A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures

Bayesian multi-study non-negative matrix factorization for mutational signatures

Mutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across conditions. We propose a Bayesian NMF method that jointly decomposes multiple datasets to identify signatures and their sharing pattern across conditions. We propose a fully unsupervised "discovery-only" model and a semi-supervised "recovery-discovery" model that simultaneously estimates known and novel signatures, and extend both to estimate covariate effects. We demonstrate our approach on extensive simulations, and apply our method to answer questions related to colorectal cancer and early-onset breast cancer.

Landscape of driver mutations and their clinical effects on Down syndrome-related myeloid neoplasms

ABSTRACT

Transient abnormal myelopoiesis (TAM) is a common complication in newborns with Down syndrome (DS). It commonly progresses to myeloid leukemia (ML-DS) after spontaneous regression. In contrast to the favorable prognosis of primary ML-DS, patients with refractory/relapsed ML-DS have poor outcomes. However, the molecular basis for refractoriness and relapse and the full spectrum of driver mutations in ML-DS remain largely unknown. We conducted a genomic profiling study of 143 TAM, 204 ML-DS, and 34 non-DS acute megakaryoblastic leukemia cases, including 39 ML-DS cases analyzed by exome sequencing. Sixteen novel mutational targets were identified in ML-DS samples. Of these, inactivations of IRX1 (16.2%) and ZBTB7A (13.2%) were commonly implicated in the upregulation of the MYC pathway and were potential targets for ML-DS treatment with bromodomain-containing protein 4 inhibitors. Partial tandem duplications of RUNX1 on chromosome 21 were also found, specifically in ML-DS samples (13.7%), presenting its essential role in DS leukemia progression. Finally, in 177 patients with ML-DS treated following the same ML-DS protocol (the Japanese Pediatric Leukemia and Lymphoma Study Group acute myeloid leukemia -D05/D11), CDKN2A, TP53, ZBTB7A, and JAK2 alterations were associated with a poor prognosis. Patients with CDKN2A deletions (n = 7) or TP53 mutations (n = 4) had substantially lower 3-year event-free survival (28.6% vs 90.5%; P < .001; 25.0% vs 89.5%; P < .001) than those without these mutations. These findings considerably change the mutational landscape of ML-DS, provide new insights into the mechanisms of progression from TAM to ML-DS, and help identify new therapeutic targets and strategies for ML-DS.

Diffsig: Associating Risk Factors with Mutational Signatures

BACKGROUND

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

METHODS

Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations.

RESULTS

We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts.

CONCLUSIONS

Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures.

IMPACT

We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

Flexible model-based non-negative matrix factorization with application to mutational signatures

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. Our novel estimation procedure is based on the expectation-maximization (EM) algorithm and regression in the log-linear quasi-Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework in a large simulation study where we compare to state of the art methods, and show results for three data sets of somatic mutation counts from patients with cancer in the breast, Liver and urinary tract.

Structural underpinnings of mutation rate variations in the human genome

Single nucleotide mutation rates have critical implications for human evolution and genetic diseases. Importantly, the rates vary substantially across the genome and the principles underlying such variations remain poorly understood. A recent model explained much of this variation by considering higher-order nucleotide interactions in the 7-mer sequence context around mutated nucleotides. This model's success implicates a connection between DNA shape and mutation rates. DNA shape, i.e. structural properties like helical twist and tilt, is known to capture interactions between nucleotides within a local context. Thus, we hypothesized that changes in DNA shape features at and around mutated positions can explain mutation rate variations in the human genome. Indeed, DNA shape-based models of mutation rates showed similar or improved performance over current nucleotide sequence-based models. These models accurately characterized mutation hotspots in the human genome and revealed the shape features whose interactions underlie mutation rate variations. DNA shape also impacts mutation rates within putative functional regions like transcription factor binding sites where we find a strong association between DNA shape and position-specific mutation rates. This work demonstrates the structural underpinnings of nucleotide mutations in the human genome and lays the groundwork for future models of genetic variations to incorporate DNA shape.

A mutation-level covariate model for mutational signatures

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

Single-cell genomic variation induced by mutational processes in cancer

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.

