The impact of Fulvestrant on Estrogen Receptor-Driven Chromatin Dynamics in Breast Cancer

The impact of Fulvestrant on Estrogen Receptor-Driven Chromatin Dynamics in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of Fulvestrant on Estrogen Receptor-Driven Chromatin Dynamics in Breast Cancer Céline Barlier, Mathias Simplicien, Elsa Moreau, Celia Fontana, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6779056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Epigenetics & Chromatin → Version 1 posted 11 You are reading this latest preprint version Abstract Background Epigenetic dysregulations are linked to various diseases, including cancer. Among them, breast cancer is the second leading cause of cancer-related deaths in women with 50% of mortalities attributable to estrogen receptor-positive (ER+) tumors. Endocrine therapies targeting the Estrogen Receptor (ER) such as Tamoxifen, Fulvestrant and Aromatase inhibitors, are widely used in the clinic. Among these therapeutic agents, Fulvestrant has been shown to fully antagonize ER activity, primarily through the rapid degradation and elimination of ER from target tissues. However, recent findings indicate that ER, when engaged with Fulvestrant, retains the ability to translocate to the nucleus and bind DNA whereas appearing transcriptionally inert. Results In this study we aimed to further investigate the effects of Fulvestrant and Estradiol, an ER natural ligand, on ER cistrome, chromatin accessibility, and H3K27ac genome-wide patterns in an ER + breast cancer cell line. Using the innovative CUT&Tag technology, we first confirmed that both Fulvestrant and Estradiol promote ER binding to DNA. Our findings revealed that Estradiol not only enhances chromatin accessibility but also increases H3K27ac levels at ER binding sites. In contrast, while Fulvestrant does not significantly alter chromatin accessibility, it can induce increases in H3K27ac levels at a subset of ER binding sites. Our observations suggest that Fulvestrant may modulate breast cancer transcriptional landscape by impacting H3K27ac dynamics, even in the absence of changes in chromatin accessibility. Conclusions This study provides new insights on the mechanistic impact of Fulvestrant on Estrogen Receptor activity and their potential implications on target gene expression, particularly highlighting a novel putative role of H3K27ac dynamics in these processes. CUT&Tag Estrogen Receptor H3K27ac ATAC-seq epigenetics chromatin landscape Estradiol Fulvestrant breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Next generation sequencing (NGS) based genome-wide chromatin profiling technologies have shed a light on the critical role played by transcription factors (TFs) and histone post-translational modifications dynamics on gene regulatory networks and their link with diseases such as cancer [ 1 ]. Various techniques have been developed and greatly increased our ability to elucidate the regulatory landscape of cells in different contexts. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) has emerged as a novel approach that addresses key limitations of ChIP-seq [ 2 ]. This technique is particularly noteworthy for its shorter processing time, reduced requirements for starting material, and enhanced signal-to-noise ratio. Since its introduction in 2019, CUT&Tag has rapidly gained popularity for mapping TFs and chromatin modifications across a range of physiological and pathological contexts, such as cancer [ 2 ]. Breast cancer is the second leading cause of cancer-related deaths in women with 50% of mortalities arising in patients bearing estrogen receptor-positive (ER+) tumors [ 3 ]. ER is a steroid hormone nuclear receptor that acts as a transcription factor when activated by the estrogen 17β-estradiol (E2), a potent and natural ER ligand [ 4 , 5 ]. The estrogen receptors, ERα and ERβ, serve as hormone-dependent transcription factors, directly regulating the expression of their target genes [ 6 ]. The binding of estrogen (E2) to ER leads to its dimerization and translocation to the nucleus, allowing its interaction with transcriptional co-activators [ 7 ]. In particular, ER alpha activation is known to promote tumorigenesis in several cancers, including breast cancer, and its inhibition/degradation has become a cornerstone of current therapeutic landscape [ 8 ]. Endocrine therapy continues to be the standard treatment for hormone receptor-positive (HR+) HER2-negative (HER2−) breast cancer. Approved endocrine agents include selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), and selective estrogen receptor downregulators or degraders (SERDs). Fulvestrant (FULV) is the only FDA-approved SERD indicated for metastatic or advanced hormone receptor-positive (HR+) breast cancer [ 9 ]. FULV stands out among approved ER therapeutics thanks to its capacity for full antagonism, thought to be achieved by rapid degradation and disappearance of ER from the target tissue [ 10 ]. However, recent findings suggest that FULV initially promotes ER translocation to the nucleus and binding to DNA as a transcriptionally inert complex before inducing its degradation [ 11 ]. In this study, we further investigated the mechanism of action underlying FULV treatment in ER + breast cancer cells. Using CUT&Tag and the Assay for Transposase-Accessible Chromatin (ATAC-seq), we compared FULV treatment with the natural ER ligand, estradiol (E2), and assessed their effects on ER binding to DNA, chromatin accessibility, and H3K27ac genome-wide dynamics, thereby dissecting the chromatin landscape influenced by these ligands. Here, we present evidence countering the hypothesis that FULV avoids the weak agonism of ER by inhibiting its binding to canonical DNA sites, thus supporting the findings previously obtained by ChIP-seq through a different profiling technology [ 11 ]. Our findings offer novel insights into the distinct mechanisms by which FULV modifies chromatin states, revealing a disconnect between chromatin accessibility and histone acetylation. Our observations suggest that FULV- bound ER complexes are not entirely inert, revealing a more complex interplay in the regulation of gene expression upon treatment. Methods Cell culture MCF-7 human breast cancer cells were hormone deprived by culturing them in phenol red-free RPMI (Catalog No. 11835, Thermo Fisher) supplemented with 10% charcoal-dextran stripped FBS (Catalog No. 100–119, Gemini bio-products), 2mM L-Glutamine, 1X MEM NEAA and 1X Antibiotic-Antimycotic (hormone deprivation media) for 4–5 days. Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂ and harvested at approximately 80% confluency. 1x10 6 cells were seeded in 6 well plates and after 24h, cells were treated with DMSO, 1nM E2 or 100nM FULV treatment for 30min, 45min or 4h. Subcellular Fractionation Subcellular fractionation was performed from 1x10 6 cells using the Subcellular Protein Fractionation Kit for Cultured Cells (Thermo Fisher Scientific, Cat. No. 78840) according to the manufacturer's instructions. Western Blot Analysis Western Blot Analysis Protein concentrations were determined using the BCA assay (Thermo Fisher Scientific, Cat. No. 23225). Equal amounts of proteins from each fraction were separated by SDS-PAGE and transferred to PVDF membranes (Biorad, Cat. No. 1704157). Membranes were probed with antibodies targeting ER (1:25, Abcam Cat. No. Ab16660), and subcellular specific markers for the cytosolic, nuclear and chromatin fractions with anti-GAPDH (1:2000, SantaCruz Cat. No. Sc-47724), anti-PARP1 (1:1000, Cell Signaling Cat. No. 9532), and anti-H3 (1:2000, Cell Signaling Cat No. 4499) respectively. Detection was performed using ECL reagent (Thermo Fisher Scientific, Cat. No. 32106). ER and H3K27ac CUT&Tag MCF7 cells were cultured in hormone deprived media for 4–5 days, followed by treatment with either DMSO, 1 nM E2, or 100 nM FULV for 45 minutes (n = 2). For the CUT&Tag procedure, cells were harvested, counted, and assessed for viability. The iDeal CUT&Tag kit (Diagenode C01070021) was employed with minor adaptation to the original protocol. In brief, 100,000 cells were permeabilized and immobilized on concanavalin A-coated magnetic beads. Cells were then incubated overnight at 4°C with primary antibodies targeting H3K27ac (1:50, Active Motif Cat. No. 39034), ER (1:50, Epicypher Cat. No. 13-2012) and IgG (Diagenode kit) as a negative control, followed by a 30-minute incubation with a rabbit secondary antibody. Next, loaded Tn5 was added, and the cleaved DNA was purified using the silica membrane column method. Library preparation was performed with Nextera™-Compatible Multiplex Primers (Active Motif, 53155), employing 14 PCR cycles. Post-PCR cleanup was carried out using AMPure XP Beads (Beckman Coulter, A63881). Library concentration was measured with Qubit (Invitrogen), and fragments profile assessed using Tapestation (Agilent). Two biological replicates were generated for each experimental condition. Libraries were normalized, pooled, denatured and loaded with 2% PhiX v3 control spike-in (Illumina) for sequencing on Illumina NextSeq550 sequencer. Sequencing parameters were set to 2 x 38 bp. CUT&Tag data analysis The processing of the CUT&Tag data was performed based on the recommendations of Dr Henikoff’s laboratory [ 12 ]. Briefly, paired-end reads were first trimmed using Trimmomatic with the following parameters: LEADING:20 TRAILING:20 SLIDINGWINDOW:4:15 MINLEN:25. Reads were then mapped to the human genome hg38 using bowtie2 and the following parameters: --dovetail --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700. Additional filtering was then applied to the aligned reads: removal of duplicates using Picard, filtering on the quality using samtools and the following parameters: -F 0x04 -q 30, removal of mitochondrial reads and finally removal of blacklist regions according to ENCODE [ 13 ]. Peaks were called first for each replicate using macs3 callpeak function and merged IgG samples with the following parameters: -f BED -g hs -q 0.05 --keep-dup all --nomodel --extsize 200 --bdg –SPMR. Biological replicate samples were then merged and peaks were called on this pooled-sample. Peaks consistently identified in both individual replicates and at the pooled-level were kept for further analysis. ATAC-seq MCF7 cells were cultured and treated as outlined for CUT&Tag experiments. For chromatin accessibility studies, we utilized the ATAC-seq kit from Active Motif (#53150), following the manufacturer's instructions. In brief, cells were harvested, counted, and assessed for viability. We processed 100,000 viable cells per condition (N = 3), lysing them to isolate nuclei. Tagmentation was carried out for 30 minutes at 37°C using an hyperactive Tn5 transposase. The tagmented DNA was then isolated with DNA purification columns and amplified through 10 cycles of PCR, employing a dual combination of indexed primers based on Illumina’s Nextera adapters. DNA cleanup was conducted using SPRI beads. Finally, DNA concentration was measured with a Qubit (Invitrogen), and library profiles were evaluated using a Tapestation (Agilent). Libraries were normalized, pooled, denatured and loaded with 2% PhiX v3 control spike-in (Illumina) for sequencing on Illumina NextSeq550 sequencer. Sequencing parameters were set to 2 x 38 bp sequencing. ATAC-seq data analysis The data were processed based on ATAC-seq standards. Briefly, reads shorter than 30bp were first trimmed using Trimmomatic. Reads were then aligned to the human genome hg38 using bowtie2 and the following parameters: --no-discordant --very-sensitive -X 2000. Then, additional filtering were applied including the removal of duplicates using Picard, the filtering of low quality mapping using samtools and the following parameters: -bf 0x2 -q 30, the removal of mitochondrial reads and blacklisted regions [ 13 ]. Finally, reads were shifted + 4bp and − 5bp respectively to account for the 9bp duplication