited because the lesion location is unknown. Here, we exploit accelerated cytosine deamination rates in UV-induced CPD (cyclobutane pyrimidine dimer) lesions to precisely map their locations at C to T mismatches in XR-seq reads, revealing general and species-specific patterns of incision site selection during NER. Our data indicate that the 5' incision site occurs preferentially in HYV (i.e. not G; C/T; not T) sequence motifs, a pattern that can be explained by sequence preferences of the XPF-ERCC1 endonuclease. In contrast, the 3' incision site does not show strong sequence preferences, once truncated reads arising from mispriming events are excluded. Instead, the 3' incision is partially determined by the 5' incision site distance, indicating that the two incision events are coupled. Finally, our data reveal unique and coupled NER incision patterns at nucleosome boundaries. These findings reveal key principles governing NER incision site selection in eukaryotic cells.

Investigating low frequency somatic mutations in Arabidopsis with Duplex Sequencing

Mutations are the source of novel genetic diversity but can also lead to disease and maladaptation. The conventional view is that mutations occur randomly with respect to their environment-specific fitness consequences. However, intragenomic mutation rates can vary dramatically due to transcription coupled repair and based on local epigenomic modifications, which are non-uniformly distributed across genomes. One sequence feature associated with decreased mutation is higher expression level, which can vary depending on environmental cues. To understand whether the association between expression level and mutation rate creates a systematic relationship with environment-specific fitness effects, we perturbed expression through a heat treatment in Arabidopsis thaliana. We quantified gene expression to identify differentially expressed genes, which we then targeted for mutation detection using Duplex Sequencing. This approach provided a highly accurate measurement of the frequency of rare somatic mutations in vegetative plant tissues, which has been a recent source of uncertainty in plant mutation research. We included mutant lines lacking mismatch repair (MMR) and base excision repair (BER) capabilities to understand how repair mechanisms may drive biased mutation accumulation. We found wild type (WT) and BER mutant mutation frequencies to be very low (mean variant frequency 1.8×10-8 and 2.6×10-8, respectively), while MMR mutant frequencies were significantly elevated (1.13×10-6). These results show that somatic variant frequencies are extremely low in WT plants, indicating that larger datasets will be needed to address the fundamental evolutionary question as to whether environmental change leads to gene-specific changes in mutation rate.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.

Hotspot propensity across mutational processes

The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis.

Epigenomic divergence correlates with sequence polymorphism in Arabidopsis paralogs

Processes affecting rates of sequence polymorphism are fundamental to the evolution of gene duplicates. The relationship between gene activity and sequence polymorphism can influence the likelihood that functionally redundant gene copies are co-maintained in stable evolutionary equilibria vs other outcomes such as neofunctionalization. Here, we investigate genic variation in epigenome-associated polymorphism rates in Arabidopsis thaliana and consider whether these affect the evolution of gene duplicates. We compared the frequency of sequence polymorphism and patterns of genetic differentiation between genes classified by exon methylation patterns: unmethylated (unM), gene-body methylated (gbM), and transposon-like methylated (teM) states, which reflect divergence in gene expression. We found that the frequency of polymorphism was higher in teM (transcriptionally repressed, tissue-specific) genes and lower in gbM (active, constitutively expressed) genes. Comparisons of gene duplicates were largely consistent with genome-wide patterns - gene copies that exhibit teM accumulate more variation, evolve faster, and are in chromatin states associated with reduced DNA repair. This relationship between expression, the epigenome, and polymorphism may lead to the breakdown of equilibrium states that would otherwise maintain genetic redundancies. Epigenome-mediated polymorphism rate variation may facilitate the evolution of novel gene functions in duplicate paralogs maintained over evolutionary time.

Identifying predictors of glioma evolution from longitudinal sequencing

Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.

Predicting regional somatic mutation rates using DNA motifs

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature.

Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox

Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole-genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole-genome sequencing (WGS) but still generate hundreds of artifacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolbox (PTATO), to accurately detect single base substitutions, insertions-deletions (indels), and structural variants in PTA-based WGS data. PTATO includes a machine learning approach and filtering based on recurrence to distinguish PTA artifacts from true mutations with high sensitivity (up to 90%), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that hematopoietic stem cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS, have normal somatic single base substitution burdens but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.

