Detecting presence of mutational signatures in cancer with confidence

Colorectal Tumors in Diverse Patient Populations Feature a Spectrum of Somatic Mutational Profiles

Admixed populations, including the Hispanic/Latino/a community, are underrepresented in cancer genetic/genomic studies. Leveraging the Latino Colorectal Cancer Consortium (LC3) and other existing datasets, we analyzed whole-exome sequencing data on tumor/normal pairs from 718 individuals with colorectal cancer to map somatic mutational features by ethnicity and genetic similarity. Global proportions of African, Asian, European, and Native American genetic ancestries were estimated using ADMIXTURE. Associations between these proportions and somatic mutational features were examined using logistic regression. APC, TP53, and KRAS were the top three mutated genes across all participants and in the subset of Latino individuals in LC3. In analyses examining recurrently mutated genes, tumors from patients of Latino ethnicity had fewer KRAS and PIK3CA mutations compared with tumors from non-Latino patients. Genetic ancestry overall was associated with CDC27 mutation status, and African genetic ancestry was associated with SMAD2 mutation status. In exome-wide analyses, African genetic ancestry was significantly associated with higher odds of mutation in KNCN and TMEM184B. Native American genetic ancestry was associated with a lower frequency of microsatellite instability-high tumors. The SBS11 mutational signature was associated with Native American genetic ancestry as well as Latino ethnicity. In an independent replication dataset, MSK-IMPACT, estimates of association were largely consistent in direction but nonsignificant. A meta-analysis of LC3 and MSK-IMPACT showed that African genetic ancestry was significantly associated with KRAS mutation status and MSI status. This work facilitates precision medicine initiatives by providing insights into the contribution of genetic ancestry to molecular features of colorectal tumors. Significance: Analysis of tumors from various populations can broadly characterize genomic landscapes and enhance precision medicine strategies.

Adenomas from individuals with pathogenic biallelic variants in the MUTYH and NTHL1 genes demonstrate base excision repair tumour mutational signature profiles similar to colorectal cancers, expanding potential diagnostic and variant classification applications

BACKGROUND

Colorectal cancers (CRCs) from people with biallelic germline likely pathogenic/pathogenic variants in MUTYH or NTHL1 exhibit specific single base substitution (SBS) mutational signatures, namely combined SBS18 and SBS36 (SBS18+SBS36), and SBS30, respectively. The aim was to determine if adenomas from biallelic cases demonstrated these mutational signatures at diagnostic levels.

METHODS

Whole-exome sequencing of FFPE tissue and matched blood-derived DNA was performed on 9 adenomas and 15 CRCs from 13 biallelic MUTYH cases, on 7 adenomas and 2 CRCs from 5 biallelic NTHL1 cases and on 27 adenomas and 26 CRCs from 46 non-hereditary (sporadic) participants. All samples were assessed for COSMIC v3.2 SBS mutational signatures.

RESULTS

In biallelic MUTYH cases, SBS18+SBS36 signature proportions in adenomas (mean±standard deviation, 65.6 %±29.6 %) were not significantly different to those observed in CRCs (76.2 % ± 20.5 %, p-value=0.37), but were significantly higher compared with non-hereditary adenomas (7.6 % ± 7.0 %, p-value=3.4 × 10-4). Similarly, in biallelic NTHL1 cases, SBS30 signature proportions in adenomas (74.5 %±9.4 %) were similar to those in CRCs (78.8 % ± 2.4 %) but significantly higher compared with non-hereditary adenomas (2.8 % ± 3.6 %, p-value=5.1 × 10-7). Additionally, a compound heterozygote with the c.1187G>A p.(Gly396Asp) pathogenic variant and the c.533G>C p.(Gly178Ala) variant of unknown significance (VUS) in MUTYH demonstrated high levels of SBS18+SBS36 in four adenomas and one CRC, providing evidence for reclassification of the VUS to pathogenic.

CONCLUSIONS

SBS18+SBS36 and SBS30 were enriched in adenomas at comparable proportions to those observed in CRCs from biallelic MUTYH and biallelic NTHL1 cases, respectively. Therefore, testing adenomas may improve the identification of biallelic cases and facilitate variant classification, ultimately enabling opportunities for CRC prevention.

Clinical impact of UV mutational signatures in Veterans with cancer

BACKGROUND

UV-related DNA damage signature (UVsig) is highly specific for cutaneous cancers. The prevalence of UVsig among tumors without a primary site and tumors of extracutaneous origin were previously reported, suggesting potential misclassification of cancers. Our study aims to assess if the knowledge of UVsig at diagnosis would change first-line treatment recommendation.

METHODS

The main outcome was the potential clinical impact (PCI) of UVsig. High PCI was defined as UVsig leading to change in diagnosis and first-line therapy. Medium PCI was a change in diagnosis, but appropriate therapy was offered. Low PCI group had diagnosis modified by clinicians and treated as cutaneous cancer independently of UVsig.

RESULTS

Among 5565 cases, 650 (12%) were positive for a UVsig. In the cancer of unknown primary group: 20 (49%), 9 (22%), and 12 (29%) cases were categorized in the high, medium, and low PCI group, respectively. In the cancer of extracutaneous origin cohort: 22 (54%), 15 (36%), and 4 (10%) cases were high, medium, and low PCI, respectively. The diagnosis would have changed in 14% of Veterans with UVsig positive tumor. Among all high PCI cases, 37 (88%) received chemotherapy that was not indicated based on a UVsig-informed diagnosis of cutaneous malignancy.

CONCLUSION

Our study suggested that UVsig would lead to revision of the working clinical diagnosis and significantly alter the first-line treatment in at least half of cancers of unknown primary or extracutaneous origin with UVsig. Knowledge of UVsig could lead to more effective and less toxic therapy for patients with cancer.

