Signatures of mutational processes in human cancer

All cancers are caused by somatic mutations. These mutations may be the consequence of the intrinsic slight infidelity of the DNA replication machinery, exogenous or endogenous mutagen exposures, enzymatic modification of DNA, or defective DNA repair. In some cancer types, a substantial proportion of somatic mutations are known to be generated by exogenous arcinogens, for example, tobacco smoking in lung cancers and ultraviolet light in skin cancers, or by abnormalities of DNA maintenance, for example, defective DNA mismatch repair in some colorectal cancers. Each biological process causing mutations leaves a characteristic imprint on the genome of a cancer cell, termed, mutational signature. In this talk, I will present mutational signatures analyses encompassing 12,023 cancer genomes across 40 distinct types of human cancer revealing more than 30 different signatures of mutational processes. Some signatures are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer etiology, prevention and therapy. Citation Format: Ludmil B. Alexandrov. Signatures of mutational processes in human cancer [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr IA11.

Mutational Signatures in Helicobacter pylori–induced Gastric Cancer: Lessons From New Sequencing Technologies

Introduction Mutational processes can be characterized by unique combinations of mutation types in the form of mutational signatures and have been associated with age, known mutagenic exposures, defects in DNA maintenance, or the APOBEC family of cytidine deaminases. We asked whether mutation signatures could be extracted from DNA sequence information in a targeted 434 gene panel covering 297 breast cancer specimens. Materials and Methods Targeted whole exome sequencing (Illumina, 2x50bp) of a 434 gene panel was performed on a set of 297 primary and metastatic breast tumor samples. Tissue of origin included breast (56%), liver (15%), lymph node (10%), lung (3%) and others (16%). Alignment was done with BWA against the human reference hg19 and variant calling was performed using VarDict. Germline variants were filtered based on allele frequencies, cohort specific population frequencies, as well as using 1000 Genomes and ExAC population frequencies. For somatic signature inference, only single nucleotide variants were retained. Panel specific trinucleotide frequencies were computed and normalized towards whole genome frequencies and somatic signatures were inferred using deconstructSigs method. Results We identified a total of 26 signatures from the set of 30 known signatures in our patient samples. Due to the small panel size, there was only a limited number of mutations available per patient to infer somatic signatures. On average, we identified two somatic signatures per sample. Most common mutation signatures identified were: Signature 1 (90.8%) - result of an endogenous mutational process initiated by spontaneous deamination of 5-methylcytosine; Signature 6 (21.8%) - defective DNA mismatch repair; Signature 15 (15.6%) - defective DNA mismatch repair; Signatue 7 (9.9%) - ultraviolet light exposure; and Signature 10 (6.5%) - altered activity of POLE. An APOBEC specific signature was identified in 20 (7%) samples. APOBEC positive samples showed significantly higher tumor mutational burden (10.7 vs. 5.7 mutations/mb) as compared to APOBEC negative samples (p<=0.001). PIK3CA was found to be mutated in 80% of APOBEC positive samples, compared to 36% of APOBEC negative samples. In addition, we found higher rates of mutations in TP53 (70% vs. 50%), MLL3 (50% vs. 19%) and MLL2 (25% vs 14%) of APOBEC positive patients. Response rates of APOBEC positive patients were significantly worse than of APOBEC negative patients, with 50 percent of patients having progressive disease compared to 25 percent of APOBEC negative patients(p=0.07, borderline). Conclusions We demonstrate the feasibility of a targeted sequencing approach to extract somatic mutation signatures from breast tumor samples, and we highlight the potential of using the APOBEC signature to predict therapeutic responses. Citation Format: Meissner T, Amallraja A, Willis S, Harris R, Leyland-Jones B, Williams C. APOBEC mutation signature in breast cancer correlates with tumor mutation burden and poor responses to therapy [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD8-10.

The Mutational and Transcriptional Landscapes of Hepatocarcinogenesis in a Rat Model.