Effect of Carcinomas on Autosomal Trait Screening: A Review Article

This review highlights the effect of carcinomas on the results of the examination of autosomal genetic traits for identification and paternity tests when carcinoid tissue is the only source and no other samples are available. In DNA typing or genetic fingerprinting, variable elements are isolated and identified within the base pair sequences that form the DNA. The person's probable identity can be determined by analysing nucleotide sequences in particular regions of DNA unique to everyone. Genetics plays an increasingly important role in the risk stratification and management of carcinoma patients. The available information from previous studies has indicated that in some incidents, including mass disasters and crimes such as terrorist incidents, biological evidence may not be available at the scene of the accident, except for some unknown human remains found in the form of undefined human tissues. If these tissues have cancerous tumours, it may affect the examination of the genetic traits derived from these samples, thereby resulting in a failure to identify the person. Pathology units, more often, verify the identity of the patients who were diagnosed with cancer in reference to their deceased tumorous relatives. Genetic fingerprinting (GF) is also used in paternity testing when the alleged parent disappeared or died and earlier was diagnosed and treated for cancer.

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.

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

BACKGROUND

Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.

METHODS

We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.

RESULTS

We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations.

CONCLUSIONS

Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.

Aldehyde-Associated Mutagenesis─Current State of Knowledge

Aldehydes are widespread in the environment, with multiple sources such as food and beverages, industrial effluents, cigarette smoke, and additives. The toxic effects of exposure to several aldehydes have been observed in numerous studies. At the molecular level, aldehydes damage DNA, cross-link DNA and proteins, lead to lipid peroxidation, and are associated with increased disease risk including cancer. People genetically predisposed to aldehyde sensitivity exhibit severe health outcomes. In various diseases such as Fanconi's anemia and Cockayne syndrome, loss of aldehyde-metabolizing pathways in conjunction with defects in DNA repair leads to widespread DNA damage. Importantly, aldehyde-associated mutagenicity is being explored in a growing number of studies, which could offer key insights into how they potentially contribute to tumorigenesis. Here, we review the genotoxic effects of various aldehydes, focusing particularly on the DNA adducts underlying the mutagenicity of environmentally derived aldehydes. We summarize the chemical structures of the aldehydes and their predominant DNA adducts, discuss various methodologies, in vitro and in vivo, commonly used in measuring aldehyde-associated mutagenesis, and highlight some recent studies looking at aldehyde-associated mutation signatures and spectra. We conclude the Review with a discussion on the challenges and future perspectives of investigating aldehyde-associated mutagenesis.

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.

Detecting recurrent passenger mutations in melanoma by targeted UV damage sequencing

Sequencing of melanomas has identified hundreds of recurrent mutations in both coding and non-coding DNA. These include a number of well-characterized oncogenic driver mutations, such as coding mutations in the BRAF and NRAS oncogenes, and non-coding mutations in the promoter of telomerase reverse transcriptase (TERT). However, the molecular etiology and significance of most of these mutations is unknown. Here, we use a new method known as CPD-capture-seq to map UV-induced cyclobutane pyrimidine dimers (CPDs) with high sequencing depth and single nucleotide resolution at sites of recurrent mutations in melanoma. Our data reveal that many previously identified drivers and other recurrent mutations in melanoma occur at CPD hotspots in UV-irradiated melanocytes, often associated with an overlapping binding site of an E26 transformation-specific (ETS) transcription factor. In contrast, recurrent mutations in the promoters of a number of known or suspected cancer genes are not associated with elevated CPD levels. Our data indicate that a subset of recurrent protein-coding mutations are also likely caused by ETS-induced CPD hotspots. This analysis indicates that ETS proteins profoundly shape the mutation landscape of melanoma and reveals a method for distinguishing potential driver mutations from passenger mutations whose recurrence is due to elevated UV damage.

Tumour mutational burden is overestimated by target cancer gene panels

Background

Tumour mutational burden (TMB) has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors (ICI) in multiple tumour types. It can be calculated from somatic mutations detected from whole exome or targeted panel sequencing data. As mutations are unevenly distributed across the cancer genome, the clinical implications from TMB calculated using different genomic regions are not clear.

Methods

Pan-cancer data of 10,179 samples were collected from The Cancer Genome Atlas cohort and 6,831 cancer patients with either ICI or non-ICI treatment outcomes were derived from published papers. TMB was calculated as the count of non-synonymous mutations and normalised by the size of genomic regions. Dirichlet method, linear regression and Poisson calibration models are used to unify TMB from different gene panels.

