Geographic variation of mutagenic exposures in kidney cancer genomes.

International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment.

MOTIVATION: Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. RESULTS: Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. AVAILABILITY AND IMPLEMENTATION: SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

CTNND1 is involved in germline predisposition to early-onset gastric cancer by affecting cell-to-cell interactions.

BACKGROUND: CDH1 and CTNNA1 remain as the main genes for hereditary gastric cancer. However, they only explain a small fraction of gastric cancer cases with suspected inherited basis. In this study, we aimed to identify new hereditary genes for early-onset gastric cancer patients (EOGC; < 50 years old). METHODS: After germline exome sequencing in 20 EOGC patients and replication of relevant findings by gene-panel sequencing in an independent cohort of 152 patients, CTNND1 stood out as an interesting candidate gene, since its protein product (p120ctn) directly interacts with E-cadherin. We proceeded with functional characterization by generating two knockout CTNND1 cellular models by gene editing and introducing the detected genetic variants using a lentiviral delivery system. We assessed ß-catenin and E-cadherin levels, cell detachment, as well as E-cadherin localization and cell-to-cell interaction by spheroid modeling. RESULTS: Three CTNND1 germline variants [c.28_29delinsCT, p.(Ala10Leu); c.1105C > T, p.(Pro369Ser); c.1537A > G, p.(Asn513Asp)] were identified in our EOGC cohorts. Cells encoding CTNND1 variants displayed altered E-cadherin levels and intercellular interactions. In addition, the p.(Pro369Ser) variant, located in a key region in the E-cadherin/p120ctn binding domain, showed E-cadherin mislocalization. CONCLUSIONS: Defects in CTNND1 could be involved in germline predisposition to gastric cancer by altering E-cadherin and, consequently, cell-to-cell interactions. In the present study, CTNND1 germline variants explained 2% (3/172) of the cases, although further studies in larger external cohorts are needed.

Germline mutations in WNK2 could be associated with serrated polyposis syndrome.

BACKGROUND: Patients with serrated polyposis syndrome (SPS) have multiple and/or large serrated colonic polyps and higher risk for colorectal cancer. SPS inherited genetic basis is mostly unknown. We aimed to identify new germline predisposition factors for SPS by functionally evaluating a candidate gene and replicating it in additional SPS cohorts. METHODS: After a previous whole-exome sequencing in 39 SPS patients from 16 families (discovery cohort), we sequenced specific genes in an independent validation cohort of 211 unrelated SPS cases. Additional external replication was also available in 297 SPS cases. The WNK2 gene was disrupted in HT-29 cells by gene editing, and WNK2 variants were transfected using a lentiviral delivery system. Cells were analysed by immunoblots, real-time PCR and functional assays monitoring the mitogen-activated protein kinase (MAPK) pathway, cell cycle progression, survival and adhesion. RESULTS: We identified 2 rare germline variants in the WNK2 gene in the discovery cohort, 3 additional variants in the validation cohort and 10 other variants in the external cohorts. Variants c.2105C>T (p.Pro702Leu), c.4820C>T (p.Ala1607Val) and c.6157G>A (p.Val2053Ile) were functionally characterised, displaying higher levels of phospho-PAK1/2, phospho-ERK1/2, CCND1, clonogenic capacity and MMP2. CONCLUSION: After whole-exome sequencing in SPS cases with familial aggregation and replication of results in additional cohorts, we identified rare germline variants in the WNK2 gene. Functional studies suggested germline WNK2 variants affect protein function in the context of the MAPK pathway, a molecular hallmark in this disease.

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator.

BACKGROUND: All cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no computationally efficient bioinformatics tool that allows visualizing and exploring these large-scale mutational events. RESULTS: Here, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. CONCLUSIONS: The new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Germline Mutations in FAF1 Are Associated With Hereditary Colorectal Cancer.

BACKGROUND & AIMS: A significant proportion of colorectal cancer (CRC) cases have familial aggregation but little is known about the genetic factors that contribute to these cases. We performed an exhaustive functional characterization of genetic variants associated with familial CRC. METHODS: We performed whole-exome sequencing analyses of 75 patients from 40 families with a history of CRC (including early-onset cases) of an unknown germline basis (discovery cohort). We also sequenced specific genes in DNA from an external replication cohort of 473 families, including 488 patients with colorectal tumors that had normal expression of mismatch repair proteins (validation cohort). We disrupted the Fas-associated factor 1 gene (FAF1) in DLD-1 CRC cells using CRISPR/Cas9 gene editing; some cells were transfected with plasmids that express FAF1 missense variants. Cells were analyzed by immunoblots, quantitative real-time polymerase chain reaction, and functional assays monitoring apoptosis, proliferation, and assays for Wnt signaling or nuclear factor (NF)-kappa-B activity. RESULTS: We identified predicted pathogenic variant in the FAF1 gene (c.1111G>A; p.Asp371Asn) in the discovery cohort; it was present in 4 patients of the same family. We identified a second variant in FAF1 in the validation cohort (c.254G>C; p.Arg85Pro). Both variants encoded unstable FAF1 proteins. Expression of these variants in CRC cells caused them to become resistant to apoptosis, accumulate beta-catenin in the cytoplasm, and translocate NF-kappa-B to the nucleus. CONCLUSIONS: In whole-exome sequencing analyses of patients from families with a history of CRC, we identified variants in FAF1 that associate with development of CRC. These variants encode unstable forms of FAF1 that increase resistance of CRC cells to apoptosis and increase activity of beta-catenin and NF-kappa-B.