IntroductionThe somatic mutations in a cancer genome are the aggregate outcome of one or more mutational processes operative through the life of the cancer patient1-3. Each mutational process leaves a characteristic mutational signature determined by the mechanisms of DNA damage and repair that constitute it. A role was recently proposed for the APOBEC family of cytidine deaminases in generating particular genome-wide mutational signatures1,4 and a signature of localized hypermutation called kataegis1,4. A germline copy number polymorphism involving APOBEC3A and APOBEC3B, which effectively deletes APOBEC3B5, has been associated with a modest increased risk of breast cancer6-8. Here, we show that breast cancers in carriers of the deletion show more mutations of the putative APOBEC-dependent genome-wide signatures than cancers in non-carriers. The results suggest that the APOBEC3A/3B germline deletion allele confers cancer susceptibility through increased activity of APOBEC-dependent mutational processes, although the mechanism by which this occurs remains unknown.

BackgroundCancer genomes harbor hundreds to thousands of somatic nonsynonymous mutations. DNA damage and deficiency of DNA repair systems are two major forces to cause somatic mutations, marking cancer genomes with specific somatic mutation patterns. Recently, several pan-cancer genome studies revealed more than 20 mutation signatures across multiple cancer types. However, detailed cancer-type specific mutation signatures and their different features within (intra-) and between (inter-) cancer types remain largely unexplored.MethodsWe employed a matrix decomposition algorithm, namely Non-negative Matrix Factorization, to survey the somatic mutations in nine major human cancers, involving a total of ~2100 genomes.ResultsOur results revealed 3-5 independent mutational signatures in each cancer, implying that a range of 3-5 predominant mutational processes likely underlie each cancer genome. Both mutagen exposure (tobacco and sun) and changes in DNA repair systems (APOBEC family, POLE, and MLH1) were found as mutagenesis forces, each of which marks the genome with an evident mutational signature. We studied the features of several signatures and their combinatory patterns within and across cancers. On one hand, we found each signature may influence a cancer genome with different influential magnitudes even in the same cancer type and the signature-specific load reflects intra-cancer heterogeneity (e.g., the smoking-related signature in lung cancer smokers and never smokers). On the other hand, inter-cancer heterogeneity is characterized by combinatory patterns of mutational signatures, where no cancers share the same signature profile, even between two lung cancer subtypes (lung adenocarcinoma and squamous cell lung cancer).ConclusionsOur work provides a detailed overview of the mutational characteristics in each of nine major cancers and highlights that the mutational signature profile is representative of each 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.

e17629 Background: Whole genome sequencing (WGS) has emerged as a tool to characterize mutational burden and potentially identify new therapeutic targets and predictive biomarkers. In this study, we describe the mutational landscape of diverse patients with endometrial cancer using WGS and explore associations between mutations and clinicopathologic characteristics. Methods: Patients with endometrial cancer were prospectively consented and WGS was performed on tumors and blood. Using the the New York Genome Center Cancer pipeline, we identified high confidence somatic mutations in our patient cohort (identified by 2 or more variant callers). Somatic variants were categorized into mutational signatures based on the Catalog of Somatic Mutations in Cancer (COSMIC) v2. Associations between COSMIC signatures and clinicopathologic variables were evaluated using ANOVA tests. Results: Sixteen patients with endometrial cancer were enrolled; 10 (62.5%) self-identified as White, 4 (25.0%) as Black, and 2 (12.5%) as Asian. Tumor histology was endometrioid (13, 81.3%) or serous/clear cell (2, 12.5%), with both low grade (9, 56.3%) and high grade (7, 43.8%) tumors. The most frequently mutated genes were PTEN (15, 93.8%), ARID1A (9, 56.3%), PIK3CA (9, 56.3%), and TP53 (6, 37.5%). PIK3CA mutations were found in all mismatch repair (MMR) deficient tumors (5), compared to only 30.0% (3) of patients with intact MMR mechanisms (FDR p = 0.0256). Signature 3, associated with BRCA mutations and loss of DNA double-strand break-repair by homologous recombination, was enriched in p53 mutated tumors (p = 0.0036). Signature 1, associated with age, was enriched in patients with MMR intact tumors (p = 0.0475), while signatures 6 and 20, associated with defective DNA MMR and microsatellite unstable tumors, were enriched in patients with in MMR deficient tumors (p < 0.0001 and p = 0.0022, respectively). Signature 8, of unknown etiology, was enriched in patients with MMR intact tumors, advanced disease (p = 0.0045), disease spread to lymph nodes (p = 0.0231), and disease recurrence (p = 0.0021). Signature 12, also of unknown etiology, was enriched in MMR deficient tumors (p = 0.0005) and stage 1 disease (p = 0.0235). Conclusions: WGS revealed distinct patterns of mutational burden associated with clinicopathologic variables. MMR status was associated with PIK3CA mutations and multiple mutational signatures. Mutational signature 8 was associated with multiple clinicopathologic characteristics associated with poor prognosis, suggesting utility as a biomarker. Further investigation regarding the mechanism of the mutational signatures and correlation with prognostic factors may shed light on disparate biologic endometrial cancer outcomes and serve as a source of hypothesis-generation for the development of targeted therapies.

