An ERα-Dependent Hypoxia Response Defines EMT-Adjacent Tumour Regions and Suppresses the Pro-survival Effects of Amiloride in Estrogen Receptor-Positive Breast Cancer

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   May 8, 2026

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   Reply to the reviewers

   Reviewer 1

   Point

   Summary

   Response

   1.1

   Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.

   To assess whether DMOG-induced changes in ERα occupancy reflect bona fide hypoxia, we measured ERα binding by ChIP-qPCR under 1% oxygen over 48 hours, compared to normoxic (21% oxygen) cells and input controls in matched cells at the GREB1 and TFF1 loci. Our findings demonstrate that 1% oxygen treatment recapitulates the ERα binding changes observed with DMOG, at the time points of our RNA-seq experiments.

   We have included these results in __Figure 1F __of the …

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   Reply to the reviewers

   Reviewer 1

   Point

   Summary

   Response

   1.1

   Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.

   To assess whether DMOG-induced changes in ERα occupancy reflect bona fide hypoxia, we measured ERα binding by ChIP-qPCR under 1% oxygen over 48 hours, compared to normoxic (21% oxygen) cells and input controls in matched cells at the GREB1 and TFF1 loci. Our findings demonstrate that 1% oxygen treatment recapitulates the ERα binding changes observed with DMOG, at the time points of our RNA-seq experiments.

   We have included these results in __Figure 1F __of the preliminary revision of the manuscript.

   1.2

   Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.

   We thank the reviewer for this comment. We have clarified that motif enrichment analysis is included to characterise the sequence context of ERα binding sites and to confirm enrichment of known ER-associated motifs (e.g. EREs), rather than to infer functional involvement of additional transcription factors under hypoxia. Corresponding interpretative statements have been removed from the Results and restricted to the Discussion.

   1.3

   Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.

   To confirm DMOG induces HIF-protein expression we have analysed HIF1α and HIF2α protein levels by western blot. We have included these in __Supplementary Figure S1A __within the preliminary revision to address this concern.

   1.4

   Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.

   We acknowledge that the ERα ChIP-seq (DMOG) and ATAC-seq datasets were generated under different conditions and are therefore not directly comparable. To address this, we have performed ChIP-qPCR under bona fide hypoxia (1% oxygen) at canonical ERα target loci (*TFF1 *and GREB1), demonstrating that the directionality of ERα binding changes observed with DMOG is recapitulated under physiological hypoxia. These data provide a direct comparison of ERα occupancy across conditions and support the use of DMOG as a proxy for hypoxia in our ChIP-seq experiments.

   If requested, we are willing to perform ATAC-seq at 16 h under 1% oxygen. However, because the original dataset was generated under 0.1% oxygen, and canonical ERα-bound sites show minimal accessibility changes under severe hypoxia, we anticipate limited additional insight from repeating this experiment.

   1.5a

   Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG.

   The statement that the ERα ChIP samples lack estrogen treatment is incorrect. Estradiol was not an experimental variable and cells were intentionally maintained under estrogen-rich conditions to preserve tumour-relevant ERα activity.

   We have now clarified within the preliminary revision by stating that cells were routinely cultured in “estrogen-rich Dulbecco’s Modified Eagle Medium” in the methods section, and clarified the use of estrogen-rich conditions in the Figure S1 legend.

   1.5b

   The single-gene examples of DMOG effects shown in Fig. S1A are not significant.

   The peak illustrated in Figure S1A (now __Figure S1D) __is intended to provide a visual confirmation of peak calling and enrichment patterns underlying the genome-wide redistribution observed in Figure 1. The peak was called by the MACS2 pipeline (code available from https://doi.org/10.5281/zenodo.17221105) with a log10(q-value) = 268.5, which passes the MACS2 cut-off q

   1.6a

   Fig. S2 lacks 1% O₂ conditions,

   We wish to clarify that __Figure S2 __(now Figure S4) serves as quality control specifically for the DMOG-treated ChIP-seq dataset presented in Figure 1C. The purpose of the plot is to visualize unfiltered motif enrichment to confirm that the identified peaks represent bona fide ERα binding events within the DMOG condition. Motif enrichment under a 1% oxygen environment would not provide this validation. In all cases the ERE is the most significantly enriched motif.

   With respect to ERα binding under 1% oxygen, we have now assessed this via targeted ChIP-qPCR validation (Figure 1F).

   1.6b

   Fig. S3 lacks DMOG-induced HIF factor assessments.

   The DMOG-induced changes in HIF1α and HIF2α expression are shown in the__ Figure S1__ of this revision proposal and have been incorporated into the manuscript as part of the changes described in response 1.3.

   1.7a

   Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality.

   We have substantially improved the labelling of Figure S4, now__ Figure S6.__

   Additionally, we have clarified that all samples were cultured in estrogen-rich media and treated with either vehicle control or 100 nM fulvestrant; thus estrogen is present in all conditions including the controls.

   1.7b

   Hypoxic conditions for assessing ER status and appropriate controls are also lacking.

   We agree that monitoring ERα stability under hypoxic conditions is essential.

   We provided a western blot assessment of ERα protein levels at 0, 8 and 48 hours of treatment with 1% oxygen or DMOG, compared to normoxic controls, included as Supplementary Figures S1B, C in the preliminary revision.

   These demonstrate the cells remain positive for ERα protein expression at 0, 8 and 48h.

   1.8

   Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.

   We thank the reviewer for this comment. To clarify the experimental design, we now signpost the reader in the figure legend of Figure S5 (now S7) to the schematic diagram provided in Figure 3B, and provide a summary stating the experiment employed a factorial design combining a 96-hour fulvestrant treatment with exposure to 1% oxygen for the final 48 hours.**

   1.9

   Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

   We have extensively revised all supplementary figure legends to ensure clarity and precision.

   1.10

   Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

   Following this comment, we carefully reviewed the presentation of all figures throughout the manuscript. We improved the organisation and labelling of the Supplementary Figures to facilitate clearer comparison of the data. In particular, full western blots are now clearly annotated and supplementary legends have been expanded to provide sufficient context for each figure to be interpreted independently.

   1.11

   i) In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer.

   ii) Additionally, a lack of a thorough comparison between DMOG and or 1 %oxygen induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript.

   iii) The lack of considering estradiol exposure under hypoxic conditions with either 1%oxygen and or DMOG also limits relevance to patients with ER+ BrCa.

   iv) The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

   i) We respectfully disagree that the manuscript does not extend prior work. Despite extensive characterisation of ERα, its role in shaping hypoxia-driven transcription in ER+ breast cancer has not been defined. Here, we identify an ERα-dependent hypoxic response (EDHR), demonstrating a reciprocal interaction between hypoxia and ERα activity.

   ii) In revision, we address concerns regarding DMOG by validating ERα binding under 1% oxygen using ChIP-qPCR thereby confirming our result in *bona fide *hypoxia. Additionally, all RNA-seq and functional assays, including ENaC targeting, were performed under 1% oxygen in the original manuscript.

   iii) All experiments were conducted under estrogen-complete conditions, now explicitly clarified, reflecting tumour-relevant ERα activity.

   iv) Together, these data establish a reciprocal interaction between ERα and hypoxia and uncover a targetable vulnerability in hypoxic ER+ breast cancer, linking transcriptional regulation to therapeutic opportunity.