Colorectal cancer genetic variants are also associated with serrated polyposis syndrome susceptibility.

BACKGROUND: Serrated polyposis syndrome (SPS) is a clinical entity characterised by large and/ormultiple serrated polyps throughout the colon and increased risk for colorectal cancer (CRC). The basis for SPS genetic predisposition is largely unknown. Common, low-penetrance genetic variants have been consistently associated with CRC susceptibility, however, their role in SPS genetic predisposition has not been yet explored. OBJECTIVE: The aim of this study was to evaluate if common, low-penetrance genetic variants for CRC risk are also implicated in SPS genetic susceptibility. METHODS: A case-control study was performed in 219 SPS patients and 548 asymptomatic controls analysing 65 CRC susceptibility variants. A risk prediction model for SPS predisposition was developed. RESULTS: Statistically significant associations with SPS were found for seven genetic variants (rs4779584-GREM1, rs16892766-EIF3H, rs3217810-CCND2, rs992157-PNKD1/TMBIM1, rs704017-ZMIZ1, rs11196172-TCF7L2, rs6061231-LAMA5). The GREM1 risk allele was remarkably over-represented in SPS cases compared with controls (OR=1.573, 1.21-2.04, p value=0.0006). A fourfold increase in SPS risk was observed when comparing subjects within the highest decile of variants (≥65) with those in the first decile (≤50). CONCLUSIONS: Genetic variants for CRC risk are also involved in SPS susceptibility, being the most relevant ones rs4779584-GREM1, rs16892766-EIF3H and rs3217810-CCND2.

Identification of New Genes Involved in Germline Predisposition to Early-Onset Gastric Cancer.

The genetic cause for several families with gastric cancer (GC) aggregation is unclear, with marked relevance in early-onset patients. We aimed to identify new candidate genes involved in GC germline predisposition. Whole-exome sequencing (WES) of germline samples was performed in 20 early-onset GC patients without previous germline mutation identified. WES was also performed in nine tumor samples to analyze the somatic profile using SigProfilerExtractor tool. Sequencing germline data were filtered to select those variants with plausible pathogenicity, rare frequency and previously involved in cancer. Then, a manual filtering was performed to prioritize genes according to current knowledge and function. These genetic variants were prevalidated with Integrative Genomics Viewer 2.8.2 (IGV). Subsequently, a further selection step was carried out according to function and information obtained from tumor samples. After IGV and selection step, 58 genetic variants in 52 different candidate genes were validated by Sanger sequencing. Among them, APC, FAT4, CTNND1 and TLR2 seem to be the most promising genes because of their role in hereditary cancer syndromes, tumor suppression, cell adhesion and Helicobacter pylori recognition, respectively. These encouraging results represent the open door to the identification of new genes involved in GC germline predisposition.

Using linkage studies combined with whole-exome sequencing to identify novel candidate genes for familial colorectal cancer.

Colorectal cancer (CRC) is a complex disorder for which the majority of the underlying germline predisposition factors remain still unidentified. Here, we combined whole-exome sequencing (WES) and linkage analysis in families with multiple relatives affected by CRC to identify candidate genes harboring rare variants with potential high-penetrance effects. Forty-seven affected subjects from 18 extended CRC families underwent WES. Genome-wide linkage analysis was performed under linear and exponential models. Suggestive linkage peaks were identified on chromosomes 1q22-q24.2 (maxSNP = rs2134095; LODlinear = 2.38, LODexp = 2.196), 7q31.2-q34 (maxSNP = rs6953296; LODlinear = 2.197, LODexp = 2.149) and 10q21.2-q23.1 (maxSNP = rs1904589; LODlinear = 1.445, LODexp = 2.195). These linkage signals were replicated in 10 independent sets of random markers from each of these regions. To assess the contribution of rare variants predicted to be pathogenic, we performed a family-based segregation test with 89 rare variants predicted to be deleterious from 78 genes under the linkage intervals. This analysis showed significant segregation of rare variants with CRC in 18 genes (weighted p-value > 0.0028). Protein network analysis and functional evaluation were used to suggest a plausible candidate gene for germline CRC predisposition. Etiologic rare variants implicated in cancer germline predisposition may be identified by combining traditional linkage with WES data. This approach can be used with already available NGS data from families with several sequenced members to further identify candidate genes involved germline predisposition to disease. This approach resulted in one candidate gene associated with increased risk of CRC but needs evidence from further studies.

