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Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics...

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Autores principales: Dhingra, Priyanka, Martinez-Fundichely, Alexander, Berger, Adeline, Huang, Franklin W., Forbes, Andre Neil, Liu, Eric Minwei, Liu, Deli, Sboner, Andrea, Tamayo, Pablo, Rickman, David S., Rubin, Mark A., Khurana, Ekta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5530464/
https://www.ncbi.nlm.nih.gov/pubmed/28750683
http://dx.doi.org/10.1186/s13059-017-1266-3
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author Dhingra, Priyanka
Martinez-Fundichely, Alexander
Berger, Adeline
Huang, Franklin W.
Forbes, Andre Neil
Liu, Eric Minwei
Liu, Deli
Sboner, Andrea
Tamayo, Pablo
Rickman, David S.
Rubin, Mark A.
Khurana, Ekta
author_facet Dhingra, Priyanka
Martinez-Fundichely, Alexander
Berger, Adeline
Huang, Franklin W.
Forbes, Andre Neil
Liu, Eric Minwei
Liu, Deli
Sboner, Andrea
Tamayo, Pablo
Rickman, David S.
Rubin, Mark A.
Khurana, Ekta
author_sort Dhingra, Priyanka
collection PubMed
description We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1266-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-55304642017-08-02 Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network Dhingra, Priyanka Martinez-Fundichely, Alexander Berger, Adeline Huang, Franklin W. Forbes, Andre Neil Liu, Eric Minwei Liu, Deli Sboner, Andrea Tamayo, Pablo Rickman, David S. Rubin, Mark A. Khurana, Ekta Genome Biol Method We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1266-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-27 /pmc/articles/PMC5530464/ /pubmed/28750683 http://dx.doi.org/10.1186/s13059-017-1266-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Dhingra, Priyanka
Martinez-Fundichely, Alexander
Berger, Adeline
Huang, Franklin W.
Forbes, Andre Neil
Liu, Eric Minwei
Liu, Deli
Sboner, Andrea
Tamayo, Pablo
Rickman, David S.
Rubin, Mark A.
Khurana, Ekta
Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title_full Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title_fullStr Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title_full_unstemmed Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title_short Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
title_sort identification of novel prostate cancer drivers using regnetdriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5530464/
https://www.ncbi.nlm.nih.gov/pubmed/28750683
http://dx.doi.org/10.1186/s13059-017-1266-3
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