Enabling Inference for Context-Dependent Models of Mutation by Bounding the Propagation of Dependency

Although the rates at which positions in the genome mutate are known to depend not only on the nucleotide to be mutated, but also on neighboring nucleotides, it remains challenging to do phylogenetic inference using models of context-dependent mutation. In these models, the effects of one mutation may in principle propagate to faraway locations, making it difficult to compute exact likelihoods. This article shows how to use bounds on the propagation of dependency to compute likelihoods of mutation of a given segment of genome by marginalizing over sufficiently long flanking sequence. This can be used for maximum likelihood or Bayesian inference. Protocols examining residuals and iterative model refinement are also discussed. Tools for efficiently working with these models are provided in an R package, which could be used in other applications. The method is used to examine context dependence of mutations since the common ancestor of humans and chimpanzee.

Mutational signatures are markers of drug sensitivity of cancer cells

Genomic analyses have revealed mutational footprints associated with DNA maintenance gone awry, or with mutagen exposures. Because cancer therapeutics often target DNA synthesis or repair, we asked if mutational signatures make useful markers of drug sensitivity. We detect mutational signatures in cancer cell line exomes (where matched healthy tissues are not available) by adjusting for the confounding germline mutation spectra across ancestries. We identify robust associations between various mutational signatures and drug activity across cancer cell lines; these are as numerous as associations with established genetic markers such as driver gene alterations. Signatures of prior exposures to DNA damaging agents - including chemotherapy - tend to associate with drug resistance, while signatures of deficiencies in DNA repair tend to predict sensitivity towards particular therapeutics. Replication analyses across independent drug and CRISPR genetic screening data sets reveal hundreds of robust associations, which are provided as a resource for drug repurposing guided by mutational signature markers.

A practical framework RNMF for exploring the association between mutational signatures and genes using gene cumulative contribution abundance

Background

Mutational signatures are somatic mutation patterns enriching operational mutational processes, which can provide abundant information about the mechanism of cancer. However, understanding of the pathogenic biological processes is still limited, such as the association between mutational signatures and genes.

Methods

We developed a simple and practical R package called RNMF (https://github.com/zhenzhang‐li/RNMF) for mutational signature analysis, including a key model of cumulative contribution abundance (CCA), which was designed to highlight the association between mutational signatures and genes and then applying it to a meta‐analysis of 1073 individuals with esophageal squamous cell carcinoma (ESCC).

Results

We revealed a number of known and previously undescribed SBS or ID signatures, and we found that APOBEC signatures (SBS2* and SBS13*) were closely associated with PIK3CA mutation, especially the E545k mutation. Furthermore, we found that age signature is closely related to the frequent mutation of TP53, of which R342* is highlighted due to strongly linked to age signature. In addition, the CCA matrix image data of genes in the signatures New, SBS3*, and SBS17b* were helpful for the preliminary evaluation of shortened survival outcome. These results can be extended to estimate the distribution of mutations or features, and study the potential impact of clinical factors.

Conclusions

In a word, RNMF can successfully achieve the correlation analysis of mutational signatures and genes, proving a strong theoretical basis for the study of mutational processes during tumor development.

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types

Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in different cancer types. Some mutations dysregulate protein function so that their accumulation is responsible for cancer development and might be associated with different cancer types. Therefore, mutations as a feature set can be used as an informative candidate to distinguish various cancer types. There are several options for demonstrating mutations. One might employ binary values to demonstrate mutation regions. Another potential method for extracting features is utilizing mutation interpreters. In this study, we investigate the trinucleotide mutational pattern of each cancer type. Moreover, we extract salient NMF-based mutational signatures across various cancer types. Then, we identify cancer-associated genes of a target cancer based on its salient signatures. We evaluate the cancer-associated genes using survival and gene expression analysis in different stages of cancer. Furthermore, we introduce DiaDeL, which is a deep learning-based binary classifier. The DiaDeL model uses mutational signatures as input features and distinct a cancer type from the others. Our proposed model outperforms six state-of-the-art methods with 0.824 and 0.88 for accuracy and AUC, respectively. The source code is available at https://github.com/sabdollahi/DiaDeL.