Benchmarking 13 tools for mutational signature attribution, including a new and improved algorithm

Mutational signatures are characteristic patterns of mutations caused by endogenous mutational processes or by exogenous mutational exposures. There has been little benchmarking of approaches for determining which signatures are present in a sample and estimating the number of mutations due to each signature. This problem is referred to as "signature attribution." We show that there are often many combinations of signatures that can reconstruct the patterns of mutations in a sample reasonably well, even after encouraging sparse solutions. We benchmarked 13 approaches to signature attribution, including a new approach called Presence Attribute Signature Activity (PASA), on large synthetic data sets (2700 synthetic samples in total). These data sets recapitulated the single-base, insertion-deletion, and doublet-base mutational signature repertoires of nine cancer types. For single-base substitution mutations, PASA and MuSiCal outperformed other approaches on all the cancer types combined. However, the ranking of approaches varied by cancer type. For doublet-base substitutions and small insertions and deletions, while PASA outperformed the other approaches in most of the nine cancer types, the ranking of approaches again varied by cancer type. We believe that this variation reflects inherent difficulties in signature attribution. These difficulties stem from the fact that there are often many attributions that can reasonably explain the pattern of mutations in a sample and from the combinatorial search space due to the need to impose sparsity. Tables herein can provide guidance on the selection of mutational signature attribution approaches that are best suited to particular cancer types and study objectives.

Widespread mutagenesis and chromosomal instability shape somatic genomes in systemic sclerosis

Systemic sclerosis is a connective tissue disorder characterized by excessive fibrosis that primarily affects women, and can present as a multisystem pathology. Roughly 4-22% of patients with systemic sclerosis develop cancer, which drastically worsens prognosis. However, the mechanisms underlying systemic sclerosis initiation, propagation, and cancer development are poorly understood. We hypothesize that the inflammation and immune response associated with systemic sclerosis can trigger DNA damage, leading to elevated somatic mutagenesis, a hallmark of pre-cancerous tissues. To test our hypothesis, we culture clonal lineages of fibroblasts from the lung tissues of controls and systemic sclerosis patients and compare their mutation burdens and spectra. We find an overall increase in all major mutation types in systemic sclerosis samples compared to control lung samples, from small-scale events such as single base substitutions and insertions/deletions, to chromosome-level changes, including copy-number changes and structural variants. In the genomes of patients with systemic sclerosis, we find evidence of somatic hypermutation or kategis (typically only seen in cancer genomes), we identify mutation signatures closely resembling the error-prone translesion polymerase Polη activity, and observe an activation-induced deaminase-like mutation signature, which overlaps with genomic regions displaying kataegis.

Adenomas from individuals with pathogenic biallelic variants in the MUTYH and NTHL1 genes demonstrate base excision repair tumour mutational signature profiles similar to colorectal cancers, expanding potential diagnostic and variant classification applications

Background

Colorectal cancers (CRCs) from people with biallelic germline likely pathogenic/pathogenic variants in MUTYH or NTHL1 exhibit specific single base substitution (SBS) mutational signatures, namely combined SBS18 and SBS36 (SBS18+SBS36), and SBS30, respectively. The aim was to determine if adenomas from biallelic cases demonstrated these mutational signatures at diagnostic levels.

Methods

Whole-exome sequencing of FFPE tissue and matched blood-derived DNA was performed on 9 adenomas and 15 CRCs from 13 biallelic MUTYH cases, on 7 adenomas and 2 CRCs from 5 biallelic NTHL1 cases and on 27 adenomas and 26 CRCs from 46 non-hereditary (sporadic) participants. All samples were assessed for COSMIC v3.2 SBS mutational signatures.

Results

In biallelic MUTYH cases, SBS18+SBS36 signature proportions in adenomas (mean±standard deviation, 65.6%±29.6%) were not significantly different to those observed in CRCs (76.2%±20.5%, p-value=0.37), but were significantly higher compared with non-hereditary adenomas (7.6%±7.0%, p-value=3.4×10-4). Similarly, in biallelic NTHL1 cases, SBS30 signature proportions in adenomas (74.5%±9.4%) were similar to those in CRCs (78.8%±2.4%) but significantly higher compared with non-hereditary adenomas (2.8%±3.6%, p-value=5.1×10-7). Additionally, a compound heterozygote with the c.1187G>A p.(Gly396Asp) pathogenic variant and the c.533G>C p.(Gly178Ala) variant of unknown significance (VUS) in MUTYH demonstrated high levels of SBS18+SBS36 in four adenomas and one CRC, providing evidence for reclassification of the VUS to pathogenic.

Conclusions

SBS18+SBS36 and SBS30 were enriched in adenomas at comparable proportions observed in CRCs from biallelic MUTYH and biallelic NTHL1 cases, respectively. Therefore, testing adenomas may improve the identification of biallelic cases and facilitate variant classification, ultimately enabling opportunities for CRC prevention.

Pan-cancer mutational signature analysis of 111,711 targeted sequenced tumors using SATS

Tumor mutational signatures are informative for cancer diagnosis and treatment. However, targeted sequencing, commonly used in clinical settings, lacks specialized analytical tools and a dedicated catalogue of mutational signatures. Here, we introduce SATS, a scalable mutational signature analyzer for targeted sequencing data. SATS leverages tumor mutational burdens to identify and quantify signatures in individual tumors, overcoming the challenges of sparse mutations and variable gene panels. Validations across simulated data, pseudo-targeted sequencing data, and matched whole-genome and targeted sequencing samples show that SATS can accurately detect common mutational signatures and estimate their burdens. Applying SATS to 111,711 tumors from the AACR Project GENIE, we created a pan-cancer mutational signature catalogue specific to targeted sequencing. We further validated signatures in lung, breast and colorectal cancers using an additional 16,774 independent samples. This signature catalogue is a valuable resource for estimating signature burdens in individual targeted sequenced tumors, facilitating the integration of mutational signatures with clinical data.

Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy

BACKGROUND

Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance.

METHODS

Whole-genome sequencing data from 59 treatment-naïve and 18 post-treatment samples from 29 OAC patients was analysed. Twenty-seven of these were enrolled in the DOCTOR trial, sponsored by the Australasian Gastro-Intestinal Trials Group. Two biopsies from each treatment-naïve tumour were assessed to define 'shared' (between both samples) and 'private' (present in one sample) mutations.

