Mutational signatures associated with tobacco smoking in human cancer

ABSTRACTTobacco smoking increases the risk of at least 15 classes of cancer. We analyzed somatic mutations and DNA methylation in 5,243 cancers of types for which tobacco smoking confers an elevated risk. Smoking is associated with increased mutation burdens of multiple distinct mutational signatures, which contribute to different extents in different cancers. One of these signatures, mainly found in cancers derived from tissues directly exposed to tobacco smoke, is attributable to misreplication of DNA damage caused by tobacco carcinogens. Others likely reflect indirect activation of DNA editing by APOBEC cytidine deaminases and of an endogenous clock-like mutational process. The results are consistent with the proposition that smoking increases cancer risk by increasing the somatic mutation load, although direct evidence for this mechanism is lacking in some smoking-related cancer types.ONE SENTENCE SUMMARYMultiple distinct mutational processes associate with tobacco smoking in cancer reflecting direct and indirect effects of tobacco smoke.

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FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures

Genome Medicine ◽

10.1186/s13073-019-0695-x ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 3

Author(s):

Hyunbin Kim ◽

Andy Jinseok Lee ◽

Jongkeun Lee ◽

Hyonho Chun ◽

Young Seok Ju ◽

...

Keyword(s):

Data Storage ◽

Human Cancer ◽

Point Mutations ◽

Variant Calling ◽

Human Tumor ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Precision Oncology ◽

Mutational Signatures ◽

Accurate Identification

Abstract Background Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. Results Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. Conclusions In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT

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

F1000Research ◽

10.12688/f1000research.24435.1 ◽

2020 ◽

Vol 9 ◽

pp. 586

Author(s):

Zhi Yang ◽

Priyatama Pandey ◽

Paul Marjoram ◽

Kimberly D. Siegmund

Keyword(s):

Web Application ◽

Human Cancer ◽

Similarity Measures ◽

Model Specification ◽

Mutational Signatures ◽

Mutational Signature ◽

Link Type ◽

Cosine Similarity Measures ◽

User Friendly ◽

Intuitive Interface

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows such comparisons to be easily performed. When using our software, the user may download published mutational signatures of either type. Mutational signatures from the pmsignature data source are expanded to probabilistic vectors of 96-possible mutation types, the same model specification used by COSMIC, and then compared to COSMIC signatures. Cosine similarity measures the extent of signature similarity. iMutSig provides a simple and user-friendly web application allowing researchers to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download on GitHub.

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Mutational signatures of complex genomic rearrangements in human cancer

10.1101/2021.05.16.444385 ◽

2021 ◽

Author(s):

Lixing Yang ◽

Lisui Bao ◽

Xiaoming Zhong ◽

Yang Yang

Keyword(s):

Human Cancer ◽

Genomic Rearrangements ◽

Cellular Structures ◽

Genome Integrity ◽

Biological Processes ◽

Extrachromosomal Dna ◽

Mutational Signatures ◽

Chromatin Bridge ◽

Open Questions ◽

Dna Loss

Complex genomic rearrangements (CGRs) are common in cancer and are known to form via two aberrant cellular structures-micronuclei and chromatin bridge. However, which mechanism is more relevant to CGR formation in cancer cells and whether there are other undiscovered mechanisms remain open questions. Here, we analyze 2,014 CGRs from 2,428 whole-genome sequenced tumors and deconvolute six CGR signatures based on the topology of CGRs. Through rigorous benchmarking, we show that our CGR signatures are highly accurate and biologically meaningful. Three signatures can be attributed to known biological processes-micronuclei- and chromatin-bridge-induced chromothripsis and extrachromosomal DNA. More than half of the CGRs belong to the remaining three newly discovered signatures. A unique signature (we named "hourglass chromothripsis") with highly localized breakpoints and small amount of DNA loss is abundant in prostate cancer. Through genetic association analysis, we find SPOP as a candidate gene causing hourglass chromothripsis and playing important role in maintaining genome integrity. Our study offers valuable insights into the formation of CGRs.

