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

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Recent studies have suggested that the human germline mutation rate and spectrum evolve rapidly. Variation in generation time has been linked to these changes, though its contribution remains unclear. We develop a framework to characterize temporal changes in polymorphisms within and between populations, while controlling for the effects of natural selection and biased gene conversion. Application to the 1000 Genomes Project dataset reveals multiple independent changes that arose after the split of continental groups, including a previously reported, transient elevation in TCC>TTC mutations in Europeans and novel signals of divergence in C>Gand T>A mutation rates among population samples. We also find a significant difference between groups sampled in and outside of Africa in old T>C polymorphisms that predate the out-of-Africa migration. This surprising signal is driven by TpG>CpG mutations and stems in part from mis-polarized CpG transitions, which are more likely to undergo recurrent mutations. Finally, by relating the mutation spectrum of polymorphisms to parental age effects on de novo mutations, we show that plausible changes in the generation time cannot explain the patterns observed for different mutation types jointly. Thus, other factors – genetic modifiers or environmental exposures – must have had a non-negligible impact on the human mutation landscape. Editor's evaluation This important study investigates temporal variation in patterns of germline mutation during the evolution of human populations. Using a compelling approach that controls for the effects of selection and biased gene conversion the authors show that changes in generation time alone cannot explain the joint patterns observed for different mutation types, suggesting that other factors such as genetic modifiers or environmental exposures must have played a role as well. This work will be of broad interest to population geneticists and evolutionary biologists. https://doi.org/10.7554/eLife.81188.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest Each human has 23 pairs of chromosomes, one set inherited from each parent. But the child's chromosomes are not an exact copy of their parents' chromosomes. Spontaneous changes or mutations in the DNA during the formation of the egg or sperm cells, or early development of the embryo, can change a small fraction of the nucleotides or 'letters' that make up the DNA. These modifications are an important source of genetic diversity in human populations and contribute to the evolution of new traits. Each genetic variant in present-day human populations represents a mutation in one of their ancestors. The types and frequencies of variants vary across human populations and have changed over time, suggesting that mutation patterns have evolved in the past. But the processes driving these population-level differences remain elusive. One possible factor may be changes in the average age of reproduction or the generation time in a population . For example, older parents contribute more – and also different types of – new mutations to their children than younger parents do. Populations, where it is customary to have children at older ages, may therefore have a different mutation landscape. To find out if this is indeed the case, Gao et al. used computer algorithms to analyze the genomes of hundreds of people living on three continents who participated in 'the 1,000 Genomes Project'. The analysis identified differences in mutation patterns across continental groups and estimated when these changes occurred. Further, they showed that although the age of reproduction had an impact on the mutation landscape, differences in generation time alone could not explain the observed changes in the human mutation spectrum. Factors other than generation time, such as environmental exposures, may have played a role in shifting these patterns. The study provides new insights into the changes in the mutation landscape over the course of human evolution. Mapping these patterns in humans worldwide may help scientists understand the causes underlying these changes. The techniques used by Gao et al. may also help analyze changes in mutation patterns in other organisms. Introduction Recent advances in high-throughput sequencing have enabled large-scale surveys of genetic variation in thousands of humans, providing a rich resource for understanding the source and mechanisms shaping the mutation landscape over time. Comparisons of polymorphism patterns across geographic population samples have uncovered numerous differences in the mutation rates and spectra (i.e., relative proportions of different types of mutations) (DeWitt et al., 2021; Goldberg and Harris, 2022; Harris, 2015; Harris and Pritchard, 2017; Hwang and Green, 2004; Mathieson and Reich, 2017; Moorjani et al., 2016a; Narasimhan et al., 2017; Speidel et al., 2019). A notable signal in humans is the enrichment of TCC>TTC variants in polymorphism data from Europeans relative to Africans and Asians (Harris and Pritchard, 2017). This signal is also observed in South Asians to a lesser degree and has been suggested to originate in ancient Neolithic farmers (Harris and Pritchard, 2017; Speidel et al., 2021). Many other subtle but statistically significant signals have also been detected; given the recent common ancestry of human populations, this finding indicates that the mutational spectrum in humans has been evolving rapidly. Several genetic and nongenetic factors have been implicated as affecting mutation rates and acting as potential drivers of observed interpopulation differences in the mutation spectrum of polymorphisms. First, some environmental exposures can increase mutation rates, especially of particular types. As humans in different geographic locations and environments may have experienced differential exposures over the past 50,000–100,000 years since the out-of-Africa (OOA) migration, rates of specific mutation types could have diverged between populations (Harris, 2015; Mathieson and Reich, 2017). Second, genetic modifiers of mutation rates, such as variants in genes that copy or repair DNA, could segregate at different frequencies across populations. Despite the deleterious effects of alleles that modify mutation rates, in recombining