   Reviewer 2

   No.

   Summary

   Response

   General Comments

   2.1

   ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype.

   We agree that further validation of ENaC involvement would strengthen this observation. We will assess ENaC protein levels under 1% hypoxia ± fulvestrant by western blot and perform siRNA-mediated depletion of ENaC subunits to test their contribution to the hypoxia-specific amiloride-sensitive phenotype by viability assay (see also response 3.3).

   2.2

   Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation.

   The experimental design already controls for potential fulvestrant-specific transcriptional effects, as all four conditions (± hypoxia, ± fulvestrant) were included. EDHR genes were defined based on induction under hypoxia, loss of this induction following ERα degradation, and absence of residual hypoxic induction in the presence of fulvestrant. Consistent with this, SCNN1B and SCNN1G do not show significant fulvestrant-responsive changes under normoxia (Figure 5C,D).

   We also note that fulvestrant has been shown to induce minimal global chromatin remodelling (Guan et al., 2019), supporting its use to assess ERα dependency without broadly confounding chromatin accessibility; this reference is now included in the manuscript.

   2.3

   The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied.

   We further examined the potential convergence of ERα and hypoxic signalling at the ENaC loci (included as __Figure 5E __in the revision proposal) showing genome browser views of the* SCNN1G* and *SCNN1B *loci, highlighting hypoxia-induced HIF1α binding and ERα association at these sites.

   To further support this, we will perform RT-qPCR validation of *SCNN1G *and SCNN1B expression following treatment ± IOX5 and ± fulvestrant. IOX5 is a selective PHD inhibitor that stabilises HIF proteins, enabling us to assess the contribution of HIF signalling independently of other oxygen-dependent effects associated with hypoxia.

   2.4

   In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed

   To assess the clinical relevance of SCNN1B and SCNN1G in ER-positive and ER-negative subgroups, we performed Cox proportional hazards analyses in TCGA and METABRIC cohorts individually, including ER status and stratifying by ER-positive and ER-negative cases (Figure 6C). These analyses support the association of SCNN1G with poorer relapse-free survival specifically in ER-positive patients.

   2.5

   The authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

   We agree that independent validation would strengthen these findings. We will perform RT-qPCR validation of key EDHR genes (including *SCNN1B *and SCNN1G) under ± hypoxia and ± fulvestrant conditions to confirm ERα-dependent hypoxic induction.

   Limitations

   2.6

   Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively.

   This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

   While ERα cistrome reprogramming has been described, our study demonstrates a reciprocal interaction in which ERα not only responds to hypoxia but actively shapes hypoxia-driven transcription, defining an ERα-dependent hypoxic response (EDHR).

   We acknowledge the limitations of DMOG and have addressed this by validating key ERα binding events under bona fide hypoxia (1% oxygen) using ChIP–qPCR, confirming our findings under physiological conditions (response 1.1).

   To further strengthen mechanistic insight, we will assess the requirement for HIF stabilisation using the selective PHD inhibitor IOX5, combined with RT-qPCR analysis of SCNN1G and SCNN1B ± IOX5 ± fulvestrant (response 2.3 and 2.5). In addition, we will validate the functional relevance of ENaC through protein-level analysis and siRNA-mediated depletion, as described in__ response 2.1.__

   Together, these additions address concerns regarding DMOG specificity and provide further support for a functional interaction between ERα and hypoxic signalling.

   Audience

   2.7

   Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

   The study incorporates multiple layers of human relevance, including spatial transcriptomic analyses demonstrating enrichment of EDHR within hypoxic tumour regions and survival analyses linking EDHR and ENaC expression to clinical outcome.

   In revision, we address the reviewer’s concerns through targeted validation (ChIP-qPCR in hypoxia, western blotting, and RT–qPCR). Together, these additions strengthen the mechanistic and translational relevance of the study.

   Reviewer 3

   No.

   Summary

   Response

   Major comments

   3.1

   The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence.

   We acknowledge that mimetics of hypoxia can introduce off-target effects. To address this, we have validated our ERα ChIP-seq findings using ChIP-qPCR at representative loci (TFF1 and GREB1), demonstrating consistent changes in ERα binding under bona fide hypoxia (1% oxygen) (now included in Figure 1F).

   As acknowledged by the reviewer, ChIP-seq under these conditions is likely not feasible due to cell number constraints. We are willing to undertake ATAC-seq if required (as stated in response 1.1); however, we do not feel it would directly address ERα occupancy at these loci. We therefore consider our targeted ChIP-qPCR to be the most appropriate approach to validate ERα redistribution under hypoxia.

   3.2a

   The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines.

   To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example,

   i) confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR

   We agree that targeted validation would strengthen the mechanistic support for ERα dependence. We will perform RT-qPCR validation of SCNN1B and *SCNN1G *under hypoxia ± fulvestrant to confirm ERα-dependent hypoxic induction (see also response 2.5).

   3.2b

   ii) test whether short-term ERα knockdown reproduces the effect.

   ERα dependency is already assessed through fulvestrant-mediated degradation within the factorial design, which provides a well-established and direct approach to evaluate ERα function. As EDHR genes are defined by loss of hypoxic induction following ERα degradation, this constitutes a robust assessment of ERα-dependent effects.

   We will therefore focus on orthogonal validation through RT-qPCR (response__ 2.5__), together with additional mechanistic and functional analyses using IOX5 and ENaC perturbation (responses 2.1 and 2.3), rather than introducing an ERα knockdown approach, although we would consider this if required.

   3.2c

   iii) A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF.

   This request aligns with point 2.3, which addresses the convergence of ERα and HIF signalling. While HIF knockdown under hypoxia would assess necessity, we will instead assess the contribution of HIF signalling using the selective PHD inhibitor IOX5, as this allows us to isolate HIF stabilisation from broader hypoxia-associated effects and avoids additional perturbation associated with transfection-based approaches. We will perform RT-qPCR analysis of SCNN1B and SCNN1G following treatment ± IOX5 ± fulvestrant to determine whether HIF stabilisation is sufficient to support ERα-dependent induction of EDHR genes.

   3.3

   The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D.

   To address the reviewer’s concern regarding pleiotropic effects, we propose (aligning with our__ response to 2.1__) to apply siRNA-mediated knockdown of SCNN1B and SCNN1G under hypoxia to determine whether this reproduces our observed viability effect, thereby providing direct evidence for ENaC involvement.

   We agree that additional pharmacological validation could further support specificity, and would consider inclusion of a more ENaC-selective inhibitor if required.

   3.4

   The RFS associations for

   SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios.

   We have analysed TCGA and METABRIC cohorts individually using Cox proportional hazards models, as this functionality is not available for merged datasets in KMplot. ER status was included in the models, and analyses were additionally stratified by ER-positive and ER-negative subgroups. The number of relapse events per subgroup is approximately 40; therefore, additional covariates such as grade and nodal status were not included given the limited number of events per model.