RESULTS

Mutational signatures SBS2/13 (APOBEC) and SBS3 (BRCA) were almost exclusively detected in private mutation populations of treatment-naïve tumours. Patients presenting these signatures had significantly worse disease specific survival. Furthermore, mutational signatures associated with platinum-based chemotherapy treatment as well as high platinum enrichment scores were only detected in post-treatment samples. Additionally, clones with high putative neoantigen binding scores were detected in some treatment-naïve samples suggesting immunoediting of clones.

CONCLUSIONS

This study demonstrates the high intra-tumour heterogeneity in OAC, as well as indicators for treatment-induced changes during tumour evolution. Intra-tumour heterogeneity remains a problem for successful treatment strategies in OAC.

The Molecular Landscape of Gastric Cancers for Novel Targeted Therapies from Real-World Genomic Profiling

BACKGROUND

Panel-based comprehensive genomic profiling is used in clinical practice worldwide; however, large real-world datasets of patients with advanced gastric cancer are not well known.

OBJECTIVE

We investigated what differences exist in clinically relevant alterations for molecularly defined or age-stratified subgroups.

METHODS

This was a collaborative biomarker study of a real-world dataset from comprehensive genomic profiling testing (Foundation Medicine, Inc.). Hybrid capture was carried out on at least 324 cancer-related genes and select introns from 31 genes frequently rearranged in cancer. Overall, 4634 patients were available for analyses and were stratified by age (≥ 40/< 40 years), microsatellite instability status, tumor mutational burden status (high 10 ≥ /low < 10 Muts/Mb), Epstein-Barr virus status, and select gene alterations. We analyzed the frequency of alterations with a chi-square test with Yate's correction.

RESULTS

Genes with frequent alterations included TP53 (60.1%), ARID1A (19.6%), CDKN2A (18.2%), KRAS (16.6%), and CDH1 (15.8%). Differences in comprehensive genomic profiling were observed according to molecularly defined or age-stratified subgroups. Druggable genomic alterations were detected in 31.4% of patients; ATM (4.4%), BRAF V600E (0.4%), BRCA1 (1.5%), BRCA2 (2.9%), ERBB2 amplification (9.2%), IDH1 (0.2%), KRAS G12C (0.7%), microsatellite instability-high (4.8%), NTRK1/2/3 fusion (0.13%), PIK3CA mutation (11.4%), and tumor mutational burden-high (9.4%). CDH1 alterations and MET amplification were significantly more frequent in patients aged < 40 years (27.7 and 6.2%) than in those aged ≥ 40 years (14.7 and 4.0%).

CONCLUSIONS

Real-world datasets from clinical panel testing revealed the genomic landscape in gastric cancer by subgroup. These findings provide insights for the current therapeutic strategies and future development of treatments in gastric cancer.

Epidemiologic Factors in Relation to Colorectal Cancer Risk and Survival by Genotoxic Colibactin Mutational Signature

BACKGROUND

The genotoxin colibactin causes a tumor single-base substitution (SBS) mutational signature, SBS88. It is unknown whether epidemiologic factors' association with colorectal cancer risk and survival differs by SBS88.

METHODS

Within the Genetic Epidemiology of Colorectal Cancer Consortium and Colon Cancer Family Registry, we measured SBS88 in 4,308 microsatellite stable/microsatellite instability low tumors. Associations of epidemiologic factors with colorectal cancer risk by SBS88 were assessed using multinomial regression (N = 4,308 cases, 14,192 controls; cohort-only cases N = 1,911), and with colorectal cancer-specific survival using Cox proportional hazards regression (N = 3,465 cases).

RESULTS

392 (9%) tumors were SBS88 positive. Among all cases, the highest quartile of fruit intake was associated with lower risk of SBS88-positive colorectal cancer than SBS88-negative colorectal cancer [odds ratio (OR) = 0.53, 95% confidence interval (CI) 0.37-0.76; OR = 0.75, 95% CI 0.66-0.85, respectively, Pheterogeneity = 0.047]. Among cohort studies, associations of body mass index (BMI), alcohol, and fruit intake with colorectal cancer risk differed by SBS88. BMI ≥30 kg/m2 was associated with worse colorectal cancer-specific survival among those SBS88-positive [hazard ratio (HR) = 3.40, 95% CI 1.47-7.84], but not among those SBS88-negative (HR = 0.97, 95% CI 0.78-1.21, Pheterogeneity = 0.066).

CONCLUSIONS

Most epidemiologic factors did not differ by SBS88 for colorectal cancer risk or survival. Higher BMI may be associated with worse colorectal cancer-specific survival among those SBS88-positive; however, validation is needed in samples with whole-genome or whole-exome sequencing available.

IMPACT

This study highlights the importance of identification of tumor phenotypes related to colorectal cancer and understanding potential heterogeneity for risk and survival.

Inherited BRCA1 and RNF43 pathogenic variants in a familial colorectal cancer type X family

Genetic susceptibility to familial colorectal cancer (CRC), including for individuals classified as Familial Colorectal Cancer Type X (FCCTX), remains poorly understood. We describe a multi-generation CRC-affected family segregating pathogenic variants in both BRCA1, a gene associated with breast and ovarian cancer and RNF43, a gene associated with Serrated Polyposis Syndrome (SPS). A single family out of 105 families meeting the criteria for FCCTX (Amsterdam I family history criteria with mismatch repair (MMR)-proficient CRCs) recruited to the Australasian Colorectal Cancer Family Registry (ACCFR; 1998-2008) that underwent whole exome sequencing (WES), was selected for further testing. CRC and polyp tissue from four carriers were molecularly characterized including a single CRC that underwent WES to determine tumor mutational signatures and loss of heterozygosity (LOH) events. Ten carriers of a germline pathogenic variant BRCA1:c.2681_2682delAA p.Lys894ThrfsTer8 and eight carriers of a germline pathogenic variant RNF43:c.988 C > T p.Arg330Ter were identified in this family. Seven members carried both variants, four of which developed CRC. A single carrier of the RNF43 variant met the 2019 World Health Organization (WHO2019) criteria for SPS, developing a BRAF p.V600 wildtype CRC. Loss of the wildtype allele for both BRCA1 and RNF43 variants was observed in three CRC tumors while a LOH event across chromosome 17q encompassing both genes was observed in a CRC. Tumor mutational signature analysis identified the homologous recombination deficiency (HRD)-associated COSMIC signatures SBS3 and ID6 in a CRC for a carrier of both variants. Our findings show digenic inheritance of pathogenic variants in BRCA1 and RNF43 segregating with CRC in a FCCTX family. LOH and evidence of BRCA1-associated HRD supports the importance of both these tumor suppressor genes in CRC tumorigenesis.