created by DNA repair by the Tn5 transposase. Peaks were called for each replicate sample using macs3 callpeaks with the following parameters: -q 0.01 --nomodel --shift − 75 --extsize 150 --keep-dup all --bdg –SPMR. Biological triplicate samples were then merged and peaks were called on this pooled-sample. Peaks consistently identified at least in two replicates and at the pooled-level were kept for further analysis. Downstream analyses Peaks annotation CUT&Tag and ATAC peaks were annotated using the Genecode v43 database (TxDb.Hsapiens.UCSC.hg38.knownGene) and the R package ChIPpeakAnno [ 14 ] to link each peak to its closest TSS gene. Promoter regions were defined as regions [-1000bp, + 500bp] from TSS. Genome-wide and ER-centered H3K27ac H3K27ac peaks called in each experimental condition were used to quantify genome-wide H3K27ac signal changes upon treatment. Summary statistics and a Principal Component Analysis (PCA) were computed based on the count matrix generated with featureCounts using all H3K27ac peaks. Venn diagram comparing genomic regions were computed using ChIPeakAnno R package. ER-centered H3K27ac regions were generated by extending 500bp upstream and 500bp downstream from the center of ER peaks identified at least in one of the experimental conditions (DMSO, E2 and/or FULV). Differential analysis Count matrices were generated using featureCounts by pooling all peaks called across conditions and counting reads falling in those genomic regions across samples. Counts were then normalized using the TMM normalization with EdgeR [ 15 ]. Differential analyses using ER, H3K27ac or ATAC-seq signals were generated using EdgeR with glmQLFit and glmQLFTest to perform pairwise comparisons between the control (DMSO) and the treatments (E2, FULV) to identify differential signals. Peaks showing at least two-fold differences and an FDR < 0.05 were considered significant. Of note, differentially enriched peaks were further filtered to retain only those called in the treated condition for over-enrichment and in the control condition for depleted signal. In addition, -inf and + inf panels were created to distinguish those cases in which no counts were found in the control (+ inf) or the treatment (-inf). Pathways enrichment A pathway enrichment analysis was performed for ER-centered H3K27ac regions found to be differentially enriched using ReactomePA R package with the following parameters: p-value adjustment using Benjamini Hochberg, a p-value cutoff < 0.05, maxGSsize of 1000 and a q-value cutoff < 0.05. Motifs analysis The function findMotifsGenome.pl from HOMER [ 16 ] was used to identify known motifs enriched at ER binding sites identified in different experimental conditions (DMSO, E2 and FULV) with default parameters. In addition, the same analysis was performed using ER-centered H3K27ac regions found to be enriched or depleted compared to the control DMSO condition. Data integration ER-centered dysregulations were first studied by integrating ER binding sites with ATAC-seq or ER-centered H3K27ac differentially enriched signals independently: Integration with ATAC-seq : ER peaks identified across all experimental conditions were intersected with differentially accessible regions upon FULV or E2 treatments to compute a heatmap summarizing chromatin opening status at ER binding sites. In addition, ER peaks were intersected with ATAC-seq peaks using bedtools –wo and, ATAC-seq log2FC as well as FDR were aggregated to obtain an average value for each unique ER binding site. Integration with H3K27ac : differentially enriched peaks were intersected using bedtools –wo and H3K27ac log2FC as well as FDR were aggregated to obtain an average value for each unique ER binding site. Finally, all datasets generated in this study were integrated to study H3K27 acetylation status and chromatin opening status at ER binding sites. ER peaks were first intersected with ATAC-seq peaks and ATAC-based log2FC as well as FDR were aggregated to compute an average value for each ER binding site. Then, these peaks were intersected with H3K27ac centered around ER binding sites and the log2FC as well as FDR of H3K27ac were aggregated for each ER binding site using the average value of the region (Supplemental Fig. 1). Results Estradiol and Fulvestrant can both induce rapid mobility and increase ER levels at chromatin To determine whether the natural and therapeutic ER ligands, E2 and FULV respectively, can promote ER mobilization to chromatin, we conducted subcellular fractionation experiments in hormone-deprived MCF7 ER + breast cancer cells 30 minutes, 45 minutes, and 4 hours post-treatment with E2 and FULV (Fig. 1 A). Our results demonstrated that both E2 and FULV treatments could induce an increase of ER protein levels in the chromatin-enriched fraction within 30 minutes, accompanied by a concomitant decrease in cytoplasmic ER levels. Notably, both treatment conditions still exhibited ER depletion in the cytoplasmic fraction up to 4 hours. However, enrichment at chromatin appeared most consistent across experimental conditions 30–45 minutes post-treatment (Fig. 1 B, C). Our results were aligned with previous observations [ 11 ] and this latter time-point was then chosen for subsequent chromatin profiling studies. Next, we investigated the extent to which E2 and FULV treatments affect ER cistrome using CUT&Tag, a novel chromatin profiling method based on enzyme-tethering, decreased input cell number and a streamlined protocol [ 12 ]. We initially detected a limited number of ER binding sites in the control DMSO condition (n ≈ 2200), as expected considering that the cells were hormone-deprived for 4–5 days prior treatment (Fig. 2 A). However, we observed a dramatic increase in ER binding sites upon E2 (n ≈ 22000) and FULV (n ≈ 40000) treatments (Fig. 2 A, Supplemental Fig. 2B) in line with previously published data obtained using ChIP-seq profiling [ 11 ]. Consistent with previous studies [ 17 ], over 75% ligand-induced ER binding sites were located at distal sites, outside promoter regions (Fig. 2 A, Supplemental Fig. 2A) [ 17 , 18 ]. We further examined whether both ligands induced similar ER binding sites upon treatment. These analyses revealed that while many sites were shared in both E2 and FULV conditions (n ≈ 20000), a similar number were unique to FULV treatment (n ≈ 21000) and a more limited number unique to E2 treatment (n ≈ 3000) (Fig. 2 B). In addition, among the few ER-binding sites detected in the control DMSO condition a majority were still found in presence of E2 and/or FULV (Fig. 1 B), suggesting a rapid and massive engagement of ER genome-wide upon ligand treatment rather than a redistribution of pre-existing chromatin bound complexes. A differential binding analysis confirmed that both ligands induced increases in ER binding to DNA when compared to the control DMSO condition but also pointed to salient differences. Despite a larger number of sites specifically detected upon FULV treatment (Fig. 2 B), ER binding appeared more robust upon E2 treatment when considering fold changes and FDR metrics with roughly twice as many sites significantly increased (2-fold enrichment and FDR < 0.05; n ≈ 8000 vs n ≈ 4500 for E2 and FULV respectively) (Fig. 2 C, Table S1 ). In line with this observation, among a total of 9951 ER binding sites significantly differentially bound upon E2 and/or FULV treatment (FDR 1.5 fold difference), four clusters emerged: E2 specific increased sites were the most common (cluster 4; n = 4780, followed by shared E2 and FULV increased sites (cluster 3; n = 2992, FULV specific increased sites (cluster 1; n = 1826 and a small number of shared decreased E2 and FULV sites (cluster 2; n = 353) (Supplemental Fig. 2C). The fact that most ER binding sites detected upon FULV treatment (Fig. 2 B) did not display significantly increased signals (Fig. 2 C) could suggest heterogeneous ER binding within the cell population, where ER would engage many of these sites in a small percentage of cells only. Finally, the annotation of pre-existing ER binding sites detected in the DMSO control condition, highlighted that a larger fraction of these sites showed significant ER differential binding upon E2 treatment (Supplemental Fig. 2D; upregulated sites in yellow and downregulated sites in blue). DNA motif enrichment analyses focused on ER binding sites pointed to several transcription factors known to functionally interact with ER in breast cancer biology such as GATA3 [ 19 , 20 ], AP-1 [ 21 ], and FOXA1 [ 21 ] in addition to the ERE motif recognized by the Estrogen Receptor- [ 22 ] (Fig. 2 D and Supplemental Fig. 2E). Both E2 and FULV ligands displayed similar motif enrichment patterns with only subtle differences, consistent with the large number of overlapping binding sites between the two treatments (Fig. 2 D, Supplemental Fig. 2B). Altogether, our observations obtained using CUT&Tag confirm previous conclusions obtained using ChIP-seq [ 11 ] and provide further support for the hypothesis that FULV does not prevent weak ER agonism as it allows ER binding to canonical DNA sites. Fulvestrant does not alter chromatin accessibility at ER binding sites To investigate the functional impact of ligand treatment on chromatin accessibility, we performed ATAC-seq in hormone-depleted MCF7 cells in the same experimental conditions used to profile ER binding sites (45 minutes post-E2 or FULV treatment). In line with recent findings [ 11 ], we observed that E2 treatment induced major changes in chromatin accessibility genome-wide (Fig. 3 A; n = 2153 increased sites and n = 93 decreased sites), when FULV treatment had virtually no impact (Fig. 3 B; n = 5 increased sites and n = 25 decreased sites). Next, we examined whether accessible regions impacted by E2 were associated with ER binding sites. We observed that E2 treatment mostly enhanced chromatin accessibility at ER binding sites (Fig. 3 B; cluster 2 and Table S2 ). Only a few ER binding sites displayed reduced chromatin accessibility upon E2 treatment (Fig. 3 B and C; cluster 1). Notably, E2 treatment resulted in 1663 ER binding sites with concomitant increases in both ER binding and chromatin accessibility (2-fold changes), most of which were located at distal genomic regions (Fig. 3 D; 91%). Our observations confirm that FULV treatment induces the binding to DNA of an ER complex unable to alter chromatin accessibility when the natural ligand E2 promotes the recruitment of a competent ER complex able to open chromatin at a large subset of its binding sites. Both Fulvestrant and E2 induce significant alterations in H3K27ac levels . Multiple studies have linked ER binding at Estrogen Responsive elements (ERE) with H3K27ac deposition, target gene activation and cancer cell fitness [ 23 – 28 ]. H3K27ac is an histone post-translational modification catalyzed by the histone acetyl-transferases CBP and p300, and is a well-established marker of transcriptionally active enhancers [ 29 , 30 ] [ 31 , 32 ]. Thus, in light of their striking differential impact on chromatin opening we next performed H3K27ac Cut&Tag to investigate whether E2 and FULV treatments induced similar effects on H3K27ac levels at ER binding sites (Fig. 4 A). In each experimental condition, H3K27ac peaks were detected both at annotated promoters and non-promoter regions (Supp Fig. 3 A). Sample clustering based on the variability in genome-wide H3K27ac signals lead to a clear separation between DMSO control condition, E2 and FULV treatments (Supp Fig. 3 B) whereas most regions enriched in H3K27ac were common to all conditions (Supp Fig. 3 C). Closer examination highlighted that both E2 and FULV induced significant genome-wide changes in H3K27ac levels, predominantly leading to the enrichment of this histone post-translational modification (2-fold enrichment and FDR < 0.05 Supplementary Fig. 3D, Table S1 ). Notably, a large proportion of H3K27ac-enriched regions were shared between E2 and