species, they could be nearly neutral and maintained for a long time, leading to genome-wide differences across populations (Milligan et al., 2022; Seoighe and Scally, 2017). In addition, direct sequencing of human pedigrees has revealed the effects of the parental ages at reproduction on the relative fractions of mutation types (Goldmann et al., 2018; Jónsson et al., 2017). For example, as parents age, fathers pass on disproportionally more T>C mutations, and mothers contribute a higher fraction of C>G mutations (Jónsson et al., 2017). Thus, differences in the average reproductive ages, or equivalently 'generation times,' alone can lead to differences in mutation spectrum across populations; indeed, such differences have been invoked to explain a large fraction of observed variation in types of polymorphisms among population samples (Coll Macià et al., 2021). The joint distribution of mutation type and frequency of polymorphisms, however, depends not only on the mutational input, but also on other evolutionary forces such as natural selection, biased gene conversion, and demography. In particular, natural selection distorts the allele frequency distribution and fixation probability of non-neutral variants, and the average effect of natural selection can differ across mutation types (Wakeley, 2010). As an example, genic regions tend to be more GC-rich, so mutations at G:C base pairs may be subject to stronger purifying or background selection compared to mutations at A:T base pairs (Lander et al., 2001; McVicker et al., 2009). GC-biased gene conversion (gBGC) is another process that exerts differential effects across mutation types by effectively acting like positive selection favoring mutations from weak alleles (A or T) to strong alleles (C or G) and negative selection against mutations from strong to weak alleles (Duret and Galtier, 2009). The strengths of selection and gBGC depend on the effective population size and thus on the demographic history of a population. Demographic history also influences allele frequencies for a given allele age (Kimura, 1969). This poses a challenge in interpreting previous studies (Harris and Pritchard, 2017; Mathieson and Reich, 2017) aimed at learning about when changes in mutational processes may have occurred by using allele frequencies, as mutations of the same frequency can have drastically different distributions of ages in distinct populations (e.g., doubletons in Africans are substantially older than doubletons in Europeans or Asians; Mathieson and McVean, 2014). Beyond the biological processes that shape polymorphism data, the characterization of the mutational spectrum can be biased by many technical issues. For instance, a recent study showed that some interpopulation differences discovered in low-coverage 1000 Genomes data may be driven by cell line artifacts or errors in PCR amplification (Anderson-Trocmé et al., 2020). Further, comparisons of mutation patterns across datasets are sensitive to differences in the accessible genomic regions across studies. Because there is large variation in mutation rates and base pair composition across genomic regions, differences in the regions sequenced across studies can have a non-negligible impact on comparisons of mutation spectrum across datasets (Monroe et al., 2022; Seplyarskiy et al., 2021). In addition, the number of genomes surveyed, in combination with the specific population demographic history, influences the chance of observing repeated mutations at the same site, and thus the observed polymorphism patterns (Lek et al., 2016). Given these challenges, it remains unclear whether the numerous observed differences across human populations stem from rapid evolution of the mutation process itself, other evolutionary processes, or technical factors. Motivated by these considerations, we propose a new framework to compare the mutation spectrum over time and across human populations. First, we infer the age of each derived allele observed in a population using a newly developed approach, Relate, which reconstructs local genealogies and estimates allele ages (Speidel et al., 2019). This approach allows us to perform more reliable comparisons across populations as well as to investigate changes in mutation processes across time. Next, we minimize confounding effects of selection by removing constrained regions and known targets of selection in the genome. We also control the effects of biased gene conversion by focusing on comparison of pairs of mutations (e.g., T>C and T>G) that are subject to similar effects of gBGC. This pairwise comparison further mitigates the issue of interdependencies in comparing mutation fractions (i.e., an increased contribution of one mutation type necessarily lowers the contribution of other mutation types). Based on this new framework, we re-evaluate the evidence for evolution of the mutation spectrum in human populations and investigate when, how, and in which populations significant changes have occurred over the course of human evolution. Finally, by relating parental age effects on the mutation spectrum estimated in contemporary pedigrees to the observed patterns of polymorphisms of varying ages, we evaluate the role of changes in generation times in shaping the human mutation landscape. Results Variation in the spectrum of human polymorphisms over time We analyzed single-nucleotide polymorphisms (SNP) identified in high-coverage whole-genome sequencing data from the 1000 Genomes Project, including 178 individuals of West African ancestry living in Ibadan, Nigeria (YRI), 179 individuals of Northern European ancestry living in the United States (CEU), and 103 individuals of East Asian ancestry living in Beijing, China (CHB) (Byrska-Bishop et al., 2022). To focus on putatively neutral mutations, we removed exons and phylogenetically conserved regions as previous studies (Harris and Pritchard, 2017; Moorjani et al., 2016a). To perform reliable comparison between datasets in downstream analysis and ensure the results are not driven by local genomic differences in mutation rate, we focused on regions that were accessible in both population and pedigree datasets ( hereafter, referred to as 'commonly accessible regions') ('Materials and Methods'). We inferred the age of each derived variant (with the ancestral allele determined based on the six primate EPO (Enredo, Pecan, Ortheus) alignment) in