Genotoxic colibactin mutational signature in colorectal cancer is associated with clinicopathological features, specific genomic alterations and better survival

Background and Aims

The microbiome has long been suspected of a role in colorectal cancer (CRC) tumorigenesis. The mutational signature SBS88 mechanistically links CRC development with the strain of Escherichia coli harboring the pks island that produces the genotoxin colibactin, but the genomic, pathological and survival characteristics associated with SBS88-positive tumors are unknown.

Methods

SBS88-positive CRCs were identified from targeted sequencing data from 5,292 CRCs from 17 studies and tested for their association with clinico-pathological features, oncogenic pathways, genomic characteristics and survival.

Results

In total, 7.5% (398/5,292) of the CRCs were SBS88-positive, of which 98.7% (392/398) were microsatellite stable/microsatellite instability low (MSS/MSI-L), compared with 80% (3916/4894) of SBS88 negative tumors (p=1.5x10-28). Analysis of MSS/MSI-L CRCs demonstrated that SBS88 positive CRCs were associated with the distal colon (OR=1.84, 95% CI=1.40-2.42, p=1x10-5) and rectum (OR=1.90, 95% CI=1.44-2.51, p=6x10-6) tumor sites compared with the proximal colon. The top seven recurrent somatic mutations associated with SBS88-positive CRCs demonstrated mutational contexts associated with colibactin-induced DNA damage, the strongest of which was the APC:c.835-8A>G mutation (OR=65.5, 95%CI=39.0-110.0, p=3x10-80). Large copy number alterations (CNAs) including CNA loss on 14q and gains on 13q, 16q and 20p were significantly enriched in SBS88-positive CRCs. SBS88-positive CRCs were associated with better CRC-specific survival (p=0.007; hazard ratio of 0.69, 95% CI=0.52-0.90) when stratified by age, sex, study, and by stage.

Conclusion

SBS88-positivity, a biomarker of colibactin-induced DNA damage, can identify a novel subtype of CRC characterized by recurrent somatic mutations, copy number alterations and better survival. These findings provide new insights for treatment and prevention strategies for this subtype of CRC.

Tumor microenvironment-mediated immune profiles and efficacy of anti-PD-L1 antibody plus chemotherapy stratified by DLL3 expression in small-cell lung cancer

BACKGROUND

Delta-like ligand 3 (DLL3) is a therapeutic target in small-cell lung cancer (SCLC). However, how DLL3 expression status affects the tumor microenvironment (TME) and clinical outcomes in SCLC remains unclear.

METHODS

This retrospective study included patients with postoperative limited-stage (LS)-SCLC and extensive-stage (ES)-SCLC treated with platinum and etoposide (PE) plus anti-programmed cell death ligand 1 (PD-L1) antibody. We investigated the relationship of DLL3 expression with TME, mutation status, tumor neoantigens, and immunochemotherapy.

RESULTS

In the LS-SCLC cohort (n = 59), whole-exome sequencing revealed that DLL3High cases had significantly more neoantigens (P = 0.004) and a significantly higher rate of the signature SBS4 associated with smoking (P = 0.02) than DLL3Low cases. Transcriptome analysis in the LS-SCLC cohort revealed that DLL3High cases had significantly suppressed immune-related pathways and dendritic cell (DC) function. SCLC with DLL3High had significantly lower proportions of T cells, macrophages, and DCs than those with DLL3Low. In the ES-SCLC cohort (n = 30), the progression-free survival associated with PE plus anti-PD-L1 antibody was significantly worse in DLL3High cases than in DLL3Low cases (4.7 vs. 7.4 months, P = 0.01).

CONCLUSIONS

Although SCLC with DLL3High had a higher neoantigen load, these tumors were resistant to immunochemotherapy due to suppressed tumor immunity by inhibiting antigen-presenting functions.

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

BACKGROUND

Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures.

RESULTS

Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access.

CONCLUSION

signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

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.

Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors

BACKGROUND

Diesel exhaust is a complex mixture, including polycyclic aromatic hydrocarbons (PAH) and nitrated PAHs (nitro-PAH), many of which are potent mutagens and possible bladder carcinogens. To explore the association between diesel exposure and bladder carcinogenesis, we examined the relationship between exposure and somatic mutations and mutational signatures in bladder tumors.

METHODS

Targeted sequencing was conducted in bladder tumors from the New England Bladder Cancer Study. Using data on 797 cases and 1,418 controls, two-stage polytomous logistic regression was used to evaluate etiologic heterogeneity between bladder cancer subtypes and quantitative, lifetime estimates of respirable elemental carbon (REC), a surrogate for diesel exposure. Poisson regression was used to evaluate associations between REC and mutational signatures.

RESULTS

We observed significant heterogeneity in the diesel-bladder cancer risk relationship, with a strong positive association among cases with high-grade, nonmuscle invasive TP53-mutated tumors compared with controls [ORTop Tertile vs.Unexposed, 4.8; 95% confidence interval (CI), 2.2-10.5; Ptrend < 0.001; Pheterogeneity = 0.002]. In muscle-invasive tumors, we observed a positive association between diesel exposure and the nitro-PAH signatures of 1,6-dintropyrene (RR, 1.93; 95% CI, 1.28-2.92) and 3-nitrobenzoic acid (RR, 1.97; 95% CI, 1.33-2.92).

CONCLUSIONS

The relationship between diesel exhaust and bladder cancer was heterogeneous based on the presence of TP53 mutations in tumors, further supporting the link between PAH exposure and TP53 mutations in carcinogenesis. Future studies that can identify nitro-PAH signatures in exposed tumors are warranted to add human data supporting the link between diesel and bladder cancer.