FULV treatments (Supplementary Fig. 3E). Furthermore, we observed that these increased levels of H3K27ac were strongly associated with ER binding sites (Fig. 4 B. Specifically, upon E2 treatment, 2,075 ER binding sites exhibited elevated H3K27ac levels, while FULV treatment led to an increase in H3K27ac signals at 1,550 ER binding sites (Fig. 4 B). Gene set enrichment analyses for the genes found near H3K27ac-increased ER binding sites upon both E2 and FULV treatment conditions revealed notably the Estrogen Response pathway as well as TNFα signalling via NF-κB (Fig. 4 C). Specific to FULV treatment, hypoxia and p53 pathways were associated with increases in H3K27ac while the Estrogen Response pathway was also associated with decreases in H3K27ac (Fig. 4 C). These findings are consistent with previous studies emphasizing the role of ER in regulating various signalling pathways, including TNFα and p53, by recruiting co-regulators and chromatin remodelling complexes [ 33 , 34 ]. An integrative analysis centered on ER binding sites revealed that 2228 regions displayed increases in both ER and H3K27ac and were primarily located at distal regions upon E2 treatment (Fig. 4 D left). A similar analysis upon FULV treatment identified 1123 regions displaying increases in both ER and H3K27ac although these sites were more evenly distributed between promoter and distal regions (Fig. 4 D right). Interestingly, DNA motif enrichment analyses focused on ER binding sites displaying differential H3K27ac signals upon ligand treatment, pointed to many similarities with the noticeable exception of the Estrogen Response Element (ERE) which was found significantly enriched at sites showing increases in H3K27ac upon E2 treatment as expected but only at H3K27ac depleted sites upon FULV treatment (Fig. 4 E) further reinforcing potential mechanistic differences upon ligand binding. In conclusion, our results then show that whereas FULV treatment does not significantly alter chromatin accessibility, it can paradoxically induce ER binding and the deposition of H3K27ac at both promoters and distal sites. Upon engagement with Fulvestrant, ER retains the ability to translocate to the nucleus and bind DNA and whereas it might fail to recruit or activate the chromatin remodelling machinery and appear transcriptionally inert, ER still appears to recruit a proficient acetyltransferase complex leading to H3K27 acetylation at a subset of its binding sites (Supplemental Fig. 3F). Discussion In this study, we explored the differential impact of the natural ligand E2 and the therapeutic agent Fulvestrant on ER activity in breast cancer cells. FULV is a synthetic, selective ER antagonist eventually promoting its degradation. By combining ER cistrome, chromatin accessibility, and H3K27ac dynamics profiling, we provide crucial insights into its mechanism of action and we endorse as well as enrich previously published findings [ 11 , 35 ] indicating that its therapeutic potential may extend beyond merely antagonizing ER activity. We purposedly chose to focus on early-stage mechanism of action post ligand treatment in hormone-deprived cells, at a time-point providing sufficient time for ER to translocate to the nucleus but still upstream protein degradation induced at later stages by Fulvestrant [ 36 ], which would make it difficult to dissociate ligand induced protein activity from the phenotypic impact of ER decreased levels. To obtain these results, we employed the novel CUT&Tag technology to confirm previous ChIP-seq results demonstrating that ER when engaged with FULV can still bind DNA genome-wide [ 11 ]. This supports the notion that CUT&Tag can replace the “gold standard” ChIP-seq, while providing a greater signal-to-noise ratio, requiring fewer cells as starting input and lower sequencing depth [ 12 ] on top of offering the possibility for automation thus enabling increased throughput, which is particularly attractive for its implementation in the drug discovery process. The dramatic increase in ER binding sites observed upon E2 and FULV treatments underscores the ability of these ligands to mobilize ER within the chromatin. However, the substantial number of low enrichment binding sites identified specifically upon FULV treatment suggests an allosteric impact on DNA binding either directly on ER DNA binding domain or indirectly on co-factors recruitment. The latter hypothesis is supported by the striking absence of chromatin opening at ER binding sites upon FULV treatment suggesting the inability to engage the chromatin remodelling machinery which could eventually lead to decreased residency at canonical binding sites and spurious binding genome-wide. Our most surprising discovery is the robust increase in H3K27ac levels observed at a non-trivial subset of ER binding sites, which challenges the idea that FULV-bound ER remains an inert DNA binding complex. Given that CBP and p300 are crucial ER co-activators that significantly influence H3K27ac dynamics [ 37 , 38 ], we hypothesize that the recruitment of this complex is not entirely disrupted upon FULV treatment. This could provide a rational for combination treatment between the standard of care Fulvestrant and CBP/p300 targeting agents, which have shown potential in pre-clinical ER + breast cancer models [ 38 , 39 ]. While both E2 and FULV induced increases in H3K27ac levels at ER binding sites, changes at a large number of sites were ligand specific thus the associated gene pathways differed significantly. This was expected from the differential binding sites at ER cistrome level. When E2 mainly affected the previously described estrogen response pathway, FULV affected several other pathways reminiscent of a poorly orchestrated program. Furthermore, a decrease in H3K27ac levels at regions containing estrogen response element (ERE) motifs upon FULV treatment suggests that FULV-bound ER may replace proficient complexes at pre-existing sites in the DMSO control condition, which would represent another way to disrupt ER activity in breast cancer cells. Conclusions Our study provides compelling evidence that Fulvestrant modulates the Estrogen Receptor molecular activity before causing its protein degradation in breast cancer by inducing genome-wide DNA binding and modulating H3K27ac levels, despite the inability to induce chromatin accessibility changes. These findings underscore the complexity of ER signalling pathways and suggest that the therapeutic potential of Fulvestrant may extend beyond mere antagonism of ER activity. Future studies could explore the molecular mechanisms behind these observations, especially focusing on how acetyltransferases such as CBP/p300 and other co-regulators are recruited and may influence these effects. Understanding these complex interactions could lead to the development of more effective treatments in ER-positive breast cancer, ultimately improving patients outcome. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The sequencing raw data of this article are available in GEO (number will be provided upon manuscript acceptance). Competing interests Funding Authors' contributions CB conducted all bioinformatic analyses, contributed to data interpretation, and drafted the manuscript. MS, EM, and IP performed the experimental work. CF and AD conducted next-generation sequencing. VP contributed to data interpretation. GB and IP were involved in the research design and implementation, data collection, interpretation, and manuscript writing. All authors read and approved the final manuscript. Acknowledgements We would like to thank Coralie Hoareau-Aveilla and Tanguy Bozec for their invaluable support and helpful advice throughout this study. References Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, et al. The chromatin accessibility landscape of primary human cancers. Science. 2018;362:eaav1898. Fu Z, Jiang S, Sun Y, Zheng S, Zong L, Li P. Cut&tag: a powerful epigenetic tool for chromatin profiling. Epigenetics. 2024;19:2293411. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA A Cancer J Clinicians. 2018;68:7–30. Stice JP, Knowlton AA. Estrogen, NFκB, and the Heat Shock Response. Mol Med. 2008;14:517–27. Yaşar P, Ayaz G, User SD, Güpür G, Muyan M. Molecular mechanism of estrogen–estrogen receptor signaling. Reprod Medicine & Biology. 2017;16:4–20. Belachew EB, Sewasew DT. Molecular Mechanisms of Endocrine Resistance in Estrogen-Receptor-Positive Breast Cancer. Front Endocrinol. 2021;12:599586. Clusan L, Ferrière F, Flouriot G, Pakdel F. A Basic Review on Estrogen Receptor Signaling Pathways in Breast Cancer. IJMS. 2023;24:6834. Rej RK, Roy J, Allu SR. Therapies for the Treatment of Advanced/Metastatic Estrogen Receptor-Positive Breast Cancer: Current Situation and Future Directions. Cancers. 2024;16:552. Bross PF, Cohen MH, Williams GA, Pazdur R. FDA Drug Approval Summaries: Fulvestrant. The Oncologist. 2002;7:477–80. Gibson MK, Nemmers LA, Beckman WC, Davis VL, Curtis SW, Korach KS. The Mechanism of ICI 164,384 Antiestrogenieity Involves Rapid Loss of Estrogen Receptor in Uterine Tissue. Endocrinology. 1991;129:2000–10. Guan J, Zhou W, Hafner M, Blake RA, Chalouni C, Chen IP, et al. Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility. Cell. 2019;178:949-963.e18. Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019;10:1930. Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019;9:9354. Zhu LJ, Gazin C, Lawson ND, Pagès H, Lin SM, Lapointe DS, et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics. 2010;11:237. Robinson MD, McCarthy DJ, Smyth GK. edgeR : a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Molecular Cell. 2010;38:576–89. Carroll JS, Liu XS, Brodsky AS, Li W, Meyer CA, Szary AJ, et al. Chromosome-Wide Mapping of Estrogen Receptor Binding Reveals Long-Range Regulation Requiring the Forkhead Protein FoxA1. Cell. 2005;122:33–43. Tabe-Bordbar S, Song YJ, Lunt BJ, Alavi Z, Prasanth KV, Sinha S. Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program. Commun Biol. 2024;7:719. Kong SL, Li G, Loh SL, Sung W, Liu ET. Cellular reprogramming by the conjoint action of ERα, FOXA1, and GATA3 to a ligand‐inducible growth state. Molecular Systems Biology. 2011;7:526. Fang SH, Chen Y, Weigel RJ. GATA-3 as a Marker of Hormone Response in Breast Cancer. Journal of Surgical Research. 2009;157:290–5. Dahlman-Wright K, Qiao Y, Jonsson P, Gustafsson J-Å, Williams C, Zhao C. Interplay between AP-1 and estrogen receptor α in regulating gene expression and proliferation networks in breast cancer cells. Carcinogenesis. 2012;33:1684–91. Joosten SEP, Gregoricchio S, Stelloo S, Yapıcı E, Huang C-CF, Yavuz K, et al. Estrogen receptor 1 chromatin profiling in human breast tumors reveals high inter-patient heterogeneity with enrichment of risk SNPs and enhancer activity at most-conserved regions. Genome Res. 2024;34:539–55. Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, et al. Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006;38:1289–97. Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell. 2011;145:622–34. Hah N, Murakami S, Nagari A, Danko CG, Kraus WL. Enhancer transcripts mark active estrogen receptor binding sites. Genome Res. 2013;23:1210–23. Hewitt SC, Grimm SA, Wu S-P, DeMayo FJ, Korach KS. Estrogen receptor α (ERα)-binding super-enhancers drive key mediators that control uterine estrogen responses in mice. J Biol Chem. 2020;295:8387–400. Lopes R, Sprouffske K, Sheng C, Uijttewaal ECH, Wesdorp AE, Dahinden J, et al. Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens. Sci Adv. 2021;7:eabf5733. Guo C, Meza-Sosa KF, Valle-Garcia D, Zhao G, Gao K, Yu L, et al. The SET oncoprotein promotes estrogen-induced transcription by facilitating establishment of active chromatin. Proc Natl Acad Sci USA. 2023;120:e2206878120. Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet. 2007;39:311–8. Visel A, Blow MJ, Li Z, Zhang T, Akiyama JA, Holt A, et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457:854–8. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA. 2010;107:21931–6. Narita T, Ito S, Higashijima Y, Chu WK, Neumann K, Walter J, et al. Enhancers are activated by p300/CBP activity-dependent PIC assembly, RNAPII recruitment, and pause release. Molecular Cell. 2021;81:2166-2182.e6. To SQ, Cheung V, Lazarus KA, Knower KC, Clyne CD. Estradiol regulates Tumor Necrosis Factor-α expression and secretion in Estrogen Receptor positive breast cancer cells. Molecular and Cellular Endocrinology. 2014;394:21–8. Cruceriu D, Baldasici O, Balacescu O, Berindan-Neagoe I. The dual role of tumor necrosis factor-alpha (TNF-α) in breast cancer: molecular insights and therapeutic approaches. Cell Oncol. 2020;43:1–18. Wardell SE, Marks JR, McDonnell DP. The turnover of estrogen receptor α by the selective estrogen receptor degrader (SERD) fulvestrant is a saturable process that is not required for antagonist efficacy. Biochemical Pharmacology. 2011;82:122–30. Long X, Nephew KP. Fulvestrant (ICI 182,780)-dependent Interacting Proteins Mediate Immobilization and Degradation of Estrogen Receptor-α. Journal of Biological Chemistry. 2006;281:9607–15. Hogg SJ, Motorna O, Cluse LA, Johanson TM, Coughlan HD, Raviram R, et al. Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition. Molecular Cell. 2021;81:2183-2200.e13. Waddell A, Mahmud I, Ding H, Huo Z, Liao D. Pharmacological Inhibition of CBP/p300 Blocks Estrogen Receptor Alpha (ERα) Function through Suppressing Enhancer H3K27 Acetylation in Luminal Breast Cancer. Cancers. 2021;13:2799. Bommi-Reddy A, Park-Chouinard S, Mayhew DN, Terzo E, Hingway A, Steinbaugh MJ, et al. CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells. Weisz A, editor. PLoS ONE. 2022;17:e0262378. Additional Declarations No competing interests reported. Supplementary Files SupFigures.pdf TableS1.xlsx TableS2.xlsx SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Epigenetics & Chromatin → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviews received at journal 12 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 02 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6779056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467119272,"identity":"0f24e8eb-79d4-4f6f-85b9-c16b49b75ff4","order_by":0,"name":"Céline Barlier","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"","lastName":"Barlier","suffix":""},{"id":467119273,"identity":"a3a5fdd5-3e56-4c48-afe5-f1f457f65e5f","order_by":1,"name":"Mathias Simplicien","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Mathias","middleName":"","lastName":"Simplicien","suffix":""},{"id":467119274,"identity":"4f66820a-c754-4033-8ffc-778500f381aa","order_by":2,"name":"Elsa Moreau","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Elsa","middleName":"","lastName":"Moreau","suffix":""},{"id":467119275,"identity":"45094114-8fec-433c-9e35-2cdd418671a2","order_by":3,"name":"Celia Fontana","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Celia","middleName":"","lastName":"Fontana","suffix":""},{"id":467119276,"identity":"4843d35d-05d6-4e58-a4cf-811cc80e99a7","order_by":4,"name":"Aurelia Delherme","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Aurelia","middleName":"","lastName":"Delherme","suffix":""},{"id":467119277,"identity":"81a98880-95d0-440b-8e23-d2f250f3a272","order_by":5,"name":"Vincent Piras","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Piras","suffix":""},{"id":467119278,"identity":"377efd30-94b7-4d0c-aed1-bab15ef02781","order_by":6,"name":"Gaylor Boulay","email":"","orcid":"","institution":"Evotec France","correspondingAuthor":false,"prefix":"","firstName":"Gaylor","middleName":"","lastName":"Boulay","suffix":""},{"id":467119279,"identity":"85258a91-06d6-450c-a281-43b835599b67","order_by":7,"name":"Isabel Paiva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBAC+QYGNgaGAiCLGYg/EKOFDYwMIFoYZwBpHuK1gHTxEKWF//CzBx8MGPL423kMP9tUHJazZ29+wPCjYhtuLRJp5oYzDBiKJQ7zGEvnnDlszMNzzICx58xtPFoYzKR5DBgSGw6zJUjntqUl9kjkMDAztuHRwn/8m/QfoJb5h9mSf1v+A2qRf0NAC0OOmTTQ+4kbDjMfk2ZssAHawkNAi0ROuWGPgUSxIVCLZc8xG2OeM2kGB/H5Rb7/+LYHPyps8uTOH2y+8aNGQo69/fBDoAhuLVAgkYDCPUBIPQgkEFQxCkbBKBgFIxcAAEIrS9zwnXWUAAAAAElFTkSuQmCC","orcid":"","institution":"Evotec France","correspondingAuthor":true,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Paiva","suffix":""}],"badges":[],"createdAt":"2025-05-29 19:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6779056/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6779056/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13072-026-00667-0","type":"published","date":"2026-03-17T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84059418,"identity":"dd64d5bf-f872-416b-b8ff-61f3cfd8bce3","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstradiol and Fulvestrant induce a rapid mobility and increase ER levels at the chromatin. \u003c/strong\u003e(A) Experimental and bioinformatic analysis design used to study ER dynamics upon E2 and FULV treatments, including subcellular fractionations, CUT\u0026amp;Tag and ATAC-seq experiments. (B-C) immunoblotting (WB) of subcellular fractionated hormone-deprived MCF7 cells (cytoplasm, nucleus and chromatin) upon E2 and FULV treatments for 30min, 45min and 4h. (C) As reported by Guan J et al, FULV increased ER levels in the chromatin-enriched fraction, concomitant with reduced cytoplasmic ER, within 30 min of treatment (red arrow). Samples were run on the same gel and transferred on the same membrane. Membrane was cut before antibody incubation and developed at the same time with the same exposure for each antibody used. Thus, comparisons can be done between cellular fractions and between conditions.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/022949396c92aac3011ad754.png"},{"id":84059420,"identity":"50e8081e-f80a-407b-b068-31ef25b7525a","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eE2 and FULV induce rapid engagement of ER at DNA genome-wide (CUT\u0026amp;Tag).\u003c/strong\u003e (A) ER peaks distribution on the human genome in DMSO, E2 and FULV treatment conditions showing that most ER binding sites are detected at distal sites outside promoter regions (B) Venn diagram displays the overlap between peaks called in the DMSO control and the two treated conditions. Most DMSO peaks are still detected in presence of E2 and/or FULV but many peaks are treatment-specific. Peaks with at least 1bp in common were considered as overlapping. (C) Volcano plots show differential ER binding upon treatments compared to the DMSO control. Both ligands majoritarily increase ER binding. In blue, ER signal with at least 2-fold decrease upon treatment and in red, ER signal with at least 2-fold increase upon treatment. Changes with an FDR \u0026gt; 0.05 and less than 2-fold differences are displayed in grey. In addition, depleted signal for which the peak was only called in the treated condition were considered as artifact and displayed as NS. –Inf and +Inf panels contain peaks for which no count was found in one of the experimental condition: in –inf no count was found in the treated condition for the specific peak and, in +inf no count was found in the control condition (D) Heatmap displaying motifs enriched at ER binding sites in the DMSO control, E2 and FULV treatments.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/48950be85331bb8c8a3580b4.png"},{"id":84059422,"identity":"aa3b3b61-df48-466b-8135-80e88f9dd18d","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":552556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eE2 - but not FULV - impacts chromatin accessibility genome-wide (ATAC-seq)\u003c/strong\u003e. (A) Volcano plots show differentially accessible regions observed genome-wide upon E2 and FULV treatments. Very few changes are observed upon FULV treatment. In blue, ATAC signal with at least 2-fold decrease upon treatment. In red, ATAC signal with at least 2-fold increase. Changes with an FDR \u0026gt; 0.05 and less than 2-fold differences are displayed in grey (B) Hierarchical clustering of differentially accessible sites upon treatment. Left: ER binding sites are shown in red (C) Boxplots show chromatin accessibility changes upon E2 or FULV treatment in the two clusters identified in B (D) Scatter plots show ER CUT\u0026amp;Tag and ATAC-seq signal changes at ER binding sites upon treatment. ER binding sites displaying 2-fold increases in both ATAC-seq and ER CUT\u0026amp;Tag signals are shown in red and are mostly observed upon E2 treatment. The shape of the dots reflects the significance of ATAC-seq signal changes with a triangle showing significant changes (FDR \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/a4c4c9569d7737225d16f62f.png"},{"id":84059989,"identity":"47e3353f-ac9e-43dd-9860-7e219aa0480a","added_by":"auto","created_at":"2025-06-06 10:06:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":728224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eE2 and FULV impact H3K27ac levels at ER binding sites (CUT\u0026amp;Tag). \u003c/strong\u003e(A) Schematic shows H3K27ac as a marker of active regulatory regions detected at both promoters and distal enhancers (B) Volcano plots show differential H3K27ac signals centered around ER binding sites. Both treatments induce robust H3K27ac changes at ER binding sites (C) Gene enrichment pathway analyses for the genes found near H3K27ac enriched ER binding sites upon E2 or FULV treatment. The statistical significance for each term is determined by a q-value (at least \u0026lt;0.05): red for highly significant terms, blue for less significant terms. The gene ratio, obtained by dividing the number of genes altered by the total number of genes belonging to the respective pathway, is determined by the size of the circles (D) Scatter plots show changes in ER and H3K27ac CUT\u0026amp;Tag signals at ER binding sites upon treatments. The shape of the dots reflects the significance of ER signal changes with a triangle showing significant changes (FDR \u0026lt; 0.05). ER binding sites displaying 2-fold increases in both ER and H3K27ac CUT\u0026amp;Tag signals are shown in red and are observed upon both E2 and FULV treatments (E) Plot shows the transcription factors recognizing the motifs enriched at ER binding sites displaying differential H3K27ac upon treatment. TF names were extracted from motifs with a p value \u0026lt; 1e-5 in at least one condition. Circle size indicates the percentage of targets. q-value significance is shown in red.