APOBEC SBS13 Mutational Signature-A Novel Predictor of Radioactive Iodine Refractory Papillary Thyroid Carcinoma

Standard surgery followed by radioactive iodine (131I, RAI) therapy are not curative for 5−20% of papillary thyroid carcinoma (PTC) patients with RAI refractory disease. Early predictors indicating therapeutic response to RAI therapy in PTC are yet to be elucidated. Whole-exome sequencing was performed (at median depth 198x) on 66 RAI-refractory and 92 RAI-avid PTCs with patient-matched germline. RAI-refractory tumors were significantly associated with distinct aggressive clinicopathological features, including positive surgical margins (p = 0.016) and the presence of lymph node metastases at primary diagnosis (p = 0.012); higher nonsilent tumor mutation burden (p = 0.011); TERT promoter (TERTp) mutation (p < 0.0001); and the enrichment of the APOBEC-related single-base substitution (SBS) COSMIC mutational signatures 2 (p = 0.030) and 13 (p < 0.001). Notably, SBS13 (odds ratio [OR] 30.4, 95% confidence intervals [CI] 1.43−647.22) and TERTp mutation (OR 41.3, 95% CI 4.35−391.60) were revealed to be independent predictors of RAI refractoriness in PTC (p = 0.029 and 0.001, respectively). Although SBS13 and TERTp mutations alone highly predicted RAI refractoriness, when combined, they significantly increased the likelihood of predicting RAI refractoriness in PTC. This study highlights the APOBEC SBS13 mutational signature as a novel independent predictor of RAI refractoriness in a distinct subgroup of PTC.

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.

Clone decomposition based on mutation signatures provides novel insights into mutational processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

A mixture model for signature discovery from sparse mutation data

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

Significance and limitations of the use of next-generation sequencing technologies for detecting mutational signatures

Next generation sequencing technologies (NGS) have been critical in characterizing the genomic landscape and untangling the genetic heterogeneity of human cancer. Since its advent, NGS has played a pivotal role in identifying the patterns of somatic mutations imprinted on cancer genomes and in deciphering the signatures of the mutational processes that have generated these patterns. Mutational signatures serve as phenotypic molecular footprints of exposures to environmental factors as well as deficiency and infidelity of DNA replication and repair pathways. Since the first roadmap of mutational signatures in human cancer was generated from whole-genome and whole-exome sequencing data, there has been a growing interest to extract mutational signatures from other NGS technologies such as targeted panel sequencing, RNA sequencing, single-cell sequencing, duplex sequencing, reduced representation sequencing, and long-read sequencing. Many of these technologies have their inherent sequencing biases and produce technical artifacts that can confound the extraction of reliable and interpretable mutational signatures. In this review, we highlight the relevance, limitations, and prospects of using different NGS technologies for examining mutational patterns and for deciphering mutational signatures.

A data-driven approach for constructing mutation categories for mutational signature analysis

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.

Cancer is a group of genetic diseases that occur as a result of an accumulation of somatic mutations in genes that regulate cellular growth and differentiation. These mutations arise from mutagenic processes such as exposure to environmental mutagens and defective DNA damage repair pathways. Each of these processes results in a characteristic pattern of mutations, referred to as a mutational signature. These signatures reveal the mutagenic mechanisms that have influenced the development of a specific tumor, and thus provide new insights into its causes and potential treatments. Originally, a mutational signature has been defined using 96 mutation categories that take into account solely the information from the mutated base and its flanking bases. Here, we aim to challenge this arbitrary categorization, which is widely used in mutational signature analysis. We have developed a novel framework for the construction of mutation categories that is based on the assumption that the activities of DNA damage repair genes are correlated with the mutational processes that are active in a given tumor. We show that using this approach we are able to identify an alternative mutation categorization that outperforms the standard categorization with respect to multiple metrics. This categorization includes categories that account for bases that extend beyond the immediate flanking bases, suggesting that mutational signatures should be studied in broader sequence contexts.

The Evolving Genomic Landscape of Esophageal Squamous Cell Carcinoma Under Chemoradiotherapy

