Modeling double strand break susceptibility to interrogate structural variation in cancer
Abstract Background Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). Results We...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-02-01
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| Series: | Genome Biology |
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| Online Access: | http://link.springer.com/article/10.1186/s13059-019-1635-1 |
| _version_ | 1828739785598959616 |
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| author | Tracy J. Ballinger Britta A. M. Bouwman Reza Mirzazadeh Silvano Garnerone Nicola Crosetto Colin A. Semple |
| author_facet | Tracy J. Ballinger Britta A. M. Bouwman Reza Mirzazadeh Silvano Garnerone Nicola Crosetto Colin A. Semple |
| author_sort | Tracy J. Ballinger |
| collection | DOAJ |
| description | Abstract Background Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). Results We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers. Conclusions We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors. |
| first_indexed | 2024-04-13T00:29:40Z |
| format | Article |
| id | doaj.art-0ed9b9678a37451d827a0ab21a8d0656 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-04-13T00:29:40Z |
| publishDate | 2019-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-0ed9b9678a37451d827a0ab21a8d06562022-12-22T03:10:31ZengBMCGenome Biology1474-760X2019-02-0120111510.1186/s13059-019-1635-1Modeling double strand break susceptibility to interrogate structural variation in cancerTracy J. Ballinger0Britta A. M. Bouwman1Reza Mirzazadeh2Silvano Garnerone3Nicola Crosetto4Colin A. Semple5MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of EdinburghScience for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska InstitutetScience for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska InstitutetScience for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska InstitutetScience for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska InstitutetMRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of EdinburghAbstract Background Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). Results We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers. Conclusions We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors.http://link.springer.com/article/10.1186/s13059-019-1635-1Double strand breakCancerStructural variationChromatinModeling |
| spellingShingle | Tracy J. Ballinger Britta A. M. Bouwman Reza Mirzazadeh Silvano Garnerone Nicola Crosetto Colin A. Semple Modeling double strand break susceptibility to interrogate structural variation in cancer Genome Biology Double strand break Cancer Structural variation Chromatin Modeling |
| title | Modeling double strand break susceptibility to interrogate structural variation in cancer |
| title_full | Modeling double strand break susceptibility to interrogate structural variation in cancer |
| title_fullStr | Modeling double strand break susceptibility to interrogate structural variation in cancer |
| title_full_unstemmed | Modeling double strand break susceptibility to interrogate structural variation in cancer |
| title_short | Modeling double strand break susceptibility to interrogate structural variation in cancer |
| title_sort | modeling double strand break susceptibility to interrogate structural variation in cancer |
| topic | Double strand break Cancer Structural variation Chromatin Modeling |
| url | http://link.springer.com/article/10.1186/s13059-019-1635-1 |
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