   Within ER-positive patients, high SCNN1G expression is associated with poorer relapse-free survival (TCGA HR 1.45, p = 0.0027), while SCNN1B shows a similar trend that does not reach statistical significance. These analyses are presented in Figure 6C and in the results section of the preliminary revision, and support the findings from the Kaplan–Meier analysis.

   3.5

   The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful.

   Spatial cell type composition and spot annotations were used as provided in the SpottedPy dataset, based on Cell2location-derived deconvolution scores and STARCH tumour annotations, without additional re-estimation.

   To address the reviewer’s suggestion, we examined the relationship between EDHR enrichment and epithelial content and observed no significant correlation at the neighbourhood level.

   These points have now been clarified in the manuscript.

   3.6

   Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

   In the preliminary revision we have added a statement to the amiloride assay figure (Figure 6D) clarifying that n = 3 independent biological replicates were performed per condition. In addition, we now provide the underlying numerical values for this assay in Table S11.

   3.7

   A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice.

   We agree that directly linking EDHR to ENaC channel activity would further strengthen the mechanistic connection. We will prioritise genetic validation of ENaC function through siRNA-mediated depletion (response 2.1), which directly tests the requirement for ENaC in the hypoxia-specific viability phenotype.

   We are willing to explore the feasibility of measuring the amiloride-sensitive Na+ currents under normoxia and acute hypoxia (via perfusion of cells with bathing solution bubbled with nitrogen during recording) ± fulvestrant to further connect hypoxic regulation to channel activity.

   Minor Comments

   3.8

   Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.

   We have now included representative ERα ChIP-seq browser snapshots for gained, conserved, and lost loci, together with input controls for both conditions, in Figure S3 of the revised manuscript.

   3.9

   In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.

   We thank the reviewer for this point. The ATAC-seq dataset was generated under 0.1% oxygen in the original study, whereas RNA-seq experiments in this work were performed at 1% oxygen to reflect tumour-relevant hypoxic conditions. The more severe hypoxia used for ATAC-seq would be expected to maximise detection of chromatin accessibility changes. Despite this, chromatin accessibility changes were limited, with ERα binding occurring predominantly at pre-accessible regions. This has now been clarified in the manuscript.

   3.10

   In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.

   The neighbourhood parameter was set to 8, corresponding to the immediate neighbouring spots in Visium data, consistent with package guidance. We have clarified this in the manuscript text.

   3.11

   For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.

   We have marked the 14 EDHR consensus genes and indicated the ENaC module in the revised heatmap to aid readability.

   3.12

   Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.

   We have reported exact sample sizes and replicate numbers in all relevant figure legends and included Table S11 summarising all statistical tests, sample sizes (n), and p values.

   3.13

   A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.

   We have added timelines for these experiments as requested.

   3.14

   Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

   We have standardised oxygen notation throughout the manuscript to use “oxygen” in the main text and “O2” where appropriate (e.g. figures).

   Reagent catalogue numbers have now been standardised for consistency of presentation in the revised manuscript.

   Gene and protein nomenclature were already formatted according to accepted conventions and were verified for consistency.

   3.15

   The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

   We thank the reviewer for this suggestion. We have expanded the manuscript to clarify that acute hypoxia (1% oxygen) and DMOG treatment capture early, dynamic hypoxic responses, in contrast to chronic CoCl2 exposure, which reflects longer-term adaptation. This distinction is relevant to tumour biology, where hypoxia is often transient due to unstable vascularisation. The following statement has been added to the manuscript:

   “In addition to such chronic hypoxic adaptation, tumour hypoxia can also be dynamic, with cells experiencing acute or transient hypoxic exposure due to unstable vascularisation; an established contributor to tumour progression (Liu et al, 2022a; Koh & Powis, 2012). Thus, in contexts where both signalling pathways remain active, the dependence of the hypoxic response on ERα in ER+ cells has not been previously characterised.”

   Primary Limitations

   3.16

   DMOG vs hypoxia in the cistrome experiment,

   To address concerns regarding the use of DMOG, we have validated key ERα binding events from the ChIP-seq dataset by ChIP–qPCR at the TFF1 and GREB1 loci under bona fide hypoxia (1% oxygen) in biological triplicate__ (Figure 1F)__. These data demonstrate consistent changes in ERα binding under hypoxia, supporting that the DMOG-induced redistribution reflects hypoxia-driven changes.

   3.17

   the absence of direct HIF or cofactor perturbations

   We acknowledge the absence of direct HIF perturbation. To address this, we will assess the contribution of HIF signalling through stabilisation approaches, including RT-qPCR analysis of* SCNN1B* and SCNN1G ± IOX5 ± fulvestrant (response 3.2), to determine whether HIF activation is sufficient to support ERα-dependent induction.

   3.18

   and the pleiotropy of amiloride.

   To address the potential pleiotropy of amiloride, we will perform siRNA-mediated knockdown of *SCNN1G *and *SCNN1B *to provide independent validation of ENaC-dependent effects (response 3.3).

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2. 

   Review Commons

   May 8, 2026

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   Referee #3

   Evidence, reproducibility and clarity

   Summary

   This study explores how hypoxia reshapes ERα signalling in ER-positive breast cancer and whether this cross-talk exposes targetable vulnerabilities. The authors first map ERα binding in MCF7 cells after dioxygenase inhibition with DMOG and observe a genome-wide redistribution with enrichment of ERE, FOXA1 and AP-1 motifs at gained sites while chromatin accessibility at these loci appears unchanged in public ATAC-seq after hypoxia. They then perform RNA-seq in MCF7 and T47D using a factorial design that combines fulvestrant-mediated ERα degradation with 1% O₂ to define an ERα-dependent hypoxia response (EDHR). A 14-gene …

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   Referee #3

   Evidence, reproducibility and clarity

   Summary

   This study explores how hypoxia reshapes ERα signalling in ER-positive breast cancer and whether this cross-talk exposes targetable vulnerabilities. The authors first map ERα binding in MCF7 cells after dioxygenase inhibition with DMOG and observe a genome-wide redistribution with enrichment of ERE, FOXA1 and AP-1 motifs at gained sites while chromatin accessibility at these loci appears unchanged in public ATAC-seq after hypoxia. They then perform RNA-seq in MCF7 and T47D using a factorial design that combines fulvestrant-mediated ERα degradation with 1% O₂ to define an ERα-dependent hypoxia response (EDHR). A 14-gene consensus EDHR signature includes ENaC regulatory subunits SCNN1B and SCNN1G, whose higher expression is associated with poorer RFS in ER+ cohorts. Functionally, amiloride increases viability in normoxia but reduces viability under hypoxia in MCF7 across a dose range. Spatial transcriptomics from ER+ tumours shows EDHR expression enriched at the margins of hypoxia and estrogen-hallmark regions and adjacent to EMT hotspots. Raw data and code availability are stated for the central datasets and accessions are provided. Together the results argue that ERα helps organise a distinct hypoxic programme and suggest a context-specific sensitivity to ENaC inhibition.

   Major comments

   The paper addresses a timely question with a clear narrative arc and brings together ChIP-seq, RNA-seq, pharmacology, survival analysis and spatial transcriptomics. The EDHR concept is interesting and the ENaC angle is original. The work is already strong and with a few targeted additions and clarifications it can be made more persuasive without becoming a new project.