Esophageal squamous cell carcinoma (ESCC) often recurs after chemoradiotherapy, and the prognosis of ESCC after chemoradiotherapy has not improved over the past few decades. The mutation process in chemoradiotherapy-resistant clones and the functional relevance of genetic alterations remain unclear. To address these problems, we performed whole-exome sequencing of 52 tumor samples from 33 patients with ESCC who received radiotherapy combined with 5-fluorouracil/platinum. In multiregion analyses of pretreatment and locally recurrent lesions from five cases, most driver gene-altered clones remained under chemoradiotherapy selection pressure, while few driver gene alterations were acquired at recurrence. The mutation signatures of recurrent ESCC, including increased deletion frequency and platinum dose-dependent base substitution signatures, were substantially different from those of primary ESCC and reflected the iatrogenic impacts of chemoradiotherapy. Single-region analysis of 28 pretreatment tumors indicated that focal copy-number gain at the MYC locus was significantly associated with poor progression-free survival and overall survival after chemoradiotherapy. MYC gain remained throughout the chemoradiotherapy course and potentially contributes to intrinsic resistance to chemoradiotherapy. Consistent with these findings, MYC copy number and mRNA and protein levels in ESCC cell lines correlated positively with resistance to radiotherapy, and MYC knockdown improved sensitivity to radiotherapy. Overall, these data characterize the clonal evolution process induced by chemoradiotherapy and clinically relevant associations for genetic alterations in ESCC. These findings increase our understanding of therapeutic resistance and support the rationale for precision chemoradiotherapy. SIGNIFICANCE: Whole-exome sequencing reveals the genetic evolution of ESCC during chemoradiotherapy, highlighting MYC gain in pretreatment tumors as a potential marker of therapy resistance.

Frequent genetic alterations in immune checkpoint-related genes in intravascular large B-cell lymphoma

Intravascular large B-cell lymphoma (IVLBCL) is a unique type of extranodal lymphoma characterized by selective growth of tumor cells in small vessels without lymphadenopathy. Greater understanding of the molecular pathogenesis of IVLBCL is hampered by the paucity of lymphoma cells in biopsy specimens, creating a limitation in obtaining sufficient tumor materials. To uncover the genetic landscape of IVLBCL, we performed whole-exome sequencing (WES) of 21 patients with IVLBCL using plasma-derived cell-free DNA (cfDNA) (n = 18), patient-derived xenograft tumors (n = 4), and tumor DNA from bone marrow (BM) mononuclear cells (n = 2). The concentration of cfDNA in IVLBCL was significantly higher than that in diffuse large B-cell lymphoma (DLBCL) (P < .0001) and healthy donors (P = .0053), allowing us to perform WES; most mutations detected in BM tumor DNA were successfully captured in cfDNA and xenograft. IVLBCL showed a high frequency of genetic lesions characteristic of activated B-cell-type DLBCL, with the former showing conspicuously higher frequencies (compared with nodal DLBCL) of mutations in MYD88 (57%), CD79B (67%), SETD1B (57%), and HLA-B (57%). We also found that 8 IVLBCL (38%) harbored rearrangements of programmed cell death 1 ligand 1 and 2 (PD-L1/PD-L2) involving the 3' untranslated region; such rearrangements are implicated in immune evasion via PD-L1/PD-L2 overexpression. Our data demonstrate the utility of cfDNA and imply important roles for immune evasion in IVLBCL pathogenesis and PD-1/PD-L1/PD-L2 blockade in therapeutics for IVLBCL.

Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers

The somatic landscape of the cancer genome results from different mutational processes represented by distinct "mutational signatures." Although several mutagenic mechanisms are known to cause specific mutational signatures in cell lines, the variation of somatic mutational activities in patients, which is mostly attributed to somatic selection, is still poorly explained. Here, we introduce a quantitative trait, mutational propensity (MP), and describe an integrated method to infer genetic determinants of variations in the mutational processes in 3,566 cancers with specific underlying mechanisms. As a result, we report 2,314 candidate determinants with both significant germline and somatic effects on somatic selection of mutational processes, of which, 485 act via cancer gene expression and 1,427 act through the tumor-immune microenvironment. These data demonstrate that the genetic determinants of MPs provide complementary information to known cancer driver genes, clonal evolution, and clinical biomarkers. SIGNIFICANCE: The genetic determinants of the somatic mutational processes in cancer elucidate the biology underlying somatic selection and evolution of cancers and demonstrate complementary predictive power across cancer types.

RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer

Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired. We demonstrate that RepairSig accurately recapitulates experimentally identified signatures, identifies autonomous signatures of deficient DNA repair processes, and explains mismatch repair deficiency in breast cancer by de novo inference of both primary and secondary signatures from patient data. RepairSig is freely available for download at https://github.com/ncbi/RepairSig.

Mutational Signatures: From Methods to Mechanisms

Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.