IMPACT

This study provides additional insight into the etiology and possible mechanisms related to diesel exhaust-induced bladder cancer.

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Pathogenic Variants in Adult-Onset Cancer Predisposition Genes in Pediatric Cancer: Prevalence and Impact on Tumor Molecular Features and Clinical Management

PURPOSE

Clinical genomic sequencing of pediatric tumors is increasingly uncovering pathogenic variants in adult-onset cancer predisposition genes (aoCPG). Nevertheless, it remains poorly understood how often aoCPG variants are of germline origin and whether they influence tumor molecular profiles and/or clinical care. In this study, we examined the prevalence, spectrum, and impacts of aoCPG variants on tumor genomic features and patient management at our institution.

EXPERIMENTAL DESIGN

This is a retrospective study of 1,018 children with cancer who underwent clinical genomic sequencing of their tumors. Tumor genomic data were queried for pathogenic variants affecting 24 preselected aoCPGs. Available tumor whole-genome sequencing (WGS) data were evaluated for second hit mutations, loss of heterozygosity (LOH), DNA mutational signatures, and homologous recombination deficiency (HRD). Patients whose tumors harbored one or more pathogenic aoCPG variants underwent subsequent germline testing based on hereditary cancer evaluation and family or provider preference.

RESULTS

Thirty-three patients (3%) had tumors harboring pathogenic variants affecting one or more aoCPGs. Among 21 tumors with sufficient WGS sequencing data, six (29%) harbored a second hit or LOH affecting the remaining aoCPG allele with four of these six tumors (67%) also exhibiting a DNA mutational signature consistent with the altered aoCPG. Two additional tumors demonstrated HRD, of uncertain relation to the identified aoCPG variant. Twenty-one of 26 patients (81%) completing germline testing were positive for the aoCPG variant in the germline. All germline-positive patients were counseled regarding future cancer risks, surveillance, and risk-reducing measures. No patients had immediate cancer therapy changed due to aoCPG data.

CONCLUSIONS

AoCPG variants are rare in pediatric tumors; however, many originate in the germline. Almost one third of tumor aoCPG variants examined exhibited a second hit and/or conferred an abnormal DNA mutational profile suggesting a role in tumor formation. aoCPG information aids in cancer risk prediction but is not commonly used to alter the treatment of pediatric cancers.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Deciphering gene contributions and etiologies of somatic mutational signatures of cancer

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.

Influence network model uncovers relations between biological processes and mutational signatures

BACKGROUND

There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures.

METHODS

To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes.

RESULTS

Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GENESIGNET also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GENESIGNET also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway.

CONCLUSIONS

GENESIGNET provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GENESIGNET method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.

A Biterm Topic Model for Sparse Mutation Data

Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-exome sequencing. Methods that process sparse mutation data typically found in practice are only in the earliest stages of development. In particular, we previously developed the Mix model that clusters samples to handle data sparsity. However, the Mix model had two hyper-parameters, including the number of signatures and the number of clusters, that were very costly to learn. Therefore, we devised a new method that was several orders-of-magnitude more efficient for handling sparse data, was based on mutation co-occurrences, and imitated word co-occurrence analyses of Twitter texts. We showed that the model produced significantly improved hyper-parameter estimates that led to higher likelihoods of discovering overlooked data and had better correspondence with known signatures.

mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery

Mutational signatures are characteristic patterns of mutations caused by endogenous or exogenous mutational processes. These signatures can be discovered by analyzing mutations in large sets of samples-usually somatic mutations in tumor samples. Most programs for discovering mutational signatures are based on non-negative matrix factorization (NMF). Alternatively, signatures can be discovered using hierarchical Dirichlet process (HDP) mixture models, an approach that has been less explored. These models assign mutations to clusters and view each cluster as being generated from the signature of a particular mutational process. Here, we describe mSigHdp, an improved approach to using HDP mixture models to discover mutational signatures. We benchmarked mSigHdp and state-of-the-art NMF-based approaches on four realistic synthetic data sets. These data sets encompassed 18 cancer types. In total, they contained 3.5 × 10⁷ single-base-substitution mutations representing 32 signatures and 6.1 × 10⁶ small insertion and deletion mutations representing 13 signatures. For three of the four data sets, mSigHdp had the best positive predictive value for discovering mutational signatures, and for all four data sets, it had the best true positive rate. Its CPU usage was similar to that of the NMF-based approaches. Thus, mSigHdp is an important and practical addition to the set of tools available for discovering mutational signatures.

Unravelling the instability of mutational signatures extraction via archetypal analysis

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity > 0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation.

Mutational Signatures as Sensors of Environmental Exposures: Analysis of Smoking-Induced Lung Tissue Remodeling

Smoking is a widely recognized risk factor in the emergence of cancers and other lung diseases. Studies of non-cancer lung diseases typically investigate the role that smoking has in chronic changes in lungs that might predispose patients to the diseases, whereas most cancer studies focus on the mutagenic properties of smoking. Large-scale cancer analysis efforts have collected expression data from both tumor and control lung tissues, and studies have used control samples to estimate the impact of smoking on gene expression. However, such analyses may be confounded by tumor-related micro-environments as well as patient-specific exposure to smoking. Thus, in this paper, we explore the utilization of mutational signatures to study environment-induced changes of gene expression in control lung tissues from lung adenocarcinoma samples. We show that a joint computational analysis of mutational signatures derived from sequenced tumor samples, and the gene expression obtained from control samples, can shed light on the combined impact that smoking and tumor-related micro-environments have on gene expression and cell-type composition in non-neoplastic (control) lung tissue. The results obtained through such analysis are both supported by experimental studies, including studies utilizing single-cell technology, and also suggest additional novel insights. We argue that the study provides a proof of principle of the utility of mutational signatures to be used as sensors of environmental exposures not only in the context of the mutational landscape of cancer, but also as a reference for changes in non-cancer lung tissues. It also provides an example of how a database collected with the purpose of understanding cancer can provide valuable information for studies not directly related to the disease.

