Estrogen Receptor Alpha Dynamics and Plasticity During Endocrine Resistance

doi:10.21203/rs.3.rs-6295413/v1

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Research Article

Estrogen Receptor Alpha Dynamics and Plasticity During Endocrine Resistance

https://doi.org/10.21203/rs.3.rs-6295413/v1

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published 13 Jun, 2025

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Background

ER-α positive breast cancer, even though they respond to endocrine treatment, half of the patients acquire resistance and progress with metastasis despite ERα status. Spatio-temporal changes in ERα and their loss under treatment pressure have been reported in a subset of patients, which is a serious problem.

Results

We have demonstrated that in vitro-generated resistance is correlated with the downregulation of ERα. To study the ERα status transition in live cells, triple-negative breast cancer cells were engineered to express EGFP-ERα, which further supported the existence of complex intracellular signaling that regulates ERα plasticity even in unperturbed conditions. Single-cell clones generate heterogeneity and loss of expression depending on proliferative cues. However, the initial response of cells to 4-hydroxytamoxifen and endoxifen involves up-regulation of ERα, likely due to its early effect on the proteasome or autophagy pathway. Supporting this, inhibition of autophagy and proteasome further enhanced the expression of ERα. Systematic analysis of RNA sequencing of ERα stable cells further confirmed that ERα regulates diverse intracellular signalling networks such as ubiquitin, proteasome pathways, cell proliferation and Unfolded Protein Responses (UPR), implicating its direct role in post-translational protein modifications. Cell cycle indicator probe expressing receptor-positive breast cancer cells confirmed the ERα expression heterogeneity both in 2D and 3D culture in a cell cycle phase independent manner.

Conclusions

Overall, the study confirms the cell’s intrinsic post-transcriptional mechanisms of ERα plasticity that could play a role in receptor heterogeneity and tumor progression under endocrine treatment that warrants further investigation.

Breast Cancer

Estrogen receptor alpha (ERα)

Receptor heterogeneity

Endocrine resistance

Unfolded Protein Response

Tamoxifen

TNBC

Real-time imaging

Breast cancer classification is primarily based on hormone receptor status and genomic signatures. Accordingly, multiple subtypes that include normal breast-like, luminal A (ER+/ PR + and Ki-67 low), luminal B (ER+/ PR + and HER2 + or HER2–, and Ki67-high), HER2-enriched (HER2+), basal-like and claudin-low [1–3]. In general, ER-positive tumors are thought to be derived from mature luminal cells and ER-negative cell types from basal or luminal progenitor-like cells [4]. However, recent studies also indicate that luminal progenitors could serve as the common cell of origin for both luminal (ER+) and basal-like (ER−) breast cancers [5]. The classification based on hormone receptors is also important for the choice of drug to be used for hormone receptor-positive tumors. Approximately 70% of all diagnosed breast cancers are ER-positive and respond to endocrine treatment such as selective ER modulators, Selective ER degraders or aromatase inhibitors [6–8]. Despite the good initial response, nearly 50% of tumors show endocrine resistance and progress to metastasis [8, 9]. The common mechanisms of resistance include alterations in the ER/PgR pathway, genomic and epigenetic alterations of ESR1, expression of truncated ER-isoforms, post-translational modification, increased receptor tyrosine kinase signalling, and altered cell cycle regulation [10]. The deregulation of ERα or loss of the receptor following endocrine treatment has been reported in approximately 10–20% of cases involving the conversion from ERα-positive to ERα-negative status [11, 12]. Many instances of receptor plasticity and shift have been reported in experimental models and clinical hormone resistance cases. Recently, it has been shown that ERα status is modulated when ER-positive cells are cultured in the presence of triple-negative breast cancer (TNBC) cells, leading to a different response to endocrine therapy [13]. It has also been noticed that cancer-associated fibroblasts (CAF)-derived factors can decrease the expression of ERα in breast tumors to induce a triple-negative phenotype [14, 15]. Similarly, serine starvation silences estrogen receptor signalling through histone hypoacetylation [16]. Many of these studies point towards oscillatory functions or epigenetic plasticity for estrogen receptors. Despite compelling evidence for genetic and epigenetic drivers of plasticity, it is still unclear if and how molecular cues from the microenvironment or the cell’s inherent signals govern the switch from ERα + to ERα- cell fate and its dynamics in live cells. It is important to know whether the shift is stochastic or whether its dynamics are ruled by the cell’s inherent signalling under proliferating conditions and endocrine pressure.

To Understand the impact of ERα heterogeneity with or without endocrine stress on hormone resistance in ERα positive cells, experimental investigation using appropriate models is necessary. Well-defined cellular models can explain the spatio-temporal alterations of hormone receptors in live cells under various conditions. EGFP-ERα-expressing stable cells in a TNBC background offers a precise model for visualizing estrogen receptor dynamics in real-time, elucidating cell-autonomous heterogeneity, and quantifying its adaptive changes under endocrine pressure. RNA sequencing of isogenic cells of ERα-positive and negative cells was carried out to understand the direct estrogen receptor transcriptional targets and signalling. In this study, we also question the correlation of estrogen receptor dynamics with the cell cycle in monolayer and organoids derived from hormone receptor-positive breast cancer cells. This study suggests a need for broader research into the relationship between ERα loss and ERα plasticity, inherent hormone receptor heterogeneity, and breast tumor biology.