Results

We found that panels based on cancer genes usually overestimate TMB compared to whole exome, potentially leading to misclassification of patients to receive ICI. The overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational hotspots. We compared different approaches to address this discrepancy and developed a generalised statistical model capable of interconverting TMB derived from whole exome and different panel sequencing data, enabling TMB correction for patient stratification for ICI treatment. We show that in a cohort of lung cancer patients treated with ICI, when using a TMB cutoff of 10 mut/Mb, our corrected TMB outperforms the original panel-based TMB.

Conclusion

Cancer gene-based panels usually overestimate TMB, and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.

Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

Genome-wide mapping of protein-DNA damage interaction by PADD-seq

Protein-DNA damage interactions are critical for understanding the mechanism of DNA repair and damage response. However, due to the relatively random distributions of UV-induced damage and other DNA bulky adducts, it is challenging to measure the interactions between proteins and these lesions across the genome. To address this issue, we developed a new method named Protein-Associated DNA Damage Sequencing (PADD-seq) that uses Damage-seq to detect damage distribution in chromatin immunoprecipitation-enriched DNA fragments. It is possible to delineate genome-wide protein-DNA damage interactions at base resolution with this strategy. Using PADD-seq, we observed that RNA polymerase II (Pol II) was blocked by UV-induced damage on template strands, and the interaction declined within 2 h in transcription-coupled repair-proficient cells. On the other hand, Pol II was clearly restrained at damage sites in the absence of the transcription-repair coupling factor CSB during the same time course. Furthermore, we used PADD-seq to examine local changes in H3 acetylation at lysine 9 (H3K9ac) around cisplatin-induced damage, demonstrating the method's broad utility. In conclusion, this new method provides a powerful tool for monitoring the dynamics of protein-DNA damage interaction at the genomic level, and it encourages comprehensive research into DNA repair and damage response.

Evo-devo perspectives on cancer

The integration of evolutionary and developmental approaches into the field of evolutionary developmental biology has opened new areas of inquiry- from understanding the evolution of development and its underlying genetic and molecular mechanisms to addressing the role of development in evolution. For the last several decades, the terms 'evolution' and 'development' have been increasingly linked to cancer, in many different frameworks and contexts. This mini-review, as part of a special issue on Evolutionary Developmental Biology, discusses the main areas in cancer research that have been addressed through the lenses of both evolutionary and developmental biology, though not always fully or explicitly integrated in an evo-devo framework. First, it briefly introduces the current views on carcinogenesis that invoke evolutionary and/or developmental perspectives. Then, it discusses the main mechanisms proposed to have specifically evolved to suppress cancer during the evolution of multicellularity. Lastly, it considers whether the evolution of multicellularity and development was shaped by the threat of cancer (a cancer-evo-devo perspective), and/or whether the evolution of developmental programs and life history traits can shape cancer resistance/risk in various lineages (an evo-devo-cancer perspective). A proper evolutionary developmental framework for cancer, both as a disease and in terms of its natural history (in the context of the evolution of multicellularity and development as well as life history traits), could bridge the currently disparate evolutionary and developmental perspectives and uncover aspects that will provide new insights for cancer prevention and treatment.

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.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

Somatic mutation distribution across tumour cohorts provides a signal for positive selection in cancer

Cancer gene discovery is reliant on distinguishing driver mutations from a multitude of passenger mutations in tumour genomes. While driver genes may be revealed based on excess mutation recurrence or clustering, there is a need for orthogonal principles. Here, we take advantage of the fact that non-cancer genes, containing only passenger mutations under neutral selection, exhibit a likelihood of mutagenesis in a given tumour determined by the tumour's mutational signature and burden. This relationship can be disrupted by positive selection, leading to a difference in the distribution of mutated cases across a cohort for driver and passenger genes. We apply this principle to detect cancer drivers independently of recurrence in large pan-cancer cohorts, and show that our method (SEISMIC) performs comparably to traditional approaches and can provide resistance to known confounding mutational phenomena. Being based on a different principle, the approach provides a much-needed complement to existing methods for detecting signals of selection.