YRI, CEU, and CHB using Relate, a method to reconstruct local genealogies based on phased haplotype sequences (Speidel et al., 2019). We then divided all SNPs into 15 bins based on the ages of the derived allele inferred by Relate, accounting for uncertainty in the inferred mutation age by assuming a uniform distribution of ages between the inferred lower and upper bounds for each variant ('Materials and methods'). We classified each SNP into six disjoint classes based on the type of base pair substitution: T>A, T>C, T>G, C>A, C>G, and C>T (each including the corresponding substitution on the reverse complement strand, e.g., T>C includes both T>C and A>G substitutions). Given the well-characterized hypermutability of methylated CpG sites (Duncan and Miller, 1980; Kong et al., 2012), we further divided C>T SNPs into subtypes occurring in CpG and non-CpG contexts by considering the flanking base pair on either side of the variant. We find marked differences in the relative proportions of different mutation types (i.e., the mutation spectrum) across varying allele age bins within CEU (Figure 1) as well as in YRI and in CHB (Figure 1—figure supplement 1), as seen earlier in the low-coverage 1000G data (Speidel et al., 2019). We obtain qualitatively similar results when considering other 1000G populations of TSI, LWK, and JPT (Figure 1—figure supplement 1). This observation echoes previous findings about the evolution of the mutation spectrum comparing polymorphisms across allele frequencies (Carlson et al., 2018; Harris and Pritchard, 2017; Mathieson and Reich, 2017). As noted previously, however, differences in mutation spectrum across frequencies alone are weak evidence for the evolution of the mutation process itself because patterns of standing polymorphisms can be affected by repeat mutations and other evolutionary forces, including selection and gene conversion. Figure 1 with 8 supplements see all Download asset Open asset Changes in the mutation spectrum of polymorphisms in CEU over evolutionary time. Figure 1—source data 1 Bedfile for the commonly accessible region excluding exons and phylogenetically conserved elements. https://cdn.elifesciences.org/articles/81188/elife-81188-fig1-data1-v3.zip Download elife-81188-fig1-data1-v3.zip Figure 1—source data 2 Text files with (pseudo-)counts of different types of mutations in YRI, LWK, CEU, TSI, CHB, JPT in each time window. https://cdn.elifesciences.org/articles/81188/elife-81188-fig1-data2-v3.zip Download elife-81188-fig1-data2-v3.zip Notably, the infinite sites model is a reasonable assumption for small sample sizes (Kimura, 1969), but recurrent mutations become highly likely in large datasets, especially at sites with higher mutation rates (Harpak et al., 2016; Lek et al., 2016). Recurrent, multi-allelic, and back mutations violate the model assumptions of Relate and are often excluded from its output. For instance, given the higher mutation rate of transitions at CpG sites, such SNPs are more likely to be subject to recurrent mutations in a large sample and thus may map to multiple branches in the tree, leading to their exclusion from Relate's output (Speidel et al., 2019). As expected from these considerations, the fraction of CpG C>T SNPs in young mutations (i.e., those estimated to have occurred in the past ~50 generations) is lower than proportions in de novo mutations (DNMs) in present-day pedigree studies (Figure 1—figure supplement 2). Differences in mutation spectrum across age bins in modern humans persist even after excluding CpG C>T mutations (Figure 1—figure supplement 3), however, indicating that other mutation types are also changing in relative frequency over time and the observed patterns are not driven solely by recurrent mutation at CpG sites. Next, we examined the effect of linked selection on different mutation types. While we excluded direct targets of selection from analysis (i.e., exons and conserved regions), much of the genome is linked to non-neutral variants and subject, notably, to background selection (Charlesworth et al., 1993; McVicker et al., 2009; Murphy et al., 2022). A common measure of the effects of background selection is the B-statistic or B-score that estimates the reduction in nucleotide diversity levels compared to the neutral expectation (McVicker et al., 2009). To characterize the impact of linked selection, we calculated the average genome-wide B-score of each mutation type. We find nearly identical average B-scores and similar distributions for all mutation types (Figure 1—figure supplement 4). Further, comparing the mutation spectrum over time in CEU, YRI, and CHB, we obtain qualitatively similar results when restricting to regions with weak background selection (B-score > 800, where the genetic diversity is reduced by <20% compared to the neutral expectation; Figure 1—figure supplement 5). These analyses suggest that although linked selection has pervasive effects, its average impact is relatively uniform across the seven mutation types in commonly accessible regions ('Materials and methods). Gene conversion is another evolutionary process that can have a profound impact on the mutation spectrum of polymorphisms. gBGC acts like selection for certain mutation types by causing the preferential transmission of strong (S) alleles (C or G) over weak (W) alleles (A or T) in heterozygotes (Duret and Galtier, 2009). Accordingly, we observe enrichments of W>S mutations (T>C and T>G) in common variants and of S>W mutations (C>A and C>T) in rare variants (Figure 1—figure supplement 6A). Moreover, gBGC violates model assumptions of Relate (for both neutrality and infinite-sites mutation model) and could lead to subtle biases in estimated allele ages (Speidel et al., 2019). Due to the effect of gBGC, W>S mutations are expected to be enriched in older variants compared to S>W variants, and this enrichment is expected to be stronger in regions with high recombination rates (Glémin et al., 2015). Indeed, we observe such enrichment and the expected correlation with recombination rate (Figure 1—figure supplement 7A), supporting the effect of gBGC on the mutation spectrum of variants across mutation ages. Furthermore, the effect of gBGC is expected to vary across populations as its strength depends on the effective population size. Accordingly, we observe that the trends of the ratio of W>S to S>W over time differ across