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/66ac0ce85ade9ce382c78cf3.png"},{"id":105223300,"identity":"1f2881c4-05fc-4eec-a050-d43a127f0821","added_by":"auto","created_at":"2026-03-23 16:03:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2806762,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/5af0fcec-80d5-436c-b6c1-0104049f35b8.pdf"},{"id":84059988,"identity":"c2d9e340-1a00-48d5-916f-37f17ff18281","added_by":"auto","created_at":"2025-06-06 10:06:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":863536,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/bd60bc424bab75c0f75b8055.pdf"},{"id":84059436,"identity":"273743b0-7738-4ea1-babf-3db8684e1e39","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2614619,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/08d5963d384c82ef3530a1ac.xlsx"},{"id":84059432,"identity":"ba789ff9-1746-4e11-b23f-9c79b6e0ebdd","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":290244,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/fe6b319e732597e689269d25.xlsx"},{"id":84059429,"identity":"cdc5f8b9-fa01-46d7-aae9-338ab8a9a721","added_by":"auto","created_at":"2025-06-06 09:58:26","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13970,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6779056/v1/3037e09ecb520d5e9de0cdce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of Fulvestrant on Estrogen Receptor-Driven Chromatin Dynamics in Breast Cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eNext generation sequencing (NGS) based genome-wide chromatin profiling technologies have shed a light on the critical role played by transcription factors (TFs) and histone post-translational modifications dynamics on gene regulatory networks and their link with diseases such as cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Various techniques have been developed and greatly increased our ability to elucidate the regulatory landscape of cells in different contexts. Among these, Cleavage Under Targets and Tagmentation (CUT\u0026amp;Tag) has emerged as a novel approach that addresses key limitations of ChIP-seq [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This technique is particularly noteworthy for its shorter processing time, reduced requirements for starting material, and enhanced signal-to-noise ratio. Since its introduction in 2019, CUT\u0026amp;Tag has rapidly gained popularity for mapping TFs and chromatin modifications across a range of physiological and pathological contexts, such as cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBreast cancer is the second leading cause of cancer-related deaths in women with 50% of mortalities arising in patients bearing estrogen receptor-positive (ER+) tumors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. ER is a steroid hormone nuclear receptor that acts as a transcription factor when activated by the estrogen 17β-estradiol (E2), a potent and natural ER ligand [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The estrogen receptors, ERα and ERβ, serve as hormone-dependent transcription factors, directly regulating the expression of their target genes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The binding of estrogen (E2) to ER leads to its dimerization and translocation to the nucleus, allowing its interaction with transcriptional co-activators [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In particular, ER alpha activation is known to promote tumorigenesis in several cancers, including breast cancer, and its inhibition/degradation has become a cornerstone of current therapeutic landscape [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEndocrine therapy continues to be the standard treatment for hormone receptor-positive (HR+) HER2-negative (HER2\u0026minus;) breast cancer. Approved endocrine agents include selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), and selective estrogen receptor downregulators or degraders (SERDs). Fulvestrant (FULV) is the only FDA-approved SERD indicated for metastatic or advanced hormone receptor-positive (HR+) breast cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. FULV stands out among approved ER therapeutics thanks to its capacity for full antagonism, thought to be achieved by rapid degradation and disappearance of ER from the target tissue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, recent findings suggest that FULV initially promotes ER translocation to the nucleus and binding to DNA as a transcriptionally inert complex before inducing its degradation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we further investigated the mechanism of action underlying FULV treatment in ER\u0026thinsp;+\u0026thinsp;breast cancer cells. Using CUT\u0026amp;Tag and the Assay for Transposase-Accessible Chromatin (ATAC-seq), we compared FULV treatment with the natural ER ligand, estradiol (E2), and assessed their effects on ER binding to DNA, chromatin accessibility, and H3K27ac genome-wide dynamics, thereby dissecting the chromatin landscape influenced by these ligands. Here, we present evidence countering the hypothesis that FULV avoids the weak agonism of ER by inhibiting its binding to canonical DNA sites, thus supporting the findings previously obtained by ChIP-seq through a different profiling technology [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our findings offer novel insights into the distinct mechanisms by which FULV modifies chromatin states, revealing a disconnect between chromatin accessibility and histone acetylation. Our observations suggest that FULV- bound ER complexes are not entirely inert, revealing a more complex interplay in the regulation of gene expression upon treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eMCF-7 human breast cancer cells were hormone deprived by culturing them in phenol red-free RPMI (Catalog No. 11835, Thermo Fisher) supplemented with 10% charcoal-dextran stripped FBS (Catalog No. 100\u0026ndash;119, Gemini bio-products), 2mM L-Glutamine, 1X MEM NEAA and 1X Antibiotic-Antimycotic (hormone deprivation media) for 4\u0026ndash;5 days. Cells were maintained at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂ and harvested at approximately 80% confluency. 1x10\u003csup\u003e6\u003c/sup\u003e cells were seeded in 6 well plates and after 24h, cells were treated with DMSO, 1nM E2 or 100nM FULV treatment for 30min, 45min or 4h.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubcellular Fractionation\u003c/h3\u003e\n\u003cp\u003eSubcellular fractionation was performed from 1x10\u003csup\u003e6\u003c/sup\u003e cells using the Subcellular Protein Fractionation Kit for Cultured Cells (Thermo Fisher Scientific, Cat. No. 78840) according to the manufacturer's instructions.\u003c/p\u003e\n\u003ch3\u003eWestern Blot Analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eWestern Blot Analysis\u003c/div\u003e \u003cp\u003eProtein concentrations were determined using the BCA assay (Thermo Fisher Scientific, Cat. No. 23225). Equal amounts of proteins from each fraction were separated by SDS-PAGE and transferred to PVDF membranes (Biorad, Cat. No. 1704157). Membranes were probed with antibodies targeting ER (1:25, Abcam Cat. No. Ab16660), and subcellular specific markers for the cytosolic, nuclear and chromatin fractions with anti-GAPDH (1:2000, SantaCruz Cat. No. Sc-47724), anti-PARP1 (1:1000, Cell Signaling Cat. No. 9532), and anti-H3 (1:2000, Cell Signaling Cat No. 4499) respectively. Detection was performed using ECL reagent (Thermo Fisher Scientific, Cat. No. 32106).\u003c/p\u003e\n\u003ch3\u003eER and H3K27ac CUT\u0026Tag\u003c/h3\u003e\n\u003cp\u003eMCF7 cells were cultured in hormone deprived media for 4\u0026ndash;5 days, followed by treatment with either DMSO, 1 nM E2, or 100 nM FULV for 45 minutes (n\u0026thinsp;=\u0026thinsp;2). For the CUT\u0026amp;Tag procedure, cells were harvested, counted, and assessed for viability. The iDeal CUT\u0026amp;Tag kit (Diagenode C01070021) was employed with minor adaptation to the original protocol.\u003c/p\u003e \u003cp\u003eIn brief, 100,000 cells were permeabilized and immobilized on concanavalin A-coated magnetic beads. Cells were then incubated overnight at 4\u0026deg;C with primary antibodies targeting H3K27ac (1:50, Active Motif Cat. No. 39034), ER (1:50, Epicypher Cat. No. 13-2012) and IgG (Diagenode kit) as a negative control, followed by a 30-minute incubation with a rabbit secondary antibody. Next, loaded Tn5 was added, and the cleaved DNA was purified using the silica membrane column method.\u003c/p\u003e \u003cp\u003eLibrary preparation was performed with Nextera\u0026trade;-Compatible Multiplex Primers (Active Motif, 53155), employing 14 PCR cycles. Post-PCR cleanup was carried out using AMPure XP Beads (Beckman Coulter, A63881). Library concentration was measured with Qubit (Invitrogen), and fragments profile assessed using Tapestation (Agilent). Two biological replicates were generated for each experimental condition. Libraries were normalized, pooled, denatured and loaded with 2% PhiX v3 control spike-in (Illumina) for sequencing on Illumina NextSeq550 sequencer. Sequencing parameters were set to 2 x 38 bp.\u003c/p\u003e\n\u003ch3\u003eCUT\u0026Tag data analysis\u003c/h3\u003e\n\u003cp\u003eThe processing of the CUT\u0026amp;Tag data was performed based on the recommendations of Dr Henikoff\u0026rsquo;s laboratory [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Briefly, paired-end reads were first trimmed using Trimmomatic with the following parameters: LEADING:20 TRAILING:20 SLIDINGWINDOW:4:15 MINLEN:25. Reads were then mapped to the human genome hg38 using bowtie2 and the following parameters: --dovetail --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700. Additional filtering was then applied to the aligned reads: removal of duplicates using Picard, filtering on the quality using samtools and the following parameters: -F 0x04 -q 30, removal of mitochondrial reads and finally removal of blacklist regions according to ENCODE [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Peaks were called first for each replicate using macs3 callpeak function and merged IgG samples with the following parameters: -f BED -g hs -q 0.05 --keep-dup all --nomodel --extsize 200 --bdg \u0026ndash;SPMR. Biological replicate samples were then merged and peaks were called on this pooled-sample. Peaks consistently identified in both individual replicates and at the pooled-level were kept for further analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eATAC-seq\u003c/h2\u003e \u003cp\u003eMCF7 cells were cultured and treated as outlined for CUT\u0026amp;Tag experiments. For chromatin accessibility studies, we utilized the ATAC-seq kit from Active Motif (#53150), following the manufacturer's instructions. In brief, cells were harvested, counted, and assessed for viability. We processed 100,000 viable cells per condition (N\u0026thinsp;=\u0026thinsp;3), lysing them to isolate nuclei. Tagmentation was carried out for 30 minutes at 37\u0026deg;C using an hyperactive Tn5 transposase. The tagmented DNA was then isolated with DNA purification columns and amplified through 10 cycles of PCR, employing a dual combination of indexed primers based on Illumina\u0026rsquo;s Nextera adapters. DNA cleanup was conducted using SPRI beads. Finally, DNA concentration was measured with a Qubit (Invitrogen), and library profiles were evaluated using a Tapestation (Agilent). Libraries were normalized, pooled, denatured and loaded with 2% PhiX v3 control spike-in (Illumina) for sequencing on Illumina NextSeq550 sequencer. Sequencing parameters were set to 2 x 38 bp sequencing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eATAC-seq data analysis\u003c/h3\u003e\n\u003cp\u003eThe data were processed based on ATAC-seq standards. Briefly, reads shorter than 30bp were first trimmed using Trimmomatic. Reads were then aligned to the human genome hg38 using bowtie2 and the following parameters: --no-discordant --very-sensitive -X 2000. Then, additional filtering were applied including the removal of duplicates using Picard, the filtering of low quality mapping using samtools and the following parameters: -bf 0x2 -q 30, the removal of mitochondrial reads and blacklisted regions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Finally, reads were shifted\u0026thinsp;+\u0026thinsp;4bp and \u0026minus;\u0026thinsp;5bp respectively to account for the 9bp duplication created by DNA repair by the Tn5 transposase. Peaks were called for each replicate sample using macs3 callpeaks with the following parameters: -q 0.01 --nomodel --shift \u0026minus;\u0026thinsp;75 --extsize 150 --keep-dup all --bdg \u0026ndash;SPMR. Biological triplicate samples were then merged and peaks were called on this pooled-sample. Peaks consistently identified at least in two replicates and at the pooled-level were kept for further analysis.\u003c/p\u003e\n\u003ch3\u003eDownstream analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePeaks annotation\u003c/h2\u003e \u003cp\u003eCUT\u0026amp;Tag and ATAC peaks were annotated using the Genecode v43 database (TxDb.Hsapiens.UCSC.hg38.knownGene) and the R package ChIPpeakAnno [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] to link each peak to its closest TSS gene. Promoter regions were defined as regions [-1000bp, +\u0026thinsp;500bp] from TSS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide and ER-centered H3K27ac\u003c/h2\u003e \u003cp\u003eH3K27ac peaks called in each experimental condition were used to quantify genome-wide H3K27ac signal changes upon treatment. Summary statistics and a Principal Component Analysis (PCA) were computed based on the count matrix generated with featureCounts using all H3K27ac peaks. Venn diagram comparing genomic regions were computed using ChIPeakAnno R package.