KMT2C-deficient tumors have elevated APOBEC mutagenesis and genomic instability in multiple cancers

The histone methyltransferase KMT2C is among the most frequently mutated epigenetic modifier genes in cancer and plays an essential role in MRE11-dependent DNA replication fork restart. However, the effects of KMT2C deficiency on genomic instability during tumorigenesis are unclear. Analyzing 9,663 tumors from 30 cancer cohorts, we report that KMT2C mutant tumors have a significant excess of APOBEC mutational signatures in several cancer types. We show that KMT2C deficiency promotes APOBEC expression and deaminase activity, and compromises DNA replication speed and delays fork restart, facilitating APOBEC mutagenesis targeting single stranded DNA near stalled forks. APOBEC-mediated mutations primarily accumulate during early replication and tend to cluster along the genome and also in 3D nuclear domains. Excessive APOBEC mutational signatures in KMT2C mutant tumors correlate with elevated genome maintenance defects and signatures of homologous recombination deficiency. We propose that KMT2C deficiency is a likely promoter of APOBEC mutagenesis, which fosters further genomic instability during tumor progression in multiple cancer types.

A phylogenetic approach to study the evolution of somatic mutational processes in cancer

Cancer cell genomes change continuously due to mutations, and mutational processes change over time in patients, leaving dynamic signatures in the accumulated genomic variation in tumors. Many computational methods detect the relative activities of known mutation signatures. However, these methods may produce erroneous signatures when applied to individual branches in cancer cell phylogenies. Here, we show that the inference of branch-specific mutational signatures can be improved through a joint analysis of the collections of mutations mapped on proximal branches of the cancer cell phylogeny. This approach reduces the false-positive discovery rate of branch-specific signatures and can sometimes detect faint signatures. An analysis of empirical data from 61 lung cancer patients supports trends based on computer-simulated datasets for which the correct signatures are known. In lung cancer somatic variation, we detect a decreasing trend of smoking-related mutational processes over time and an increasing influence of APOBEC mutational processes as the tumor evolution progresses. These analyses also reveal patterns of conservation and divergence of mutational processes in cell lineages within patients.

Identifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signatures

Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 × 10-23 and p = 6 × 10-11, respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers.

The genomic landscape of cholangiocarcinoma reveals the disruption of post-transcriptional modifiers

Molecular variation between geographical populations and subtypes indicate potential genomic heterogeneity and novel genomic features within CCA. Here, we analyze exome-sequencing data of 87 perihilar cholangiocarcinoma (pCCA) and 261 intrahepatic cholangiocarcinoma (iCCA) cases from 3 Asian centers (including 43 pCCAs and 24 iCCAs from our center). iCCA tumours demonstrate a higher tumor mutation burden and copy number alteration burden (CNAB) than pCCA tumours, and high CNAB indicates a poorer pCCA prognosis. We identify 12 significantly mutated genes and 5 focal CNA regions, and demonstrate common mutations in post-transcriptional modification-related potential driver genes METTL14 and RBM10 in pCCA tumours. Finally we demonstrate the tumour-suppressive role of METTL14, a major RNA N6-adenosine methyltransferase (m6A), and illustrate that its loss-of-function mutation R298H may act through m6A modification on potential driver gene MACF1. Our results may be valuable for better understanding of how post-transcriptional modification can affect CCA development, and highlight both similarities and differences between pCCA and iCCA.

Copy number alteration signatures as biomarkers in cancer: a review

Within certain cancers, extensive copy number alterations (CNAs) contribute to a complex and heterogenic genomic profile. This makes it difficult to understand and unravel the distinct molecular dynamics shaping the disease while preventing clinically effective patient stratification. CNA signature analysis represents a novel genomic stratification tool for probing this complexity, offering an intricate framework for deriving CNA patterns at the molecular level. This allows the underlying genomic mechanisms of specific cancers to be revealed, leading to the potential identification of therapeutic targets and prognostic associations. This review outlines the molecular and methodological basis of CNA signatures and focuses on recent advances highlighting their clinical utility, limitations and prospective future as novel diagnostic and prognostic cancer biomarkers.

SUITOR: Selecting the number of mutational signatures through cross-validation

For de novo mutational signature analysis, the critical first step is to decide how many signatures should be expected in a cancer genomics study. An incorrect number could mislead downstream analyses. Here we present SUITOR (Selecting the nUmber of mutatIonal signaTures thrOugh cRoss-validation), an unsupervised cross-validation method that requires little assumptions and no numerical approximations to select the optimal number of signatures without overfitting the data. In vitro studies and in silico simulations demonstrated that SUITOR can correctly identify signatures, some of which were missed by other widely used methods. Applied to 2,540 whole-genome sequenced tumors across 22 cancer types, SUITOR selected signatures with the smallest prediction errors and almost all signatures of breast cancer selected by SUITOR were validated in an independent breast cancer study. SUITOR is a powerful tool to select the optimal number of mutational signatures, facilitating downstream analyses with etiological or therapeutic importance.

MetaMutationalSigs: comparison of mutational signature refitting results made easy

MOTIVATION

The analysis of mutational signatures is becoming increasingly common in cancer genetics, with emerging implications in cancer evolution, classification, treatment decision and prognosis. Recently, several packages have been developed for mutational signature analysis, with each using different methodology and yielding significantly different results. Because of the non-trivial differences in tools' refitting results, researchers may desire to survey and compare the available tools, in order to objectively evaluate the results for their specific research question, such as which mutational signatures are prevalent in different cancer types.

RESULTS

Due to the need for effective comparison of refitting mutational signatures, we introduce a user-friendly software that can aggregate and visually present results from different refitting packages.

AVAILABILITY AND IMPLEMENTATION

MetaMutationalSigs is implemented using R and python and is available for installation using Docker and available at: https://github.com/EESI/MetaMutationalSigs.