human populations (Figure 1—figure supplement 6B, Figure 1—figure supplement 7B). These results highlight the need to account for gBGC in order to reliably interpret the source of observed differences within and between populations (whether using allele frequency bins or allele age estimates). Pairwise comparisons of mutation types accounting for gBGC In light of the impact of gBGC on the mutation spectrum, we focused on comparisons of pairs of mutation types subject to similar effects of gBGC (i.e., in which both are favored, disfavored, or unaffected by gBGC). Specifically, we focused on four pairwise comparisons including (1) C>T at non-CpGs vs. C>A at non-CpGs; (2) C>T CpGs vs. C>A CpGs; (3) C>G vs. T>A; and (4) T>C vs. T>G. In principle, it is possible that the strength of gBGC is distinct for different types of variantsinvolving S and W alleles (Tsai-Wu et al., 1992). However, in mice, roughly similar conversion rates are observed for C>A and C>T non-crossover gene conversion events as well as for T>C and T>G events (Li et al., 2019), lending support to using pairwise mutation ratios for controlling the effects of gBGC at least to a first approximation. Three of the four pairwise comparisons involve mutation types with the same mutational opportunity (e.g., both T>C and T>G mutations involve changes at ancestral T bases in the genome), which further minimizes the confounding effects of regional variation on the chance of recurrent mutation or strength of background selection. Moreover, the pairwise ratios impose no co-dependency among mutation types as the four comparisons are mathematically independent of each other (although they may be biologically dependent if multiple ratios are affected simultaneously by some change in the mutational process). Investigating the mutation spectrum using these four pairwise comparisons, we observe marked differences in the ratios both over evolutionary time and across populations. Specifically, we find multiple independent signals of mutation rate evolution, reflected by both temporal variation within a population and differences between YRI, CHB, and CEU (p<0.01 by chi-square test after correcting for multiple hypothesis testing; 'Materials and methods'; Figure 2A). These differences may represent geographic or population differences as we these findings in other population samples from the same continents – LWK, TSI, and JPT – from the 1000 Genomes Project (Figure supplement 1). Figure 2 with supplements see all Download asset Open asset of pairwise mutation ratios for polymorphisms in different time pairwise mutation ratios are each of which mutation types that are for mutational opportunity and effects of GC-biased gene conversion The indicates the with the out-of-Africa (OOA) migration. The represent the observed polymorphism while the the assuming a distribution of polymorphism in are three ratios that show significant interpopulation with into each in lower to the from a chi-square test after a for and of levels were used in Figure in ratio in CEU at non-CpG sites, after excluding the four contexts and previously identified to be with the in Europeans by Harris and Pritchard, as well as contexts affected by of in mutational of and (Harris, 2015; Mathieson and Reich, 2017). divergence in ratio among three population ratio in YRI than CEU and CHB samples among old variants, driven by Figure data 1 Text files with (pseudo-)counts of mutations classified into types in genomic regions and within the C>G mutation in YRI, LWK, CEU, TSI, CHB, and JPT in each time window. Download We multiple to out technical artifacts or to the mutation process in to the observed interpopulation We obtain qualitatively similar results when restricting the analysis to putatively neutral regions with B-score > (Figure supplement or comparing regions with high and recombination rates (Figure supplement 3), that the of pairwise comparisons effectively controls for the effects of selection and gBGC. In to account for potential in mutation ages estimated by Relate, we variants by allele frequencies of inferred mutation ages and the signals in mutation age analysis (Figure supplement 4). We observe similar results with different for allele age, based on inferred mutation ages in YRI or CHB (Figure supplement 5). these results strong evidence that the human germline mutation spectrum has evolved over time and across populations. we the and population of the mutation rate changes to each of the signals we in of non-CpG ratio in Europeans The signal that we observed is the transient elevation in the ratio of mutations at non-CpG sites in CEU compared to the ancestral the in the non-CpG ratios of CHB and YRI not a similar at recent This signal the previously enrichment of C>T polymorphisms in a in as well as other contexts (Harris and Pritchard, 2017; Mathieson and Reich, 2017; Speidel et al., 2019). Investigating the temporal patterns in CEU, we find that the increase in the ratio of mutations at non-CpG sites from the time the years or and and in the recent age of (Figure 2A). Because there is large uncertainty in inferred allele ages and approach often the contribution of each variant over or more age bins ('Materials and the and of variation be the transient change in non-CpG C>T mutations likely and of higher than results However, the temporal and geographic enrichment patterns from analysis are with previous based on low-coverage or other datasets (Harris and Pritchard, 2017; Mathieson and Reich, 2017; Speidel et al., 2019). all non-CpG the interpopulation differences are in the four previously contexts and Harris and Pritchard, but are in other non-CpG contexts as well (Figure analysis that these mutational contexts are enriched in of the mutational from mutations in the of in and with exposures to light and et al., Harris, 2015; Mathieson and Reich, 2017). To test whether one of these mutational may be for the observed differences in polymorphism data, we the mutation ratio at non-CpG sites after excluding the contexts affected by or ('Materials and methods'). While we observe some reduction in the of non-CpG ratio in the interpopulation differences remain significant (Figure These results suggest the transient change in non-CpG ratio is not driven by the mutational mechanisms corresponding to either or Thus, the of this signal in Europeans remains unclear. of ratio among populations The