DNA replication initiation shapes the mutational landscape and expression of the human genome

The interplay between active biological processes and DNA repair is central to mutagenesis. Here, we show that the ubiquitous process of replication initiation is mutagenic, leaving a specific mutational footprint at thousands of early and efficient replication origins. The observed mutational pattern is consistent with two distinct mechanisms, reflecting the two-step process of origin activation, triggering the formation of DNA breaks at the center of origins and local error-prone DNA synthesis in their immediate vicinity. We demonstrate that these replication initiation-dependent mutational processes exert an influence on phenotypic diversity in humans that is disproportionate to the origins' genomic size: By increasing mutational loads at gene promoters and splice junctions, the presence of an origin significantly influences both gene expression and mRNA isoform usage. Last, we show that mutagenesis at origins not only drives the evolution of origin sequences but also contributes to sculpting regulatory domains of the human genome.

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.

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.

Discovering the drivers of clonal hematopoiesis

Mutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) drive clonal hematopoiesis (CH). While some CH drivers have been identified, the compendium of all genes able to drive CH upon mutations in HSCs remains incomplete. Exploiting signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, analogously to cancer. Using the tumor sample in blood/tumor pairs as reference, we identify blood somatic mutations across more than 12,000 donors from two large cancer genomics cohorts. The application of IntOGen, a driver discovery pipeline, to both cohorts, and more than 24,000 targeted sequenced samples yields a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch . This approach recovers known CH genes, and discovers other candidates.

Recurrent somatic mutations as predictors of immunotherapy response

Immune checkpoint blockade (ICB) has transformed the treatment of metastatic cancer but is hindered by variable response rates. A key unmet need is the identification of biomarkers that predict treatment response. To address this, we analyzed six whole exome sequencing cohorts with matched disease outcomes to identify genes and pathways predictive of ICB response. To increase detection power, we focus on genes and pathways that are significantly mutated following correction for epigenetic, replication timing, and sequence-based covariates. Using this technique, we identify several genes (BCLAF1, KRAS, BRAF, and TP53) and pathways (MAPK signaling, p53 associated, and immunomodulatory) as predictors of ICB response and develop the Cancer Immunotherapy Response CLassifiEr (CIRCLE). Compared to tumor mutational burden alone, CIRCLE led to superior prediction of ICB response with a 10.5% increase in sensitivity and a 11% increase in specificity. We envision that CIRCLE and more broadly the analysis of recurrently mutated cancer genes will pave the way for better prognostic tools for cancer immunotherapy.

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.

Pan-cancer analysis of mutations in open chromatin regions and their possible association with cancer pathogenesis

BACKGROUND

Open chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions.

METHODS

Whole-genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis.

RESULTS

In some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types.

CONCLUSIONS

These results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis.

High-throughput characterization of the role of non-B DNA motifs on promoter function

lternative DNA conformations, termed non-B DNA structures, can affect transcription, but the underlying mechanisms and their functional impact have not been systematically characterized. Here, we used computational genomic analyses coupled with massively parallel reporter assays (MPRAs) to show that certain non-B DNA structures have a substantial effect on gene expression. Genomic analyses found that non-B DNA structures at promoters harbor an excess of germline variants. Analysis of multiple MPRAs, including a promoter library specifically designed to perturb non-B DNA structures, functionally validated that Z-DNA can significantly affect promoter activity. We also observed that biophysical properties of non-B DNA motifs, such as the length of Z-DNA motifs and the orientation of G-quadruplex structures relative to transcriptional direction, have a significant effect on promoter activity. Combined, their higher mutation rate and functional effect on transcription implicate a subset of non-B DNA motifs as major drivers of human gene-expression-associated phenotypes.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Distinct genomic landscape of Chinese pediatric acute myeloid leukemia impacts clinical risk classification

Studies have revealed key genomic aberrations in pediatric acute myeloid leukemia (AML) based on Western populations. It is unknown to what extent the current genomic findings represent populations with different ethnic backgrounds. Here we present the genomic landscape of driver alterations of Chinese pediatric AML and discover previously undescribed genomic aberrations, including the XPO1-TNRC18 fusion. Comprehensively comparing between the Chinese and Western AML cohorts reveal a substantially distinct genomic alteration profile. For example, Chinese AML patients more commonly exhibit mutations in KIT and CSF3R, and less frequently mutated of genes in the RAS signaling pathway. These differences in mutation frequencies lead to the detection of previously uncharacterized co-occurring mutation pairs. Importantly, the distinct driver profile is clinical relevant. We propose a refined prognosis risk classification model which better reflected the adverse event risk for Chinese AML patients. These results emphasize the importance of genetic background in precision medicine.