Molecular classification and diagnostics of upper urinary tract urothelial carcinoma

Upper urinary tract urothelial carcinoma (UTUC) is one of the common urothelial cancers. Its molecular pathogenesis, however, is poorly understood, with no useful biomarkers available for accurate diagnosis and molecular classification. Through an integrated genetic study involving 199 UTUC samples, we delineate the landscape of genetic alterations in UTUC enabling genetic/molecular classification. According to the mutational status of TP53, MDM2, RAS, and FGFR3, UTUC is classified into five subtypes having discrete profiles of gene expression, tumor location/histology, and clinical outcome, which is largely recapitulated in an independent UTUC cohort. Sequencing of urine sediment-derived DNA has a high diagnostic value for UTUC with 82.2% sensitivity and 100% specificity. These results provide a solid basis for better diagnosis and management of UTUC.

De novo mutational signature discovery in tumor genomes using SparseSignatures

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features.

MutViz 2.0: visual analysis of somatic mutations and the impact of mutational signatures on selected genomic regions

Patterns of somatic single nucleotide variants observed in human cancers vary widely between different tumor types. They depend not only on the activity of diverse mutational processes, such as exposure to ultraviolet light and the deamination of methylated cytosines, but largely also on the sequence content of different genomic regions on which these processes act. With MutViz (http://gmql.eu/mutviz/), we have presented a user-friendly web tool for the identification of mutation enrichments that offers preloaded mutations from public datasets for a variety of cancer types, well organized within an effective database architecture. Somatic mutation patterns can be visually and statistically analyzed within arbitrary sets of small, user-provided genomic regions, such as promoters or collections of transcription factor binding sites. Here, we present MutViz 2.0, a largely extended and consolidated version of the tool: we took into account the immediate (trinucleotide) sequence context of mutations, improved the representation of clinical annotation of tumor samples and devised a method for signature refitting on limited genomic regions to infer the contribution of individual mutational processes to the mutation patterns observed in these regions. We described both the features of MutViz 2.0, concentrating on the novelties, and the substantial re-engineering of the cloud-based architecture.

Cancer mutational signatures representation by large-scale context embedding

Motivation

The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analysing the complex mutational patterns.

Results

Here, we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context. Our method is motivated by the observation that the mutation rate at megabase scale and the local mutational patterns jointly contribute to distinguishing cancer subtypes, both of which can be simultaneously captured by MutSpace. Simulation evaluations show that MutSpace can effectively characterize mutational features from known patient subgroups and achieve superior performance compared with previous methods. As a proof-of-principle, we apply MutSpace to 560 breast cancer patient samples and demonstrate that our method achieves high accuracy in subtype identification. In addition, the learned embeddings from MutSpace reflect intrinsic patterns of breast cancer subtypes and other features of genome structure and function. MutSpace is a promising new framework to better understand cancer heterogeneity based on somatic mutations.

Availability and implementation

Source code of MutSpace can be accessed at: https://github.com/ma-compbio/MutSpace.

Supplementary information

Supplementary data are available at Bioinformatics online.

Unbiased Detection of Driver Mutations in Extramammary Paget Disease

PURPOSE

Extramammary Paget disease (EMPD) is an uncommon skin malignancy whose genetic alterations are poorly characterized. Previous reports identified mutations in chromatin remodeling genes and PIK3CA. In order to unambiguously determine driver mutations in EMPD, we analyzed 87 EMPD samples using exome sequencing in combination with targeted sequencing.

EXPERIMENTAL DESIGN

First, we analyzed 37 EMPD samples that were surgically resected using whole-exome sequencing. Based on several in silico analysis, we built a custom capture panel of putative driver genes and analyzed 50 additional formalin-fixed, paraffin-embedded samples using target sequencing. ERBB2 expression was evaluated by HER2 immunohisotochemistry. Select samples were further analyzed by fluorescence in situ hybridization.

RESULTS

A median of 92 mutations/sample was identified in exome analysis. A union of driver detection algorithms identified ERBB2, ERBB3, KMT2C, TP53, PIK3CA, NUP93, AFDN, and CUX1 as likely driver mutations. Copy-number alteration analysis showed regions spanning CDKN2A as recurrently deleted, and ERBB2 as recurrently amplified. ERBB2, ERBB3, and FGFR1 amplification/mutation showed tendency toward mutual exclusivity. Copy-number alteration load was associated with likelihood to recur. Mutational signatures were dominated by aging and APOBEC activation and lacked evidence of ultraviolet radiation. HER2 IHC/fluorescence in situ analysis validated ERBB2 amplification but was underpowered to detect mutations. Tumor heterogeneity in terms of ERBB2 amplification status was observed in some cases.