Cell Culture

Human breast cancer cell lines MDA-MB-231 and MCF-7 were obtained from the Central Cell Line (CCL) Repository of BRIC-Rajiv Gandhi Centre for Biotechnology. Both cell lines were routinely cultured in RPMI Medium (Gibco, USA) containing 10% fetal bovine serum (Gibco, USA), and 1X antibiotic-antimycotic (Gibco, USA) in the incubator at 37⁰C, supplemented with 5% CO₂. The cells were used within 10 passages after revival from the original stock.

Generation of Endocrine resistance in MCF-7 cells and its functional evaluation

The breast cancer cell lines were continuously maintained in phenol red-free RPMI containing 1µM of endoxifen ( #E8284, Sigma ) and or 4µM of 4-hydroxy tamoxifen (#508225, Sigma) for three months. The cells were fed with fresh drug-containing media every 3 days. The endocrine-resistant clones were functionally evaluated for resistance and cross-resistance; the cells were exposed to multiple drugs/endocrine (Supplementary Table S1) treatment for 48 hours and imaged using a fluorescence microscope (Nikon Eclipse Ti). Before drug/ endocrine treatment, cells were stained using nucleic acid dye Hoechst 33342 (1µg/ml for 10 minutes). The parental and endocrine-resistant cell whole cell extract was used for the immunoblot for ERα.

Whole-cell extract was prepared using RIPA lysis buffer. 40 µg of proteins were loaded for Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and the separated proteins were transferred to polyvinylidene difluoride (PVDF) membrane by wet transfer method. The PVDF membrane was blocked with 5% BSA in TBST and incubated using an appropriate primary antibody (Supplementary Table ST2) overnight at 4⁰C, and later with HRP conjugated secondary antibody for enhanced chemiluminescence (Thermo Scientific, Rockford, IL, USA) based detection.

Immunofluorescent staining of ERα and confocal imaging

To detect ERα in MCF 7 parental and resistant cells, the cells were grown on 8 well-chambered coverglass ( Nunc, Thermo) and fixed with 4% paraformaldehyde. After permeabilization and blocking, the cells were incubated overnight at 4°C in primary antibody against ERα (CST #8644). After washing, the cells were further incubated with Alexa Fluor 647 conjugated secondary antibody (Molecular Probes, Invitrogen) for 2 hours at room temperature. The cells were also counter-stained with nucleic acid dye Hoechst 33342 (1µg/ml for 10 minutes). After staining, cells were imaged using a Nikon AIR confocal imager with a 20X objective. For imaging, ERα Alexa Fluor 647 is performed at 640/665 nm, and Hoechst is imaged at 350/461 nm. All the images were analyzed using Nikon's NIS element software.

Construction of MDA-MB-231 EGFP-ERα Cell Line

The MDA-MB-231 cells were used to transfect the pEGFP-C1-ER alpha, a gift from Michael Mancini (Addgene plasmid # 28230) [17]. Following the manufacturer's instructions, the plasmid was transfected using a Neon Electroporation system (Invitrogen, USA). After transfection, the cells were maintained in G418 containing media for one month and further sorted based on EGFP expression using FACS Aria III (Becton Dickinson, USA). To ensure stable expression of EGFP ERα, cells were further expanded and verified by live cell imaging under fully motorized Epi fluorescence microscopy for three days (Nikon Eclipse Ti) using 20X objective. The cells were maintained in an onstage incubator from Okolab (Okolab, Italy). The transfected cell clones were verified by RT-PCR and Western blot analysis and were used for further experiments.

Understanding ERα heterogeneity using Flow cytometry

To understand the ERα heterogeneity in stable MDA-MB-231 EGFP ERα, cells were sorted based on EGFP ERα expression levels (high and low) using the flow cytometer sorter FACS Aria III (Becton Dickinson, USA). The sorted cells were maintained in G418 selection media for 2 weeks. Further, flow cytometric analysis and confocal imaging were performed to understand the heterogeneity. Analysis and sorting of EGFP ERα was performed based on EGFP expression under a 488 nm laser line. The doubling time of cells with EGFP ERα high and low expression levels was also evaluated from confocal microscopic images and plotted.

Confocal real-time imaging to understand ERα heterogeneity upon endocrine treatment

The cells were seeded in 96-well optical bottom plates (Corning), and endocrine treatment was performed. After 2 hours of treatment, Real-time imaging was carried out using a Nikon confocal imager A1R equipped with a live cell incubation chamber from Okolab (Okolab, Italy) for 3 days, 20 X objective with NA of 0.75 was used with 488 nm laser. All the images were analyzed using Nikon's NIS element software.

Evaluation of ERα dynamics and cell cycle analysis

To understand the correlation of ERα dynamics and cell cycle, MDA-MB-231 EGFP-ERα cells stained with Hoechst 33342 were used for cell cycle analysis. In brief, cells are stained with 1µg/ml of Hoechst 33342 for 30 minutes and washed with serum-free media. The cells were analyzed using BD FACSAria III equipped with 355nm laser lines. Doublet discrimination was done on the Hoechst area signal against the Hoechst width. We have also analysed the population distribution of Hoechst-stained cells based on EGFP ERα expression.