interpopulation difference is in the ratio (Figure the among all three populations. In the past both YRI and CEU samples show an increase in the ratio of different while in the CHB, the ratio and then relatively for roughly (Figure 2A). the previous interpopulation differences in remain highly significant for the recent variants as well to factors the relative rates of C>G and T>A mutations at The fraction of C>G in de novo germline mutations is sensitive to parental ages, with the age at (Jónsson et al., 2017). This the that the interpopulation differences in ratio are driven by different average reproductive ages among populations (Coll Macià et al., 2021). To test this we the regional enrichment of C>G mutations – enriched – as of the genome with the C>G SNP that to of the age effect (i.e., the increase in with Jónsson et al., 2017). The ratio within the C>G enriched regions not show significant interpopulation differences (Figure reduced to the much lower SNP in these regions of all but see Figure supplement for of the C>G enriched regions, the three populations differ as much as they genome-wide (Figure indicating that the differential of C>G mutations with ages is not the of the differences

BackgroundMutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts.ResultsIn this work we present Mutational Signatures in Cancer (MuSiCa), a new web tool based on MutationalPatterns and built using the Shiny framework in R language. By means of a simple interface suited to non-specialized researchers, it provides a comprehensive analysis of the somatic mutational status of the supplied cancer samples. It permits characterizing the profile and burden of mutations, as well as quantifying COSMIC-reported mutational signatures. It also allows classifying samples according to the above signature contributions.ConclusionsMuSiCa is a helpful web application to characterize mutational signatures in cancer samples. It is accessible online at http://bioinfo.ciberehd.org/GPtoCRC/en/tools.html and source code is freely available at https://github.com/marcos-diazg/musica.

The majority of mutational signatures have been characterized in tumors from American and European countries, and the degree to which mutational signatures are similar to or different from those in Chinese populations has not been fully explored. We leveraged a large-scale clinical sequencing cohort of tumors from the Chinese population developed by OrigiMed (OM). A total of 2,115 tumors from 25 major tumor types that had at least 10 single-base substitutions (SBS) were used for mutational signature analysis. Sixteen mutational signatures from the Catalogue of Somatic Mutations in Cancer (COSMIC) database were identified in the OM cohort using the musicatk package. The activity levels for most of the signatures were similar to those in a cohort from Memorial Sloan Kettering in the United States. The activity level for the apolipoprotein B mRNA editing catalytic polypeptide-like–related signature SBS2 was significantly lower in breast tumors in the OM cohort. The aristolochic acid signature SBS22 was observed in four soft-tissue sarcomas, and the POLE-associated signature SBS10 was observed in a gallbladder carcinoma. In lung adenocarcinoma, the polycyclic aromatic hydrocarbon signature SBS4 was significantly higher in males compared with females but not associated with smoking status. UV-related mutations were significantly lower in cutaneous melanomas from the Chinese cohort compared with the American cohort and may contribute to lower response rates to immunotherapy observed in this population. Overall, these results add to our understanding of the mutational processes that contribute to tumors from the Chinese population.Significance:Analysis of a large Chinese cohort from 25 tumor types reveals similarities and differences in the activity of mutational signatures compared with other populations.

BackgroundAn important step towards personalizing cancer treatment is to integrate heterogeneous evidences to catalog mutational hotspots that are biologically and therapeutically relevant and thus represent where targeted therapy would likely be beneficial. However, existing methods do not sufficiently delineate varying functionality of individual mutations within the same genes.ResultsWe observed a large discordancy of mutation rates across different mutation subtypes and tumor types, and nominated 702 hotspot mutations in 549 genes in the Catalog of Somatic Mutations in Cancer (COSMIC) by considering context specific mutation characteristics such as genes, cancer types, mutation rates, mutation subtypes and sequence contexts. We observed that hotspot mutations were highly prevalent in Non CpG-island C/G transition and transversion sequence contexts in 10 tumor types, and specific insertion hotspot mutations were enriched in breast cancer and deletion hotspot mutations in colorectal cancer. We found that the hotspot mutations nominated by our approach were significantly more conserved than non-hotspot mutations in the corresponding cancer genes. We also examined the biological significance and pharmacogenomics properties of these hotspot mutations using data in the Cancer Genome Atlas (TCGA) and the Cancer Cell-Line Encyclopedia (CCLE), and found that 53 hotspot mutations are independently associated with diverse functional evidences in 1) mRNA and protein expression, 2) pathway activity, or 3) drug sensitivity and 82 were highly enriched in specific tumor types. We highlighted the distinct functional indications of hotspot mutations under different contexts and nominated novel hotspot mutations such as MAP3K4 A1199 deletion, NR1H2 Q175 insertion, and GATA3 P409 insertion as potential biomarkers or drug targets.ConclusionWe identified a set of hotspot mutations across 17 tumor types by considering the background mutation rate variations among genes, tumor subtypes, mutation subtypes, and sequence contexts. We illustrated the common and distinct mutational signatures of hotspot mutations among different tumor types and investigated their variable functional relevance under different contexts, which could potentially serve as a resource for explicitly selecting targets for diagnosis, drug development, and patient management.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2727-x) contains supplementary material, which is available to authorized users.