CONCLUSIONS

Our comprehensive, unbiased analysis shows EMPD is characterized by alterations involving the PI3K-AKT pathway. EMPD is distinct from other skin cancers in both molecular pathways altered and etiology behind mutagenesis.

iMutSig: a web application to identify the most similar mutational signature using shiny

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows comparisons of pairs of mutational signatures to be easily performed. Cosine similarity measures the extent of signature similarity. To compare mutational signatures of different formats, one signature type (COSMIC or pmsignature) is converted to the format of the other before the signatures are compared. iMutSig provides a simple and user-friendly web application allowing researchers to download published mutational signatures of either type and to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download from GitHub.

CaMuS: simultaneous fitting and de novo imputation of cancer mutational signature

The identification of the mutational processes operating in tumour cells has implications for cancer diagnosis and therapy. These processes leave mutational patterns on the cancer genomes, which are referred to as mutational signatures. Recently, 81 mutational signatures have been inferred using computational algorithms on sequencing data of 23,879 samples. However, these published signatures may not always offer a comprehensive view on the biological processes underlying tumour types that are not included or underrepresented in the reference studies. To circumvent this problem, we designed CaMuS (Cancer Mutational Signatures) to construct de novo signatures while simultaneously fitting publicly available mutational signatures. Furthermore, we propose to estimate signature similarity by comparing probability distributions using the Hellinger distance. We applied CaMuS to infer signatures of mutational processes in poorly studied cancer types. We used whole genome sequencing data of 56 neuroblastoma, thus providing evidence for the versatility of CaMuS. Using simulated data, we compared the performance of CaMuS to sigfit, a recently developed algorithm with comparable inference functionalities. CaMuS and sigfit reconstructed the simulated datasets with similar accuracy; however two main features may argue for CaMuS over sigfit: (i) superior computational performance and (ii) a reliable parameter selection method to avoid spurious signatures.

Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar)

Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling - more specifically, Latent Dirichlet Allocation (LDA) - in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO₂/O₂/N₂): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N₂ (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.

MutSignatures: an R package for extraction and analysis of cancer mutational signatures

Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.

Genetic and clinical landscape of breast cancers with germline BRCA1/2 variants

The genetic and clinical characteristics of breast tumors with germline variants, including their association with biallelic inactivation through loss-of-heterozygosity (LOH) and second somatic mutations, remain elusive. We analyzed germline variants of 11 breast cancer susceptibility genes for 1,995 Japanese breast cancer patients, and identified 101 (5.1%) pathogenic variants, including 62 BRCA2 and 15 BRCA1 mutations. Genetic analysis of 64 BRCA1/2-mutated tumors including TCGA dataset tumors, revealed an association of biallelic inactivation with more extensive deletions, copy neutral LOH, gain with LOH and younger onset. Strikingly, TP53 and RB1 mutations were frequently observed in BRCA1- (94%) and BRCA2- (9.7%) mutated tumors with biallelic inactivation. Inactivation of TP53 and RB1 together with BRCA1 and BRCA2, respectively, involved LOH of chromosomes 17 and 13. Notably, BRCA1/2 tumors without biallelic inactivation were indistinguishable from those without germline variants. Our study highlights the heterogeneity and unique clonal selection pattern in breast cancers with germline variants.

Yukiko Inagaki-Kawata et al. report an analysis of germline variants in breast cancer susceptibility genes in 1,995 Japanese breast cancer patients. They find that 5.1% of the patients carry germline variants in cancer-linked genes and investigate the characteristics of patients with germline mutations in BRCA1/2.

Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes

Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.