\u003c/p\u003e \u003cp\u003eER-centered H3K27ac regions were generated by extending 500bp upstream and 500bp downstream from the center of ER peaks identified at least in one of the experimental conditions (DMSO, E2 and/or FULV).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential analysis\u003c/h2\u003e \u003cp\u003eCount matrices were generated using featureCounts by pooling all peaks called across conditions and counting reads falling in those genomic regions across samples. Counts were then normalized using the TMM normalization with EdgeR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferential analyses using ER, H3K27ac or ATAC-seq signals were generated using EdgeR with glmQLFit and glmQLFTest to perform pairwise comparisons between the control (DMSO) and the treatments (E2, FULV) to identify differential signals. Peaks showing at least two-fold differences and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Of note, differentially enriched peaks were further filtered to retain only those called in the treated condition for over-enrichment and in the control condition for depleted signal. In addition, -inf and +\u0026thinsp;inf panels were created to distinguish those cases in which no counts were found in the control (+\u0026thinsp;inf) or the treatment (-inf).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePathways enrichment\u003c/h2\u003e \u003cp\u003eA pathway enrichment analysis was performed for ER-centered H3K27ac regions found to be differentially enriched using ReactomePA R package with the following parameters: p-value adjustment using Benjamini Hochberg, a p-value cutoff\u0026thinsp;\u0026lt;\u0026thinsp;0.05, maxGSsize of 1000 and a q-value cutoff\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMotifs analysis\u003c/h2\u003e \u003cp\u003eThe function findMotifsGenome.pl from HOMER [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was used to identify known motifs enriched at ER binding sites identified in different experimental conditions (DMSO, E2 and FULV) with default parameters. In addition, the same analysis was performed using ER-centered H3K27ac regions found to be enriched or depleted compared to the control DMSO condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData integration\u003c/h2\u003e \u003cp\u003eER-centered dysregulations were first studied by integrating ER binding sites with ATAC-seq or ER-centered H3K27ac differentially enriched signals independently:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntegration with ATAC-seq\u003c/span\u003e: ER peaks identified across all experimental conditions were intersected with differentially accessible regions upon FULV or E2 treatments to compute a heatmap summarizing chromatin opening status at ER binding sites. In addition, ER peaks were intersected with ATAC-seq peaks using bedtools \u0026ndash;wo and, ATAC-seq log2FC as well as FDR were aggregated to obtain an average value for each unique ER binding site.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntegration with H3K27ac\u003c/span\u003e: differentially enriched peaks were intersected using bedtools \u0026ndash;wo and H3K27ac log2FC as well as FDR were aggregated to obtain an average value for each unique ER binding site.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFinally, all datasets generated in this study were integrated to study H3K27 acetylation status and chromatin opening status at ER binding sites. ER peaks were first intersected with ATAC-seq peaks and ATAC-based log2FC as well as FDR were aggregated to compute an average value for each ER binding site. Then, these peaks were intersected with H3K27ac centered around ER binding sites and the log2FC as well as FDR of H3K27ac were aggregated for each ER binding site using the average value of the region (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEstradiol and Fulvestrant can both induce rapid mobility and increase ER levels at chromatin\u003c/h2\u003e \u003cp\u003eTo determine whether the natural and therapeutic ER ligands, E2 and FULV respectively, can promote ER mobilization to chromatin, we conducted subcellular fractionation experiments in hormone-deprived MCF7 ER\u0026thinsp;+\u0026thinsp;breast cancer cells 30 minutes, 45 minutes, and 4 hours post-treatment with E2 and FULV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Our results demonstrated that both E2 and FULV treatments could induce an increase of ER protein levels in the chromatin-enriched fraction within 30 minutes, accompanied by a concomitant decrease in cytoplasmic ER levels. Notably, both treatment conditions still exhibited ER depletion in the cytoplasmic fraction up to 4 hours. However, enrichment at chromatin appeared most consistent across experimental conditions 30\u0026ndash;45 minutes post-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C). Our results were aligned with previous observations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and this latter time-point was then chosen for subsequent chromatin profiling studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we investigated the extent to which E2 and FULV treatments affect ER cistrome using CUT\u0026amp;Tag, a novel chromatin profiling method based on enzyme-tethering, decreased input cell number and a streamlined protocol [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We initially detected a limited number of ER binding sites in the control DMSO condition (n\u0026thinsp;\u0026asymp;\u0026thinsp;2200), as expected considering that the cells were hormone-deprived for 4\u0026ndash;5 days prior treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, we observed a dramatic increase in ER binding sites upon E2 (n\u0026thinsp;\u0026asymp;\u0026thinsp;22000) and FULV (n\u0026thinsp;\u0026asymp;\u0026thinsp;40000) treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplemental Fig.\u0026nbsp;2B) in line with previously published data obtained using ChIP-seq profiling [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consistent with previous studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], over 75% ligand-induced ER binding sites were located at distal sites, outside promoter regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplemental Fig.\u0026nbsp;2A) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further examined whether both ligands induced similar ER binding sites upon treatment. These analyses revealed that while many sites were shared in both E2 and FULV conditions (n\u0026thinsp;\u0026asymp;\u0026thinsp;20000), a similar number were unique to FULV treatment (n\u0026thinsp;\u0026asymp;\u0026thinsp;21000) and a more limited number unique to E2 treatment (n\u0026thinsp;\u0026asymp;\u0026thinsp;3000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In addition, among the few ER-binding sites detected in the control DMSO condition a majority were still found in presence of E2 and/or FULV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), suggesting a rapid and massive engagement of ER genome-wide upon ligand treatment rather than a redistribution of pre-existing chromatin bound complexes.\u003c/p\u003e \u003cp\u003eA differential binding analysis confirmed that both ligands induced increases in ER binding to DNA when compared to the control DMSO condition but also pointed to salient differences. Despite a larger number of sites specifically detected upon FULV treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), ER binding appeared more robust upon E2 treatment when considering fold changes and FDR metrics with roughly twice as many sites significantly increased (2-fold enrichment and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; n\u0026thinsp;\u0026asymp;\u0026thinsp;8000 \u003cem\u003evs\u003c/em\u003e n\u0026thinsp;\u0026asymp;\u0026thinsp;4500 for E2 and FULV respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In line with this observation, among a total of 9951 ER binding sites significantly differentially bound upon E2 and/or FULV treatment (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026gt; 1.5 fold difference), four clusters emerged: E2 specific increased sites were the most common (cluster 4; n\u0026thinsp;=\u0026thinsp;4780, followed by shared E2 and FULV increased sites (cluster 3; n\u0026thinsp;=\u0026thinsp;2992, FULV specific increased sites (cluster 1; n\u0026thinsp;=\u0026thinsp;1826 and a small number of shared decreased E2 and FULV sites (cluster 2; n\u0026thinsp;=\u0026thinsp;353) (Supplemental Fig.\u0026nbsp;2C). The fact that most ER binding sites detected upon FULV treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) did not display significantly increased signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) could suggest heterogeneous ER binding within the cell population, where ER would engage many of these sites in a small percentage of cells only.\u003c/p\u003e \u003cp\u003eFinally, the annotation of pre-existing ER binding sites detected in the DMSO control condition, highlighted that a larger fraction of these sites showed significant ER differential binding upon E2 treatment (Supplemental Fig.\u0026nbsp;2D; upregulated sites in yellow and downregulated sites in blue).\u003c/p\u003e \u003cp\u003eDNA motif enrichment analyses focused on ER binding sites pointed to several transcription factors known to functionally interact with ER in breast cancer biology such as GATA3 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], AP-1 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and FOXA1 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] in addition to the ERE motif recognized by the Estrogen Receptor- [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Supplemental Fig.\u0026nbsp;2E). Both E2 and FULV ligands displayed similar motif enrichment patterns with only subtle differences, consistent with the large number of overlapping binding sites between the two treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Supplemental Fig.\u0026nbsp;2B).\u003c/p\u003e \u003cp\u003eAltogether, our observations obtained using CUT\u0026amp;Tag confirm previous conclusions obtained using ChIP-seq [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and provide further support for the hypothesis that FULV does not prevent weak ER agonism as it allows ER binding to canonical DNA sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFulvestrant does not alter chromatin accessibility at ER binding sites\u003c/h2\u003e \u003cp\u003eTo investigate the functional impact of ligand treatment on chromatin accessibility, we performed ATAC-seq in hormone-depleted MCF7 cells in the same experimental conditions used to profile ER binding sites (45 minutes post-E2 or FULV treatment). In line with recent findings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], we observed that E2 treatment induced major changes in chromatin accessibility genome-wide (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; n\u0026thinsp;=\u0026thinsp;2153 increased sites and n\u0026thinsp;=\u0026thinsp;93 decreased sites), when FULV treatment had virtually no impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; n\u0026thinsp;=\u0026thinsp;5 increased sites and n\u0026thinsp;=\u0026thinsp;25 decreased sites).