Comparison of somatic mutation landscapes in Chinese versus European breast cancer patients

Recent genomic studies suggest that Asian breast cancer (BC) may have distinct somatic features; however, most comparisons of BC genomic features across populations did not account for differences in age, subtype, and sequencing methods. In this study, we analyzed whole-exome sequencing (WES) data to characterize somatic copy number alterations (SCNAs) and mutation profiles in 98 Hong Kong BC (HKBC) patients and compared with those from The Cancer Genome Atlas of European ancestry (TCGA-EA, N = 686), which had similar distributions of age at diagnosis and PAM50 subtypes as in HKBC. We developed a two-sample Poisson model to compare driver gene selection pressure, which reflects the effect sizes of cancer driver genes, while accounting for differences in sample size, sequencing platforms, depths, and mutation calling methods. We found that somatic mutation and SCNA profiles were overall very similar between HKBC and TCGA-EA. The selection pressure for small insertions and deletions (indels) in GATA3 (false discovery rate (FDR) corrected p < 0.01) and single-nucleotide variants (SNVs) in TP53 (nominal p = 0.02, FDR corrected p = 0.28) was lower in HKBC than in TCGA-EA. Among the 13 signatures of single-base substitutions (SBS) that are common in BC, we found a suggestively higher contribution of SBS18 and a lower contribution of SBS1 in HKBC than in TCGA-EA, while the two APOBEC-induced signatures showed similar prevalence. Our results suggest that the genomic landscape of BC was largely very similar between HKBC and TCGA-EA, despite suggestive differences in some driver genes and mutational signatures that warrant future investigations in large and diverse Asian populations.

Accuracy of mutational signature software on correlated signatures

Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.

Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance

We concurrently examine the whole genome, transcriptome, methylome, and immune cell infiltrates in baseline tumors from 77 patients with advanced cutaneous melanoma treated with anti-PD-1 with or without anti-CTLA-4. We show that high tumor mutation burden (TMB), neoantigen load, expression of IFNγ-related genes, programmed death ligand expression, low PSMB8 methylation (therefore high expression), and T cells in the tumor microenvironment are associated with response to immunotherapy. No specific mutation correlates with therapy response. A multivariable model combining the TMB and IFNγ-related gene expression robustly predicts response (89% sensitivity, 53% specificity, area under the curve [AUC], 0.84); tumors with high TMB and a high IFNγ signature show the best response to immunotherapy. This model validates in an independent cohort (80% sensitivity, 59% specificity, AUC, 0.79). Except for a JAK3 loss-of-function mutation, for patients who did not respond as predicted there is no obvious biological mechanism that clearly explained their outlier status, consistent with intratumor and intertumor heterogeneity in response to immunotherapy.

Mutational pattern of PIK3CA exon 20 in circulating DNA in breast cancer

Breast cancer (BC) is one of the most common cancers with diverse mutations, etiology and causes. Mutational signature of the driver genes could allow for better understanding disease etiology and progression. This study aims to assess PIK3CA Exon 20 somatic mutational signature in relation to potential underlying etiology. Circulating DNA of 71 Egyptian BC patients was isolated, amplified for PIK3CA Exon 20, and sequenced. Mutational signature was determined according to COSMIC v2 signature. Public BC dataset was analysed to assess PIK3CA mutations effect on the transcriptomic profile. Somatic mutations of PIK3CA exon 20 were found in 66.2% of the study cohort. Nucleotide substitution patterns were similar to general nucleotide substitution patterns in BC. Signature 3 and 9 were the most common signatures in the studied BC patients. Signature of Aristolochic acid exposure was found in some cases. The most common nucleotide substitution was T > A transversion, but substitutions T > G and T > C were correlated to each other and to the total mutation number. PIK3CA mutations were found to disrupt several pathways including RAC1, PDGF, Wnt, and integrin signalling. PIK3CA exon 20 mutational signatures in Egyptian BC patients could suggest a disease etiology involving homologous recombination deficiency (HRD) and polymerase eta (Pol η). Nucleotide substitution patterns could indicate the role of exposure to oxidative stress and some carcinogens such as 4-aminobiphenyl and Aristolochic acid.

ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures

Over the past decade, a promising line of cancer research has utilized machine learning to mine statistical patterns of mutations in cancer genomes for information. Recent work shows that these statistical patterns, commonly referred to as "mutational signatures," have diverse therapeutic potential as biomarkers for cancer therapies. However, translating this potential into reality is hindered by limited access to sequencing in the clinic. Almost all methods for mutational signature analysis (MSA) rely on whole genome or whole exome sequencing data, while sequencing in the clinic is typically limited to small gene panels. To improve clinical access to MSA, we considered the question of whether targeted panels could be designed for the purpose of mutational signature detection. Here we present ScalpelSig, to our knowledge the first algorithm that automatically designs genomic panels optimized for detection of a given mutational signature. The algorithm learns from data to identify genome regions that are particularly indicative of signature activity. Using a cohort of breast cancer genomes as training data, we show that ScalpelSig panels substantially improve accuracy of signature detection compared to baselines. We find that some ScalpelSig panels even approach the performance of whole exome sequencing, which observes over 10 × as much genomic material. We test our algorithm under a variety of conditions, showing that its performance generalizes to another dataset of breast cancers, to smaller panel sizes, and to lesser amounts of training data.

MSA: reproducible mutational signature attribution with confidence based on simulations

BACKGROUND

Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments.

RESULTS

We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures.

CONCLUSIONS

MSA is a tool for optimised mutational signature attribution based on simulations, providing confidence intervals using parametric bootstrap. It comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA .

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 .

Evaluating the utility of tumour mutational signatures for identifying hereditary colorectal cancer and polyposis syndrome carriers

OBJECTIVE

Germline pathogenic variants (PVs) in the DNA mismatch repair (MMR) genes and in the base excision repair gene MUTYH underlie hereditary colorectal cancer (CRC) and polyposis syndromes. We evaluated the robustness and discriminatory potential of tumour mutational signatures in CRCs for identifying germline PV carriers.

DESIGN

Whole-exome sequencing of formalin-fixed paraffin-embedded (FFPE) CRC tissue was performed on 33 MMR germline PV carriers, 12 biallelic MUTYH germline PV carriers, 25 sporadic MLH1 methylated MMR-deficient CRCs (MMRd controls) and 160 sporadic MMR-proficient CRCs (MMRp controls) and included 498 TCGA CRC tumours. COSMIC V3 single base substitution (SBS) and indel (ID) mutational signatures were assessed for their ability to differentiate CRCs that developed in carriers from non-carriers.