RNA Isolation and RNA Seq analysis

Total RNA from MDA-MB-231 EGFP-ERα and parental cells were extracted by TRIzol reagent as per standard protocol. Libraries were then generated from 500 ng of intact RNA using the QIAseq Stranded RNA Lib Kit (Qiagen, cat. No. 180451) as specified by the manufacturer. Library quality control was performed using Agilent D1000 ScreenTape (Agilent, Cat. No. 506–5582) and Qubit DNA HS (Thermo, Q33230). The libraries underwent sequencing on the Illumina NovaSeq 6000 platform. The expression levels for each gene to the Reads Per Kilobase of Transcript per Million Fragments Mapped (RPKM) were normalised to facilitate the comparison of transcripts between samples. To validate the RNA-seq data, six genes listed in Supplementary Table ST3 were randomly selected for quantitative RT-PCR.

Identification of Differentially Expressed Genes (DEGs)

The biological replicates were grouped for differential expression analysis as reference (MDA-MB-231) and Test (MDA-MB-231 EGFP ERα). Pseudo-genes were removed from the analysis. Differential expression analysis was done using the DESeq2 package after normalizing the data using the relative log expression normalization method. Genes with absolute log2 fold change ≥ 1.5 and p adj-value ≤ 0.05 were considered significant.

Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis

Enrichment analysis for biological process, molecular function, cellular component, and KEGG Pathway was performed using Cluster Profiler R Bioconductor package. Gene Ontology (GO) and pathway terms with p adj-value ≤ 0.05 are considered significant.

Effect of autophagy, proteasome and translational inhibitors on ERα dynamics

To understand the correlation of ERα dynamics with autophagy (bafilomycin) and proteasome (MG132) inhibition, MDA-MB-231 EGFP-ERα cells were treated with bafilomycin and MG132, respectively, for 24 hours. As mentioned above, real-time imaging and flow cytometry analysis were performed for ERα dynamics. Cycloheximide-treated cells were imaged for 12 hours using a Nikon confocal imager A1R to understand the role of translational inhibition.

Development of cell cycle indicator cells expressing Cdt1-Kusabira orange

As previously reported, the expression vector for Cdt1 Kusabira orange, pcDNA3-mKO2-hCdt1(30/120), a G1 cell cycle indicator, was sourced from Dr. Atsushi Miyawaki at the Riken Centre for Brain Science, Japan [17] [18]. Following the manufacturer's instructions, the plasmid Cdt1 was transfected into the MCF-7 cell line using the Neon Electroporation system (Invitrogen, USA). The cells were maintained in G418 containing media for one month and sorted to enrich cells expressing high levels of Cdt1 based on the red fluorescence intensity at 562 nm laser excitation using the cell sorter FACS AriaIII. To ensure stable expression of Cdt1, cells were further expanded and verified by live cell imaging under fully motorized Epi fluorescence microscopy for three days (Nikon Eclipse Ti) using a 20X objective. The cells were maintained in an onstage incubator from Okolab (Okolab, Italy). Clones of cells confirmed to be stably expressing the Cdt1 were used for further experiments.

Three-dimensional (3D) cell culture, ERα immunostaining and confocal imaging

Twenty-four multiwell plates (Corning) were coated with 1% agarose (Sigma). An equal number of MCF-7 and MCF-7 Cdt cells were added to each well with serum-free 3D Tumor sphere medium (PromoCell, #C-39870). Cultures were maintained at 37⁰C for 17 days in a 5% CO₂-humidified shaking incubator to get spheres with an average diameter of 200 µm. The spheres are then transferred to 96 well optical bottom plates (Corning) after immunostaining for ERα for confocal imaging, as described above. The imaging was performed in Nikon AIR confocal microscope with a 20X objective to generate a z-stack of full tumor spheres. A pinhole size of 25.5µM was used for z-sectioning. Parallelly, MCF-7 and MCF-7 Cdt monolayer cells were also immunostained with ERα and imaged. The laser lines 562nm and 647nm were used for Cdt KO and Alexa Fluor 647, respectively. Emission signals were collected using different spectral PMT in sequential mode imaging.

Statistical Analysis

Statistical analysis was carried out, and a value of p < 0.05 was considered statistically significant. The statistical tests were analyzed using Two-way ANOVA. The data are shown as the mean ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001.

Hormone resistance involves stable down-regulation and altered heterogeneity of estrogen receptor (ERα) expression.