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we examined whether accessible regions impacted by E2 were associated with ER binding sites. We observed that E2 treatment mostly enhanced chromatin accessibility at ER binding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; cluster 2 and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Only a few ER binding sites displayed reduced chromatin accessibility upon E2 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C; cluster 1). Notably, E2 treatment resulted in 1663 ER binding sites with concomitant increases in both ER binding and chromatin accessibility (2-fold changes), most of which were located at distal genomic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD; 91%).\u003c/p\u003e \u003cp\u003eOur observations confirm that FULV treatment induces the binding to DNA of an ER complex unable to alter chromatin accessibility when the natural ligand E2 promotes the recruitment of a competent ER complex able to open chromatin at a large subset of its binding sites.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBoth Fulvestrant and E2 induce significant alterations in H3K27ac levels\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eMultiple studies have linked ER binding at Estrogen Responsive elements (ERE) with H3K27ac deposition, target gene activation and cancer cell fitness [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. H3K27ac is an histone post-translational modification catalyzed by the histone acetyl-transferases CBP and p300, and is a well-established marker of transcriptionally active enhancers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, in light of their striking differential impact on chromatin opening we next performed H3K27ac Cut\u0026amp;Tag to investigate whether E2 and FULV treatments induced similar effects on H3K27ac levels at ER binding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn each experimental condition, H3K27ac peaks were detected both at annotated promoters and non-promoter regions (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sample clustering based on the variability in genome-wide H3K27ac signals lead to a clear separation between DMSO control condition, E2 and FULV treatments (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) whereas most regions enriched in H3K27ac were common to all conditions (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eCloser examination highlighted that both E2 and FULV induced significant genome-wide changes in H3K27ac levels, predominantly leading to the enrichment of this histone post-translational modification (2-fold enrichment and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 Supplementary Fig.\u0026nbsp;3D, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Notably, a large proportion of H3K27ac-enriched regions were shared between E2 and FULV treatments (Supplementary Fig.\u0026nbsp;3E). Furthermore, we observed that these increased levels of H3K27ac were strongly associated with ER binding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. Specifically, upon E2 treatment, 2,075 ER binding sites exhibited elevated H3K27ac levels, while FULV treatment led to an increase in H3K27ac signals at 1,550 ER binding sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eGene set enrichment analyses for the genes found near H3K27ac-increased ER binding sites upon both E2 and FULV treatment conditions revealed notably the Estrogen Response pathway as well as TNFα signalling via NF-κB (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Specific to FULV treatment, hypoxia and p53 pathways were associated with increases in H3K27ac while the Estrogen Response pathway was also associated with decreases in H3K27ac (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These findings are consistent with previous studies emphasizing the role of ER in regulating various signalling pathways, including TNFα and p53, by recruiting co-regulators and chromatin remodelling complexes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn integrative analysis centered on ER binding sites revealed that 2228 regions displayed increases in both ER and H3K27ac and were primarily located at distal regions upon E2 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD left). A similar analysis upon FULV treatment identified 1123 regions displaying increases in both ER and H3K27ac although these sites were more evenly distributed between promoter and distal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD right).\u003c/p\u003e \u003cp\u003eInterestingly, DNA motif enrichment analyses focused on ER binding sites displaying differential H3K27ac signals upon ligand treatment, pointed to many similarities with the noticeable exception of the Estrogen Response Element (ERE) which was found significantly enriched at sites showing increases in H3K27ac upon E2 treatment as expected but only at H3K27ac depleted sites upon FULV treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) further reinforcing potential mechanistic differences upon ligand binding.\u003c/p\u003e \u003cp\u003eIn conclusion, our results then show that whereas FULV treatment does not significantly alter chromatin accessibility, it can paradoxically induce ER binding and the deposition of H3K27ac at both promoters and distal sites. Upon engagement with Fulvestrant, ER retains the ability to translocate to the nucleus and bind DNA and whereas it might fail to recruit or activate the chromatin remodelling machinery and appear transcriptionally inert, ER still appears to recruit a proficient acetyltransferase complex leading to H3K27 acetylation at a subset of its binding sites (Supplemental Fig.\u0026nbsp;3F).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the differential impact of the natural ligand E2 and the therapeutic agent Fulvestrant on ER activity in breast cancer cells. FULV is a synthetic, selective ER antagonist eventually promoting its degradation. By combining ER cistrome, chromatin accessibility, and H3K27ac dynamics profiling, we provide crucial insights into its mechanism of action and we endorse as well as enrich previously published findings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] indicating that its therapeutic potential may extend beyond merely antagonizing ER activity. We purposedly chose to focus on early-stage mechanism of action post ligand treatment in hormone-deprived cells, at a time-point providing sufficient time for ER to translocate to the nucleus but still upstream protein degradation induced at later stages by Fulvestrant [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which would make it difficult to dissociate ligand induced protein activity from the phenotypic impact of ER decreased levels.\u003c/p\u003e \u003cp\u003eTo obtain these results, we employed the novel CUT\u0026amp;Tag technology to confirm previous ChIP-seq results demonstrating that ER when engaged with FULV can still bind DNA genome-wide [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This supports the notion that CUT\u0026amp;Tag can replace the \u0026ldquo;gold standard\u0026rdquo; ChIP-seq, while providing a greater signal-to-noise ratio, requiring fewer cells as starting input and lower sequencing depth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] on top of offering the possibility for automation thus enabling increased throughput, which is particularly attractive for its implementation in the drug discovery process.\u003c/p\u003e \u003cp\u003eThe dramatic increase in ER binding sites observed upon E2 and FULV treatments underscores the ability of these ligands to mobilize ER within the chromatin. However, the substantial number of low enrichment binding sites identified specifically upon FULV treatment suggests an allosteric impact on DNA binding either directly on ER DNA binding domain or indirectly on co-factors recruitment. The latter hypothesis is supported by the striking absence of chromatin opening at ER binding sites upon FULV treatment suggesting the inability to engage the chromatin remodelling machinery which could eventually lead to decreased residency at canonical binding sites and spurious binding genome-wide.\u003c/p\u003e \u003cp\u003eOur most surprising discovery is the robust increase in H3K27ac levels observed at a non-trivial subset of ER binding sites, which challenges the idea that FULV-bound ER remains an inert DNA binding complex. Given that CBP and p300 are crucial ER co-activators that significantly influence H3K27ac dynamics [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], we hypothesize that the recruitment of this complex is not entirely disrupted upon FULV treatment. This could provide a rational for combination treatment between the standard of care Fulvestrant and CBP/p300 targeting agents, which have shown potential in pre-clinical ER\u0026thinsp;+\u0026thinsp;breast cancer models [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile both E2 and FULV induced increases in H3K27ac levels at ER binding sites, changes at a large number of sites were ligand specific thus the associated gene pathways differed significantly. This was expected from the differential binding sites at ER cistrome level. When E2 mainly affected the previously described estrogen response pathway, FULV affected several other pathways reminiscent of a poorly orchestrated program. Furthermore, a decrease in H3K27ac levels at regions containing estrogen response element (ERE) motifs upon FULV treatment suggests that FULV-bound ER may replace proficient complexes at pre-existing sites in the DMSO control condition, which would represent another way to disrupt ER activity in breast cancer cells.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study provides compelling evidence that Fulvestrant modulates the Estrogen Receptor molecular activity before causing its protein degradation in breast cancer by inducing genome-wide DNA binding and modulating H3K27ac levels, despite the inability to induce chromatin accessibility changes. These findings underscore the complexity of ER signalling pathways and suggest that the therapeutic potential of Fulvestrant may extend beyond mere antagonism of ER activity. Future studies could explore the molecular mechanisms behind these observations, especially focusing on how acetyltransferases such as CBP/p300 and other co-regulators are recruited and may influence these effects. Understanding these complex interactions could lead to the development of more effective treatments in ER-positive breast cancer, ultimately improving patients outcome.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing raw data of this article are available in GEO (number will be provided upon manuscript acceptance).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCB conducted all bioinformatic analyses, contributed to data interpretation, and drafted the manuscript. MS, EM, and IP performed the experimental work. CF and AD conducted next-generation sequencing. VP contributed to data interpretation. GB and IP were involved in the research design and implementation, data collection, interpretation, and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Coralie Hoareau-Aveilla and Tanguy Bozec for their invaluable support and helpful advice throughout this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCorces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, et al. The chromatin accessibility landscape of primary human cancers. Science. 2018;362:eaav1898. \u003c/li\u003e\n\u003cli\u003eFu Z, Jiang S, Sun Y, Zheng S, Zong L, Li P. Cut\u0026amp;tag: a powerful epigenetic tool for chromatin profiling. Epigenetics. 2024;19:2293411. \u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA A Cancer J Clinicians. 2018;68:7\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eStice JP, Knowlton AA. Estrogen, NF\u0026kappa;B, and the Heat Shock Response. Mol Med. 2008;14:517\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eYaşar P, Ayaz G, User SD, G\u0026uuml;p\u0026uuml;r G, Muyan M. Molecular mechanism of estrogen\u0026ndash;estrogen receptor signaling. Reprod Medicine \u0026amp; Biology. 2017;16:4\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eBelachew EB, Sewasew DT. Molecular Mechanisms of Endocrine Resistance in Estrogen-Receptor-Positive Breast Cancer. Front Endocrinol. 