Abstract

BackgroundEstrogen 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.MethodsER 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.ResultsAnalysis 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.ConclusionsIn 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 materialThe online version of this article (doi:10.1186/s13073-016-0382-0) contains supplementary material, which is available to authorized users.

Highlights

  • Estrogen receptor (ER) activity is critical for the development and progression of the majority of breast cancers

  • We applied our computational workflow to nine ER chromatin immunoprecipitation (ChIP)-seq datasets from five different studies of MCF7 cells performed under similar experimental conditions (Additional file 1: Table S1) [1, 22,23,24,25]

  • The datasets were merged by combining the reads and 303,964,039 sequencing reads were mapped to the human genome and identified a total of 1,409,406 Single nucleotide variants (SNV) and short indels

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Summary

Introduction

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 developed a computational pipeline to identify SNVs in ER binding sites using chromatin immunoprecipitation sequencing (ChIP-seq) data from ER+ breast cancer models. Breast cancer is a major public health issue with an increasing incidence over the past decade in the US. Endocrine therapy, such as the antiestrogen tamoxifen and aromatase inhibitors, are the most successful treatment for breast cancer in which estrogen signaling is active. The potential genomic changes underlying unique ER ChIP-binding sites in different models are still unclear

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