Frequent mutations in HLA and related genes in extranodal NK/T cell lymphomas

Extranodal NK/T cell lymphomas (ENKTCLs) are aggressive Epstein-Barr virus-associated T/NK neoplasms that predominantly affect Asians. To explore the causative somatic events, we conducted a comprehensive genetic analysis of 19 ENKTCL patients by whole-genome (N = 2), whole-exome (N = 16), and targeted sequencing (N = 15). Commonly deregulated gene pathways in ENKTCLs included epigenetic modifiers (58%, 11/19) followed by human leukocyte antigens (HLAs) and related genes including HLA-A, B2M, TAP1, CD274, and PDCD1LG2 (32%, 6/19), and JAK-STAT pathway (26%, 5/19). Conspicuously, loss-of-function mutations in HLA-A were recurrently identified in ENKTCLs (16%, 3/19). HLA protein expression was examined by immunohistochemistry in 16 patients and lower expression was associated with advanced stages at presentation (p = .007). In conclusion, the defective antigen presenting pathway is common and related to disease progression, suggesting immune escape as a pathogenic mechanism of ENKTCLs.

Whole exome profiling and mutational analysis of Ocular Surface Squamous Neoplasia

PURPOSE

To determine genetic mutational profiles in patients with Ocular Surface Squamous Neoplasia (OSSN) using whole exome sequencing.

METHODS

Prospective, case-series study. Patient recruitment was conducted in a single tertiary referral center from April to September 2017. Specimens were obtained by incisional biopsies of tumors from ten eyes with histopathologic confirmation of OSSN. DNA whole exome sequencing and mutation analysis were performed.

RESULTS

Ten patients with clinically-diagnosed OSSN underwent DNA whole exome sequencing analysis. Deleterious mutations in 305 genes known to drive tumor development and progression were found. These mutations centered around two main pathways: DNA repair/cell cycle and development/growth. All ten samples had at least one mutation in a DNA repair/cell cycle gene and all but one sample had one in a development/growth gene. The most common mutation was found in TP53 and HGF (both present in 50% of cases) and mutually exclusive mutations were found in BRCA1 and BRCA2 (50% of cases). Mutations in APC, MSH6, PDGFRA, and PTCH1 were found in 40% of cases. Global mutation analysis identified ultraviolet induced radiation as the only mutational signature present in the dataset.

CONCLUSIONS

Mutations found in samples from patients with OSSN are mainly induced by ultraviolet radiation and occur within two main pathways related to DNA repair/cell cycle and development/growth. There are many clinically available drugs and several others being evaluated in clinical trials that target the genes found mutated in this study, offering new therapeutic options for OSSN.

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g., CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open-source license.

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer.

Inference and visualization of DNA damage patterns using a grade of membership model

MOTIVATION

Quality control plays a major role in the analysis of ancient DNA (aDNA). One key step in this quality control is assessment of DNA damage: aDNA contains unique signatures of DNA damage that distinguish it from modern DNA, and so analyses of damage patterns can help confirm that DNA sequences obtained are from endogenous aDNA rather than from modern contamination. Predominant signatures of DNA damage include a high frequency of cytosine to thymine substitutions (C-to-T) at the ends of fragments, and elevated rates of purines (A & G) before the 5' strand-breaks. Existing QC procedures help assess damage by simply plotting for each sample, the C-to-T mismatch rate along the read and the composition of bases before the 5' strand-breaks. Here we present a more flexible and comprehensive model-based approach to infer and visualize damage patterns in aDNA, implemented in an R package aRchaic. This approach is based on a 'grade of membership' model (also known as 'admixture' or 'topic' model) in which each sample has an estimated grade of membership in each of K damage profiles that are estimated from the data.

RESULTS

We illustrate aRchaic on data from several aDNA studies and modern individuals from 1000 Genomes Project Consortium (2012). Here, aRchaic clearly distinguishes modern from ancient samples irrespective of DNA extraction, lab and sequencing protocols. Additionally, through an in-silico contamination experiment, we show that the aRchaic grades of membership reflect relative levels of exogenous modern contamination. Together, the outputs of aRchaic provide a concise visual summary of DNA damage patterns, as well as other processes generating mismatches in the data.

AVAILABILITY AND IMPLEMENTATION

aRchaic is available for download from https://www.github.com/kkdey/aRchaic.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

Legacy Data Confound Genomics Studies

Recent reports have identified differences in the mutational spectra across human populations. Although some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data are used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower quality data from the early phases of the 1kGP thus continue to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

The repertoire of mutational signatures in human cancer

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

Identification of cancer driver genes based on nucleotide context

Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.

