A data-driven approach for constructing mutation categories for mutational signature analysis.

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base...

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Autores principales:	Gal Gilad, Mark D M Leiserson, Roded Sharan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2e2db682464b4c72ad2e2f5eb12868df
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id	oai:doaj.org-article:2e2db682464b4c72ad2e2f5eb12868df
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spelling	oai:doaj.org-article:2e2db682464b4c72ad2e2f5eb12868df2021-12-02T19:57:28ZA data-driven approach for constructing mutation categories for mutational signature analysis.1553-734X1553-735810.1371/journal.pcbi.1009542https://doaj.org/article/2e2db682464b4c72ad2e2f5eb12868df2021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009542https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.Gal GiladMark D M LeisersonRoded SharanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009542 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Gal Gilad Mark D M Leiserson Roded Sharan A data-driven approach for constructing mutation categories for mutational signature analysis.
description	Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.
format	article
author	Gal Gilad Mark D M Leiserson Roded Sharan
author_facet	Gal Gilad Mark D M Leiserson Roded Sharan
author_sort	Gal Gilad
title	A data-driven approach for constructing mutation categories for mutational signature analysis.
title_short	A data-driven approach for constructing mutation categories for mutational signature analysis.
title_full	A data-driven approach for constructing mutation categories for mutational signature analysis.
title_fullStr	A data-driven approach for constructing mutation categories for mutational signature analysis.
title_full_unstemmed	A data-driven approach for constructing mutation categories for mutational signature analysis.
title_sort	data-driven approach for constructing mutation categories for mutational signature analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2e2db682464b4c72ad2e2f5eb12868df
work_keys_str_mv	AT galgilad adatadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis AT markdmleiserson adatadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis AT rodedsharan adatadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis AT galgilad datadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis AT markdmleiserson datadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis AT rodedsharan datadrivenapproachforconstructingmutationcategoriesformutationalsignatureanalysis
_version_	1718375830965452800

A data-driven approach for constructing mutation categories for mutational signature analysis.

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