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Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

BMC Bioinformatics ◽

10.1186/s12859-020-03772-3 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Erik N. Bergstrom ◽

Mark Barnes ◽

Iñigo Martincorena ◽

Ludmil B. Alexandrov

Keyword(s):

Null Hypothesis ◽

Cancer Genomics ◽

Human Cancer ◽

Mutational Signatures ◽

Driver Genes ◽

Link Type ◽

Bioinformatics Tools ◽

Base Substitutions ◽

Extensive Documentation ◽

Cancer Genomes

Abstract Background Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Results Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. Conclusions SigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.

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The APOBEC3A deaminase drives episodic mutagenesis in cancer cells

10.1101/2021.02.14.431145 ◽

2021 ◽

Author(s):

Mia Petljak ◽

Kevan Chu ◽

Alexandra Dananberg ◽

Erik N. Bergstrom ◽

Patrick von Morgen ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Cell Lines ◽

Human Cancer ◽

B Cell Lymphoma ◽

Lymphoma Cell ◽

Mutational Signatures ◽

Cytidine Deaminases ◽

Human Cancer Cells ◽

Cancer Genomes

ABSTRACTThe APOBEC3 family of cytidine deaminases is widely speculated to be a major source of somatic mutations in cancer1–3. However, causal links between APOBEC3 enzymes and mutations in human cancer cells have not been established. The identity of the APOBEC3 paralog(s) that may act as prime drivers of mutagenesis and the mechanisms underlying different APOBEC3-associated mutational signatures are unknown. To directly investigate the roles of APOBEC3 enzymes in cancer mutagenesis, candidate APOBEC3 genes were deleted from cancer cell lines recently found to naturally generate APOBEC3-associated mutations in episodic bursts4. Deletion of the APOBEC3A paralog severely diminished the acquisition of mutations of speculative APOBEC3 origins in breast cancer and lymphoma cell lines. APOBEC3 mutational burdens were undiminished in APOBEC3B knockout cell lines. APOBEC3A deletion reduced the appearance of the clustered mutation types kataegis and omikli, which are frequently found in cancer genomes. The uracil glycosylase UNG and the translesion polymerase REV1 were found to play critical roles in the generation of mutations induced by APOBEC3A. These data represent the first evidence for a long-postulated hypothesis that APOBEC3 deaminases generate prevalent clustered and non-clustered mutational signatures in human cancer cells, identify APOBEC3A as a driver of episodic mutational bursts, and dissect the roles of the relevant enzymes in generating the associated mutations in breast cancer and B cell lymphoma cell lines.

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The APOBEC3A deaminase drives episodic mutagenesis in cancer cells

10.21203/rs.3.rs-240585/v1 ◽

2021 ◽

Author(s):

John Maciejowski ◽

Mia Petljak ◽

Kevan Chu ◽

Alexandra Dananberg ◽

Erik Bergstrom ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Cell Lines ◽

Human Cancer ◽

B Cell Lymphoma ◽

Lymphoma Cell ◽

Mutational Signatures ◽

Cytidine Deaminases ◽

Human Cancer Cells ◽

Cancer Genomes

Abstract The APOBEC3 family of cytidine deaminases is widely speculated to be a major source of somatic mutations in cancer1–3. However, causal links between APOBEC3 enzymes and mutations in human cancer cells have not been established. The identity of the APOBEC3 paralog(s) that may act as prime drivers of mutagenesis and the mechanisms underlying different APOBEC3-associated mutational signatures are unknown. To directly investigate the roles of APOBEC3 enzymes in cancer mutagenesis, candidate APOBEC3 genes were deleted from cancer cell lines recently found to naturally generate APOBEC3-associated mutations in episodic bursts4. Deletion of the APOBEC3A paralog severely diminished the acquisition of mutations of speculative APOBEC3 origins in breast cancer and lymphoma cell lines. APOBEC3 mutational burdens were undiminished in APOBEC3B knockout cell lines. APOBEC3A deletion reduced the appearance of the clustered mutation types kataegis and omikli, which are frequently found in cancer genomes. The uracil glycosylase UNG and the translesion polymerase REV1 were found to play critical roles in the generation of mutations induced by APOBEC3A. These data represent the first evidence for a long-postulated hypothesis that APOBEC3 deaminases generate prevalent clustered and non-clustered mutational signatures in human cancer cells, identify APOBEC3A as a driver of episodic mutational bursts, and dissect the roles of the relevant enzymes in generating the associated mutations in breast cancer and B cell lymphoma cell lines.