2021;12:599586. \u003c/li\u003e\n\u003cli\u003eClusan L, Ferri\u0026egrave;re F, Flouriot G, Pakdel F. A Basic Review on Estrogen Receptor Signaling Pathways in Breast Cancer. IJMS. 2023;24:6834. \u003c/li\u003e\n\u003cli\u003eRej RK, Roy J, Allu SR. Therapies for the Treatment of Advanced/Metastatic Estrogen Receptor-Positive Breast Cancer: Current Situation and Future Directions. Cancers. 2024;16:552. \u003c/li\u003e\n\u003cli\u003eBross PF, Cohen MH, Williams GA, Pazdur R. FDA Drug Approval Summaries: Fulvestrant. The Oncologist. 2002;7:477\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eGibson MK, Nemmers LA, Beckman WC, Davis VL, Curtis SW, Korach KS. The Mechanism of ICI 164,384 Antiestrogenieity Involves Rapid Loss of Estrogen Receptor in Uterine Tissue. Endocrinology. 1991;129:2000\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eGuan J, Zhou W, Hafner M, Blake RA, Chalouni C, Chen IP, et al. Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility. Cell. 2019;178:949-963.e18. \u003c/li\u003e\n\u003cli\u003eKaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, et al. CUT\u0026amp;Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019;10:1930. \u003c/li\u003e\n\u003cli\u003eAmemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019;9:9354. \u003c/li\u003e\n\u003cli\u003eZhu LJ, Gazin C, Lawson ND, Pag\u0026egrave;s H, Lin SM, Lapointe DS, et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics. 2010;11:237. \u003c/li\u003e\n\u003cli\u003eRobinson MD, McCarthy DJ, Smyth GK. edgeR : a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eHeinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Molecular Cell. 2010;38:576\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eCarroll JS, Liu XS, Brodsky AS, Li W, Meyer CA, Szary AJ, et al. Chromosome-Wide Mapping of Estrogen Receptor Binding Reveals Long-Range Regulation Requiring the Forkhead Protein FoxA1. Cell. 2005;122:33\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eTabe-Bordbar S, Song YJ, Lunt BJ, Alavi Z, Prasanth KV, Sinha S. Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program. Commun Biol. 2024;7:719. \u003c/li\u003e\n\u003cli\u003eKong SL, Li G, Loh SL, Sung W, Liu ET. Cellular reprogramming by the conjoint action of ER\u0026alpha;, FOXA1, and GATA3 to a ligand‐inducible growth state. Molecular Systems Biology. 2011;7:526. \u003c/li\u003e\n\u003cli\u003eFang SH, Chen Y, Weigel RJ. GATA-3 as a Marker of Hormone Response in Breast Cancer. Journal of Surgical Research. 2009;157:290\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eDahlman-Wright K, Qiao Y, Jonsson P, Gustafsson J-\u0026Aring;, Williams C, Zhao C. Interplay between AP-1 and estrogen receptor \u0026alpha; in regulating gene expression and proliferation networks in breast cancer cells. Carcinogenesis. 2012;33:1684\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eJoosten SEP, Gregoricchio S, Stelloo S, Yapıcı E, Huang C-CF, Yavuz K, et al. Estrogen receptor 1 chromatin profiling in human breast tumors reveals high inter-patient heterogeneity with enrichment of risk SNPs and enhancer activity at most-conserved regions. Genome Res. 2024;34:539\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eCarroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, et al. Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006;38:1289\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eHah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell. 2011;145:622\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eHah N, Murakami S, Nagari A, Danko CG, Kraus WL. Enhancer transcripts mark active estrogen receptor binding sites. Genome Res. 2013;23:1210\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eHewitt SC, Grimm SA, Wu S-P, DeMayo FJ, Korach KS. Estrogen receptor \u0026alpha; (ER\u0026alpha;)-binding super-enhancers drive key mediators that control uterine estrogen responses in mice. J Biol Chem. 2020;295:8387\u0026ndash;400. \u003c/li\u003e\n\u003cli\u003eLopes R, Sprouffske K, Sheng C, Uijttewaal ECH, Wesdorp AE, Dahinden J, et al. Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens. Sci Adv. 2021;7:eabf5733. \u003c/li\u003e\n\u003cli\u003eGuo C, Meza-Sosa KF, Valle-Garcia D, Zhao G, Gao K, Yu L, et al. The SET oncoprotein promotes estrogen-induced transcription by facilitating establishment of active chromatin. Proc Natl Acad Sci USA. 2023;120:e2206878120. \u003c/li\u003e\n\u003cli\u003eHeintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet. 2007;39:311\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eVisel A, Blow MJ, Li Z, Zhang T, Akiyama JA, Holt A, et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457:854\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCreyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA. 2010;107:21931\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eNarita T, Ito S, Higashijima Y, Chu WK, Neumann K, Walter J, et al. Enhancers are activated by p300/CBP activity-dependent PIC assembly, RNAPII recruitment, and pause release. Molecular Cell. 2021;81:2166-2182.e6. \u003c/li\u003e\n\u003cli\u003eTo SQ, Cheung V, Lazarus KA, Knower KC, Clyne CD. Estradiol regulates Tumor Necrosis Factor-\u0026alpha; expression and secretion in Estrogen Receptor positive breast cancer cells. Molecular and Cellular Endocrinology. 2014;394:21\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCruceriu D, Baldasici O, Balacescu O, Berindan-Neagoe I. The dual role of tumor necrosis factor-alpha (TNF-\u0026alpha;) in breast cancer: molecular insights and therapeutic approaches. Cell Oncol. 2020;43:1\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eWardell SE, Marks JR, McDonnell DP. The turnover of estrogen receptor \u0026alpha; by the selective estrogen receptor degrader (SERD) fulvestrant is a saturable process that is not required for antagonist efficacy. Biochemical Pharmacology. 2011;82:122\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eLong X, Nephew KP. Fulvestrant (ICI 182,780)-dependent Interacting Proteins Mediate Immobilization and Degradation of Estrogen Receptor-\u0026alpha;. Journal of Biological Chemistry. 2006;281:9607\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eHogg SJ, Motorna O, Cluse LA, Johanson TM, Coughlan HD, Raviram R, et al. Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition. Molecular Cell. 2021;81:2183-2200.e13. \u003c/li\u003e\n\u003cli\u003eWaddell A, Mahmud I, Ding H, Huo Z, Liao D. Pharmacological Inhibition of CBP/p300 Blocks Estrogen Receptor Alpha (ER\u0026alpha;) Function through Suppressing Enhancer H3K27 Acetylation in Luminal Breast Cancer. Cancers. 2021;13:2799. \u003c/li\u003e\n\u003cli\u003eBommi-Reddy A, Park-Chouinard S, Mayhew DN, Terzo E, Hingway A, Steinbaugh MJ, et al. CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells. Weisz A, editor. PLoS ONE. 2022;17:e0262378. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"epigenetics-and-chromatin","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epch","sideBox":"Learn more about [Epigenetics \u0026 Chromatin](http://epigeneticsandchromatin.biomedcentral.com/)","snPcode":"13072","submissionUrl":"https://submission.nature.com/new-submission/13072/3","title":"Epigenetics \u0026 Chromatin","twitterHandle":"@EpigenChromatin","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CUT\u0026Tag, Estrogen Receptor, H3K27ac, ATAC-seq, epigenetics, chromatin landscape, Estradiol, Fulvestrant, breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-6779056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6779056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpigenetic dysregulations are linked to various diseases, including cancer. Among them, breast cancer is the second leading cause of cancer-related deaths in women with 50% of mortalities attributable to estrogen receptor-positive (ER+) tumors. Endocrine therapies targeting the Estrogen Receptor (ER) such as Tamoxifen, Fulvestrant and Aromatase inhibitors, are widely used in the clinic. Among these therapeutic agents, Fulvestrant has been shown to fully antagonize ER activity, primarily through the rapid degradation and elimination of ER from target tissues. However, recent findings indicate that ER, when engaged with Fulvestrant, retains the ability to translocate to the nucleus and bind DNA whereas appearing transcriptionally inert.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study we aimed to further investigate the effects of Fulvestrant and Estradiol, an ER natural ligand, on ER cistrome, chromatin accessibility, and H3K27ac genome-wide patterns in an ER\u0026thinsp;+\u0026thinsp;breast cancer cell line. Using the innovative CUT\u0026amp;Tag technology, we first confirmed that both Fulvestrant and Estradiol promote ER binding to DNA. Our findings revealed that Estradiol not only enhances chromatin accessibility but also increases H3K27ac levels at ER binding sites. In contrast, while Fulvestrant does not significantly alter chromatin accessibility, it can induce increases in H3K27ac levels at a subset of ER binding sites. Our observations suggest that Fulvestrant may modulate breast cancer transcriptional landscape by impacting H3K27ac dynamics, even in the absence of changes in chromatin accessibility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides new insights on the mechanistic impact of Fulvestrant on Estrogen Receptor activity and their potential implications on target gene expression, particularly highlighting a novel putative role of H3K27ac dynamics in these processes.\u003c/p\u003e","manuscriptTitle":"The impact of Fulvestrant on Estrogen Receptor-Driven Chromatin Dynamics in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-06 09:58:21","doi":"10.21203/rs.3.rs-6779056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T05:25:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T19:22:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T19:23:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301249774513186279702239304163653636695","date":"2025-07-03T18:43:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339777888440477437698134327955298594151","date":"2025-07-03T17:18:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T13:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175928599168155511588836323488692288557","date":"2025-06-05T15:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333835761520329604210779374672446276932","date":"2025-06-04T09:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T06:35:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T04:51:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Epigenetics \u0026 Chromatin","date":"2025-06-02T09:48:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"epigenetics-and-chromatin","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epch","sideBox":"Learn more about [Epigenetics \u0026 Chromatin](http://epigeneticsandchromatin.biomedcentral.com/)","snPcode":"13072","submissionUrl":"https://submission.nature.com/new-submission/13072/3","title":"Epigenetics \u0026 Chromatin","twitterHandle":"@EpigenChromatin","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06ad730a-b284-418b-bc69-0b9df02217c8","owner":[],"postedDate":"June 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:00:49+00:00","versionOfRecord":{"articleIdentity":"rs-6779056","link":"https://doi.org/10.1186/s13072-026-00667-0","journal":{"identity":"epigenetics-and-chromatin","isVorOnly":false,"title":"Epigenetics \u0026 Chromatin"},"publishedOn":"2026-03-17 15:58:00","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-06-06 09:58:21","video":"","vorDoi":"10.1186/s13072-026-00667-0","vorDoiUrl":"https://doi.org/10.1186/s13072-026-00667-0","workflowStages":[]},"version":"v1","identity":"rs-6779056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6779056","identity":"rs-6779056","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}