Breast cancer cell line MCF − 7 is the promising cell model for studying hormone resistance in in-vitro conditions. This cell acquires resistance phenotype while maintaining under endocrine therapies such as endofixen and tamoxifen. We have used this cell line to generate an endocrine-resistant phenotype by maintaining the cells with 4-hydroxytamoxifen and endoxifen for 3 months. Figure 1A shows the quantification of cell death in parental and resistant clones upon different drug treatments. The resistant clones showed cross-resistance to multiple drugs like cisplatin, paclitaxel, and podophyllotoxin, and they also survived even higher concentrations of 4OH tamoxifen (8µM). Further, to understand the ERα status of resistant clones, whole cell extracts of 48-hour drug-exposed parental clones were compared with resistant clones using immunoblot. As shown in Fig. 1B and Supplementary Fig S4 A-D, both endoxifen and 4OH tamoxifen-resistant cells demonstrated downregulation of ERα expression. Interestingly, the early response of cells to both endoxifen and 4OH tamoxifen involves the up-regulation of ERα. Confocal immunofluorescent imaging was carried out to see the expression heterogeneity of ERα in parental and resistant clones (Fig. 1C). Similar to the population-level analysis by immunoblot, cell-to-cell expression variability is evident in the confocal images, and many cells lose ERα expression in the resistant clones. Overall, the analysis of ERα indicated an increase in expression during the initial days of drug treatment, with clear and stable downregulation of ERα in endocrine-resistant cells creating evident ERα expression heterogeneity.

Single-cell analysis of MDA-MB-231 EGFP ERα cells reveals cell-to-cell heterogeneity in unstressed conditions and ERα loss upon hormone resistance.

As shown above, the single-cell analysis of the ERα signal in treated and untreated parental MCF-7 cells revealed significant cell-to-cell variability in expression. Resistant cells demonstrated loss of ERα signal in the majority of cells, along with cells having ERα expression. It is important to note the basal and altered level of ERα heterogeneity upon hormone resistance, suggesting a role for the cell’s inherent non-genetic ERα regulation. To study the dynamics of ERα in live cells, we have generated EGFP ERα stable cells in a hormone receptor-negative background using the TNBC cell MDA-MB-231. As described in the methods, stable cells were generated by transfection following flow sorting and single-cell cloning. As shown in Fig. 1C, consistent with the ERα expression heterogeneity in MCF-7 cells, the overexpressed EGFP ERα in MDA-MB-231 cells also demonstrated cell-to-cell variation in expression despite being a single-cell clone. Cells showed ERα expression heterogeneity on 24 hours of seeding after sorting (Fig. 2A), which was more evident upon confluence (Fig. 2B). The immuno blot and real-time PCR analysis of ERα in parental and EGFP ERα stable cells confirmed the overexpression of transgene (Fig. 2C, D and Supplementary Fig S4 E and F). As shown in Fig. 2B, a good number of cells also showed mild cytoplasmic expression and a few cells with loss of expression despite being EGFP ERα stable clones. Further, to confirm the ERα expression plasticity in unstressed conditions, the heterogenous cell population was sorted into two distinct groups based on expression levels as high and low by flow cytometry sorting as per the gate shown in Fig. 2E. The sorted cells were further maintained for two weeks and analyzed by flow cytometry to know the quantitative divergence of the cell population for EGFP ERα expression (Fig. 2E). As shown in Fig. 2F and G, the confocal images of cells sorted for higher expression generated almost equal levels of higher and lower expressing cell populations as the original heterogeneity. Even the lower expressing stable cells generated a higher expressing population with a slower rate than the higher expressing sorted population. The rate of ERα conversion seems to be higher among the high-expressing cells. We have calculated the doubling time of EGFP ERα high and low expressing cells from 48 hours of real-time imaging to know if the dynamics of expression shift is influenced by the cell proliferation rate. As shown in Fig. 2F and G, ERα higher expressing cells have a faster proliferation rate with an average doubling time of 27 hours than the low expressing cells with a doubling time of 38 hours. The graphical representation of the doubling time of MDA-MB-231 ERα cells having higher and lower expression is shown in Fig. 2I. Overall, the study demonstrates the cell's ability to maintain inherent ERα receptor expression heterogeneity and the potential utility of the system to report ERα dynamics in live cells.

Real-time imaging reveals complex oscillation of EGFP ERα with cell cycle progression and temporal alterations under endocrine treatment.

The above results using EGFP ERα stable cells confirmed that cells always tend to maintain heterogenic receptor expression and oscillation in expression with proliferation, even if sorted to get cells with uniform expression. Real-time confocal imaging was carried out for 90 hours to understand whether this oscillation is regulated by cell proliferation and to know the dynamics. As shown in Fig. 3A, the oscillation of ERα expression is evident in the cycling cells; many times, the cells showed a decline in expression prior to cell division and regained the expression immediately after cell division. Upon confluence, cells demonstrated increased expression of both nuclear and cytoplasmic EGFP ERα (Fig. 2B, 3A and Supplementary Video S1). As shown in Fig. 2E, F and G, sorted cells initially showed a homogeneous expression level; however, upon confluence, many cells demonstrated expression heterogeneity and loss in a subset of cells. The flow cytometry also confirmed the generation of original heterogeneity under normal proliferation in in-vitro conditions. To understand whether original heterogeneity is being affected by 4OH tamoxifen, real-time confocal imaging was carried out for 90 hours, starting from 2 hours of treatment (Fig. 3B and Supplementary Video S2). Compared to the untreated control, 90 hours of 4OH tamoxifen treatment increased the ERα expression, and marked upregulation is observed after 48 hours, as seen in MCF-7 cells for its initial response to endocrine treatment (Fig. 1). As shown in Fig. 3B, there is no detectable cell death up to 90 hours, with the concentration of 4OH tamoxifen used for the generation of endocrine resistance. The cell models also depict the loss of ERα in the later period, where in four weeks of treatment with endoxifen and 4OH tamoxifen, many surviving cells lose ERα expression (Fig. 3C).

