scholarly journals The Repertoire of Mutational Signatures in Human Cancer

2018 ◽  
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.

scholarly journals Comprehensive analysis of clustered mutations in cancer reveals recurrent APOBEC3 mutagenesis of ecDNA

2021 ◽  
Author(s):  
Erik N Bergstrom ◽  
Jens-Christian Luebeck ◽  
Mia Petljak ◽  
Vineet Bafna ◽  
Paul S. Mischel ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Human Cancer ◽  
Cancer Genes ◽  
Base Substitutions ◽  
Cancer Genomes ◽  
Cancer Types ◽  
Mutational Processes ◽  
Comprehensive Characterization

Clustered somatic mutations are common in cancer genomes with prior analyses revealing several types of clustered single-base substitutions, including doublet- and multi-base substitutions, diffuse hypermutation termed omikli, and longer strand-coordinated events termed kataegis. Here, we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome sequenced cancers from 30 cancer types. While only 3.7% of substitutions and 0.9% of indels were found to be clustered, they contributed 8.4% and 6.9% of substitution and indel drivers, respectively. Multiple distinct mutational processes gave rise to clustered indels including signatures enriched in tobacco smokers and homologous-recombination deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, while the majority of multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, previously attributed to the activity of APOBEC3 deaminases, accounted for a large proportion of clustered substitutions. However, only 16.2% of omikli matched APOBEC3 patterns with experimental validation confirming additional mutational processes giving rise to omikli. Kataegis was generated by multiple mutational processes with 76.1% of all kataegic events exhibiting AID/APOBEC3-associated mutational patterns. Co-occurrence of APOBEC3 kataegis and extrachromosomal-DNA (ecDNA) was observed in 31% of samples with ecDNA. Multiple distinct APOBEC3 kataegic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kataegic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fueling the evolution of ecDNA.

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

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.

scholarly journals Defining the mutation signatures of DNA polymerase θ in cancer genomes

NAR Cancer ◽  
2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Taejoo Hwang ◽  
Shelley Reh ◽  
Yerkin Dunbayev ◽  
Yi Zhong ◽  
Yoko Takata ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Dna Polymerase ◽  
Human Cancer ◽  
Strand Break ◽  
Base Substitution ◽  
Cancer Therapies ◽  
End Joining ◽  
Dsb Repair ◽  
Human Cancer Cells ◽  
Insertion And Deletion

Abstract DNA polymerase theta (POLQ)-mediated end joining (TMEJ) is a distinct pathway for mediating DNA double-strand break (DSB) repair. TMEJ is required for the viability of BRCA-mutated cancer cells. It is crucial to identify tumors that rely on POLQ activity for DSB repair, because such tumors are defective in other DSB repair pathways and have predicted sensitivity to POLQ inhibition and to cancer therapies that produce DSBs. We define here the POLQ-associated mutation signatures in human cancers, characterized by short insertions and deletions in a specific range of microhomologies. By analyzing 82 COSMIC (Catalogue of Somatic Mutations in Cancer) signatures, we found that BRCA-mutated cancers with a higher level of POLQ expression have a greatly enhanced representation of the small insertion and deletion signature 6, as well as single base substitution signature 3. Using human cancer cells with disruptions of POLQ, we further show that TMEJ dominates end joining of two separated DSBs (distal EJ). Templated insertions with microhomology are enriched in POLQ-dependent distal EJ. The use of this signature analysis will aid in identifying tumors relying on POLQ activity.

scholarly journals Recurrent somatic mutations in regulatory regions of human cancer genomes

2015 ◽  
Vol 47 (7) ◽  
pp. 710-716 ◽  
Author(s):  
Collin Melton ◽  
Jason A Reuter ◽  
Damek V Spacek ◽  
Michael Snyder
Keyword(s):  
Somatic Mutations ◽  
Human Cancer ◽  
Regulatory Regions ◽  
Cancer Genomes

scholarly journals Detecting presence of mutational signatures in cancer with confidence

2017 ◽  
Author(s):  
Xiaoqing Huang ◽  
Damian Wojtowicz ◽  
Teresa M. Przytycka
Keyword(s):  
Breast Cancer ◽  
Somatic Mutations ◽  
Decomposition Methods ◽  
R Package ◽  
The Novel ◽  
Cancer Etiology ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Cancer Genomes ◽  
Additional Support

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.

scholarly journals Mutational signatures associated with tobacco smoking in human cancer

2016 ◽  
Author(s):  
Ludmil B. Alexandrov ◽  
Young Seok Ju ◽  
Kerstin Haase ◽  
Peter Van Loo ◽  
Iñigo Martincorena ◽  
...  
Keyword(s):  
Tobacco Smoke ◽  
Tobacco Smoking ◽  
Human Cancer ◽  
Mutational Signatures ◽  
Cytidine Deaminases ◽  
Endogenous Clock ◽  
Cancer Types ◽  
Tobacco Carcinogens ◽  
Mutational Processes ◽  
Mutational Process

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.

scholarly journals RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer

2020 ◽  
Author(s):  
Damian Wojtowicz ◽  
Jan Hoinka ◽  
Bayarbaatar Amgalan ◽  
Yoo-Ah Kim ◽  
Teresa M. Przytycka
Keyword(s):  
Dna Damage ◽  
Dna Repair ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Therapeutic Implications ◽  
Mutational Landscape ◽  
Repair Processes ◽  
Cancer Genomes ◽  
Current Paradigm ◽  
Mutational Processes

AbstractMany mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RepairSig, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RepairSig was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RepairSig is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RepairSig is publicly available at https://github.com/ncbi/RepairSig.

scholarly journals Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

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/.

scholarly journals Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

2018 ◽  
Author(s):  
Omichessan Hanane ◽  
Severi Gianluca ◽  
Perduca Vittorio
Keyword(s):  
Somatic Mutations ◽  
Empirical Evaluation ◽  
Simulated Data ◽  
Evaluation Study ◽  
Systematic Evaluation ◽  
Mutational Signatures ◽  
Computational Tools ◽  
Probability Profile ◽  
Mathematical Algorithms ◽  
Mutational Processes

AbstractMutational signatures refer to patterns in the occurrence of somatic mutations that reflect underlying mutational processes. To date, after the analysis of tens of thousands of genomes and exomes from about 40 different cancers types, 30 mutational signatures characterized by a unique probability profile across the 96 mutation types have been identified, validated and listed on the COSMIC (Catalogue of Somatic Mutations in Cancer) website. Simultaneously with this development, the last few years saw the publication of several concurrent methods (mathematical algorithms implemented in publicly available software packages) for either the quantification of the contribution of prespecified signatures (e.g. COSMIC signatures) in a given cancer genome or the identification of new signatures from a sample of cancer genomes. A review about existing computational tools has been recently published to guide researchers and practitioners in conducting their mutational signatures analysis, however, other tools have been introduced since its publication and, to date, there has not been a systematic evaluation and comparison of the performance of such tools. In order to fill this gap, we carry on an empirical evaluation study of all available packages to date, using both real and simulated data.

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