e18547 Background: Even though local and regional controls have been substantially improved in nasopharyngeal carcinoma (NPC) in the contemporary era of intensity-modulated radiotherapy with extensive use of combined chemotherapy, the distant metastasis becomes the major cause of treatment failure and cancer-related death. To date, the genes contributed to metastasis of NPC is still unclear. The aim of this study was to identify the genes which lead to distant metastasis. Methods: A total of forty primary nonkeratinizing NPC patients were diagnosed at Shandong Cancer Hospital in this study. The formaldehyde-fixed paraffin embedded (FFPE) taken from primary sites or metastatic lymph nodes were performed next-generation sequencing (NGS) panel (Shanghai OrigiMed Co., Ltd.) to determine variated genes, such as single nucleotide variants (SNV), copy number variants (CNV) and rearrangement. These patients were followed up until Febr. 8, 2020. The genes related to distant metastasis were identified by logistic regression. Moreover, this study compared the frequency of mutated gene between our data and Catalog of Somatic Mutations in Cancer (COSMIC) database by the Chi-square test or Fisher’s exact test. Results: The study included 31 men and 9 women. The median age of the patients at diagnosis was 47 years (range 15–71 years). With the median follow-up of 10.6 months (range 16.8–72.3 months), 7 patients had distant metastasis and 1 undergone recurrent. Notably, EMSY and MCL1 variants were contributed to NPC distant metastasis (OR = 31, P = 0.049). The top eight SNV of genes in our study were CYLD, KMT2D, BAP1, EP300, TP53, ATM, NFKBIA and SPEN. When compared to COSMIC database, the mutant frequencies of CYLD, EP300 and BAP1 in our study were significantly higher than that of COSMIC database. However, the mutant frequencies of IDH2 and KMT2C were significantly lower than COSMIC database. Conclusions: This is the first study which suggests that EMSY and MCL1 variants were involved in the metastasis of NPC. The study identified 5 genes, which mutation frequency is significantly different from the COSMIC database. The study provided a molecular basis for a comprehensive understanding of, and exploring targeted therapies for nasopharyngeal carcinoma.

Unlike most tumor suppressor genes, the most common genetic alterations in TP53 are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers, and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3–8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53-wild-type and -null cell lines. We found that loss or dominant-negative inhibition of p53 function reliably enhanced cellular fitness. By integrating these data with the COSMIC mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

Among breast cancer subtypes, triple-negative breast cancer (TNBC) is associated with the poorest prognosis and currently lacks effective targeted therapies. While gene mutation profiling provides valuable insights into recurrent mutations and genomic heterogeneity, the comparative mutational characteristics distinguishing TNBC from non-TNBC tumors have not been comprehensively investigated. This study aims to elucidate these differences through a systematic comparative analysis. We retrieved clinical data and somatic mutation profiles of 704 breast cancer patients from The Cancer Genome Atlas (TCGA) database. Based on hormone receptor expression status, the breast cancer samples were stratified into TNBC cohort (n = 109) and non-TNBC cohort (n = 595). Comprehensive genomic analysis was performed to compare differences in mutational profiles between the two cohorts, including differential frequencies of recurrent mutations, interaction patterns of co-occurring and mutually exclusive mutations, driver genes, decomposition of mutational signatures using the Catalogue of Somatic Mutations in Cancer (COSMIC) database, and pathway enrichment of significantly altered genes. Compared to the non-TNBC cohort, the TNBC patients had a younger age at onset. Significant differences were observed in the mutation rate of TP53 (81% vs. 27%, p < 2.2 × 10- 16), PIK3CA (11% vs. 38%, p = 5.4 × 10- 9) and GATA3 (1% vs. 16%, p = 1.0 × 10- 6) between TNBC and non-TNBC cohorts. TNBC also exhibited a higher tumor mutation burden (TMB) than non-TNBC (1.2 vs. 0.6 mutations/Mb). Genes in TNBC predominantly exhibited a co-mutation pattern, whereas non-TNBC showed either co-occurring or mutually exclusive mutational relationships. TTN and PIK3CA were identified as key driver genes in TNBC, while non-TNBC exhibited greater diversity in driver alterations, including MAP3K1 and KMT2C. Mutational signature analysis revealed that Single-Base Substitution2 (SBS2) and SBS6 were common to both cohorts, while SBS3 and SBS26 were predominantly enriched in TNBC, and SBS10b was more frequent in non-TNBC. Pathway enrichment analysis showed that the most affected oncogenic pathways in TNBC were TP53, TGF-Beta, RTK-RAS, and Hippo pathways, whereas non-TNBC involved TP53, TGF-Beta, NRF2, PI3K, and RTK-RAS pathways. Our study delineates the distinct genomic mutational landscapes between TNBC and non-TNBC cohorts, and reveals somatic interactions, driver genes, mutational signatures and pathway enrichment. These findings highlight the distinct molecular features of TNBC, which may contribute to its aggressive clinical behavior.

The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability. To improve data accessibility and interoperability, somatic variants have received stable genomic identifiers that are associated with their genomic coordinates in GRCh37 and GRCh38, and new export files with reduced data redundancy have been made available for download.

