Mutational signatures: the patterns of somatic mutations hidden in cancer genomes

AbstractCancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA mainte-nance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Decomposition of cancer’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from diﬀerent decomposition methods are not always consistent. Hence, one needs to not only be able to decompose a patient’s mutational profile into signatures but also to establish the accuracy of such decomposition. We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified very stable and highly unstable signatures, as well as signatures that have been missed altogether. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#signatureestimation.

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Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

10.1101/568568 ◽

2019 ◽

Author(s):

Yoo-Ah Kim ◽

Damian Wojtowicz ◽

Rebecca Sarto Basso ◽

Itay Sason ◽

Welles Robinson ◽

...

Keyword(s):

Breast Cancer ◽

Dna Damage ◽

Somatic Mutations ◽

Cancer Genome ◽

Mutational Signatures ◽

Dna Damage And Repair ◽

Cancer Genomes ◽

Cancer Mutations ◽

Functional Pathways ◽

Dna Maintenance

AbstractStudies of cancer mutations typically focus on identifying cancer driving mutations. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of passenger somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Understanding the etiology of the mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Considering mutational signatures as phenotypes, we asked two complementary questions (i) what are functional pathways whose geneexpressionprofiles are associated with mutational signatures, and (ii) what aremutated pathways(if any) that might underlie specific mutational signatures? We have been able to identify pathways associated with mutational signatures on both expression and mutation levels. In particular, our analysis provides novel insights into mutagenic processes in breast cancer by capturing important differences in the etiology of different APOBEC related signatures and the two clock-like signatures. These results are important for understanding mutagenic processes in cancer and for developing personalized drug therapies.

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The Repertoire of Mutational Signatures in Human Cancer

10.1101/322859 ◽

2018 ◽

Cited By ~ 74

Author(s):

Ludmil B Alexandrov ◽

Jaegil Kim ◽

Nicholas J Haradhvala ◽

Mi Ni Huang ◽

Alvin WT Ng ◽

...

Keyword(s):

Somatic Mutations ◽

Human Cancer ◽

Base Substitution ◽

Mutational Signatures ◽

Insertion And Deletion ◽

Damage Repair ◽

Cancer Genomes ◽

Dataset Size ◽

Dna Maintenance ◽

Mutational Processes

ABSTRACTSomatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.

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DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis

eLife ◽

10.7554/elife.00534 ◽

2013 ◽

Vol 2 ◽

Cited By ~ 221

Author(s):

Benjamin JM Taylor ◽

Serena Nik-Zainal ◽

Yee Ling Wu ◽

Lucy A Stebbings ◽

Keiran Raine ◽

...

Keyword(s):

Breast Cancer ◽

Abasic Site ◽

Dna Breaks ◽

Mutational Signatures ◽

Genome Wide ◽

Cancer Genomes ◽

Strand Breakage ◽

Cytidine Deamination ◽

Apobec Family ◽

Insight Into

Breast cancer genomes have revealed a novel form of mutation showers (kataegis) in which multiple same-strand substitutions at C:G pairs spaced one to several hundred nucleotides apart are clustered over kilobase-sized regions, often associated with sites of DNA rearrangement. We show kataegis can result from AID/APOBEC-catalysed cytidine deamination in the vicinity of DNA breaks, likely through action on single-stranded DNA exposed during resection. Cancer-like kataegis can be recapitulated by expression of AID/APOBEC family deaminases in yeast where it largely depends on uracil excision, which generates an abasic site for strand breakage. Localized kataegis can also be nucleated by an I-SceI-induced break. Genome-wide patterns of APOBEC3-catalyzed deamination in yeast reveal APOBEC3B and 3A as the deaminases whose mutational signatures are most similar to those of breast cancer kataegic mutations. Together with expression and functional assays, the results implicate APOBEC3B/A in breast cancer hypermutation and give insight into the mechanism of kataegis.

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Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

10.1101/249219 ◽

2018 ◽

Cited By ~ 1

Author(s):

Alexandre Coudray ◽

Anna M. Battenhouse ◽

Philipp Bucher ◽

Vishwanath R. Iyer

Keyword(s):

Exome Sequencing ◽

Somatic Mutations ◽

Low Cost ◽

Systematic Evaluation ◽

Rna Seq ◽

Whole Exome ◽

Cancer Genomes ◽

Additional Support ◽

Coding Variants ◽

Measure Gene Expression

ABSTRACTTo detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from GATK to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

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Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

10.1101/850453 ◽

2019 ◽

Cited By ~ 3

Author(s):

Harald Vöhringer ◽

Arne van Hoeck ◽

Edwin Cuppen ◽

Moritz Gerstung

Keyword(s):

Somatic Hypermutation ◽

Translesion Synthesis ◽

Strand Break ◽

Signature Analysis ◽

Transcription Start ◽

Mutational Signatures ◽

Transcription Start Sites ◽

Mutational Signature ◽

Cancer Genomes ◽

Cancer Types

AbstractMutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types.Key findingsSimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants.Analysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes.Distinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage.APOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively.Somatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types.

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