Closer analysis of real-time imaging showed an oscillatory expression pattern for ERα with the progression of the cell cycle. So, to address whether EGFP ERα expression is linked with cell cycle status, the cells were stained using Hoechst 33342 for cell cycle analysis. In the Flow cytometry analysis data shown in Fig. 3D, the cells showed normal cell cycle distribution with 55% G1, 28% S, and 17% as G2/M cell population. An arbitrary gate drawn based on DNA content intensity on marking G1 and S/G2 against EGFP ERα expression is shown in Fig. 3E. The expression of high and low EGFP ERα cells is uniformly spread both in G1 and S/G2. However, more low-expressing cells are accumulated in the G1 phase. In general, EGFP ERα failed to show a complete correlation with change in DNA content. The study suggests that EGFP ERα shows cell inherent plasticity in expression and 4OH tamoxifen-induced early upregulation of the receptor during initial treatment, despite its loss of expression upon resistance acquisition in later stages.

Transcriptomics reveals ERα regulated pathways that include proteasome, ubiquitin, and redox as key players

An RNA sequencing was conducted using Illumina 6000 to understand the global transcript variation between parental MDA-MB-231 and MDA-MB-231 EGFP ERα cells. After data filtering, differentially expressed genes (DEGs), up and downregulated, were identified based on the p-adj value of less than 0.05 and fold change of ≥ 1.5 and ≤-1.5. The transcriptome data was submitted under BioProject ID SUB14701276. The volcano plot showing the up-regulated and down-regulated DEGs is given in Fig. 4A. The curated gene list for upregulated and downregulated genes is shown in Supplementary Table ST3. As shown, a total of 603 differentially expressed genes were identified based on the above criteria. Real-time PCR was carried out for randomly selected six DEGs (NQO1, SOD3, IDH1, CDH11, RPL15, and RPS6KA2) to confirm the validity of the RNA-seq results (Supplementary Fig. S1A). The higher expression levels of NQO1, SOD3, and IDH1 were detected in ERα transgenic MDA-MB-231 cells than in wild-type cells. While CDH11, RPL15, and RPS6KA2 were identified with lower expression levels in ERα transgenic MDA-MB-231 cells than wild-type cells. The expression differences obtained by RT-PCR were consistent with the results of the RNA-seq transcriptomic analysis (Supplementary Fig. S1B).

The Gene Ontology (GO) analysis was performed to understand the key pathways regulated by DEGs and their specific functional attributes. 267 DEGs were supplied to GO analysis. The significantly enriched GO terms of both up and down-regulated DEGs between ERα overexpressed MDA-MB-231 and wild-type cells are shown in Fig. 4B and C, respectively. In the biological process (BP) analysis, most of the DEGs are mapped on the regulation of cell proliferation, cell migration, protein modification process, negative regulation of cell adhesion, and stress response. In the cellular components (CC) analysis, the significantly enriched cellular machineries were extracellular region, anchoring junction, adhesion junction, and membrane. In the molecular function (MF) category, protein binding, signalling receptor binding, and protein-containing complex binding were the significantly mapped functions.

To further specify the direct correspondence of the pathways and to clarify the biological insights of ERα, the Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis was performed. The results demonstrated that the DEGs were enriched in signal pathways of the cell cycle, ESR mediated signalling, KEAP1-NFE2L2 pathway, G1/S Transition, unfolded protein response (UPR), nuclear events mediated by NFE2L2, FOXO mediated transcription, etc. (Supplementary Fig. S2A and B). Overall, the transcriptomic analysis reveals many functional pathways such as redox, proteasome, and UPR as the key signalling traits gained by ER alpha expression in MDA-MB-231 cells. The major DEGs are responsible for proteasome-mediated signalling and unfolded protein response is represented in Supplementary Fig. S2B.

ER alpha expression is upregulated under proteasome and autophagy inhibition

The results so far confirmed the expression plasticity and oscillatory behaviour of ERα. The transcriptomics analysis of EGFP ERα cells compared to parental cells showed UPR and ubiquitin pathways as the critically influenced pathways by ERα, indicating an indirect role of proteasome or autophagy in ERα expression. To check the role of proteasome and autophagy on ERα expression, the MDA-MB-231 EGFP ERα cells were exposed to a proteasome inhibitor, MG132, and a late autophagy inhibitor, bafilomycin and chloroquine. Proteasomal and autophagy inhibition showed increased EGFP ERα expression compared to control untreated cells, which was more prominent in MG132 when analyzed after 24 hours of treatment by flow cytometry (Fig. 5A). Bafilomycin treatment showed enhanced expression of EGFP ERα compared to chloroquine. Further, to understand the dynamics of ERα regulation by proteasome and autophagy in real time, live cell imaging was carried out for 24 hours after treatment with MG132 and Bafilomycin. Real-time imaging further confirmed the time-dependent increase in EGFP ERα expression upon inhibition of proteasome and late-stage autophagy (Fig. 5B and C, Supplementary Videos S3 and S4). As seen in the video, bafilomycin treatment gradually increased EGFP ERα expression from 6 hours; the trend declined after 20 hours, and MG132 treatment enhanced expression of EGFP ERα from 8 hours and declined after 18 hours. As seen in the video, many cells without EGFP ERα expression during the initial time points gradually regained expression, and 95% of cells showed enhanced expression, losing their parental heterogeneity. The results further confirm that multiple post-transcriptional pathways, including ubiquitin-proteasomal and autophagy, regulate EGFP ERα expression, thereby maintaining cell-to-cell heterogeneity. This provides an additional layer of regulation that could be important in ERα oscillation and expression plasticity. We have also tested the EGFP ERα expression kinetics in live cells after translation inhibition by cycloheximide (CHX) treatment to see how the expression heterogeneity is being affected (Fig. 5D and E, Supplementary Videos S5 and S6). The surface intensity plot of untreated and cycloheximide-treated cells at 12 hours showed a significant reduction in EGFP ERα expression (Fig. 5F and G). As seen from the image and single-cell tracing of EGFP ERα expression over time, cycloheximide treatment gradually reduced the expression, losing cell-to-cell expression variability by 12 hours (Supplementary Fig. S3A and B).