RESULTS

The combination of mutational signatures SBS18 and SBS36 contributing >30% of a CRC's signature profile was able to discriminate biallelic MUTYH carriers from all other non-carrier control CRCs with 100% accuracy (area under the curve (AUC) 1.0). SBS18 and SBS36 were associated with specific MUTYH variants p.Gly396Asp (p=0.025) and p.Tyr179Cys (p=5×10^-5), respectively. The combination of ID2 and ID7 could discriminate the 33 MMR PV carrier CRCs from the MMRp control CRCs (AUC 0.99); however, SBS and ID signatures, alone or in combination, could not provide complete discrimination (AUC 0.79) between CRCs from MMR PV carriers and sporadic MMRd controls.

CONCLUSION

Assessment of SBS and ID signatures can discriminate CRCs from biallelic MUTYH carriers and MMR PV carriers from non-carriers with high accuracy, demonstrating utility as a potential diagnostic and variant classification tool.

Mutational signatures: emerging concepts, caveats and clinical applications

Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient's cancer, we have been able to discern generic patterns of mutations, termed 'mutational signatures', that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.

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.

Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis

Summary

Mutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution for de novo signature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution, doublet base substitution, small insertion and deletion and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. Sigflow pipeline can be applied to both human and mouse genomes.

Availability and implementation

Sigflow is an open source software under academic free license v3.0 and it is freely available at https://github.com/ShixiangWang/sigflow or https://hub.docker.com/r/shixiangwang/sigflow.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeted Deep Sequencing of Bladder Tumors Reveals Novel Associations between Cancer Gene Mutations and Mutational Signatures with Major Risk Factors

PURPOSE

Exome- and whole-genome sequencing of muscle-invasive bladder cancer has revealed important insights into the molecular landscape; however, there are few studies of non-muscle-invasive bladder cancer with detailed risk factor information.

EXPERIMENTAL DESIGN

We examined the relationship between smoking and other bladder cancer risk factors and somatic mutations and mutational signatures in bladder tumors. Targeted sequencing of frequently mutated genes in bladder cancer was conducted in 322 formalin-fixed paraffin-embedded bladder tumors from a population-based case-control study. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI), evaluating mutations and risk factors. We used SignatureEstimation to extract four known single base substitution mutational signatures and Poisson regression to calculate risk ratios (RR) and 95% CIs, evaluating signatures and risk factors.

RESULTS

Non-silent KDM6A mutations were more common in females than males (OR = 1.83; 95% CI, 1.05-3.19). There was striking heterogeneity in the relationship between smoking status and established single base substitution signatures: current smoking status was associated with greater ERCC2-Signature mutations compared with former (P = 0.024) and never smoking (RR = 1.40; 95% CI, 1.09-1.80; P = 0.008), former smoking was associated with greater APOBEC-Signature13 mutations (P = 0.05), and never smoking was associated with greater APOBEC-Signature2 mutations (RR = 1.54; 95% CI, 1.17-2.01; P = 0.002). There was evidence that smoking duration (the component most strongly associated with bladder cancer risk) was associated with ERCC2-Signature mutations and APOBEC-Signature13 mutations among current (P trend = 0.005) and former smokers (P = 0.0004), respectively.

CONCLUSIONS

These data quantify the contribution of bladder cancer risk factors to mutational burden and suggest different signature enrichments among never, former, and current smokers.

MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations

BACKGROUND

Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, necessitating complicated integration of a plethora of computational and statistical approaches.

FINDINGS

Here, we present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for 2 reported MRS datasets of hepatocellular carcinoma and colorectal cancer identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution.

CONCLUSIONS

In summary, MesKit is useful for interpreting ITH and tracing evolutionary trajectory based on MRS data. MesKit is implemented in R and available at https://bioconductor.org/packages/MesKit under the GPL v3 license.

Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes

Genome alteration signatures reflect recurring patterns caused by distinct endogenous or exogenous mutational events during the evolution of cancer. Signatures of single base substitution (SBS) have been extensively studied in different types of cancer. Copy number alterations are important drivers for the progression of multiple cancer. However, practical tools for studying the signatures of copy number alterations are still lacking. Here, a user-friendly open source bioinformatics tool "sigminer" has been constructed for copy number signature extraction, analysis and visualization. This tool has been applied in prostate cancer (PC), which is particularly driven by complex genome alterations. Five copy number signatures are identified from human PC genome with this tool. The underlying mutational processes for each copy number signature have been illustrated. Sample clustering based on copy number signature exposure reveals considerable heterogeneity of PC, and copy number signatures show improved PC clinical outcome association when compared with SBS signatures. This copy number signature analysis in PC provides distinct insight into the etiology of PC, and potential biomarkers for PC stratification and prognosis.

Mutational signatures associated with exposure to carcinogenic microplastic compounds bisphenol A and styrene oxide

Microplastic pollutants in oceans and food chains are concerning to public health. Common plasticizing compounds Bisphenol-A (BPA) and Styrene-7,8-Oxide (SO) are now labeled as carcinogens. We show that BPA and SO cause deoxyribonucleic acid damage and mutagenesis in human cells, and analyze the genome-wide point mutation and genomic rearrangement patterns associated with BPA and SO exposure. A subset of the single- and doublet base substitutions shows mutagenesis near or at guanine, consistent with these compounds' preferences to form guanosine adducts. Presence of other mutational signatures suggest additional mutagenesis probably due to complex effects of BPA and SO on diverse cellular processes. Analyzing data for 19 cancer cohorts, we find that tumors of digestive and urinary organs show relatively high similarity in mutational profiles, and the burden of such mutations increases with age. Even within the same cancer type, proportions of corresponding mutational patterns vary among the cohorts from different countries, as does the amount of microplastic waste in ocean waters. BPA and SO are relatively mild mutagens, and other environmental agents can also potentially generate similar, complex mutational patterns in cancer genomes. Nonetheless, our findings call for systematic evaluation of public health consequences of microplastic exposure worldwide.