Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 128 |
| Journal | Genome Medicine |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 13 2016 |
Funding
This work was supported in part by funds from the Breast Cancer Research Foundation (AVL, SO), National Cancer Institute of the National Institutes of Health (NIH) award number R01CA94118 (to AVL) and P30CA047904, National Library of Medicine of the NIH award number R01LM012087 (to PVB), Fashion Footwear Association of New York (FFANY), and the Shear Family Foundation. AVL and SO are recipients of Scientific Advisory Council awards from Susan G. Komen for the Cure; and AVL is a Hillman Foundation Fellow. This work used the Data Exacell, which is supported by National Science Foundation award number ACI-1261721 at the Pittsburgh Supercomputing Center (PSC). We thank David Boone and Ryan Hartmaier for reading and commenting on the manuscript. This project used the Pittsburgh Genome Research Repository developed and funded by the Institute for Precision Medicine (IPM) and University of Pittsburgh Cancer Institute (UPCI) and includes collaboration of faculty and staff from IPM, UPCI, the Department of Biomedical Informatics (DBMI), the University of Pittsburgh Center for Simulation and Modeling (SaM), the Pittsburgh Supercomputing Center (PSC), and University of Pittsburgh Medical Center (UPMC).
Keywords
- Breast cancer
- DNA binding
- Estrogen receptor
- IGF1R
- Non-coding SNVs
ASJC Scopus subject areas
- Genetics(clinical)
- Genetics
- Molecular Medicine
- Molecular Biology