Expression heterogeneity is independent of cell cycle status both in monolayer and tumor sphere models

So far, we have only analysed ERα expression heterogeneity in monolayer. Given that spheroids mimic the in vivo growth conditions of breast tumors, it is crucial to understand how ERα expression is influenced within these tumor sphere models. For this, MCF-7 cells were grown as spheres in low attachment growth conditions until tumor spheres reached a diameter of 200µm size. After fixation and immunostaining, ERα was analyzed in MCF-7 cells in monolayer against 3D spheres by confocal imaging (Fig. 6A). The mean intensity distribution of ERα expression correlates with its heterogeneity in MCF-7 cells in monolayer (Fig. 6B). An increased ERα expression was noticed in tumor spheres than in the monolayer. Despite the increase in the expression of ERα, cell-to-cell expression heterogeneity is more pronounced in tumor spheres than the monolayer, as observed in the volume view. The z-stack video clearly explains the complex ERα heterogeneity between cells in tumor spheres (Supplementary Video S7). Now that we have an improved model to mimic in vivo tumor growth conditions, further studies to find whether the expression variation is cell cycle stage-dependent is done. The MCF 7 cells were transfected and stably developed to express the G1-S cell cycle stage sensor Cdt1 Kusabira orange (KO) to visualize G1-S cells with the red fluorescent colour of Cdt1. The G1-S cell cycle indicator transfected MCF-7 cells were cultured as monolayer and tumor spheres parallelly to evaluate the ERα expression (Fig. 6C). Despite the significant heterogeneity observed in tumor spheres, strong correlations between ERα expression variations and cell cycle stages are not evident in either monolayer or tumor sphere cultures. High and low-ERα expressing cells were observed in both G1-S and non-G1-S cells in 2D and 3D models (Fig. 6C and Supplementary Video S8).

Tumor heterogeneity is a serious clinical problem with implications for diagnosis, prognosis, and treatment decisions. Breast cancer classification based on ERα, HER2, and PR is the standard protocol for predicting prognosis and treatment decisions. Among these subtypes, both HER2-positive tumours and ERα-positive tumours achieve better responses with targeted therapy such as HER2-targeted antibodies like trastuzumab or endocrine treatments, respectively. However, increasing clinical studies suggest that therapeutic benefit is unpredictable in many cases despite the presence of well-characterized targets. Intra-tumoral and inter-metastatic receptor heterogeneity have been observed within a specific subtype of breast cancers. A more challenging issue is spatio-temporal heterogeneity in breast cancers affecting treatment outcomes. The treatment approaches, even the most recently developed advanced therapies, target molecular signatures based on the diagnosis of mixed populations of cancer cells, mostly from a single biopsy, assuming stable receptor status throughout the treatment, and seeing cancer as a homogenous disease. However, several studies have demonstrated receptor status alterations upon neo-adjuvant therapy, necessitating serial biopsies and comprehensive analytics for better treatment outcomes [20–23]. In addition, receptor heterogeneity is observed between matched primary and metastatic breast cancer lesions [24]. Few early studies demonstrate that spatio-temporal changes in receptor status with tumor progression and under treatment stress are indicators of poor prognosis [22, 25, 26]. More frequent alterations have been observed for HER2 and PR. However, increasing studies and clinical reports implicate estrogen receptor mutation and alterations in its expression as a key driving factor of endocrine resistance [27]. Both intrinsic and acquired resistance are more common in the clinical setting in ER-positive patients receiving endocrine treatment [8]. Intrinsic resistance could be due to the ER independence of the tumors or contributed by pre-existing ER expression heterogeneity. Even though genomics alterations in the ERα gene (ESR1) are also seen occasionally in acquired resistance, increasing studies suggest that ligand-independent ER activation, unbalanced ER co-regulator activity, and alterations in ER dynamics as underlying reasons for the resistance mechanism [28].