Quantitative analysis of somatically acquired and constitutive uniparental disomy in gastrointestinal cancers.

Somatically acquired uniparental disomies (aUPDs) are frequent events in solid tumors and have been associated with cancer-related genes. Studies assessing their functional consequences across several cancer types are therefore necessary. Here, we aimed at integrating aUPD profiles with the mutational status of cancer-related genes in a tumor-type specific manner. Using TCGA datasets for 1,032 gastrointestinal cancers, including colon (COAD), rectum (READ), stomach (STAD), esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), we show a non-random distribution of aUPD, suggesting the existence of a cancer-specific landscape of aUPD events. Our analysis indicates that aUPD acts as a "second hit" in Knudson's model in order to achieve biallelic inactivation of tumor suppressor genes. In particular, APC, ARID1A and NOTCH1 were recurrently inactivated by the presence of homozygous mutation as a consequence of aUPD in COAD and READ, STAD and ESCC, respectively. Furthermore, while TP53 showed inactivation caused by aUPD at chromosome arm 17p across all tumor types, copy number losses at this genomic position were also frequent. By experimental and computationally inferring genome ploidy, we demonstrate that an increased number of aUPD events, both affecting the whole chromosome or segments of it, were present in highly aneuploid genomes compared to near-diploid tumors. Finally, the presence of mosaic UPD was detected at a higher frequency in DNA extracted from peripheral blood lymphocytes of patients with colorectal cancer compared to healthy individuals. In summary, our study defines specific profiles of aUPD in gastrointestinal cancers and provides unequivocal evidence of their relevance in cancer.

Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples.

BACKGROUND: Mutational 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. RESULTS: In 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. CONCLUSIONS: MuSiCa 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 .

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.

PURPOSE: Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available. METHODS: We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints. RESULTS: DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts. CONCLUSION: DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers.

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers.

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

The Complexity of Tobacco Smoke-Induced Mutagenesis in Head and Neck Cancer.

Tobacco smoke, alone or combined with alcohol, is the predominant cause of head and neck cancer (HNC). Here, we further explore how tobacco exposure contributes to cancer development by mutational signature analysis of 265 whole-genome sequenced HNC from eight countries. Six tobacco-associated mutational signatures were detected, including some not previously reported. Differences in HNC incidence between countries corresponded with differences in mutation burdens of tobacco-associated signatures, consistent with the dominant role of tobacco in HNC causation. Differences were found in the burden of tobacco-associated signatures between anatomical subsites, suggesting that tissue-specific factors modulate mutagenesis. We identified an association between tobacco smoking and three additional alcohol-related signatures indicating synergism between the two exposures. Tobacco smoking was associated with differences in the mutational spectra and repertoire of driver mutations in cancer genes, and in patterns of copy number change. Together, the results demonstrate the multiple pathways by which tobacco smoke can influence the evolution of cancer cell clones.

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator.

Background: All cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events. Results: Here, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. Conclusions: The new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Topography of mutational signatures in human cancer.

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

Unraveling the impact of a germline heterozygous POLD1 frameshift variant in serrated polyposis syndrome.

Serrated polyposis syndrome (SPS) is one of the most frequent polyposis syndromes characterized by an increased risk for developing colorectal cancer (CRC). Although SPS etiology has been mainly associated with environmental factors, germline predisposition to SPS could also be relevant for cases with familial aggregation or a family history of SPS/CRC. After whole-exome sequencing of 39 SPS patients from 16 families, we identified a heterozygous germline frameshift variant in the POLD1 gene (c.1941delG, p.(Lys648fs*46)) in a patient with SPS and CRC. Tumor presented an ultra-hypermutated phenotype and microsatellite instability. The POLD1 germline variant segregated in three additional SPS-affected family members. We attempted to create yeast and cellular models for this variant but were no viable. Alternatively, we generated patient-derived organoids (PDOs) from healthy rectal tissue of the index case, as well as from a control donor. Then, we challenged PDOs with a DNA-damaging agent to induce replication stress. No significant differences were observed in the DNA damage response between control and POLD1-Lys648fs PDOs, nor specific mutational signatures were observed. Our results do not support the pathogenicity of the analyzed POLD1 frameshift variant. One possible explanation is that haplosufficiency of the wild-type allele may be compensating for the absence of expression of the frameshift allele. Overall, future work is required to elucidate if functional consequences could be derived from POLD1 alterations different from missense variants in their proofreading domain. To our knowledge, our study presents the first organoid model for germline POLD1 variants and establishes the basis for its use as a model for disease in SPS, CRC and other malignancies.

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment.

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor.

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.