Inhibitory effects of estrogen receptor beta on specific hormone-responsive gene expression and association with disease outcome in primary breast cancer
© Lin et al.; licensee BioMed Central Ltd. 2007
Received: 24 October 2006
Accepted: 10 April 2007
Published: 10 April 2007
The impact of interactions between the two estrogen receptor (ER) subtypes, ERα and ERβ, on gene expression in breast cancer biology is not clear. The goal of this study was to examine transcriptomic alterations in cancer cells co-expressing both receptors and the association of gene expression signatures with disease outcome.
Transcriptional effects of ERβ overexpression were determined in a stably transfected cell line derived from ERα-positive T-47D cells. Microarray analysis was carried out to identify differential gene expression in the cell line, and expression of key genes was validated by quantitative polymerase chain reaction. Microarray and clinical data from patient samples were then assessed to determine the in vivo relevance of the expression profiles observed in the cell line.
A subset of 14 DNA replication and cell cycle-related genes was found to be specifically downregulated by ERβ. Expression profiles of four genes, CDC2, CDC6, CKS2, and DNA2L, were significantly inversely correlated with ERβ transcript levels in patient samples, consistent with in vitro observations. Kaplan-Meier analysis revealed better disease outcome for the patient group with an expression signature linked to higher ERβ expression as compared to the lower ERβ-expressing group for both disease-free survival (p = 0.00165) and disease-specific survival (p = 0.0268). These findings were further validated in an independent cohort.
Our findings revealed a transcriptionally regulated mechanism for the previously described growth inhibitory effects of ERβ in ERα-positive breast tumor cells and provide evidence for a functional and beneficial impact of ERβ in primary breast tumors.
Estrogens are involved in a number of vertebrate developmental and physiological processes and have been implicated in certain types of endocrine-related tumors [1–4]. Hormone response in target tissues is mediated by nuclear receptors that function as ligand-dependent transcription factors. Receptor function is further modulated by post-translational modifications and interactions with other nuclear proteins. Originally, only one type of estrogen receptor (ER) was thought to be involved in hormone signaling. However, a second ER, termed ERβ, was subsequently discovered, adding another dimension of complexity to the regulation of hormone response. The original receptor was renamed ERα .
ERα and ERβ show 55% identity in their ligand-binding domains and approximately 97% similarity in the DNA-binding domains (DBDs). Both ERs bind estradiol with high affinity but vary in their ability to bind other natural and synthetic ligands and the types of response elicited upon ligand binding [6–8]. Reflecting the high degree of similarity in their DBDs, both receptors interact with the same conserved estrogen response element (ERE) (5'-GGTCAnnnTGACC-3') as either homodimers or α/β heterodimers [9–11]. Tissue-specific expression and co-expression of receptor subtypes suggest that ER homodimers and heterodimers may mediate distinct hormone responses [12–15]. Moreover, the discovery of ERβ variants with different structural and functional characteristics and tissue distribution further highlighted the potential complexity of the interactions between ERs and the mechanisms by which estrogen response is modulated [16–20].
The predominant impact of ER activation appears to be alterations in the transcriptional activity and expression profiles of target genes. A number of genes, including trefoil factor 1/pS2, cathepsin D, cyclin D1, c-Myc, and the progesterone receptor, are positively regulated by estrogen treatment . Transcriptional repression by ER has not been as well studied. However, by means of SAGE (Serial Analysis of Gene Expression) and DNA microarrays, many more estrogen-responsive genes, induced or repressed by the hormone, have been identified and characterized [22–29]. Much of the work on gene expression has been focused on the role of ERα, but little is known about genes specifically targeted by ERβ or by α/β heterodimers. Recent microarray experiments using knockout animals indicate that target tissues in ERβ knockouts exhibited an overall increased transcriptional response to hormone treatment as compared to wild-type controls . Expression studies of osteosarcoma cells stably transfected with each receptor subtype suggest that ERα and ERβ share some common target genes, although each receptor also appears to have distinct sets of downstream targets . Despite these efforts, the exact transcriptional effects of ERα and ERβ in breast cancer remain obscure.
To characterize the impact of ERβ expression on hormone response in ERα-positive breast tumor cells, we have stably transfected T-47D (ERα+/ERβ-) cells with an inducible ERβ expression construct to generate subline T-47Dbeta. Induction of ERβ expression in this cell line was shown to inhibit estrogen-responsive cell proliferation . These observations are consistent with other reports that describe the growth-inhibitory effects of ERβ [33, 34]. Using high-density DNA microarrays under conditions that induce ERβ expression and following hormone treatment, we screened for potential transcriptional effects of the ERαβ co-expression. Here, we present a set of cell cycle and DNA replication genes responsive to ERβ expression and estrogen treatment and their potential roles in the biology of primary breast tumors.