&lt;div&gt;Abstract&lt;p&gt;Women of African descent have the highest breast cancer mortality in the United States and are more likely than women from other population groups to develop an aggressive disease. It remains uncertain to what extent breast cancer in Africa is reminiscent of breast cancer in African American or European American patients. Here, we performed whole-exome sequencing of genomic DNA from 191 breast tumor and non-cancerous adjacent tissue pairs obtained from 97 African American, 69 European American, 2 Asian American, and 23 Kenyan patients. Our analysis of the sequencing data revealed an elevated tumor mutational burden in both Kenyan and African American patients, when compared with European American patients. &lt;i&gt;TP53&lt;/i&gt; mutations were most prevalent, particularly in African American patients, followed by &lt;i&gt;PIK3CA&lt;/i&gt; mutations, which showed similar frequencies in European American, African American, and the Kenyan patients. Mutations targeting &lt;i&gt;TBX3&lt;/i&gt; were confined to European Americans and those targeting the &lt;i&gt;FBXW7&lt;/i&gt; tumor suppressor to African American patients whereas mutations in the &lt;i&gt;ARID1A&lt;/i&gt; gene that are known to confer resistance to endocrine therapy were distinctively enriched among Kenyan patients. A Kyoto Encyclopedia of Genes and Genomes pathway analysis could link &lt;i&gt;FBXW7&lt;/i&gt; mutations to an increased mitochondrial oxidative phosphorylation capacity in tumors carrying these mutations. Finally, Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures in tumors correlated with the occurrence of driver mutations, immune cell profiles, and neighborhood deprivation with associations ranging from being mostly modest to occasionally robust. To conclude, we found mutational profiles that were different between these patient groups. The differences concentrated among genes with low mutation frequencies in breast cancer.&lt;/p&gt;Significance:&lt;p&gt;The study describes differences in tumor mutational profiles between African American, European American, and Kenyan breast cancer patients. It also investigates how these profiles may relate to the tumor immune environment and the neighborhood environment in which the patients had residence. Finally, it describes an overrepresentation of &lt;i&gt;ARID1A&lt;/i&gt; gene mutations in breast tumors of the Kenyan patients.&lt;/p&gt;&lt;/div&gt;

In the absence of clear pathologic differences, clinical history may differentiate potential primary parotid squamous cell carcinomas (SCC) from metastases. The presence of an ultraviolet (UV) signature can distinguish between tumors of cutaneous and non-cutaneous origin. This study aimed to investigate rates of UV signature mutations in squamous cell carcinomas of the parotid gland as well as differences in clinical features between tumors of cutaneous and non-cutaneous origin. Clinical and pathologic data were collected from 71 patients with SCC involving the parotid gland, of which 48 had cutaneous, 10 had mucosal, and 13 had no history of SCC. In 34 available cases, genomic DNA was isolated from formalin-fixed paraffin-embedded tissue specimens and sequenced using a targeted hybrid capture 1213 gene panel. Tumor mutational burden and COSMIC (Catalogue of Somatic Mutations in Cancer) mutational signatures were calculated. Most (74%) were UV-positive. Patients with UV-positive tumors were significantly older, white, and had higher rates of sun exposure. Patients with UV-negative tumors had a significantly higher mortality rate and shorter time to death: 6 (67%) died of disease with a median time to death of 9months compared to 5 (20%) UV-positive patients who died of disease with a median time to death of 32months. Pathologic features did not significantly vary by clinical history or UV status. The presence of a UV-signature combined with clinical history can be used to determine the primary source of SCC involving the parotid gland. UV-positivity may reflect a less aggressive disease course in an older population.

Databases used for clinical interpretation in oncology rely on genetic data derived primarily from patients of European ancestry, leading to biases in cancer genetics research and clinical practice. One practical issue that arises in this context is the potential misclassification of multi-ancestral population variants as tumor-associated because they are not represented in reference genomes against which tumor sequencing data is aligned. To systematically find misclassified variants, we compared somatic variants in census genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) V99 with multi-ancestral population variants from the Genome Aggregation Databases' Linkage Disequilibrium (GnomAD). By comparing genomic coordinates, reference, and alternate alleles, we could identify misclassified variants in genes associated with cancer. We found 192 of 208 genes in COSMIC's cancer-associated census genes (92.31%) to be associated with variant misclassifications. Among the 1,906,732 variants in COSMIC, 6,957 variants (0.36%) aligned with normal population variants in GnomAD, concerning for misclassification. The African / African American ancestral population included the greatest number of misclassified variants and also had the greatest number of unique misclassified variants. The direct, systematic comparison of variants from COSMIC for co-occurrence in GnomAD supports a more accurate interpretation of tumor sequencing data and reduces bias related to genomic ancestry.

