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      Concordance of hydrogen peroxide–induced 8-oxo-guanine patterns with two cancer mutation signatures of upper GI tract tumors

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          Abstract

          Oxidative DNA damage has been linked to inflammation, cancer, and aging. Here, we have mapped two types of oxidative DNA damage, oxidized guanines produced by hydrogen peroxide and oxidized thymines created by potassium permanganate, at a single-base resolution. 8-Oxo-guanine occurs strictly dependent on the G/C sequence context and shows a pronounced peak at transcription start sites (TSSs). We determined the trinucleotide sequence pattern of guanine oxidation. This pattern shows high similarity to the cancer-associated single-base substitution signatures SBS18 and SBS36. SBS36 is found in colorectal cancers that carry mutations in MUTYH, encoding a repair enzyme that operates on 8-oxo-guanine mispairs. SBS18 is common in inflammation-associated upper gastrointestinal tract tumors including esophageal and gastric adenocarcinomas. Oxidized thymines induced by permanganate occur with a distinct dinucleotide specificity, 5′T-A/C, and are depleted at the TSS. Our data suggest that two cancer mutational signatures, SBS18 and SBS36, are caused by reactive oxygen species.

          Abstract

          Abstract

          Hydrogen peroxide induces a DNA damage pattern with high similarity to two cancer mutational signatures.

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          Most cited references71

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          The repertoire of mutational signatures in human cancer

          Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
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            BEDTools: The Swiss-Army Tool for Genome Feature Analysis.

            Technological advances have enabled the use of DNA sequencing as a flexible tool to characterize genetic variation and to measure the activity of diverse cellular phenomena such as gene isoform expression and transcription factor binding. Extracting biological insight from the experiments enabled by these advances demands the analysis of large, multi-dimensional datasets. This unit describes the use of the BEDTools toolkit for the exploration of high-throughput genomics datasets. Several protocols are presented for common genomic analyses, demonstrating how simple BEDTools operations may be combined to create bespoke pipelines addressing complex questions. Curr. Protoc. Bioinform. 47:11.12.1-11.12.34. © 2014 by John Wiley & Sons, Inc. Copyright © 2014 John Wiley & Sons, Inc.
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              Chromatin-state discovery and genome annotation with ChromHMM.

              Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing - original draft
                Role: Investigation
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                June 2022
                03 June 2022
                : 8
                : 22
                : eabn3815
                Affiliations
                [1]Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA.
                Author notes
                [* ]Corresponding author. Email: gerd.pfeifer@ 123456vai.org
                Author information
                https://orcid.org/0000-0001-5041-0559
                https://orcid.org/0000-0001-9314-7009
                https://orcid.org/0000-0002-5080-9604
                Article
                abn3815
                10.1126/sciadv.abn3815
                9166614
                35658030
                9b460e73-56b7-4a5e-982a-284514784dea
                Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 22 November 2021
                : 15 April 2022
                Funding
                Funded by: NIH National Cancer Institute;
                Award ID: CA228089
                Categories
                Research Article
                Biomedicine and Life Sciences
                SciAdv r-articles
                Cancer
                Molecular Biology
                Cancer
                Custom metadata
                Penchie Limbo

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