Materials and methods
T-47D cells were cultured in a mixture of Dulbecco's modified Eagle's medium (DMEM) and Ham's F-12 1:1 supplemented with 5% fetal bovine serum (FBS), penicillin, and streptomycin. For experiments using 17β-estradiol, DMEM without phenol red and FBS treated with dextran-coated charcoal were used.
Transfection and plasmids
T-47D cells stably transfected with tetracycline (TET)-regulated ERβ expression plasmid were generated in two steps: (a) The cells were first transfected with pTet-tTAk (Invitrogen Corporation, Carlsbad, CA, USA) modified to contain puromycin resistance using lipofectin according to manufacturer's instructions (Invitrogen Corporation). Selection was performed with 0.5 μg/ml of puromycin in the presence of 1 μg/ml of TET. A good clone showing a high level of induction and low basal activity was selected using the pUHC13-3 control plasmid (Invitrogen Corporation). The short form of ERβ encoding 485 amino acids was fused to the flag tag (ERβ485) and cloned into PBI-EGFP (Clontech, Mountain View, CA, USA). (b) This construct was then transfected into the previously described inducible clone together with a neomycin-resistance plasmid. The selection was performed using 500 μg/ml of G418 (Calbiochem, brand of EMD Biosciences, Inc., San Diego, CA, USA).
Preparation of RNA
Cells were added to 150-mm plates at a confluency of 40%; after 1 day, the normal medium was replaced by the medium described above, supplemented with 5% dextran-coated charcoal-treated FBS (DCCFBS). After 24 hours, 10 nM ICI 182780 (fulvestrant) was added to the cultures and incubation proceeded for an additional 48 hours. For expression of ERβ, TET was removed 12 hours before initiation of treatment with 17β-estradiol. At time 0 hours, the medium was changed to 0.5% DCCFBS and 10 nM of 17β-estradiol or the equivalent volume of dimethyl sulfoxide for mock-treated controls was added. RNA was prepared by adding 10 ml of Trizol (Invitrogen Corporation) to each 150-mm plate at different time points after the start of treatment, and RNA was prepared according to the manufacturer's instructions.
Microarrays were generated by spotting the Compugen USA, Inc. (Jamesburg, NJ, USA) 19 K human oligo library, made by Sigma-Genosys (The Woodlands, TX, USA), on poly-L-lysine-coated glass slides. Twenty-five micrograms each of sample total RNA and human universal reference RNA (Stratagene, La Jolla, CA, USA) were labeled with cyanine (Cy) 5-conjugated dUTP and Cy3-conjugated dUTP (PerkinElmer Life and Analytical Sciences, Inc., Waltham, MA, USA), respectively, and hybridized to the arrays using protocols established by the Patrick O. Brown Laboratory (Stanford University, Palo Alto, CA, USA) accessible at . Array images and data were obtained and processed using the GenePix4000B scanner and GenePix Pro software (Axon Instruments, Inc., now part of Molecular Devices Corporation, Sunnyvale, CA, USA). A single sample from each time point and treatment condition was subjected to microarray analysis. Differentially expressed genes were determined using a 1.15-fold difference cutoff at multiple time points and were clustered and visualized using the Eisen Cluster and TreeView programs . Gene ontology of responsive genes was derived from annotations (2002 version) made by Compugen USA, Inc. Significant enrichment of functional categories was determined by chi statistics by comparing the distribution of genes in a given functional category for all of the genes represented on the microarray (expected distribution) with the distribution of genes in the same functional category in differentially expressed genes (observed distribution). The p value was then calculated based on a two-tailed chi distribution, and a p value of less than 0.05 was considered a significant difference between the observed and expected values. The data are presented as the log2-transformed ratio between the treated/induced samples and the mock treated-uninduced controls for the same time point. The cell line microarray data have been deposited with the Gene Expression Omnibus under accession number GSE7206, and the normalized log2-transformed fold-change data are presented in Additional file 1.
Real-time relative quantification polymerase chain reaction
For validation of microarray data, RNA samples from the 30-hour time point were converted to cDNA by means of the PowerScript (Clontech) reverse transcriptase in accordance with the recommended protocol with 1 μg of total RNA. Quantification of transcript levels was performed using SYBR Green chemistry on the ABI PRISM® 7900 HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Polymerase chain reaction (PCR) amplification was carried out in 20 μl of reaction mixture consisting of 50 nM of primer pairs. The following primers were used for PCRs: cell division cycle 2 (CDC2), CCCAAATGGAAACCAGGAAG (forward), CGTTTGGCTGGATCATAGAT (reverse); cell division cycle 6 (CDC6), GGAAACCCGTTTGACAAAGG (forward), CGGGTGGGGTGTAAGAGAAGAATT (reverse); cell division cycle 28 protein kinase regulatory subunit 2 (CKS2), CTGATGTCTGAAGAGGAGTG (forward), GGAAGAGGTCGTCTAAAGAG (reverse); DNA replication helicase 2-like (DNA2L), GGATGGAACTGTTGGTGAAC (forward), ATTTAGTGAGGGCACACACC (reverse); ERβ, TCCATGCGCCTGGCTAAC (forward), CAGATGTTCCATGCCCTTGTTA (reverse); and β actin, ACCCACACTGTGCCCATCTACGAG (forward), TCTCCTTAATGTCACGCACGATTTCC (reverse). 18S rRNA Pre-developed TaqMan® Assays by Applied Biosystems were used as reference gene.
Patients and tumor specimens
The original patient material consisted of freshly frozen breast tumors that came from a population-based cohort of 315 women and that represented 65% of all breast tumors resected in Uppsala County, Sweden, from 1 January 1987 to 31 December 1989 [37, 38]. The follow-up RNA expression profiling study was approved by the ethical committee at the Karolinska Institute, Stockholm, Sweden. Affymetrix (Santa Clara, CA, USA) oligonucleotide microarrays (U133A&B) were used to determine gene expression profiles in breast tumor samples. After exclusions based on RNA integrity and array quality control, expression profiles of 260 tumors were deemed suitable for further analysis . To assess the role of ERβ-regulated cell cycle and DNA replication genes in breast tumor biology, we selected the 69 ER-positive tumors from patients who underwent adjuvant tamoxifen therapy (see Additional file 2). Clinico-pathological characteristics were derived from the patient records and from routine clinical measurements at the time of diagnosis as described elsewhere . Histopathological re-examination and grading according to Elston-Ellis were performed by an experienced breast cancer pathologist without prior knowledge of selected therapies and outcomes. Gene expression data are presented as mean-centered log2-transformed normalized intensity for the probe set. The distances between expression profiles were calculated based on Pearson correlation, and the average-linkage method from the Eisen Cluster program was used to cluster genes and tumor samples. All cluster data were generated in the same way and using the same parameters to maintain the consistency of the analysis.
Screen for ERβ-regulated E2-responsive genes
ERβ disrupts expression of cell cycle and DNA replication genes
Statistics of key gene ontology categories
χ2 p value
E2 + ERβ upregulated
E2 + ERβ downregulated
All genes on array
Statistics of key gene ontology categories by mechanism of regulation in 728 E2 + ERβ downregulated genes
χ2 p value
All genes on array
Gene expression profiles in patient samples
We sought to address whether this in vitro effect of ERβ on proliferation could explain some of the heterogeneity in biological behavior of ERα-positive breast cancer. We posited that higher ERβ levels would be correlated with lower expression of these specific DNA replication and cell cycle genes and, in turn, would be associated with better clinical outcome. To test this hypothesis, we examined ERβ transcript levels in primary breast tumors with the expression profiles of ERβ-regulated cell cycle genes identified in our in vitro analysis and their association with clinical outcome.
Association with estrogen receptor beta expression in 69 breast tumors
Original characterization of ERβ function indicated its ability to form homodimers and heterodimers (with ERα) and to bind the same consensus ERE sequences in electrophoretic mobility shift experiments . However, the biological effects of ERβ are considerably different from those of ERα: ERβ expression alone, unlike hormone treatment, was not sufficient to drive cell proliferation . In cell lines, overexpression of ERβ inhibited E2-driven and ERα-mediated cell proliferation . In our microarray screen for transcriptional effects of ERβ expression in ERα-positive cells, statistical analysis indicated a significant enrichment of genes from two cell growth-associated functional categories within the population of genes downregulated by ERβ in the presence of E2 (Tables 2 and 3). However, because both receptor subtypes are present in the cells, it is not clear from the experimental system whether the observed transcriptional effects were due to the activity of ERβ homodimers or α/β heterodimers.