BackgroundBecause driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples.ResultsWe used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations– positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified.ConclusionsBy comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

BackgroundT-cell lymphoma (TCL) is highly aggressive and has a poor prognosis; thus, it is worth exploring biomarkers that may predict clinical outcomes and investigate their potential role in developing targeted therapies. In this study, we characterized the mutation pattern of tumor necrosis factor-alpha-inducing protein 3 (TNFAIP3) and its role in the prognosis of TCL patients.MethodsCoding sequence (CDS) mutations in TNFAIP3 in TCL patients was explored using exome-sequencing data from 79 patients in our center (Guangdong Provincial People’s Hospital, GDPH) and 544 samples from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Additionally, non-CDS mutations in TNFAIP3 in 41 TCL patients from our center (JNU) were investigated by polymerase chain reaction (PCR) and Sanger sequencing. Furthermore, non-CDS mutations in TNFAIP3 in 47 TCL patients from Gene Expression Omnibus (GEO) dataset were explored.ResultsIn the COSMIC database, TNFAIP3 mutations in TCL patients were located in the CDS, and the overall mutation frequency was 2.2%. However, TNFAIP3 mutations were not detected in the CDS of any of the samples in our center’s datasets. Interestingly, non-CDS TNFAIP3 mutations were found in 14.6% and 4.3% of TCL patients in the JNU and GSE15842 dataset, respectively. Importantly, there was a clear trend showing that TCL patients with a TNFAIP3 mutation were associated with a longer 5-year restricted mean survival time (RMST) and favorable OS rate compared with those without a TNFAIP3 mutation in the JNU dataset [hazard ratio (HR) = 0.29, 95% confidence interval (CI) 0.07 to 1.31, P = 0.089]. Furthermore, TNFAIP3 mutations significantly correlated with T-cell large granular lymphocytic leukemia (T-LGLL) with a favorable prognosis in the JNU dataset (P = 0.002). Notably, the different mutation patterns of TNFAIP3 when comparing our center and the COSMIC datasets might be due to different ethnic and genetic backgrounds.ConclusionsTo the best of our knowledge, we for the first time describe that TNFAIP3 mutations in non-CDS regions are associated with favorable OS for TCL patients, which might be a potential biomarker for the prognostic stratification of Chinese TCL patients.

Background: Liver cancer accounts for more than 700,000 deaths each year making it the third leading cause of cancer-related deaths worldwide. Late diagnosis of the disease is the reason behind most deaths. Driver mutations are genetic alterations in tumor cells, which are responsible for the development of liver cancer; therefore, the identification of genetic biomarkers is necessary for the prediction and early diagnosis of liver cancer. Objectives: The main objective of this study is to identify pathogenic alleles that may act as potential biomarkers for the prediction of liver cancer. It also identifies the role of novel genes in liver cancer that are not known to cause the disease. Methods: The mutation data of non-coding variants were downloaded from the catalogue of somatic mutations in cancer (COSMIC) databases. Different bioinformatics tools were, then, used to retrieve mutations in liver cancer. The genetic alterations in hepatocellular carcinoma (HCC) were analyzed. Results: The present study successfully identified pathogenic alleles (consistent mutations) along with a set of novel genes that might be involved in the development of liver cancer. It identified non-coding mutations near human genes and transcription factor binding sites of HepG2 cells. This study also identified mutations near the genes that are involved in the Ras/MAFK signaling pathway of the Hepatitis B virus. Conclusions: The pathogenic alleles identified in this study may provide targeted therapy for the treatment of liver cancer. The identification of novel genes may help to understand the progression of liver cancer at the molecular level. The identified driver mutations may act as potential biomarkers and therapeutic targets for early prediction and treatment of liver cancer.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://www.sanger.ac.uk/cosmic), is one of the most comprehensive web systems describing the impact of somatic mutations in human cancer. The information is sourced directly from the published scientific literature and the Cancer Genome Project's high throughput sequencing laboratories at the Wellcome Trust Sanger Institute UK (CGP), allowing a high data quality to be maintained. The v44 release (Nov. 2009) contained the curation of 8336 papers covering 71 known cancer genes by point mutation and 30 by gene fusion. The mutation data for all these genes is being maintained up-to-date and new genes are regularly added to this list. Additionally, 4871 genes and 2764 samples have been scrutinized by the CGP laboratories. As cancer genetics moves toward the analysis of whole genomes, COSMIC is being updated to handle and display these data and increase the functionality of the website. With the rapid development of sequencing technologies, the quantity of data that can be put into COSMIC is increasing fast. We have extended our curation process to include large exome-wide candidate gene screens, recently including the first such screen from Sjoblom et al (2006), and the first TCGA screen (2008) with other large scale screening datasets being curated. In addition, whole-genome screens are becoming available with many non-coding variations. From the CGP laboratories, we already have two low-coverage genome rearrangement screens in COSMIC and this will rapidly expand, beginning with the imminent release of rearrangement scans in a set of 24 breast tumours. High-coverage genome analyses are also beginning to become available; with full genome coverage, the majority of somatic mutations in a tumour can be identified and COSMIC is being prepared to receive and display this data. With the inclusion of these new data, the COSMIC website has evolved to allow more easy access to required data. New specialization filters are in place to provide methods to search for subsets of COSMIC data in the usual user-friendly web pages. More extensive investigations can be performed using the new COSMIC Biomart, an industry-standard data mining system allowing access through web pages or programmatic interfaces. As the inclusion of new genomic data accelerates and the systems through which it can be accessed evolve, COSMIC is well placed to remain a central resource in human cancer genetics. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 93.