In the current study, we have demonstrated the potential application of EGFP ERα stable cells in a TNBC background to understand the expression plasticity and dynamics of ERα in both physiological conditions as well as under and endocrine stress. We have expanded single-cell clones of cells stably expressing EGFP ERα, demonstrating high ERα plasticity levels under normal proliferating conditions. Even the sorted cells showed inherent heterogeneity as in ERα positive cells with two weeks of in vitro culture. Previous work indicated a role for ERα as an important regulator of growth and differentiation in normal breast tissues [29]. Real-time imaging confirmed that many cells progress in the cell cycle with an oscillatory expression of ERα spontaneously generating negative cells upon confluence. Even though the current study has not specifically addressed the role of the cell cycle in the ERα heterogeneity, transcriptomics revealed a significant number of genes involved in cell proliferation as upregulated in ERα expressing cells. However, the study failed to demonstrate any correlation of its expression alteration with the steady-state cell cycle stages analyzed by flow cytometry, suggesting the existence of cell cycle stage independent signalling as the reason for its oscillation. Similarly, MCF − 7 cells expressing the G1-S cell cycle indicator also failed to show a strong correlation with the cell cycle stage in monolayer and tumor sphere models. Although most proliferating cells do not express ERα, the proportion of ERα positive cells is increased in highly proliferating structures [30]. In terms of prognosis, ERα positive breast tumors have a more favourable prognosis than breast tumors with little or no expression of ERα [31–33]. Similarly, TNBC that lacks ERα is inherently aggressive with a poor prognosis. ERα expression and its impact on proliferation and prognosis need careful evaluation considering complex expression plasticity. Supporting this, our study further confirmed secondarily acquired complex heterogeneity in tumor spheres and hormone-resistant clones. Such phenotype variations and phenotype transitions without genetic changes could be regulated at the epigenetic, transcriptomic, and post-translational levels, influencing external micro-environmental changes or internal cellular cues. The context-dependent tumor micro-environmental changes could be due to drug pressure, immune cellular components, extracellular matrix (ECM), and hypoxia. As hormone receptor signalling is key in normal development, it is imperative to have a complex regulatory network for adapting the cells depending on the hormone status and developmental cues. Systematic analysis of RNA sequencing of EGFP ERα stable cells further confirmed that ERα regulates diverse intracellular signalling networks such as ubiquitin, proteasome pathways, and UPR, implicating its direct role in post-translational protein modifications. The importance of these temporal changes in the global proteome and ERα plasticity, in driving tumor progression under endocrine treatment requires further evaluation. Also, studies are needed to know if any of the ERα associated client proteins are involved in the rapid oscillation and expression shift through the recruitment of client-bound complex into ubiquitin degradation in a cell cycle phase-independent manner. Hsp90 and Hsp70 are associated with hormone receptors for their stability and nuclear signalling, and their role in tumorigenesis is emerging [34]. Considering the complex and multiple expression regulatory networks for ERα, protein degradation could be specifically regulated by multiple signalling, such as autophagy and ubiquitination-proteasomal degradation, rendering complex levels of resistance generation possibilities for intrinsic and acquired resistance. Consistent with this, transcriptomic analysis revealed noticeable alterations in the protein modification process and UPR along with cell proliferation and differentiation, indicating a direct role for ERα in global protein modification. It is a fact that protein modification is key in determining the differentiation or proliferation decisions of cancer cells. Consistent with this, many genes involved in ubiquitination and UPR, such as PERK, IRE1, CHOP, and BiP, were significantly upregulated in EGFP ERα stable cells. The study also revealed an additional layer of ERα expression regulation by proteasome in EGFP ERα cells. Treatment of cells with autophagy inhibitors such as chloroquine and bafilomycin also enhanced ERα expression. Supporting the results, epigenetic events are also known to contribute to ERα silencing and enhance resistance to endocrine therapies, such as tamoxifen [35–38]. Previous studies suggested that class I and II histone deacetylase (HDACs) act as transcriptional repressors of ERα in ER-negative breast cancer [39–41].

The switch from ERα positive to ERα negative phenotype has been investigated experimentally and in clinical samples [42]. Consistent with this, using the EGFP ERα model system, we have observed an initial increase in EGFP ERα during the early cycles of endocrine treatment. However, upon prolonged exposure that contributes to hormone resistance, there is a downregulation of ERα at the population level. Interestingly, a stable nuclear accumulation of EGFP ERα also marks the early phenotype during endocrine treatment. ERα expression is lost at the time of tumor relapse from 20% [43, 44] to nearly 50% [45, 46] in tamoxifen-treated patients.

