Dataset Information

Endogenous Purification Reveals GREB1 as a Key Estrogen Receptor Regulatory Factor

ABSTRACT: Estrogen receptor-alpha (ER) is the driving transcription factor in most breast cancers, and its associated proteins can influence drug response, but direct methods for identifying interacting proteins have been limited. We purified endogenous ER using an approach termed RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins) and discovered the interactome under agonist- and antagonist-liganded conditions in breast cancer cells, revealing transcriptional networks in breast cancer. The most estrogen-enriched ER interactor is GREB1, a potential clinical biomarker with no known function. GREB1 is shown to be a chromatin-bound ER coactivator and is essential for ER-mediated transcription, because it stabilizes interactions between ER and additional cofactors. We show a GREB1-ER interaction in three xenograft tumors, and using a directed protein-protein approach, we find GREB1-ER interactions in half of ER+ primary breast cancers. This finding is supported by histological expression of GREB1, which shows that GREB1 is expressed in half of ER+ cancers, and predicts good clinical outcome. These findings reveal an unexpected role for GREB1 as an estrogen-specific ER cofactor that is expressed in drug-sensitive contexts. Additional information: All microarray data have been deposited in the GEO database with the accession number GSE37386. All ChIP-seq data have been deposited in the GEO database with the accession number GSE41561. RawMSdata files were processed using Proteome Discoverer v.1.3 (Thermo Scientific). Processed files were searched against the SwissProt human database using the Mascot search engine version 2.3.0. Searches were done with tryptic specificity allowing up to one miscleavage and a tolerance on mass measurement of 10 ppm in MS mode and 0.6 Da for MS/MS ions. Structure modifications allowed were oxidized methionine, and deamidation of asparagine and glutamine residues, which were searched as variable modifications. Using a reversed decoy database, false discovery rate (FDR) was less than 1%. Detailed results including peptide sequences, peptide scores, ion scores, expect values, and Mascot scores have been included in Table S1. SILAC data were analyzed using Proteome Discoverer v.1.3. This analysis only uses unique peptides for ratio analysis. The resulting fold-change ratios for proteins found in both replicates were then converted into log fold-change ratios as represented in the figures. In a few instances where protein ratios were absent due to signal from only one of the two channels, these were ranked highest in the log fold-change table following manual inspection of the raw data (MS and MS/MS) to confirm specificity to a single label.

OTHER RELATED OMICS DATASETS IN: PRJNA177430PRJNA159713

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Hisham Mohammed

LAB HEAD: Hisham Mohammed

PROVIDER: PXD000047 | Pride | 2013-08-09

REPOSITORIES: Pride

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Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor.

Mohammed Hisham H D'Santos Clive C Serandour Aurelien A AA Ali H Raza HR Brown Gordon D GD Atkins Alan A Rueda Oscar M OM Holmes Kelly A KA Theodorou Vasiliki V Robinson Jessica L L JL Zwart Wilbert W Saadi Amel A Ross-Innes Caryn S CS Chin Suet-Feung SF Menon Suraj S Stingl John J Palmieri Carlo C Caldas Carlos C Carroll Jason S JS

Cell reports 20130209 2

Estrogen receptor-α (ER) is the driving transcription factor in most breast cancers, and its associated proteins can influence drug response, but direct methods for identifying interacting proteins have been limited. We purified endogenous ER using an approach termed RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins) and discovered the interactome under agonist- and antagonist-liganded conditions in breast cancer cells, revealing transcriptional networks in breast cancer. ...[more]

PMID: 23403292

Similar Datasets

Project description:Estrogen receptor positive breast cancer is the most prevalent form of breast cancer. Although a number of available drugs are highly effective at blocking estrogen mediated receptor activity, thousands of patients die every year from ER positive breast cancers because the disease progresses to a stage at which these drugs are no longer effective. Thus, it is crucial to establish a comprehensive understanding of the biology of the estrogen receptor (ER) in ER:positive breast cancers that progress despite hormone therapy, a gap in knowledge that remains a serious impediment to successful treatment of patients with ER positive breast cancer. A key question that must be answered is how the estrogen receptor retains the capacity to activate transcription in the absence or near absence of estrogen. We have found a partial answer to this question upon investigating the effect of amplification and overexpression of Wolf Hirschhorn Syndrome Candidate 1:Like 1 (WHSC1L1), a gene that is amplified in 15% of breast cancers that codes for a histone:lysine methyltransferase. WHSC1L1 lies in the 8p11:p12 amplicon, a region of gene amplification that is strongly associated with breast cancer. In this study, we performed shRNA knockdown of the catalytically inactive short isoform of WHSC1L1 in SUM44PE breast cancer cells and found that expression of the short isoform of WHSC1L1 is necessary for expression of the estrogen receptor in this highly ER:positive cell line. In addition, we found that the estrogen receptor binds chromatin extensively in the absence of exogenous estrogen, including several actively transcribed canonical ER target genes, indicating that estrogen receptor signaling is active in SUM44 cells in estrogen free conditions. These findings represent a novel model for ER biology in luminal B breast cancers harboring amplification of WHSC1L1 and provide insight into the mechanisms by which ER: positive breast cancers become unresponsive to SERMs or aromatase inhibitors.

Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk. 30 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. All samples were age-matched between histologically normal epithelial samples from ER+ breast cancer patients (n=15) and histologically normal epithelial samples from ER- breast cancer patients (n=15). Sample numbers correspond to individual patient samples. Of the 4 publicly available datasets mentioned above, the only dataset with a GEO number was GSE3494, corresponding to the Miller dataset. The supplementary file below lists the 251 Samples used from the GSE3494 study. We did not reanalyze the data - there was no change made to the Miller dataset; we only used these data for confirmation of our own dataset.

			Action	DRS
	E2f4_ICI.raw	Raw
	E2f4_TAM.raw	Raw
	PR053_HIM_03.raw	Raw
	PR053_HIM_04.raw	Raw
	PR053_HIM_12.raw	Raw