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Non-coding single nucleotide variants affecting estrogen receptor binding and activity

BACKGROUND: Estrogen receptor (ER) activity is critical for the development and progression of the majority of breast cancers. It is known that ER is differentially bound to DNA leading to transcriptomic and phenotypic changes in different breast cancer models. We investigated whether single nucleot...

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Autores principales: Bahreini, Amir, Levine, Kevin, Santana-Santos, Lucas, Benos, Panayiotis V., Wang, Peilu, Andersen, Courtney, Oesterreich, Steffi, Lee, Adrian V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5154163/
https://www.ncbi.nlm.nih.gov/pubmed/27964748
http://dx.doi.org/10.1186/s13073-016-0382-0
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author Bahreini, Amir
Levine, Kevin
Santana-Santos, Lucas
Benos, Panayiotis V.
Wang, Peilu
Andersen, Courtney
Oesterreich, Steffi
Lee, Adrian V.
author_facet Bahreini, Amir
Levine, Kevin
Santana-Santos, Lucas
Benos, Panayiotis V.
Wang, Peilu
Andersen, Courtney
Oesterreich, Steffi
Lee, Adrian V.
author_sort Bahreini, Amir
collection PubMed
description BACKGROUND: Estrogen receptor (ER) activity is critical for the development and progression of the majority of breast cancers. It is known that ER is differentially bound to DNA leading to transcriptomic and phenotypic changes in different breast cancer models. We investigated whether single nucleotide variants (SNVs) in ER binding sites (regSNVs) contribute to ER action through changes in the ER cistrome, thereby affecting disease progression. Here we developed a computational pipeline to identify SNVs in ER binding sites using chromatin immunoprecipitation sequencing (ChIP-seq) data from ER+ breast cancer models. METHODS: ER ChIP-seq data were downloaded from the Gene Expression Omnibus (GEO). GATK pipeline was used to identify SNVs and the MACS algorithm was employed to call DNA-binding sites. Determination of the potential effect of a given SNV in a binding site was inferred using reimplementation of the is-rSNP algorithm. The Cancer Genome Atlas (TCGA) data were integrated to correlate the regSNVs and gene expression in breast tumors. ChIP and luciferase assays were used to assess the allele-specific binding. RESULTS: Analysis of ER ChIP-seq data from MCF7 cells identified an intronic SNV in the IGF1R gene, rs62022087, predicted to increase ER binding. Functional studies confirmed that ER binds preferentially to rs62022087 versus the wild-type allele. By integrating 43 ER ChIP-seq datasets, multi-omics, and clinical data, we identified 17 regSNVs associated with altered expression of adjacent genes in ER+ disease. Of these, the top candidate was in the promoter of the GSTM1 gene and was associated with higher expression of GSTM1 in breast tumors. Survival analysis of patients with ER+ tumors revealed that higher expression of GSTM1, responsible for detoxifying carcinogens, was correlated with better outcome. CONCLUSIONS: In conclusion, we have developed a computational approach that is capable of identifying putative regSNVs in ER ChIP-binding sites. These non-coding variants could potentially regulate target genes and may contribute to clinical prognosis in breast cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0382-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-51541632016-12-20 Non-coding single nucleotide variants affecting estrogen receptor binding and activity Bahreini, Amir Levine, Kevin Santana-Santos, Lucas Benos, Panayiotis V. Wang, Peilu Andersen, Courtney Oesterreich, Steffi Lee, Adrian V. Genome Med Research BACKGROUND: Estrogen receptor (ER) activity is critical for the development and progression of the majority of breast cancers. It is known that ER is differentially bound to DNA leading to transcriptomic and phenotypic changes in different breast cancer models. We investigated whether single nucleotide variants (SNVs) in ER binding sites (regSNVs) contribute to ER action through changes in the ER cistrome, thereby affecting disease progression. Here we developed a computational pipeline to identify SNVs in ER binding sites using chromatin immunoprecipitation sequencing (ChIP-seq) data from ER+ breast cancer models. METHODS: ER ChIP-seq data were downloaded from the Gene Expression Omnibus (GEO). GATK pipeline was used to identify SNVs and the MACS algorithm was employed to call DNA-binding sites. Determination of the potential effect of a given SNV in a binding site was inferred using reimplementation of the is-rSNP algorithm. The Cancer Genome Atlas (TCGA) data were integrated to correlate the regSNVs and gene expression in breast tumors. ChIP and luciferase assays were used to assess the allele-specific binding. RESULTS: Analysis of ER ChIP-seq data from MCF7 cells identified an intronic SNV in the IGF1R gene, rs62022087, predicted to increase ER binding. Functional studies confirmed that ER binds preferentially to rs62022087 versus the wild-type allele. By integrating 43 ER ChIP-seq datasets, multi-omics, and clinical data, we identified 17 regSNVs associated with altered expression of adjacent genes in ER+ disease. Of these, the top candidate was in the promoter of the GSTM1 gene and was associated with higher expression of GSTM1 in breast tumors. Survival analysis of patients with ER+ tumors revealed that higher expression of GSTM1, responsible for detoxifying carcinogens, was correlated with better outcome. CONCLUSIONS: In conclusion, we have developed a computational approach that is capable of identifying putative regSNVs in ER ChIP-binding sites. These non-coding variants could potentially regulate target genes and may contribute to clinical prognosis in breast cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0382-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-12-13 /pmc/articles/PMC5154163/ /pubmed/27964748 http://dx.doi.org/10.1186/s13073-016-0382-0 Text en © The Author(s). 2016 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 Research
Bahreini, Amir
Levine, Kevin
Santana-Santos, Lucas
Benos, Panayiotis V.
Wang, Peilu
Andersen, Courtney
Oesterreich, Steffi
Lee, Adrian V.
Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title_full Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title_fullStr Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title_full_unstemmed Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title_short Non-coding single nucleotide variants affecting estrogen receptor binding and activity
title_sort non-coding single nucleotide variants affecting estrogen receptor binding and activity
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5154163/
https://www.ncbi.nlm.nih.gov/pubmed/27964748
http://dx.doi.org/10.1186/s13073-016-0382-0
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