A new technique for genome-wide mapping of nucleotide excision repair without immunopurification of damaged DNA

Nucleotide excision repair functions to protect genome integrity, and ongoing studies using excision repair sequencing (XR-seq) have contributed to our understanding of how cells prioritize repair across the genome. In this method, the products of excision repair bearing damaged DNA are captured, sequenced, and then mapped genome-wide at single-nucleotide resolution. However, reagent requirements and complex procedures have limited widespread usage of this technique. In addition to the expense of these reagents, it has been hypothesized that the immunoprecipitation step using antibodies directed against damaged DNA may introduce bias in different sequence contexts. Here, we describe a newly developed adaptation called dA-tailing and adaptor ligation (ATL)–XR-seq, a relatively simple XR-seq method that avoids the use of immunoprecipitation targeting damaged DNA. ATL-XR-seq captures repair products by 3′-dA-tailing and 5′-adapter ligation instead of the original 5′- and 3′-dual adapter ligation. This new approach avoids adapter dimer formation during subsequent PCR, omits inefficient and time-consuming purification steps, and is very sensitive. In addition, poly(dA) tail length heterogeneity can serve as a molecular identifier, allowing more repair hotspots to be mapped. Importantly, a comparison of both repair mapping methods showed that no major bias is introduced by the anti-UV damage antibodies used in the original XR-seq procedure. Finally, we also coupled the described dA-tailing approach with quantitative PCR in a new method to quantify repair products. These new methods provide powerful and user-friendly tools to qualitatively and quantitatively measure excision repair.

G-Quadruplex Matters in Tissue-Specific Tumorigenesis by BRCA1 Deficiency

How and why distinct genetic alterations, such as BRCA1 mutation, promote tumorigenesis in certain tissues, but not others, remain an important issue in cancer research. The underlying mechanisms may reveal tissue-specific therapeutic vulnerabilities. Although the roles of BRCA1, such as DNA damage repair and stalled fork stabilization, obviously contribute to tumor suppression, these ubiquitously important functions cannot explain tissue-specific tumorigenesis by BRCA1 mutations. Recent advances in our understanding of the cancer genome and fundamental cellular processes on DNA, such as transcription and DNA replication, have provided new insights regarding BRCA1-associated tumorigenesis, suggesting that G-quadruplex (G4) plays a critical role. In this review, we summarize the importance of G4 structures in mutagenesis of the cancer genome and cell type-specific gene regulation, and discuss a recently revealed molecular mechanism of G4/base excision repair (BER)-mediated transcriptional activation. The latter adequately explains the correlation between the accumulation of unresolved transcriptional regulatory G4s and multi-level genomic alterations observed in BRCA1-associated tumors. In summary, tissue-specific tumorigenesis by BRCA1 deficiency can be explained by cell type-specific levels of transcriptional regulatory G4s and the role of BRCA1 in resolving it. This mechanism would provide an integrated understanding of the initiation and development of BRCA1-associated tumors.

Somatic structural variant formation is guided by and influences genome architecture

The occurrence and formation of genomic structural variants (SVs) is known to be influenced by the 3D chromatin architecture, but the extent and magnitude have been challenging to study. Here, we apply Hi-C to study chromatin organization before and after induction of chromothripsis in human cells. We use Hi-C to manually assemble the derivative chromosomes following the occurrence of massive complex rearrangements, which allows us to study the sources of SV formation and their consequences on gene regulation. We observe an action–reaction interplay whereby the 3D chromatin architecture directly impacts the location and formation of SVs. In turn, the SVs reshape the chromatin organization to alter the local topologies, replication timing, and gene regulation in cis. We show that SVs have a strong tendency to occur between similar chromatin compartments and replication timing regions. Moreover, we find that SVs frequently occur at 3D loop anchors, that SVs can cause a switch in chromatin compartments and replication timing, and that this is a major source of SV-mediated effects on nearby gene expression changes. Finally, we provide evidence for a general mechanistic bias of the 3D chromatin on SV occurrence using data from more than 2700 patient-derived cancer genomes.

MutationalPatterns: the one stop shop for the analysis of mutational processes

BACKGROUND

The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data.

RESULTS

Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights.

CONCLUSIONS

This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns .