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In estrogen receptor (ER)-negative breast cancer (BC), high tumor glucocorticoid receptor (GR) expression has been associated with a relatively poor outcome. In contrast, using a meta-analysis of several genomic datasets, here we find that tumor GR mRNA expression is associated with improved ER+ relapse-free survival (RFS) (independently of progesterone receptor (PR) expression). To understand the mechanism by which GR expression is associated with a better ER+ BC outcome, the global effect of GR-mediated transcriptional activation in ER+ BC cells was studied. Analysis of GR chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) in ER+/GR+ MCF-7 cells revealed that upon co-activation of GR and ER, GR chromatin association became enriched at proximal promoter regions. Furthermore, following ER activation, increased GR chromatin association was observed at ER, FOXO, and AP1 response elements. In addition, ER associated with GR response elements, suggesting that ER and GR interact in a complex. Co-activation of GR and ER resulted in increased expression (relative to ER activation alone) of transcripts that encode proteins promoting cellular differentiation (e.g. KDM4B, VDR) and inhibiting the Wnt-signaling pathway (IGFBP4). Finally, expression of these individual pro-differentiation genes was associated with significantly improved RFS in ER+ BC patients. Together, these data suggest that the co-expression and subsequent activity of tumor cell GR and ER contribute to the less aggressive natural history of early-stage BC by coordinating the altered expression of genes favoring differentiation.
The interaction between estrogen and glucocorticoid receptor activity highlights the importance of context-dependent nuclear receptor function in cancer.
In the past decade, it has become increasingly clear that breast cancer (BC) subtypes have very different biological behaviors. For example, improved early-stage estrogen receptor-positive (ER+) BC prognosis appears to correlate with high ER and progesterone receptor (PR) expression and activity (1), while mechanisms of triple-negative breast cancer (TNBC) prognosis have yet to be well-characterized (2). Our laboratory used a retrospective meta-analysis of over 1000 early-stage ER+ BC patients and found that high tumor glucocorticoid receptor (GR/NR3C1) mRNA expression was associated with an improved prognosis compared to low or absent GR/NR3C1 expression (3). This result suggested that in the presence of ER, high GR expression leads to a less malignant phenotype by altering the regulation of genes associated with tumor aggressiveness. This finding was surprising because the same retrospective meta-analysis of over 300 early-stage ER-negative tumors found that high versus low GR mRNA expression was associated with a relatively poor prognosis (3); the latter conclusion is supported by a recent immunohistochemical (IHC) analysis of GR expression in ER-negative BC (4). Interestingly, consistent with results in early-stage ER-negative BC (5-7), GR activity appears to be associated with the expression of tumor cell survival and chemoresistance genes in a number of other cancers including ovarian (8-10) and castration-resistant prostate cancer (11,12).
Studies examining GR association with chromatin in the presence of activated ER have suggested that both ER and GR have chromatin remodeling activity (13-15). For example, both ER and GR can increase chromatin accessibility for each other in ER+/GR+ murine mammary cells (13). In a model system using ER response elements (EREs), known EREs from the prolactin receptor gene regulatory region demonstrated increased GR association following ER activation, suggesting ER and GR act cooperatively (14). Conversely, GR activation was shown to displace liganded ER from AP1-associated chromatin regions near the transcriptional start sites (TSSs) of the well-established ER target genes CyclinD1 and TFF1 (15). Based on these observations and our own work, we hypothesized that ER could modulate the chromatin localization of activated GR, thereby affecting GR-dependent gene expression pathways that contribute to the better outcome of ER+/GR+ BC patients.
To begin to test this hypothesis, we first examined whether ER and GR expression is independent of the known good prognostic association of high PR expression in early-stage ER+ BCs. We then employed GR and ER chromatin immunoprecipitation with and without their respective ligands followed by high-throughput sequencing (ChIP-seq) and genome-wide transcript profiling in GR+/ER+ MCF-7 cells. We found that co-activation of GR and ER was associated with a relative increase in the expression of pro-differentiating genes compared to individual receptor activation alone. ER and GR co-activation also resulted in decreased expression of GR-regulated endothelial to mesenchymal transition (EMT)-related genes compared to GR activation alone. These findings form the framework for understanding how high GR expression, chromatin remodeling, and transcriptional activity result in improved patient outcome in early-stage ER+/GR+ BC.
MCF-7 cells were obtained from ATCC (Manassas, VA) and was re-validated within 6 months of use using Short Tandem Repeat Profiling (DDC Medical). MCF-7 cells were grown at 37°C in 5% CO2 in Dulbecco's Modified Eagle's Medium (DMEM, Lonza) supplemented with 10% fetal bovine serum (FBS, Gemini Bio-Products), and 1% penicillin/streptomycin (Lonza). Estradiol (E2, Sigma-Aldrich) and Dexamethasone (Dex, Sigma-Aldrich) were dissolved in vehicle (ethanol, EtOH) in 1mM stock solutions and further diluted in EtOH for the various cell culture experiments. Protease and phosphatase inhibitor tablets (Roche Diagnostics) were dissolved in respective lysis buffers per the manufacturers' protocol.
MCF-7 cells were incubated in phenol red-free DMEM containing 2.5% charcoal-stripped serum for 4 days total (with a media change after 48h) and treated with either EtOH (60 min), 100 nM Dex (60 min), or 100 nM E2 (75 min) or pretreated with E2 for fifteen minutes followed by co-treatment with Dex (i.e. 75 min E2 and 60 min Dex). These hormone concentrations were used previously in GR ChIP-seq experiments (13). DNA and associated proteins were cross-linked with 1% formaldehyde, and lysates were sonicated and IP'd as described previously (3). ChIP experiments were conducted using the ChIP Assay kit and manufacturer's protocol (EMD Millipore). ChIP-grade rabbit polyclonal anti-GR (sc-1003x, Santa Cruz, recognizing the N-terminal region of GR) and anti-ER HC-20 (sc-543x, Santa Cruz, recognizing the C-terminal region of ER-α) were used for the IP. We also used normal rabbit IgG (sc-2027; Santa Cruz) as a negative control. Eluted ChIP DNA was purified using the PCR Purification kit (Qiagen). GR ChIP samples, ER ChIP samples, and corresponding input samples were analyzed in triplicate.
GR ChIP-seq and ER ChIP-seq was performed on the Illumina HiSeq platform, generating raw reads for analysis (see Supplementary Materials and Methods). The sequence alignment and identification of peaks is described briefly. Sequence quality was assessed and aligned to the human genome (version hg19), and peaks were detected using two algorithms: rgt-ODIN v0.3.2 (16) and MACS2 v126.96.36.19940616 (17). The final lists of peaks contained concordant peaks between the two algorithms, with a false discovery rate of 0.05 (MACS2). Next, ChIP-seq peaks were annotated based on genome features using Homer v4.5 and binding region sequences were determined using CentriMo v4.10.0. Androgen receptor (AR) and GR share the same response element (RE) sequence (18); thus, a CentriMo-identified “AR RE” is likely a functional GRE in the context of Dex treatment and anti-GR ChIP-seq. Likewise, EREs in this study include Homer-defined REs that include ER full and half-sites (such as REs for ESR1, ESR2, ESRRA, ESRRB, NR2F1 and NR2F2). GR enrichment at specific genomic features was calculated and log2-enrichment scores for Homer-defined regions were obtained (see Supplementary Materials and Methods for additional description). GR and ER ChIP-seq peaks for specific genes (KDM4B, IGFBP4, and VDR) were visualized using the Integrative Genomics Viewer (Broad Institute).
MCF-7 cells were incubated in phenol red-free DMEM containing 2.5% charcoal-stripped serum for 4 days total (with a media change after 48h). Next, the cells were treated with either vehicle, Dex (100 nM), E2 (10 nM) or Dex/E2 for 4 h (to identify early gene expression resulting from GR and/or ER chromatin association) in 2.5% charcoal-stripped serum (following incubation in stripped serum as described above). Cells were lysed in a RNA lysis buffer (Ambion, Invitrogen) and RNA was isolated and submitted to The University of Chicago Genomics Facility for reverse transcription and Affymetrix microarray as described previously (7), using the Human Genome U133 Plus 2.0 array platform. Global transcript profiling was performed in triplicate for each sample and resulting data (available on GEO:GSE79761) was normalized using the RMA method from Affymetrix Power Tools v1.10.2. The Dex, Dex/E2 and E2 treatments were compared with the vehicle to estimate relative expression values. Probes were filtered to include those with at least two out of three replicates exhibiting absolute relative expression values of +/- 1.5 fold-change (3). Ingenuity Pathway Analysis (IPA®, Qiagen) was performed with averaged expression values over two biological replicates to generate lists of canonical pathways for the Dex/E2 and E2 treatment conditions (compared to vehicle).
To confirm differential gene expression, the Custom Profiler RT2 PCR Array (Qiagen, 4h compound treatment) and/or individual quantitative reverse-transcription polymerase chain reaction (Q-RT-PCR) of mRNA was performed on three separately prepared biological replicates. Transcript levels were normalized to the housekeeping gene RPLP0. Genomic contamination control primers for GRIE3 were also used to assure RNA purity. Relative differences (at least 15% change in either direction) in fold-change gene expression (normalized initially to Veh treatment) between E2 vs. Dex/E2 treatment groups were calculated using a dataset of averaged significantly up- and down-regulated microarray and custom array genes. Additional Q-RT-PCR was performed for individual genes of interest as follows: KDM4B, 8h compound treatment; IGFPB4, 8h; VDR, 2h; these hormone treatment times were selected after a timecourse optimization for maximal transcript levels between 0 – 8h. PCR primer sets for individual Q-RT-PCR are listed in the Supplementary Materials and Methods. Fold-change was calculated by normalizing CT values to RPLP0 and then vehicle controls, error was calculated as standard deviation (propagated error)(19) and p-values were generated by a paired t-test.
Affymetrix ESR1 probe 205225_at expression was used to classify patients as ESR1+ and ESR1–based on the cut-off previously determined using the receiver operating characteristic (ROC) analysis that best predicted ER+ status by immunohistochemistry (IHC) (3). PR (PGR)-high and -low expression groups were defined as being above or below the median expression of the Affymetrix PGR probe 208305_at among all 1378 patients (3). Among the 1024 (74.3%) ESR1+ patients, 664 (64.8%) were classified as PGR-high. For all other genes (NR3C1, KDM4B, VDR, and IGFBP4), high and low expression groups were defined as the top and bottom 25% of expression levels, respectively. For KDM4B, VDR, and IGFBP4, we used the corresponding Affymetrix probeID in which we observed differential gene expression in the initial genome-wide microarray experiments.
Relapse-free survival was estimated using the method of Kaplan-Meier, and groups were compared using the logrank test. Hazard ratios (HR) were estimated using Cox proportional hazards regression model. We determined whether tumor NR3C1 expression was associated with differential RFS among all ESR1+ patients, followed by the high- and low-PGR gene expression subgroups of ESR1+ patients. All NR3C1 probes hybridize to GR-alpha, and probe 216321_at maps to both GR-alpha and GR-beta (3). Pearson's correlation coefficient between expression levels of the PGR probe (208305_at) and each of the four NR3C1 probes was calculated among ER+ patients. Similarly, we determined whether tumor KDM4B, VDR, and IGFBP4 expression was associated with differential RFS for all ESR1+ patients.
Individual transient siRNA knockdown of KDM4B, IGFBP4, and VDR were performed in MCF-7 cells. Briefly, the MCF-7 cells were cultured in 5% charcoal-stripped serum for 4 days, with a media change after 48h. On day 4, reverse transfection was performed using RNAiMax (Invitrogen) and a pool of four siRNAs for each gene as well as a non-targeting control sequence siRNA pool (ON-TARGETplus human siRNA SMARTpool, Dharmacon, siRNA sequences in the Supplementary Information). The RNAiMax protocol was performed using 150 pmol siRNA per well of a 12-well plate. Live cell number was counted over the course of 72 h by using either phase-contrast images (20) from Incucyte Live Cell Imager (Essen) or trypan blue exclusion assay. Knock-down efficiency of each gene compared to control siRNA was measured by Q-RT-PCR (primers are in the Supplementary Information).
Previous studies suggest that high tumor GR expression is significantly associated with a shortened RFS in ER-negative BC patients and, conversely, with an improved RFS in ER+ BC (3). These findings prompted us to further examine the association between GR and RFS in ER+ BC. Thus, we performed a retrospective meta-analysis of 1,024 ER+ BC patients (3) to determine whether GR mRNA (NRC31) expression associated with RFS. Indeed, we found that high tumor NRC31 expression was significantly associated with improved outcome in four of four Affymetrix NR3C1 probes, with hazard ratios for recurrence (HRs) ranging from 0.35 to 0.65 (Figure 1A).
It is well-established that ER+ BC patients with high tumor PR expression have improved RFS compared to patients with low tumor PR expression (1). PR has been recently shown to alter ER chromatin association as a possible mechanism for an improved outcome in ER+/PR+ BC patients (21). To determine if the significant association of high tumor GR expression with improved RFS in ER+ patients is dependent on PR expression, we examined our meta-analysis dataset and stratified ER+ BCs based on high versus low PR transcript (PGR) expression. We found that high tumor GR expression was associated with improved RFS in patients with both ER+/PR–high tumors (Figure 1B, HRs ranging 0.35 – 0.55) and in ER+/PR-low tumors (Figure 1C, HRs ranging 0.39 – 0.84). Additionally, the statistical interaction between PR positivity (based on the single available PGR probe) and each GR (NR3C1) probe were non-significant in Cox regression models, suggesting that tumor GR and PR expression independently affects patient outcome. To further support this observation, we found that the correlation between tumor GR mRNA expression (all four NR3C1 probes) and PR expression (one PGR probe) was low in the ER+ patient subset (Pearson's correlation coefficients ranging from -0.02 to 0.2). Taken together, these data suggest that tumor GR expression is associated with improved RFS in ER+ BC patients, independently of tumor PR expression.
Given that high GR expression was associated with a significantly improved RFS in ER+ patients independently of PR expression (Figure 1), we sought to uncover how GR and ER could coordinately activate pathways that contribute to a better patient outcome. To study the role of GR association with chromatin, we treated GR+/ER+ MCF-7 cells with the GR agonist dexamethasone (Dex, 100 nM, 60 min), ER agonist estradiol (E2, 100 nM, 75 min), or the combination of both (Dex/E2). Bioinformatics analysis of GR ChIP-seq revealed GR binding regions (GBRs), indicated by ChIP-seq peaks. To reduce the occurrence of false positives, GR-associated chromatin sequences were identified using concordant peaks from two algorithms, ODIN (16) and MACS (17). In the absence of ligand, only 726 genome-wide GR peaks were observed (Supplementary Figure 1A). While the total number of Dex-treated GR peaks remained approximately the same regardless of ER activation (Figure 2A, Dex vs. Dex/E2), about 60% of Dex/E2 peaks were unique to the Dex/E2 condition (N=5,963 genome-wide), demonstrating an altered GR chromatin landscape upon dual receptor activation. Furthermore, upon GR and ER co-activation, the change in GR location reflected significantly increased relative enrichment of GR chromatin association at promoter and DNA sequences that encode the 5′ and 3′ untranslated regions (5′UTRs and 3′UTRs) (Figure 2B). This dramatic change in GR chromatin topography suggests that activated ER can remodel chromatin, allowing increased accessibility of GR to proximal gene regulatory regions.
To determine which of the genome-wide GBRs might be contributing to changes in transcriptional activity, we examined GBRs within 100 kb in either direction of known (annotated) transcriptional start sites (TSSs, Version hg19). We identified 7,500 GBRs (peaks) upon GR activation alone and 7,292 GBRs upon GR and ER co-activation (Figure 2A, center column). Similar to our genome-wide peak analysis, about 60% of the GBRs within 100 kb of an annotated TSS were unique to the Dex/E2 condition. To connect chromatin association with potential transcriptional activity, we identified the TSS-associated genes closest to the GR peaks in any treatment condition (Figure 2A, right column). We found a total of 5,289 GR-associated genes following GR activation alone and 5,128 genes following dual receptor activation, while approximately 40% of the annotated genes (N= 2,097) were unique to the Dex/E2 treatment groups. Together, these data suggest that ER has an important role in determining the topography of GR chromatin association.
Because we found that co-activation of GR and ER resulted in altered GR association with DNA (GBRs), we next sought to characterize GBRs by identifying their associated response elements (REs). Using a CentriMo-based motif analysis, we found that in the absence of ER activation, GR associated most significantly with GREs (E-value=9.3e-1147, Dex vs. vehicle, Figure 2C). To study how ER activation changes GR enrichment, we performed a differential motif analysis (CentriMo) comparing Dex/E2 versus Dex-only conditions. When GR and ER were co-activated, GR demonstrated significantly enriched association with GREs compared to GR activation alone (E-value=5.2e-1258, Dex/E2 vs. Dex, Figure 2C). Also, upon dual receptor activation, GR associated more with EREs (E-value=4.3e-202, Figure 2C), consistent with GR ChIP-seq data in murine mammary cells (13). Furthermore, ER activation led to an increase in GR association at REs for the cooperating transcription factor AP1 (E-value=1.5e-7) and the ER-associated pioneering factor FOXO (E-value=2.9e-49) (22,23). Because GR is not believed to recognize or associate directly with EREs (24), these results suggest that ER remodels chromatin and allows GR increased indirect association with EREs, as well as to GR to have greater accessibility to GREs.
The relative increase in GR association at known ER, AP1, and FOXO REs prompted us to investigate whether, conversely, ER associates with GBRs during co-activation. Thus, ER ChIP-sequencing was performed in MCF-7 cells treated with Dex/E2 and compared to vehicle treatment (peak counts summarized in Supplementary Figures 1A-B). Indeed, genome-wide CentriMo motif analysis of ER ChIP-seq showed that upon Dex/E2 treatment, ER was significantly enriched not only at EREs (E-value=8.4e-1530), AP1 REs (E-value=3.2e-62), and FOXO REs (E-value=8.1e-92), but also at GREs (E-value=6.5e-379, Supplementary Figure 1C). These observations further suggest that GR and ER can interact in a complex and associate with each other's known primary chromatin binding regions.
To determine the proximity of GBRs and ER binding regions (EBRs), we analyzed the location of GR (GR ChIP-seq) and ER (ER ChIP-seq) peaks following Dex/E2. Of the total genome-wide GR (N=9,740, Figure 2) and ER (N=14,353, Supplementary Figure 1B) peaks, 4,390 GR and 6,355 ER peaks were found within +/- 100 kb of the same TSS (see Table 1A). Based on gene annotation by the nearest TSS, we identified 2,674 genes in which GR and ER associated within +/- 100 kb of the TSS (Table 1A, right column). In a genome-wide analysis, we also identified 2,834 regions where ChIP'd GR and ChIP'd ER peaks overlapped (Table 1B, partial peak overlap). Of these GR- and ER- overlapped peaks, 2,205 were within 100 kb of a TSS, revealing 1,734 TSS-annotated genes (Table 1B). To determine those GR and ER peaks that overlapped exactly, we identified all instances where the summits of a GR and a ER peak fell within six or fewer nucleotides of each other (Table 1C). Throughout the entire genome, 348 overlapping GR/ER summits were identified, and the majority of these summits (N=266) were found within 100 kb of a nearest TSS. These 266 overlapping summits were associated with 259 genes (based on proximal TSSs, Table 1C). Finally, we used motif analysis to determine the highest ranked transcription factor REs represented among proximal GBRs and EBRs. GREs and EREs were the top two significant sequences found when GR and ER bound in proximity, partially overlapped, or showed perfect overlap (Table 1A - C). Overall, these analyses demonstrate that upon GR and ER co-activation approximately 30% (2,205 from Table 1) of the genome-wide GBRs (7,292 from Fig. 2A) demonstrate some overlap with EBRs. In these surprisingly frequent cases, GR and ER may concurrently complex with DNA, or alternatively, bind to the same chromatin region in a temporally independent fashion.
Because the co-activation of GR and ER extensively altered GBR locations, we sought to determine accompanying changes in early target gene expression. Using independent Affymetrix gene array experiments (N=3), we measured steady-state gene expression changes after treatment with Vehicle, Dex, E2, or Dex/E2 (4h). Known GR and ER target genes (e.g. SGK1, SNAI2, PER1 in Dex treatment) and ER target genes (e.g. PGR, TFF1, GREB1, and IGFBP4 in E2 treatment) were identified as significantly induced ≥ 1.5-fold compared to control (Vehicle) at four hours. Figure 3A shows a Venn diagram of all differentially expressed genes identified (+/- 1.5-fold compared to vehicle) under each hormone condition. At 4h, we found that upon ER (E2) activation, a total of 1,420 genes were differentially expressed (Figure 3A). Upon GR (Dex) activation, we identified only 497 differentially expressed genes, while upon ER and GR (E2 and Dex) co-activation, 1,093 genes were differentially expressed compared to vehicle. Of the 1,420 E2-regulated genes, 51% overlapped with the dual Dex/E2 treatment; and of the 497 Dex-regulated genes, 39% overlapped with genes regulated by the Dex/E2 treatment. Upon Dex/E2 treatment, 297 unique genes were differentially expressed.
Since the overall goal of this study was to understand how GR expression and activation contributes to a more indolent ER+/GR-high BC phenotype compared to ER+/GR-low BCs, we next examined genome-wide gene expression with GR and ER co-activation (Dex/E2) compared to ER activation alone (E2). To determine differences in gene expression patterns and associated pathways, we performed Ingenuity Pathway Analysis (IPA®) using the differentially expressed genes in the Dex/E2 (versus vehicle) and E2 (versus vehicle) treatment groups. The most significant gene expression pathways for the E2 alone-regulated genes (E2) were related to cancer, cell differentiation, and cell proliferation (Figure 3B). Interestingly, the most significant pathway for the Dex/E2-regulated genes was related to cell differentiation, followed by pathways involved in cancer, inflammation, cell proliferation (25), and cell cycle checkpoint regulation. We next intersected GR ChIP-seq (Figure 2) and ER ChIP-seq (Supplementary Figure 1) TSS-annotated genes to determine which Dex/E2 pathway genes had direct GR- and/or ER-associated chromatin binding (Figure 3B, right three columns). Most of the Dex/E2 regulated pathways contained genes in which at least half had either a GBR or EBR or both within +/-100 kb of their gene TSS. Furthermore, many Dex/E2 pathway genes had both GR and ER localized within +/-100 kb of the TSS (Figure 3B, right-most column). Together, these data suggest that ER and GR co-activation directly and coordinately induce gene expression pathways affecting differentiation and proliferation.
To further explore the transcriptional consequences of GR and ER co-activation versus ER activation alone, we next determined which genes were increased or decreased by at least 15% in the Dex/E2 treatment compared to the E2 treatment alone. This analysis revealed that Dex/E2 treatment resulted in the relatively differential expression of 308 genes (142 upregulated and 166 down-regulated) compared to E2 alone. This comparison, when limited to genes with GBRs and/or EBRs within 100 kb of a TSS, revealed several putative direct GR (N= 225; 123 up and 102 down) and ER (N= 191; 110 up and 81 down) target genes (Figure 3C). Moreover, the majority of these GR and ER target genes contained both GR and ER binding regions within +/- 100 kb of the gene TSS (Figure 3C), suggesting that ER and GR coordinately contribute their differential regulation. Finally, among the GR- and/or ER-bound genes with differential expression in the Dex/E2 treatment group (compared to E2 alone) were several genes that encode “master regulator” transcription factors, their upstream kinases, and chromatin remodeling proteins (see Supplementary Table 1). Therefore, in addition to affecting gene expression involved in promoting cellular differentiation, the activation of GR and ER alters the expression of genes encoding master transcriptional regulators, suggesting that GR may act as a “master regulator of master regulators.”
To further understand why ER+/GR-high tumors exhibit a better clinical outcome compared to ER+/GR-low tumors, we next analyzed our microarray and Q-RT-PCR data to determine which differentiation-related genes differed in expression. We sought to limit our study to genes where GR associated within +/-100 kb of their TSS (GR ChIP-seq in Dex/E2, see Supplementary Data Files). We found relatively increased gene expression in Dex/E2 vs. E2 of several genes associated with cellular differentiation such as VDR, chromatin remodelers such as KDM4B, as well as negative regulators of pro-oncogenic Wnt signaling such as IGFBP4 and CCDC88C (DAPLE) (Supplementary Table 1).
In addition to relatively increased expression of genes expected to favor differentiation and less aggressive tumor growth, conversely, hallmark EMT genes SNAI2 and SOX2 (26,27) were markedly decreased upon co-activation of ER and GR compared to GR activation alone (Supplementary Data files). This is consistent with our previous observation that in TNBC, GR drives pathways promoting cell survival and EMT(3). Furthermore, we also observed relatively repressed expression of other genes encoding proteins related to cellular de-differentiation and EMT such as zinc-finger transcription factors (EGR3, KLF9, TRERF), chromatin remodelers (SUV39H2), cytoskeletal organization (ARHGEF26, RHOU, RHOBTB1, ARHGAP36, TBC1D8), and cell junctions and cell polarity (RET, DOCK4, CXCL12, LAMA3) (see Supplementary Table 1 and Supplementary Data files for additional genes).
To further understand how GR chromatin association and individual gene expression may be altered by ER and GR co-activation, we chose three exemplary genes from the gene expression pathways (Figure 3B) to study: Wnt signaling inhibitor insulin-like growth factor binding protein 4 IGFBP4 (28), a chromatin remodeling enzyme associated with GR and ER function – lysine (K)-specific demethylase 4B KDM4B (29-31), and the vitamin D receptor VDR (32,33). We first compared GR and ER chromatin binding within regulatory regions of these genes following single and dual hormone treatments (Figure 4A). For IGFBP4, a known ER target gene, we observed ER chromatin association in the E2 and Dex/E2 treatments, as well as some unliganded ER association in the Vehicle and Dex treatments (Figure 4A, left). However, GR recruitment was only detected in the Dex/E2 treatment, suggesting that ER remodels chromatin for GR binding (Figure 4A and 4B, left). Indeed, following an analysis of RE motifs within the EBR and GBR peak of IGFBP4, we found FOXO REs and EREs among the most represented motifs in the Dex/E2 treatment (Figure 4A and Supplementary Figure 3). An examination of regulatory regions for KDM4B revealed that the co-activation of GR and ER (Dex/E2) resulted in novel GBRs as well as EBRs, implicating both GR and ER as chromatin remodeling factors (Figure 4A and 4B, middle). Motif analysis of the Dex/E2 EBR peaks revealed a high frequency of FOXO, AP2, and ER REs (Figure 4A, middle). GREs were also identified within the EBR peak, but less frequently (Supplementary Figure 3). Finally, for VDR, we observed that activated GR associated with chromatin in both Dex and Dex/E2 treatment groups, whereas ER was only associated in the Dex/E2 treatment. Motif analysis of the EBRs and GBRs resulting from the Dex/E2 treatment for VDR showed the presence of GREs and EREs, as well as several FOXO and AP2 REs (Figure 4A, right and Supplementary Figure 3). These data suggest that GR remodels chromatin creating a new EBR in the regulatory region of VDR (Figure 4A and 4B, right).
We next confirmed key gene expression changes initially detected by whole genome transcript profiling using Q-RT-PCR. The dual activation of GR and ER resulted in increased expression of IGFBP4, KDM4B, and VDR, compared to ER activation alone (Figure 4C). Together with the chromatin binding data shown in Figure 4A, we conclude that ER, GR, or both receptors can remodel chromatin in association with enhanced gene expression.
To determine the phenotypic effect of these three genes in primary BC, we examined the RFS of early-stage ER+ BC patients in the meta-dataset analyzed in Figure 1 (3). A Kaplan-Meier plot of the top quartile versus bottom quartile of gene expression was generated. High tumor gene expression for IGFBP4 (p = 6.5e-11) (34,35), KDM4B (p = 6e-6), or VDR (p = 0.0028) (33) was significantly associated with improved long-term RFS (Figure 4D).
To further explore how expression of IGFBP4, KDM4B, and VDR affects ER+ BC phenotype in a cell-based assay, we measured cell number over time following individual gene knockdown. Increased proliferation is associated with poorly-differentiated, aggressive ER+ BC, and decreased proliferative indices are observed in well-differentiated, more indolent ER+ BCs (36,37). We therefore predicted that depletion of IGFBP4, KDM4B, or VDR gene expression would result in increased cell proliferation. Indeed, in comparison to MCF-7 cells expressing a control sequence, we observed a significant increase in cell proliferation (Figure 4E) following siRNA depletion of each gene (Supplementary Figure 4), suggesting their pro-differentiating function. These studies suggest that GR and ER coordinately alter gene expression in ER+ BC, thereby contributing to a favorable tumor phenotype and patient outcome.
The efforts of many laboratories have contributed to our current understanding of the GR cistrome and transcriptome in mammalian epithelial cells (38-42). For example, GR (and other nuclear receptors, NRs) interact with DNA in multifaceted complexes composed of co-regulators (43,44), transcription factors (22,45), and more recently appreciated, other NRs (11-15,21,46). Thus far, the interplay between GR and other NRs is most evident in hormone-dependent cancers such as prostate and breast cancer. Data from this study and others suggest that GR and ER influence each other's chromatin accessibility, as well as subsequent hormone-dependent transcriptional activity (3,13-15).
Because GR is known to activate TNBC cell survival gene expression pathways (5,6,10-12) and high tumor GR expression is associated with poor ER-negative patient outcome (3,4), we were struck by the observation that high GR expression was conversely associated with a significantly improved outcome in ER+ BC. Through a retrospective meta-analysis, we found an association between high tumor GR expression and longer RFS in patients with ER+ tumors. Moreover, we found that the association of GR expression with improved RFS was independent of PR expression. Overall, it appears that ER+/PR-negative BC patients with low-GR-expressing tumors have markedly shortened RFS compared to those with high GR expression (Figure 1), suggesting that GR status could be used to guide adjuvant treatment choice.
Our studies also support the notion that the cistrome resulting from GR and ER co-activation likely contributes to a more indolent phenotype in ER+ BC. Interestingly, we found that the higher individual expression of putative cell-differentiating genes (KDM4B, VDR, IGFBP4) was associated with improved RFS in ER+ BC patients. The chromatin remodeling protein KDM4B has been shown previously to be required for E2-mediated proliferation (29-31). However, our results suggest that increased KDM4B expression may play a different role in the context of GR and ER co-activation. Several genes related to EMT, such as SNAI2 and SOX2, were down-regulated upon co-activation of ER and GR compared to GR activation alone. These data suggest that altered patterns of GR and ER chromatin binding are associated with changes in the expression of differentiation-related genes; future experiments using CRISPR to delete these binding regions could more definitively demonstrate their requirement. Finally, in addition to the increase in gene expression associated with cellular differentiation, GR activation may also inhibit ER-regulated proliferative genes (47). We are currently studying how GR activation alters global ER chromatin association, ER target gene expression, and ER-mediated cell proliferation.
Other NRs, such as PR and androgen receptor (AR) also appear to play a role in ER+ BC by altering ER-mediated gene expression. The well-characterized MCF-7 cell line has been used extensively to study ER activity (48); however, these cells also express PR and some AR (49), both of which can crosstalk with GR and ER (11-12, 21, 39, 50-52). The consensus GRE is similar to AR and PR REs(18), suggesting a potential interplay between GR, AR, and PR at the chromatin level. It is possible that other NRs (such as PR and AR) may form complexes with GR and ER to influence each other's activity in subsets of BC. We are currently examining whether the individual members of the NR3C nuclear receptor family share common chromatin rearrangement characteristics in ER-positive BC models.
The unexpectedly high incidence of partial (Table 1B) or exact (Table 1C) overlapping GBRs and EBRs suggests that GR and ER interact at functional regulatory regions (e.g. enhancers) and that GR and ER complex together when interacting with chromatin. Our identification of RE motifs for co-operative transcription factors (such as FOXO and GATA REs) within the shared GR/ER binding regions of IGFBP4, KDM4B, and VDR (Figure 4A) is consistent with finding these motifs at ER-dependent promoter and enhancer regions (22,53). Additional studies to map known enhancers with these shared GR and ER binding regions should be pursued. Although we did not perform experiments distinguishing simultaneous versus sequential GR and ER association with chromatin, we (Supplementary Figure 2) and others (15) have observed GR and ER protein complex formation using immunoprecipitation in whole cell MCF-7 lysates with and without ligand. We found that available ER co-immunoprecipitates with GR most efficiently in the presence of Dex or Dex/E2 (Supplementary Figure 2B-C), despite low level steady-state GR (Dex) and ER (Dex/E2) due to ligand-induced receptor degradation (54,55). Studies are ongoing to determine if ligand activation recruits cooperative transcription factors, such as FOXO, to GR/ER complexes thereby facilitating chromatin interaction.
Our analysis of GR genome-wide chromatin association suggests that liganded ER acts to remodel chromatin, resulting in altered GR accessibility to DNA. A role for ER in chromatin remodeling has been described previously (13,56-58), but this function may be underestimated in ER+ BC biology. Our observation of enriched GR binding at promoter regions upon ER activation suggests that ER alters chromatin configuration significantly. ER appears to remodel chromatin to expose de novo GBRs, and this in turn might allow GR to further modulate chromatin accessibility for other transcription factors (see model in Figure 5).
Because ER has been successfully targeted for treatment in BC, our studies suggest that the addition of GR modulation might be evaluated for therapeutic benefit. Indeed, a clinical trial of tamoxifen, chemotherapy, and the GR agonist prednisone was performed; however, patients were not stratified by GR or ER tumor expression and thus, the results are difficult to interpret (59). Moreover, adjuvant use of daily prednisone was not tolerable. Our results suggest that treatment with an alternative GR modulator (with less toxicity than prednisone or dexamethasone) might promote the activation of pro-differentiation and anti-proliferative gene expression pathways in ER+ BC.
We thank Dr. Pieter Faber and Dr. Jaejung Kim of the University of Chicago Genomics Core Facility. We also thank Dr. Doug Turnbull of the University of Oregon Genomics Core Facility.
Grant Support: The study was supported by NIH R01 CA089208, The University of Chicago Comprehensive Cancer Center NIH P30 CA014599, Susan G. Komen for the Cure IIR12223772, and the Prostate Cancer Foundation Movember Challenge Award
Disclosure of Potential Conflict of Interest: Drs. Pan, Kocherginsky, and Conzen have a patent issued “Methods and compositions related to glucocorticoid receptor (GR) antagonists and breast cancer.” This patent covers ER-negative breast cancer and therefore is not directly relevant to this work in estrogen receptor-positive (ER+) breast cancer. However, it may be considered broadly relevant to the work. There are no other conflicts of interest to report.
Authors' Contributions: Conception and design: D.C. West, D. Pan, C.F. Pierce, S.D. ConzenDevelopment of methodology: D.C. West, D. Pan, C.F. Pierce, S.C. Styke, E.Y. Tonsing-Carter, K.M. Hernandez, T.I. Garcia, M. Kocherginsky, S.D. Conzen
Acquisition of data: D.C. West, D. Pan, E.Y. Tonsing-Carter, K.M. Hernandez, C.F. Pierce, S.C. Styke, K.R. Bowie, T.I. Garcia, M. Kocherginsky, S.D. Conzen
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D.C. West, D. Pan, E.Y. Tonsing-Carter, K.M. Hernandez, C.F. Pierce, T.I. Garcia, M. Kocherginsky, S.D. Conzen
Writing, review, and/or revision of the manuscript: D.C. West, D. Pan, E.Y. Tonsing-Carter, K.M. Hernandez, C.F. Pierce, S.C. Styke, K.R. Bowie, T.I. Garcia, M. Kocherginsky, S.D. Conzen
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): D.C. West, K.M. Hernandez, C.F. Pierce, S.C. Styke, K.R. Bowie, E.Y. Tonsing-Carter, S.D. Conzen
Study supervision: D.C. West, D. Pan, M. Kocherginsky, S.D. Conzen