2007 Annual Meeting of the National Lung Cancer Partnership: A Summary of Meeting Highlights

SummaryAll cancers carry somatic mutations. The patterns of mutation in cancer genomes reflect the DNA damage and repair processes to which cancer cells and their precursors have been exposed. To explore these mechanisms further, we generated catalogs of somatic mutation from 21 breast cancers and applied mathematical methods to extract mutational signatures of the underlying processes. Multiple distinct single- and double-nucleotide substitution signatures were discernible. Cancers with BRCA1 or BRCA2 mutations exhibited a characteristic combination of substitution mutation signatures and a distinctive profile of deletions. Complex relationships between somatic mutation prevalence and transcription were detected. A remarkable phenomenon of localized hypermutation, termed “kataegis,” was observed. Regions of kataegis differed between cancers but usually colocalized with somatic rearrangements. Base substitutions in these regions were almost exclusively of cytosine at TpC dinucleotides. The mechanisms underlying most of these mutational signatures are unknown. However, a role for the APOBEC family of cytidine deaminases is proposed.PaperClip

Background: Endometrioid endometrial carcinomas (EECs) frequently harbor mutations in the PI3K pathway. In contrast with other cancer types (e.g. breast cancer) where PIK3CA mutations are generally mutually exclusive with PTEN mutations, in EECs mutations affecting these genes often co-occur. Here we sought to determine whether the type and pattern of mutations targeting different components of the PI3K pathway are distinct between microsatellite stable (MSS) and high-level microsatellite instable (MSI-H) EECs, and to define the mutational signatures in MSI-H and MSS EECs. Methods: Whole exome massively parallel sequencing-based mutation data from EECs of The Cancer Genome Atlas (TCGA) project were used to define the number, type and pattern of mutations affecting PI3K pathway-related genes (i.e., AKT1, INPP4B, MTOR, PIK3CA, PIK3R1 and PTEN). Based on seven MSI markers assessed by TCGA, EECs were classified as MSI-H (n = 70) and MSS (n = 109). POLE ultramutated cases were excluded. Mutational signatures were defined using EMu, a method based upon the expectation-maximization algorithm. Results: Although the mutation rates of MSS and MSI-H EECs were significantly different, the prevalence of mutations affecting PI3K pathway genes was similar between these two groups (all p>0.05), with the exception of PTEN mutations, which were more prevalent in MSI-H (87%) than in MSS EECs (72%; p = 0.017). The PIK3CA hotspot mutations E542K, E545K, and H1047R were found to be significantly more prevalent in PIK3CA-mutant MSS EECs (36%) than in PIK3CA-mutant MSI-H EECs (13.5%; p = 0.019). In both MSI-H and MSS EECs a mutational signature related to age was identified, characterized by C>T transitions at NpCpG trinucleotides; in MSS tumors a C>T and C>G at TpCpN trinucleotides mutational signature, attributed to the APOBEC family of cytidine deaminases, was identified, whereas in MSI-H tumors, a DNA-MMR deficiency-like signature was found. Conclusion: Although the prevalence of mutations targeting different components of the PI3K pathway is similar between MSS and MSI-H EECs, PIK3CA hotspot mutations, which result in constitutive kinase activation, are significantly more prevalent in MSS than in MSI-H EECs. We have observed that the mutational processes operating in MSI-H and MSS EECs are distinct, and that the landscape of mutations affecting PI3K pathway-related genes might be shaped by multiple mutational processes in these cancers. Our findings warrant further investigation of the role of different types of PIK3CA mutations in and their predictive impact on distinct subtypes of EECs. Citation Format: Caterina Marchio, Maria R. De Filippo, Charlotte KY Ng, Robert A. Soslow, Jorge S. Reis-Filho, Britta Weigelt. Microsatellite instability status in endometrioid endometrial carcinomas is associated with distinct types and patterns of PI3K pathway mutations. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4817. doi:10.1158/1538-7445.AM2015-4817