Single-cell analysis based dissection of clonality in myelofibrosis

Cancer development is an evolutionary genomic process with parallels to Darwinian selection. It requires acquisition of multiple somatic mutations that collectively cause a malignant phenotype and continuous clonal evolution is often linked to tumor progression. Here, we show the clonal evolution structure in 15 myelofibrosis (MF) patients while receiving treatment with JAK inhibitors (mean follow-up 3.9 years). Whole-exome sequencing at multiple time points reveal acquisition of somatic mutations and copy number aberrations over time. While JAK inhibition therapy does not seem to create a clear evolutionary bottleneck, we observe a more complex clonal architecture over time, and appearance of unrelated clones. Disease progression associates with increased genetic heterogeneity and gain of RAS/RTK pathway mutations. Clonal diversity results in clone-specific expansion within different myeloid cell lineages. Single-cell genotyping of circulating CD34 + progenitor cells allows the reconstruction of MF phylogeny demonstrating loss of heterozygosity and parallel evolution as recurrent events.

Myelofibrosis is a myeloproliferative neoplasm. Here, the authors show the clonal evolution of myelofibrosis during JAK inhibitor therapy, revealing how the treatment results in an increase in clonal complexity and a gain of RAS pathway mutations.

DNA Repair Footprint Uncovers Contribution of DNA Repair Mechanism to Mutational Signatures

Cancer genomes accumulate a large number of somatic mutations resulting from imperfection of DNA processing during normal cell cycle as well as from carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These processes often lead to distinctive patterns of mutations, called mutational signatures. Several computational methods have been developed to uncover such signatures from catalogs of somatic mutations. However, cancer mutational signatures are the end-effect of several interplaying factors including carcinogenic exposures and potential deficiencies of the DNA repair mechanism. To fully understand the nature of each signature, it is important to disambiguate the atomic components that contribute to the final signature. Here, we introduce a new descriptor of mutational signatures, DNA Repair FootPrint (RePrint), and show that it can capture common properties of deficiencies in repair mechanisms contributing to diverse signatures. We validate the method with published mutational signatures from cell lines targeted with CRISPR-Cas9-based knockouts of DNA repair genes.

Mutational signatures in colon cancer

OBJECTIVE

Recently, many tumor sequencing studies have inferred and reported on mutational signatures, short nucleotide patterns at which particular somatic base substitutions appear more often. A number of signatures reflect biological processes in the patient and factors associated with cancer risk. Our goal is to infer mutational signatures appearing in colon cancer, a cancer for which environmental risk factors vary by cancer subtype, and compare the signatures to those in adult stem cells from normal colon. We also compare the mutational signatures to others in the literature.

RESULTS

We apply a probabilistic mutation signature model to somatic mutations previously reported for six adult normal colon stem cells and 431 colon adenocarcinomas. We infer six mutational signatures in colon cancer, four being specific to tumors with hypermutation. Just two signatures explained the majority of mutations in the small number of normal aging colon samples. All six signatures are independently identified in a series of 295 Chinese colorectal cancers.

Discovering novel mutation signatures by latent Dirichlet allocation with variational Bayes inference

MOTIVATION

A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear.

RESULTS

In this study, we present a novel method for estimating the number of mutation signatures-latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported.

AVAILABILITY AND IMPLEMENTATION

All the predicted mutation signatures with clustering results are freely available at http://www.f.waseda.jp/mhamada/MS/index.html. All the C++ source code and python scripts utilized in this study can be downloaded on the Internet (https://github.com/qkirikigaku/MS_LDA).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance

Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.

SigsPack, a package for cancer mutational signatures

Background

Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology.

Results

We present here SigsPack, a Bioconductor package to estimate a sample’s exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example.

Conclusion

SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3043-7) contains supplementary material, which is available to authorized users.

HiLDA: a statistical approach to investigate differences in mutational signatures

We propose a hierarchical latent Dirichlet allocation model (HiLDA) for characterizing somatic mutation data in cancer. The method allows us to infer mutational patterns and their relative frequencies in a set of tumor mutational catalogs and to compare the estimated frequencies between tumor sets. We apply our method to two datasets, one containing somatic mutations in colon cancer by the time of occurrence, before or after tumor initiation, and the second containing somatic mutations in esophageal cancer by sex, age, smoking status, and tumor site. In colon cancer, the relative frequencies of mutational patterns were found significantly associated with the time of occurrence of mutations. In esophageal cancer, the relative frequencies were significantly associated with the tumor site. Our novel method provides higher statistical power for detecting differences in mutational signatures.