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Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different...

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Autores principales: Martinez-Fundichely, Alexander, Dixon, Austin, Khurana, Ekta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512825/
https://www.ncbi.nlm.nih.gov/pubmed/36163358
http://dx.doi.org/10.1038/s41467-022-32945-2
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author Martinez-Fundichely, Alexander
Dixon, Austin
Khurana, Ekta
author_facet Martinez-Fundichely, Alexander
Dixon, Austin
Khurana, Ekta
author_sort Martinez-Fundichely, Alexander
collection PubMed
description Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection.
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spelling pubmed-95128252022-09-28 Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers Martinez-Fundichely, Alexander Dixon, Austin Khurana, Ekta Nat Commun Article Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection. Nature Publishing Group UK 2022-09-26 /pmc/articles/PMC9512825/ /pubmed/36163358 http://dx.doi.org/10.1038/s41467-022-32945-2 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
Martinez-Fundichely, Alexander
Dixon, Austin
Khurana, Ekta
Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title_full Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title_fullStr Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title_full_unstemmed Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title_short Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
title_sort modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512825/
https://www.ncbi.nlm.nih.gov/pubmed/36163358
http://dx.doi.org/10.1038/s41467-022-32945-2
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