   1. The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence. Estimated time: 6-8 weeks for a focused follow up with two conditions and biological duplicates/triplicates.

   2. The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines. To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example, confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR and test whether short-term ERα knockdown reproduces the effect. A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF. Estimated time: 3-4 weeks for qPCR and siRNA validations.

   3. The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D. Estimated time: 4-6 weeks.

   4. The RFS associations for SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios. Estimated time: 1-2 weeks.

   5. The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful. Estimated time: 1 week.

   Reproducibility and statistics

   Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

   Optional

   A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice. Estimated time: 6-8 weeks.

   Minor comments

   1. Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.
   2. In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.
   3. In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.
   4. For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.
   5. Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.
   6. A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.
   7. Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

   Prior studies

   The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

   Significance

   General assessment

   The strongest aspects are the carefully designed factorial RNA-seq that cleanly separates ERα and hypoxia effects, the discovery of a concise EDHR signature reproducible across two ER+ lines, and the integration with spatial transcriptomics that places EDHR near EMT-rich tumour regions. The ENaC connection is new and potentially actionable, and the context-dependent amiloride response is a practical lead. Limitations are primarily mechanistic: DMOG vs hypoxia in the cistrome experiment, the absence of direct HIF or cofactor perturbations, and the pleiotropy of amiloride.

   Advance

   To my knowledge, this is the first description of a distinct ERα-dependent hypoxic programme in ER+ breast cancer that includes ENaC regulatory subunits and links to an EMT-adjacent spatial niche. The conceptual advance is the positioning of ERα as a coordinator of a subset of hypoxia-induced genes rather than as a parallel pathway, together with an initial functional readout that suggests a therapeutic angle through ENaC modulation. With the targeted additions outlined above, the study would move from strong association to a more mechanistic and translationally relevant model.

   Audience

   The work will interest a specialised audience in nuclear receptor biology, hypoxia signalling, tumour microenvironment, and ion transport in cancer. It has potential relevance for basic researchers studying ERα cistrome dynamics, for groups using spatial transcriptomics to define micro-niches, and for translational researchers exploring metabolic and ionic vulnerabilities in ER+ disease.

   Expertise disclosure

   Keywords: nuclear receptors,, chromatin profiling, transcriptomics, spatial transcriptomics, breast cancer biology.

   I am not a domain expert in ion channel electrophysiology; my comments on ENaC pharmacology focus on specificity and study design rather than detailed channel biophysics.

   Tone

   I find the paper well conceived and already compelling. The suggested experiments are focused, realistic in scope, and primarily aim to turn several strong associations into concise mechanistic statements that would further increase confidence and impact.

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   Referee #2

   Evidence, reproducibility and clarity

   ERα drives most luminal breast cancers. However, how hypoxia reshapes ERα activity and how ERα itself might influence the hypoxic response remain unclear. Understanding this interaction is crucial, as hypoxia is strongly linked to endocrine resistance and poor outcomes. In this study, authors investigated how hypoxia modifies ERα signalling in ER+ breast cancer and whether ERα contributes to the transcriptional response to low oxygen. Using MCF7 and T47D cells, they combined genome-wide profiling of the ERα cistrome under DMOG, hypoxic transcriptomics with or without ERα degradation, and spatial transcriptomics in tumours. This …

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   Referee #2

   Evidence, reproducibility and clarity

   ERα drives most luminal breast cancers. However, how hypoxia reshapes ERα activity and how ERα itself might influence the hypoxic response remain unclear. Understanding this interaction is crucial, as hypoxia is strongly linked to endocrine resistance and poor outcomes. In this study, authors investigated how hypoxia modifies ERα signalling in ER+ breast cancer and whether ERα contributes to the transcriptional response to low oxygen. Using MCF7 and T47D cells, they combined genome-wide profiling of the ERα cistrome under DMOG, hypoxic transcriptomics with or without ERα degradation, and spatial transcriptomics in tumours. This revealed an ERα-dependent hypoxic response (EDHR), prominently involving regulation of epithelial sodium channel (ENaC) subunits, whose expression requires both hypoxia and active ERα signalling. Functionally, ENaC inhibition with amiloride reduced cell viability under hypoxia. Together, these findings uncover a previously unrecognised ERα-dependent layer of the hypoxic transcriptome and identify ENaC as a potential therapeutic vulnerability in hypoxic ER+ breast cancer. Although the study is interesting, the manuscript lacks several essential functional and experimental validations. ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype. Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation. The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied. In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed Finally, the authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

   Significance

   General assessment strengths:

   This study uncovers a previously unrecognised ERα-dependent hypoxic response in breast cancer, revealing that ERα actively shapes the hypoxic transcriptome rather than functioning as an isolated pathway. To me, the main strength of this work is the identification of ENaC as a novel hypoxia-specific therapeutic vulnerability in ER+ breast cancer, suggesting that ion-channel regulation may play a broader and underappreciated role in endocrine resistance.

   Limitation:

   Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively. This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

   Audience:

   Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

   Expertise:

   My evaluation is based on my background in breast cancer, ERα signaling and breast tumorigenesis. However, I have limited expertise in spacial transcriptomic analyses and advanced CHiP-seq bioinformatic analyses, which may affect my assessment of some computational analyses.

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   Referee #1

   Evidence, reproducibility and clarity

   In this manuscript, Malcom et al. present evidence that, under hypoxic conditions, hypoxia-inducible factors (HIFs) alter the estrogen receptor alpha (ERα) epigenomic landscape in a model of estrogen receptor-positive (ER+) breast cancer (BrCa). The response of ER+ BrCa to estradiol (E2) in MCF7 (ER+) cells, as well as ERα signaling in both primary and metastatic breast cancer, has been well studied, and the epigenomic landscape of ERα+ BrCa is well documented. The differentially expressed genes (DEGs) identified under treatment with the hypoxia mimetic dimethyloxalylglycine (DMOG) revealed a subset of ERα-dependent hypoxic response …

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   Referee #1

   Evidence, reproducibility and clarity

   In this manuscript, Malcom et al. present evidence that, under hypoxic conditions, hypoxia-inducible factors (HIFs) alter the estrogen receptor alpha (ERα) epigenomic landscape in a model of estrogen receptor-positive (ER+) breast cancer (BrCa). The response of ER+ BrCa to estradiol (E2) in MCF7 (ER+) cells, as well as ERα signaling in both primary and metastatic breast cancer, has been well studied, and the epigenomic landscape of ERα+ BrCa is well documented. The differentially expressed genes (DEGs) identified under treatment with the hypoxia mimetic dimethyloxalylglycine (DMOG) revealed a subset of ERα-dependent hypoxic response (EDHR) genes. The outcome was a reprogramming of the basal ERα cistrome, coinciding with sites enriched for estrogen response elements (EREs) and co-transcription factor binding motifs for ERα, including FOXA1 and AP-1. This was demonstrated by ERα ChIP-seq (i.e. DMOG) and ATAC-seq (i.e. 1% O2) performed under different hypoxic conditions. The transcripts identified following DMOG treatment were leveraged and compared to publicly available RNA-seq datasets from various breast cancer subtypes exposed to 1% hypoxic oxygen. Although the comparison methods varied, the results suggested that BrCa cell lines under 1% hypoxic oxygen conditions showed strong similarity to MCF7 cells treated with DMOG. Genes upregulated in response to DMOG correlated with poorer survival outcomes. To demonstrate the requirement for ERα in this model, MCF7 cells were treated with the selective estrogen receptor degrader (SERD) fulvestrant-the only FDA-approved SERD for ER+ BrCa-showing a dampening of the HIF response among EDHR genes. This suggests that ERα is necessary for the expression of DEGs under hypoxic conditions induced by DMOG. Finally, the sodium channel protein ENaC subunits (i.e., SCNN1B and SCNN1G) were further characterized as candidate EDHR genes. Analyses of publicly available datasets indicated that high mRNA expression levels of these subunits were associated with worse survival outcomes, supporting the clinical relevance of EDHR genes SCNN1B and SCNN1G. To further validate clinical relevance, utilize the Spatial Transcriptome in a small subset of ER+ BrCa.