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Understanding mutagenesis through delineation of mutational signatures in human cancer

Carcinogenesis ◽

10.1093/carcin/bgw055 ◽

2016 ◽

Vol 37 (6) ◽

pp. 531-540 ◽

Cited By ~ 56

Author(s):

Mia Petljak ◽

Ludmil B. Alexandrov

Keyword(s):

Human Cancer ◽

Mutational Signatures

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Mutational signatures of complex genomic rearrangements in human cancer

10.21203/rs.3.rs-535468/v1 ◽

2021 ◽

Author(s):

Lixing Yang ◽

Lisui Bao ◽

Xiaoming Zhong ◽

Yang Yang

Keyword(s):

Human Cancer ◽

Genomic Rearrangements ◽

Cellular Structures ◽

Genome Integrity ◽

Biological Processes ◽

Extrachromosomal Dna ◽

Mutational Signatures ◽

Chromatin Bridge ◽

Open Questions ◽

Dna Loss

Abstract Complex genomic rearrangements (CGRs) are common in cancer and are known to form via two aberrant cellular structures—micronuclei and chromatin bridge. However, which mechanism is more relevant to CGR formation in cancer cells and whether there are other undiscovered mechanisms remain open questions. Here, we analyze 2,014 CGRs from 2,428 whole-genome sequenced tumors and deconvolute six CGR signatures based on the topology of CGRs. Through rigorous benchmarking, we show that our CGR signatures are highly accurate and biologically meaningful. Three signatures can be attributed to known biological processes—micronuclei- and chromatin-bridge-induced chromothripsis and extrachromosomal DNA. More than half of the CGRs belong to the remaining three newly discovered signatures. A unique signature (we named “hourglass chromothripsis”) with highly localized breakpoints and small amount of DNA loss is abundant in prostate cancer. Through genetic association analysis, we find SPOP as a candidate gene causing hourglass chromothripsis and playing important role in maintaining genome integrity. Our study offers valuable insights into the formation of CGRs.

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Performance Characteristics of Mutational Signature Analysis in Targeted Panel Sequencing

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2020-0536-oa ◽

2021 ◽

Author(s):

Lauren Lawrence ◽

Christian A. Kunder ◽

Eula Fung ◽

Henning Stehr ◽

James Zehnder

Keyword(s):

Ultraviolet Radiation ◽

Tobacco Smoking ◽

High Specificity ◽

Primary Site ◽

Clinical Samples ◽

Signature Analysis ◽

Mutational Signatures ◽

Targeted Next Generation Sequencing ◽

Mutational Signature ◽

Poorly Differentiated

Context.— Mutational signatures have been described in the literature and a few centers have implemented pipelines for clinical reporting. Objective.— To describe the performance of a mutational signature caller with clinical samples sequenced on a targeted next-generation sequencing panel with a small genomic footprint. Design.— One thousand six hundred eighty-two (n = 1682) clinical samples were analyzed for the presence of mutational signatures using deconstructSigs on variant calls with at least 20 variant reads. Results.— Signature 10 (associated with POLe mutation) achieved separation of cases and controls in hypermutated samples. Signatures 4 (associated with tobacco smoking) and 7 (associated with ultraviolet radiation) as an indicator of pulmonary or cutaneous primary sites showed moderate sensitivity and high specificity at optimal cutpoints. Mutational signatures in malignancies with unknown primaries were somewhat consistent with the clinically suspected primary site, with an apparent dose-response relationship between the number of variants analyzed and the ability of mutational signature analysis to correctly suggest a primary site. Conclusions.— Mutational signatures represent an opportunity for orthogonal testing of primary site, which may be particularly useful in supporting cutaneous or pulmonary sites in poorly differentiated neoplasms. Tobacco smoking, ultraviolet radiation, and POLe mutational signatures are the most appropriate signatures for implementation. Even relatively small numbers of variants appear capable of supporting a clinically suspected primary.

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