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Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions

Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and...

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Autores principales: Wong, John K. L., Aichmüller, Christian, Schulze, Markus, Hlevnjak, Mario, Elgaafary, Shaymaa, Lichter, Peter, Zapatka, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748499/
https://www.ncbi.nlm.nih.gov/pubmed/35013316
http://dx.doi.org/10.1038/s41467-021-27792-6
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author Wong, John K. L.
Aichmüller, Christian
Schulze, Markus
Hlevnjak, Mario
Elgaafary, Shaymaa
Lichter, Peter
Zapatka, Marc
author_facet Wong, John K. L.
Aichmüller, Christian
Schulze, Markus
Hlevnjak, Mario
Elgaafary, Shaymaa
Lichter, Peter
Zapatka, Marc
author_sort Wong, John K. L.
collection PubMed
description Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.
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spelling pubmed-87484992022-01-20 Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions Wong, John K. L. Aichmüller, Christian Schulze, Markus Hlevnjak, Mario Elgaafary, Shaymaa Lichter, Peter Zapatka, Marc Nat Commun Article Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome. Nature Publishing Group UK 2022-01-10 /pmc/articles/PMC8748499/ /pubmed/35013316 http://dx.doi.org/10.1038/s41467-021-27792-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wong, John K. L.
Aichmüller, Christian
Schulze, Markus
Hlevnjak, Mario
Elgaafary, Shaymaa
Lichter, Peter
Zapatka, Marc
Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title_full Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title_fullStr Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title_full_unstemmed Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title_short Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
title_sort association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748499/
https://www.ncbi.nlm.nih.gov/pubmed/35013316
http://dx.doi.org/10.1038/s41467-021-27792-6
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