One of the difficulties in assessing the biological significance of such associations between in vitro gene expression changes and the cellular response is that a number of genes coordinately expressed (for example, the ERβ-responsive gene set) may be involved. One-by-one targeting of all responsive genes not only is experimentally problematic but also raises the question of relevance given that an entire gene set may need to be altered. We pursued an alternative approach to determining the in vivo relevance of these findings in the engineered cell line by exploring the association of ERβ transcript levels and the key ERβ-suppressed cell cycle and DNA replication genes in disease outcome of patients with breast cancer. We hypothesized that if our in vitro findings were biologically important, their configuration should be captured in clinical tumors and their biological impact should recapitulate that found in cell culture.
To this end, we first found significant association of the expression profiles of four genes, CDC2, CKS2, DNA2L, and CDC6, with ERβ transcript levels in the breast tumors, consistent with the inverse correlation (high ERβ; low cell cycle/DNA replication genes) observed in the cell line model. CDC2 is a cyclin-dependent kinase that regulates G1-S and G2-M phases of the cell cycle; CKS2 encodes the essential regulatory subunit of cyclin-dependent kinases; and CDC6 and DNA2L function in the pre-replication complex formation and unwinding of replicating DNA, respectively. Tumor samples were then hierarchically clustered into two distinct groups, using the expression profiles of this four-gene set and ERβ (Figure 3). We found significant association of the patient groups with tumor grade (which we consider a clinical surrogate for cell cycle and DNA replication) and with 10-year relapse and death. Moreover, the patient cluster with high ERβ and low ERβ downstream gene expression has significantly higher probability of DFS and DSS over time as compared to those with the opposite profiles. These results suggest that the ERβ-regulated genes identified in our in vitro studies are also involved in the biology of primary malignant breast tumors and reveal protective effects of ERβ in disease prognosis. That this effect is limited to the ERα-positive subgroup is also consistent with our observations that the main ERβ effect is in the presence of ERα and the E2 ligand. Previously, we reported that ER-positive tumors exhibited significant differences detected by expression profiling associated with variances in overall survival . Using the same dataset, we assessed the impact of ERβ and its unique downstream responsive gene set and found that this minimal gene set alone could account for the majority of the survival differences . This suggests that ERβ status may be a major driver for clinical heterogeneity in ER-positive tumors.
Our findings are consistent with a recent report by Hopp and colleagues  in which they found that low ERβ protein levels are associated with poor DFS and overall survival in ER-positive patients treated with adjuvant tamoxifen therapy. In contrast, another group reported the lack of association between ERβ transcript levels and disease outcome following endocrine therapy in a prospective study . One possible explanation for the discrepancy between the prospective study and the previous protein level study and the microarray expression data reported here is that protein levels and expression profiles of downstream ERβ-regulated genes are a more proximal measure of ERβ activity than the transcript levels alone.
A reasonable question is whether the ERβ downstream genes harbor the transcriptional control elements associated with the ERs (that is, EREs). Our assessment of the 5' regulatory regions of the four key downstream genes CDC2, CKS2, DNA2L, and CDC6 showed no evidence for an acceptable ERE (data not shown). This raises the possibility either that the expression of these genes involves trans-elements such as other transcription factors induced by ERβ or that ERβ is acting as a co-factor for other transcription factors such as AP-1 [43, 44]. In an earlier microarray study of estrogen-responsive genes, Lobenhofer and colleagues  observed an increase in the expression of a number of DNA replication fork genes, including those that have been shown to be regulated by the E2F transcription factor. Indeed, a number of studies showed that E2F is involved in regulating the expression of CDC6 [45–47]. These findings suggest that ERβ may affect cell proliferation, in part, through interactions with the E2F transcriptional regulatory pathways.
Taken together, our observations point to specific inhibitory effects of ERβ on hormone-dependent breast cancer cell proliferation and also suggest that molecules directly targeting ERα/ERβ interactions or ERβ mimics could be a novel strategy to develop estrogen response modifiers for the management of ERα-positive breast tumors.
cell division cycle 2
cell division cycle 6
cell division cycle 28 protein kinase regulatory subunit 2
DNA replication helicase 2-like
dextran-coated charcoal-treated fetal bovine serum
Dulbecco's modified Eagle's medium
estrogen response element
fetal bovine serum
polymerase chain reaction
This study was supported by the Singapore Agency for Science Technology and Research (A*STAR) and grants from the Stockholm Cancer Society, the King Gustav Fifth Jubilee Fund, the Swedish Cancer Fund, and Karo Bio AB.
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