   Major:

   1. Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.
   2. Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.
   3. Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.
   4. Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.
   5. Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG. The single-gene examples of DMOG effects shown in Fig. S1A are not significant.
   6. Figures S2 and S3: Fig. S2 lacks 1% O₂ conditions, and Fig. S3 lacks DMOG-induced HIF factor assessments.
   7. Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality. Hypoxic conditions for assessing ER status and appropriate controls are also lacking.
   8. Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.
   9. Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

   Minor:

   1. Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

   Significance

   In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer. Additionally, a lack of a through comparison between DMOG and or 1 %O2 induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript. The lack of considering estradiol exposure under hypoxic conditions with either 1%O2 and or DMOG also limits relevance to patients with ER+ BrCa. The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

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   Reply to the reviewers

   Reviewer 1

   Point

   Summary

   Response

   1.1

   Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.

   To assess whether DMOG-induced changes in ERα occupancy reflect bona fide hypoxia, we measured ERα binding by ChIP-qPCR under 1% oxygen over 48 hours, compared to normoxic (21% oxygen) cells and input controls in matched cells at the GREB1 and TFF1 loci. Our findings demonstrate that 1% oxygen treatment recapitulates the ERα binding changes observed with DMOG, at the time points of our RNA-seq experiments.

   We have included these results in __Figure 1F __of the …

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   Reply to the reviewers

   Reviewer 1

   Point

   Summary

   Response

   1.1

   Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.

   To assess whether DMOG-induced changes in ERα occupancy reflect bona fide hypoxia, we measured ERα binding by ChIP-qPCR under 1% oxygen over 48 hours, compared to normoxic (21% oxygen) cells and input controls in matched cells at the GREB1 and TFF1 loci. Our findings demonstrate that 1% oxygen treatment recapitulates the ERα binding changes observed with DMOG, at the time points of our RNA-seq experiments.

   We have included these results in __Figure 1F __of the preliminary revision of the manuscript.

   1.2

   Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.

   We thank the reviewer for this comment. We have clarified that motif enrichment analysis is included to characterise the sequence context of ERα binding sites and to confirm enrichment of known ER-associated motifs (e.g. EREs), rather than to infer functional involvement of additional transcription factors under hypoxia. Corresponding interpretative statements have been removed from the Results and restricted to the Discussion.

   1.3

   Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.

   To confirm DMOG induces HIF-protein expression we have analysed HIF1α and HIF2α protein levels by western blot. We have included these in __Supplementary Figure S1A __within the preliminary revision to address this concern.

   1.4

   Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.

   We acknowledge that the ERα ChIP-seq (DMOG) and ATAC-seq datasets were generated under different conditions and are therefore not directly comparable. To address this, we have performed ChIP-qPCR under bona fide hypoxia (1% oxygen) at canonical ERα target loci (*TFF1 *and GREB1), demonstrating that the directionality of ERα binding changes observed with DMOG is recapitulated under physiological hypoxia. These data provide a direct comparison of ERα occupancy across conditions and support the use of DMOG as a proxy for hypoxia in our ChIP-seq experiments.

   If requested, we are willing to perform ATAC-seq at 16 h under 1% oxygen. However, because the original dataset was generated under 0.1% oxygen, and canonical ERα-bound sites show minimal accessibility changes under severe hypoxia, we anticipate limited additional insight from repeating this experiment.

   1.5a

   Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG.

   The statement that the ERα ChIP samples lack estrogen treatment is incorrect. Estradiol was not an experimental variable and cells were intentionally maintained under estrogen-rich conditions to preserve tumour-relevant ERα activity.

   We have now clarified within the preliminary revision by stating that cells were routinely cultured in “estrogen-rich Dulbecco’s Modified Eagle Medium” in the methods section, and clarified the use of estrogen-rich conditions in the Figure S1 legend.

   1.5b

   The single-gene examples of DMOG effects shown in Fig. S1A are not significant.

   The peak illustrated in Figure S1A (now __Figure S1D) __is intended to provide a visual confirmation of peak calling and enrichment patterns underlying the genome-wide redistribution observed in Figure 1. The peak was called by the MACS2 pipeline (code available from https://doi.org/10.5281/zenodo.17221105) with a log10(q-value) = 268.5, which passes the MACS2 cut-off q

   1.6a

   Fig. S2 lacks 1% O₂ conditions,

   We wish to clarify that __Figure S2 __(now Figure S4) serves as quality control specifically for the DMOG-treated ChIP-seq dataset presented in Figure 1C. The purpose of the plot is to visualize unfiltered motif enrichment to confirm that the identified peaks represent bona fide ERα binding events within the DMOG condition. Motif enrichment under a 1% oxygen environment would not provide this validation. In all cases the ERE is the most significantly enriched motif.

   With respect to ERα binding under 1% oxygen, we have now assessed this via targeted ChIP-qPCR validation (Figure 1F).

   1.6b

   Fig. S3 lacks DMOG-induced HIF factor assessments.

   The DMOG-induced changes in HIF1α and HIF2α expression are shown in the__ Figure S1__ of this revision proposal and have been incorporated into the manuscript as part of the changes described in response 1.3.

   1.7a

   Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality.

   We have substantially improved the labelling of Figure S4, now__ Figure S6.__

   Additionally, we have clarified that all samples were cultured in estrogen-rich media and treated with either vehicle control or 100 nM fulvestrant; thus estrogen is present in all conditions including the controls.

   1.7b

   Hypoxic conditions for assessing ER status and appropriate controls are also lacking.

   We agree that monitoring ERα stability under hypoxic conditions is essential.

   We provided a western blot assessment of ERα protein levels at 0, 8 and 48 hours of treatment with 1% oxygen or DMOG, compared to normoxic controls, included as Supplementary Figures S1B, C in the preliminary revision.

   These demonstrate the cells remain positive for ERα protein expression at 0, 8 and 48h.

   1.8

   Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.

   We thank the reviewer for this comment. To clarify the experimental design, we now signpost the reader in the figure legend of Figure S5 (now S7) to the schematic diagram provided in Figure 3B, and provide a summary stating the experiment employed a factorial design combining a 96-hour fulvestrant treatment with exposure to 1% oxygen for the final 48 hours.**

   1.9

   Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

   We have extensively revised all supplementary figure legends to ensure clarity and precision.