Previous studies using TNBC cell line MDA-MB-231 expressing either ERα or HER2 to understand transcriptional targets of the receptors. Mudvari et al. reported 200 genes as differentially regulated using a microarray approach upon HER2 reintroduction, revealing the importance of the cell model [47]. Similarly, RNA sequencing experiments using MDA-MB-231 ERα cells demonstrated 620 differentially expressed genes compared to parental cells, where the expression level of ERα was negatively associated with metastasis and epithelial-mesenchymal transition (EMT) in breast cancer [48]. We also observed similar negative regulation of the EMT pathway and increased antioxidant signalling, including NRF2, upon re-expression of ERα in TNBC cells. Since redox oscillations and cell cycle dependency have been observed in proliferating cells, further studies are needed to understand whether redox alterations play a role in ERα plasticity in proliferating conditions and endocrine resistance. The importance of periodic oscillations in intracellular redox in regulating the cell cycle has been studied in multiple cellular models. Recently, we have shown an interesting redox alteration during the cell division stages [49]. A previous study also indicated that activation of estrogen receptors transfected into a receptor-negative breast cancer cell line decreases the metastatic and invasive potential of the cells [50]. An early study also substantiated that oscillatory expression of the ERα receptor is required for a proper cell division involving c-myc in a positive feedback loop [51]. A recent study by Mohammed et al. revealed the extent of transcriptional and epigenetic heterogeneity induced by estrogen, highlighting the need for precision in analysing tumor changes as they evolve for better diagnosis and treatment decisions [52]. The complex receptor oscillation and shift, even in unperturbed conditions, seen using the reporter cell is to be carefully analyzed in the context of clinical receptor heterogeneity and its implication in diagnosis and prognosis. Few studies have shown that tumors with ERα positivity as low as 10% can show a significant response to anti-estrogen therapy [53, 54]. This suggests a possible receptor switch as a likely factor that could generate a high-level ERα responsiveness over treatment time, benefiting the patient group and necessitating better models for understanding the ERα status and dynamics.

Studies addressing intra-tumoral heterogeneity of receptors suggest that breast tumors are not homogenous for their expression of ERα, HER2, and PR, with possible complex spatially organized subpopulations in a tumor that could influence tumor progression and treatment to therapy. Since a temporal regulatory network also drives ERα status alterations, better methods for the simultaneous detection of key receptors in large tissue samples are to be used in routine clinical practice. Recently, we have described a method for the simultaneous detection of ERα, PR, and HER2 using multiple fluorescent approaches that revealed complex spatial receptor heterogeneity within a tumor [55]. Initial heterogeneity at diagnosis and alteration of receptor status during the progression and treatment conditions must be carefully analyzed, at least among patients with an increased risk of recurrences during hormonal therapy. Routine implementation of multiplex detection of hormone receptors status in core needle biopsy could be evaluated in recurrent or metastasis tumors for therapeutic decision.

This present study concludes that loss of Estrogen-receptor expression in ERα positive patients following tumor progression could involve post-translational signalling where proteasome-ubiquitin and autophagy pathways are to be considered as potential players. The study also points towards larger investigations on how ERα loss and ERα plasticity relate to larger signalling that could impact breast tumor biology and evolution.

ER+ Estrogen receptor alpha positive

UPR Unfolded protein response

ESR1 Estrogen receptor1 gene

CAF Cancer-associated fibroblasts

TNBC Triple-negative breast cancer

DEG Differentially expressed genes

TME Tumor microenvironment

Acknowledgements

AS and AH are supported by the University Grants Commission. AGJ is supported by the Indian Council of Medical Research-Senior Research Fellowship (No.3/2/2/40/2020-NCD-III). JT is supported by CSIR, and SVJ received CM-Nava Kerala-Post Doctoral Fellowship, Govt. of Kerala (No. KSHEC-A3/344/Govt. Kerala-NKPDF/2022).

Author contribution

Conceptualization: TRSK, AS; Methodology: AS, TRSK; Validation: AS, KSK, AGJ, SVJ, JTPJ; Formal analysis: AS, KSK, AMH, UPS, NSS, VSS, SSV, KCS; Writing-Original draft preparation: TRSK, AS; Data curation: AS, KSK, AGJ, SVJ, JTPJ, UPS; Writing-review and editing: TRSK, AS, KSK, AGJ, SVJ, JTPJ, UPS; Visualization: AS, NSS, VSS, SSV; Supervision: TRSK; Project administration: TRSK; Funding acquisition: TRSK.

Funding information

This work was supported by a grant from the Department of Biotechnology- Government of India Department of Biotechnology (No. BT/01/CEIB/01/CB/2016).

Data and materials availability

All data needed to evaluate the conclusions in the paper are present in the article and the supplementary materials. Additional data associated with this paper may be requested from the authors.

Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

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Estrogen Receptor Alpha Dynamics and Plasticity During Endocrine Resistance

Status:

Journal Publication

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Abstract

Background

Results

Conclusions

Figures

Background

Materials and Methods

Cell Culture

Generation of Endocrine resistance in MCF-7 cells and its functional evaluation

Immunofluorescent staining of ERα and confocal imaging

Construction of MDA-MB-231 EGFP-ERα Cell Line

Understanding ERα heterogeneity using Flow cytometry

Confocal real-time imaging to understand ERα heterogeneity upon endocrine treatment

Evaluation of ERα dynamics and cell cycle analysis

RNA Isolation and RNA Seq analysis

Identification of Differentially Expressed Genes (DEGs)

Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis

Effect of autophagy, proteasome and translational inhibitors on ERα dynamics

Development of cell cycle indicator cells expressing Cdt1-Kusabira orange

Three-dimensional (3D) cell culture, ERα immunostaining and confocal imaging

Statistical Analysis

Results

Transcriptomics reveals ERα regulated pathways that include proteasome, ubiquitin, and redox as key players

ER alpha expression is upregulated under proteasome and autophagy inhibition

Expression heterogeneity is independent of cell cycle status both in monolayer and tumor sphere models

DISCUSSION

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

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