Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784797918304141312 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9512825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT martinezfundichelyalexander modelingtissuespecificbreakpointproximityofstructuralvariationsfromwholegenomestoidentifycancerdrivers AT dixonaustin modelingtissuespecificbreakpointproximityofstructuralvariationsfromwholegenomestoidentifycancerdrivers AT khuranaekta modelingtissuespecificbreakpointproximityofstructuralvariationsfromwholegenomestoidentifycancerdrivers |