   1.10

   Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

   Following this comment, we carefully reviewed the presentation of all figures throughout the manuscript. We improved the organisation and labelling of the Supplementary Figures to facilitate clearer comparison of the data. In particular, full western blots are now clearly annotated and supplementary legends have been expanded to provide sufficient context for each figure to be interpreted independently.

   1.11

   i) In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer.

   ii) Additionally, a lack of a thorough comparison between DMOG and or 1 %oxygen induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript.

   iii) The lack of considering estradiol exposure under hypoxic conditions with either 1%oxygen and or DMOG also limits relevance to patients with ER+ BrCa.

   iv) The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

   i) We respectfully disagree that the manuscript does not extend prior work. Despite extensive characterisation of ERα, its role in shaping hypoxia-driven transcription in ER+ breast cancer has not been defined. Here, we identify an ERα-dependent hypoxic response (EDHR), demonstrating a reciprocal interaction between hypoxia and ERα activity.

   ii) In revision, we address concerns regarding DMOG by validating ERα binding under 1% oxygen using ChIP-qPCR thereby confirming our result in *bona fide *hypoxia. Additionally, all RNA-seq and functional assays, including ENaC targeting, were performed under 1% oxygen in the original manuscript.

   iii) All experiments were conducted under estrogen-complete conditions, now explicitly clarified, reflecting tumour-relevant ERα activity.

   iv) Together, these data establish a reciprocal interaction between ERα and hypoxia and uncover a targetable vulnerability in hypoxic ER+ breast cancer, linking transcriptional regulation to therapeutic opportunity.

   Reviewer 2

   No.

   Summary

   Response

   General Comments

   2.1

   ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype.

   We agree that further validation of ENaC involvement would strengthen this observation. We will assess ENaC protein levels under 1% hypoxia ± fulvestrant by western blot and perform siRNA-mediated depletion of ENaC subunits to test their contribution to the hypoxia-specific amiloride-sensitive phenotype by viability assay (see also response 3.3).

   2.2

   Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation.

   The experimental design already controls for potential fulvestrant-specific transcriptional effects, as all four conditions (± hypoxia, ± fulvestrant) were included. EDHR genes were defined based on induction under hypoxia, loss of this induction following ERα degradation, and absence of residual hypoxic induction in the presence of fulvestrant. Consistent with this, SCNN1B and SCNN1G do not show significant fulvestrant-responsive changes under normoxia (Figure 5C,D).

   We also note that fulvestrant has been shown to induce minimal global chromatin remodelling (Guan et al., 2019), supporting its use to assess ERα dependency without broadly confounding chromatin accessibility; this reference is now included in the manuscript.

   2.3

   The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied.

   We further examined the potential convergence of ERα and hypoxic signalling at the ENaC loci (included as __Figure 5E __in the revision proposal) showing genome browser views of the* SCNN1G* and *SCNN1B *loci, highlighting hypoxia-induced HIF1α binding and ERα association at these sites.

   To further support this, we will perform RT-qPCR validation of *SCNN1G *and SCNN1B expression following treatment ± IOX5 and ± fulvestrant. IOX5 is a selective PHD inhibitor that stabilises HIF proteins, enabling us to assess the contribution of HIF signalling independently of other oxygen-dependent effects associated with hypoxia.

   2.4

   In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed

   To assess the clinical relevance of SCNN1B and SCNN1G in ER-positive and ER-negative subgroups, we performed Cox proportional hazards analyses in TCGA and METABRIC cohorts individually, including ER status and stratifying by ER-positive and ER-negative cases (Figure 6C). These analyses support the association of SCNN1G with poorer relapse-free survival specifically in ER-positive patients.

   2.5

   The authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

   We agree that independent validation would strengthen these findings. We will perform RT-qPCR validation of key EDHR genes (including *SCNN1B *and SCNN1G) under ± hypoxia and ± fulvestrant conditions to confirm ERα-dependent hypoxic induction.

   Limitations

   2.6

   Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively.

   This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

   While ERα cistrome reprogramming has been described, our study demonstrates a reciprocal interaction in which ERα not only responds to hypoxia but actively shapes hypoxia-driven transcription, defining an ERα-dependent hypoxic response (EDHR).

   We acknowledge the limitations of DMOG and have addressed this by validating key ERα binding events under bona fide hypoxia (1% oxygen) using ChIP–qPCR, confirming our findings under physiological conditions (response 1.1).

   To further strengthen mechanistic insight, we will assess the requirement for HIF stabilisation using the selective PHD inhibitor IOX5, combined with RT-qPCR analysis of SCNN1G and SCNN1B ± IOX5 ± fulvestrant (response 2.3 and 2.5). In addition, we will validate the functional relevance of ENaC through protein-level analysis and siRNA-mediated depletion, as described in__ response 2.1.__

   Together, these additions address concerns regarding DMOG specificity and provide further support for a functional interaction between ERα and hypoxic signalling.

   Audience

   2.7

   Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

   The study incorporates multiple layers of human relevance, including spatial transcriptomic analyses demonstrating enrichment of EDHR within hypoxic tumour regions and survival analyses linking EDHR and ENaC expression to clinical outcome.

   In revision, we address the reviewer’s concerns through targeted validation (ChIP-qPCR in hypoxia, western blotting, and RT–qPCR). Together, these additions strengthen the mechanistic and translational relevance of the study.

   Reviewer 3

   No.

   Summary

   Response

   Major comments

   3.1

   The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence.

   We acknowledge that mimetics of hypoxia can introduce off-target effects. To address this, we have validated our ERα ChIP-seq findings using ChIP-qPCR at representative loci (TFF1 and GREB1), demonstrating consistent changes in ERα binding under bona fide hypoxia (1% oxygen) (now included in Figure 1F).

   As acknowledged by the reviewer, ChIP-seq under these conditions is likely not feasible due to cell number constraints. We are willing to undertake ATAC-seq if required (as stated in response 1.1); however, we do not feel it would directly address ERα occupancy at these loci. We therefore consider our targeted ChIP-qPCR to be the most appropriate approach to validate ERα redistribution under hypoxia.

   3.2a

   The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines.

   To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example,

   i) confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR

   We agree that targeted validation would strengthen the mechanistic support for ERα dependence. We will perform RT-qPCR validation of SCNN1B and *SCNN1G *under hypoxia ± fulvestrant to confirm ERα-dependent hypoxic induction (see also response 2.5).

   3.2b

   ii) test whether short-term ERα knockdown reproduces the effect.

   ERα dependency is already assessed through fulvestrant-mediated degradation within the factorial design, which provides a well-established and direct approach to evaluate ERα function. As EDHR genes are defined by loss of hypoxic induction following ERα degradation, this constitutes a robust assessment of ERα-dependent effects.

   We will therefore focus on orthogonal validation through RT-qPCR (response__ 2.5__), together with additional mechanistic and functional analyses using IOX5 and ENaC perturbation (responses 2.1 and 2.3), rather than introducing an ERα knockdown approach, although we would consider this if required.

   3.2c

   iii) A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF.

   This request aligns with point 2.3, which addresses the convergence of ERα and HIF signalling. While HIF knockdown under hypoxia would assess necessity, we will instead assess the contribution of HIF signalling using the selective PHD inhibitor IOX5, as this allows us to isolate HIF stabilisation from broader hypoxia-associated effects and avoids additional perturbation associated with transfection-based approaches. We will perform RT-qPCR analysis of SCNN1B and SCNN1G following treatment ± IOX5 ± fulvestrant to determine whether HIF stabilisation is sufficient to support ERα-dependent induction of EDHR genes.

   3.3

   The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D.

   To address the reviewer’s concern regarding pleiotropic effects, we propose (aligning with our__ response to 2.1__) to apply siRNA-mediated knockdown of SCNN1B and SCNN1G under hypoxia to determine whether this reproduces our observed viability effect, thereby providing direct evidence for ENaC involvement.

   We agree that additional pharmacological validation could further support specificity, and would consider inclusion of a more ENaC-selective inhibitor if required.

   3.4

   The RFS associations for

   SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios.

   We have analysed TCGA and METABRIC cohorts individually using Cox proportional hazards models, as this functionality is not available for merged datasets in KMplot. ER status was included in the models, and analyses were additionally stratified by ER-positive and ER-negative subgroups. The number of relapse events per subgroup is approximately 40; therefore, additional covariates such as grade and nodal status were not included given the limited number of events per model.

   Within ER-positive patients, high SCNN1G expression is associated with poorer relapse-free survival (TCGA HR 1.45, p = 0.0027), while SCNN1B shows a similar trend that does not reach statistical significance. These analyses are presented in Figure 6C and in the results section of the preliminary revision, and support the findings from the Kaplan–Meier analysis.

   3.5

   The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful.

   Spatial cell type composition and spot annotations were used as provided in the SpottedPy dataset, based on Cell2location-derived deconvolution scores and STARCH tumour annotations, without additional re-estimation.

   To address the reviewer’s suggestion, we examined the relationship between EDHR enrichment and epithelial content and observed no significant correlation at the neighbourhood level.

   These points have now been clarified in the manuscript.

   3.6

   Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

   In the preliminary revision we have added a statement to the amiloride assay figure (Figure 6D) clarifying that n = 3 independent biological replicates were performed per condition. In addition, we now provide the underlying numerical values for this assay in Table S11.

   3.7

   A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice.

   We agree that directly linking EDHR to ENaC channel activity would further strengthen the mechanistic connection. We will prioritise genetic validation of ENaC function through siRNA-mediated depletion (response 2.1), which directly tests the requirement for ENaC in the hypoxia-specific viability phenotype.

   We are willing to explore the feasibility of measuring the amiloride-sensitive Na+ currents under normoxia and acute hypoxia (via perfusion of cells with bathing solution bubbled with nitrogen during recording) ± fulvestrant to further connect hypoxic regulation to channel activity.

   Minor Comments

   3.8

   Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.

   We have now included representative ERα ChIP-seq browser snapshots for gained, conserved, and lost loci, together with input controls for both conditions, in Figure S3 of the revised manuscript.

   3.9

   In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.

   We thank the reviewer for this point. The ATAC-seq dataset was generated under 0.1% oxygen in the original study, whereas RNA-seq experiments in this work were performed at 1% oxygen to reflect tumour-relevant hypoxic conditions. The more severe hypoxia used for ATAC-seq would be expected to maximise detection of chromatin accessibility changes. Despite this, chromatin accessibility changes were limited, with ERα binding occurring predominantly at pre-accessible regions. This has now been clarified in the manuscript.

   3.10

   In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.

   The neighbourhood parameter was set to 8, corresponding to the immediate neighbouring spots in Visium data, consistent with package guidance. We have clarified this in the manuscript text.

   3.11

   For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.

   We have marked the 14 EDHR consensus genes and indicated the ENaC module in the revised heatmap to aid readability.

   3.12

   Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.

   We have reported exact sample sizes and replicate numbers in all relevant figure legends and included Table S11 summarising all statistical tests, sample sizes (n), and p values.

   3.13

   A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.

   We have added timelines for these experiments as requested.

   3.14

   Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

   We have standardised oxygen notation throughout the manuscript to use “oxygen” in the main text and “O2” where appropriate (e.g. figures).

   Reagent catalogue numbers have now been standardised for consistency of presentation in the revised manuscript.

   Gene and protein nomenclature were already formatted according to accepted conventions and were verified for consistency.

   3.15

   The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

   We thank the reviewer for this suggestion. We have expanded the manuscript to clarify that acute hypoxia (1% oxygen) and DMOG treatment capture early, dynamic hypoxic responses, in contrast to chronic CoCl2 exposure, which reflects longer-term adaptation. This distinction is relevant to tumour biology, where hypoxia is often transient due to unstable vascularisation. The following statement has been added to the manuscript:

   “In addition to such chronic hypoxic adaptation, tumour hypoxia can also be dynamic, with cells experiencing acute or transient hypoxic exposure due to unstable vascularisation; an established contributor to tumour progression (Liu et al, 2022a; Koh & Powis, 2012). Thus, in contexts where both signalling pathways remain active, the dependence of the hypoxic response on ERα in ER+ cells has not been previously characterised.”

   Primary Limitations

   3.16

   DMOG vs hypoxia in the cistrome experiment,

   To address concerns regarding the use of DMOG, we have validated key ERα binding events from the ChIP-seq dataset by ChIP–qPCR at the TFF1 and GREB1 loci under bona fide hypoxia (1% oxygen) in biological triplicate__ (Figure 1F)__. These data demonstrate consistent changes in ERα binding under hypoxia, supporting that the DMOG-induced redistribution reflects hypoxia-driven changes.

   3.17

   the absence of direct HIF or cofactor perturbations

   We acknowledge the absence of direct HIF perturbation. To address this, we will assess the contribution of HIF signalling through stabilisation approaches, including RT-qPCR analysis of* SCNN1B* and SCNN1G ± IOX5 ± fulvestrant (response 3.2), to determine whether HIF activation is sufficient to support ERα-dependent induction.

   3.18

   and the pleiotropy of amiloride.

   To address the potential pleiotropy of amiloride, we will perform siRNA-mediated knockdown of *SCNN1G *and *SCNN1B *to provide independent validation of ENaC-dependent effects (response 3.3).