Pancreatic acinar cell carcinoma (ACC) is a rare pancreatic cancer. The advancement of treatment is hampered because of the limited knowledge of its molecular mechanism. Whole-exome sequencing was performed on DNA extracted from 11 pure ACC surgical samples. Potential germline variants were removed on the basis of polymorphic databases, alternative allele frequency, coverage depth, and Catalogue of Somatic Mutations in Cancer (COSMIC) annotations after variant calling procedure. Mutation profiles and signatures were assessed through the Mutational Patterns package. A median of 34 somatic mutations were detected (range, 19-60). Three novel recurrent small deletions were identified. Common pancreatic ductal adenocarcinoma mutations or neuroendocrine tumor mutants were not found. FAT atypical cadherin 4, mucin 5B, titin, and zinc finger homeobox 3 were consistently mutated across 4 independent ACC studies. A high contribution of COSMIC mutational signature 1 was seen in ACC, indicating deamination of 5-methylcytosine. The majority of the patients had COSMIC signatures 6, 15, or 20, relating to defective DNA mismatch repair. Six patients showed COSMIC mutational signature 10 because of the altered activity of DNA polymerase epsilon. Distinct mutational signatures pathways were found in ACC and targeting them may improve clinical outcome.

Mammals have developed clever adaptive and innate immune defense mechanisms to protect against invading bacterial and viral pathogens. Human innate immunity is continuously evolving to expand the repertoire of restriction factors and one such family of intrinsic restriction factors is the APOBEC3 (A3) family of cytidine deaminases. The coordinated expression of seven members of the A3 family of cytidine deaminases provides intrinsic immunity against numerous foreign infectious agents and protects the host from exogenous retroviruses and endogenous retroelements. Four members of the A3 proteins—A3G, A3F, A3H, and A3D—restrict HIV-1 in the absence of virion infectivity factor (Vif); their incorporation into progeny virions is a prerequisite for cytidine deaminase-dependent and -independent activities that inhibit viral replication in the host target cell. HIV-1 encodes Vif, an accessory protein that antagonizes A3 proteins by targeting them for polyubiquitination and subsequent proteasomal degradation in the virus producing cells. In this review, we summarize our current understanding of the role of human A3 proteins as barriers against HIV-1 infection, how Vif overcomes their antiviral activity, and highlight recent structural and functional insights into A3-mediated restriction of lentiviruses.

The Anti-HIV-1 Cytidine Deaminase APOBEC3G Is a Cellular Site-Specific RNA Editing Enzyme

Cancers arise owing to somatic mutations, and the characteristic combinations of somatic mutations form mutational signatures. Despite many mutational signatures being identified, mutational processes underlying a number of mutational signatures remain unknown, which hinders the identification of interventions that may reduce somatic mutation burdens and prevent the development of cancer. We demonstrate that the unknown cause of a mutational signature can be inferred by the associated signatures with known etiology. However, existing association tests are not statistically powerful due to excess zeros in mutational signatures data. To address this limitation, we propose a semiparametric kernel independence test (SKIT). The SKIT statistic is defined as the integrated squared distance between mixed probability distributions and is decomposed into four disjoint components to pinpoint the source of dependency. We derive the asymptotic null distribution and prove the asymptotic convergence of power. Due to slow convergence to the asymptotic null distribution, a bootstrap method is employed to compute p-values. Simulation studies demonstrate that when zeros are prevalent, SKIT is more resilient to power loss than existing tests and robust to random errors. We applied SKIT to The Cancer Genome Atlas mutational signatures data for over 9000 tumors across 32 cancer types, and identified a novel association between signature 17 curated in the Catalogue of Somatic Mutations in Cancer and apolipoprotein B mRNA editing enzyme (APOBEC) signatures in gastrointestinal cancers. It indicates that APOBEC activity is likely associated with the unknown cause of signature 17. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.