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   Referee #3

   Evidence, reproducibility and clarity

   Summary

   This study explores how hypoxia reshapes ERα signalling in ER-positive breast cancer and whether this cross-talk exposes targetable vulnerabilities. The authors first map ERα binding in MCF7 cells after dioxygenase inhibition with DMOG and observe a genome-wide redistribution with enrichment of ERE, FOXA1 and AP-1 motifs at gained sites while chromatin accessibility at these loci appears unchanged in public ATAC-seq after hypoxia. They then perform RNA-seq in MCF7 and T47D using a factorial design that combines fulvestrant-mediated ERα degradation with 1% O₂ to define an ERα-dependent hypoxia response (EDHR). A 14-gene …

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   Referee #3

   Evidence, reproducibility and clarity

   Summary

   This study explores how hypoxia reshapes ERα signalling in ER-positive breast cancer and whether this cross-talk exposes targetable vulnerabilities. The authors first map ERα binding in MCF7 cells after dioxygenase inhibition with DMOG and observe a genome-wide redistribution with enrichment of ERE, FOXA1 and AP-1 motifs at gained sites while chromatin accessibility at these loci appears unchanged in public ATAC-seq after hypoxia. They then perform RNA-seq in MCF7 and T47D using a factorial design that combines fulvestrant-mediated ERα degradation with 1% O₂ to define an ERα-dependent hypoxia response (EDHR). A 14-gene consensus EDHR signature includes ENaC regulatory subunits SCNN1B and SCNN1G, whose higher expression is associated with poorer RFS in ER+ cohorts. Functionally, amiloride increases viability in normoxia but reduces viability under hypoxia in MCF7 across a dose range. Spatial transcriptomics from ER+ tumours shows EDHR expression enriched at the margins of hypoxia and estrogen-hallmark regions and adjacent to EMT hotspots. Raw data and code availability are stated for the central datasets and accessions are provided. Together the results argue that ERα helps organise a distinct hypoxic programme and suggest a context-specific sensitivity to ENaC inhibition.

   Major comments

   The paper addresses a timely question with a clear narrative arc and brings together ChIP-seq, RNA-seq, pharmacology, survival analysis and spatial transcriptomics. The EDHR concept is interesting and the ENaC angle is original. The work is already strong and with a few targeted additions and clarifications it can be made more persuasive without becoming a new project.

   1. The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence. Estimated time: 6-8 weeks for a focused follow up with two conditions and biological duplicates/triplicates.

   2. The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines. To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example, confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR and test whether short-term ERα knockdown reproduces the effect. A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF. Estimated time: 3-4 weeks for qPCR and siRNA validations.

   3. The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D. Estimated time: 4-6 weeks.

   4. The RFS associations for SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios. Estimated time: 1-2 weeks.

   5. The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful. Estimated time: 1 week.

   Reproducibility and statistics

   Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

   Optional

   A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice. Estimated time: 6-8 weeks.

   Minor comments

   1. Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.
   2. In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.
   3. In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.
   4. For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.
   5. Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.
   6. A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.
   7. Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

   Prior studies

   The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

   Significance

   General assessment

   The strongest aspects are the carefully designed factorial RNA-seq that cleanly separates ERα and hypoxia effects, the discovery of a concise EDHR signature reproducible across two ER+ lines, and the integration with spatial transcriptomics that places EDHR near EMT-rich tumour regions. The ENaC connection is new and potentially actionable, and the context-dependent amiloride response is a practical lead. Limitations are primarily mechanistic: DMOG vs hypoxia in the cistrome experiment, the absence of direct HIF or cofactor perturbations, and the pleiotropy of amiloride.

   Advance

   To my knowledge, this is the first description of a distinct ERα-dependent hypoxic programme in ER+ breast cancer that includes ENaC regulatory subunits and links to an EMT-adjacent spatial niche. The conceptual advance is the positioning of ERα as a coordinator of a subset of hypoxia-induced genes rather than as a parallel pathway, together with an initial functional readout that suggests a therapeutic angle through ENaC modulation. With the targeted additions outlined above, the study would move from strong association to a more mechanistic and translationally relevant model.

   Audience

   The work will interest a specialised audience in nuclear receptor biology, hypoxia signalling, tumour microenvironment, and ion transport in cancer. It has potential relevance for basic researchers studying ERα cistrome dynamics, for groups using spatial transcriptomics to define micro-niches, and for translational researchers exploring metabolic and ionic vulnerabilities in ER+ disease.

   Expertise disclosure

   Keywords: nuclear receptors,, chromatin profiling, transcriptomics, spatial transcriptomics, breast cancer biology.

   I am not a domain expert in ion channel electrophysiology; my comments on ENaC pharmacology focus on specificity and study design rather than detailed channel biophysics.

   Tone

   I find the paper well conceived and already compelling. The suggested experiments are focused, realistic in scope, and primarily aim to turn several strong associations into concise mechanistic statements that would further increase confidence and impact.

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   Referee #2

   Evidence, reproducibility and clarity

   ERα drives most luminal breast cancers. However, how hypoxia reshapes ERα activity and how ERα itself might influence the hypoxic response remain unclear. Understanding this interaction is crucial, as hypoxia is strongly linked to endocrine resistance and poor outcomes. In this study, authors investigated how hypoxia modifies ERα signalling in ER+ breast cancer and whether ERα contributes to the transcriptional response to low oxygen. Using MCF7 and T47D cells, they combined genome-wide profiling of the ERα cistrome under DMOG, hypoxic transcriptomics with or without ERα degradation, and spatial transcriptomics in tumours. This …

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   Referee #2

   Evidence, reproducibility and clarity

   ERα drives most luminal breast cancers. However, how hypoxia reshapes ERα activity and how ERα itself might influence the hypoxic response remain unclear. Understanding this interaction is crucial, as hypoxia is strongly linked to endocrine resistance and poor outcomes. In this study, authors investigated how hypoxia modifies ERα signalling in ER+ breast cancer and whether ERα contributes to the transcriptional response to low oxygen. Using MCF7 and T47D cells, they combined genome-wide profiling of the ERα cistrome under DMOG, hypoxic transcriptomics with or without ERα degradation, and spatial transcriptomics in tumours. This revealed an ERα-dependent hypoxic response (EDHR), prominently involving regulation of epithelial sodium channel (ENaC) subunits, whose expression requires both hypoxia and active ERα signalling. Functionally, ENaC inhibition with amiloride reduced cell viability under hypoxia. Together, these findings uncover a previously unrecognised ERα-dependent layer of the hypoxic transcriptome and identify ENaC as a potential therapeutic vulnerability in hypoxic ER+ breast cancer. Although the study is interesting, the manuscript lacks several essential functional and experimental validations. ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype. Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation. The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied. In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed Finally, the authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

   Significance

   General assessment strengths:

   This study uncovers a previously unrecognised ERα-dependent hypoxic response in breast cancer, revealing that ERα actively shapes the hypoxic transcriptome rather than functioning as an isolated pathway. To me, the main strength of this work is the identification of ENaC as a novel hypoxia-specific therapeutic vulnerability in ER+ breast cancer, suggesting that ion-channel regulation may play a broader and underappreciated role in endocrine resistance.

   Limitation:

   Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively. This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

   Audience:

   Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

   Expertise:

   My evaluation is based on my background in breast cancer, ERα signaling and breast tumorigenesis. However, I have limited expertise in spacial transcriptomic analyses and advanced CHiP-seq bioinformatic analyses, which may affect my assessment of some computational analyses.

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   Referee #1

   Evidence, reproducibility and clarity

   In this manuscript, Malcom et al. present evidence that, under hypoxic conditions, hypoxia-inducible factors (HIFs) alter the estrogen receptor alpha (ERα) epigenomic landscape in a model of estrogen receptor-positive (ER+) breast cancer (BrCa). The response of ER+ BrCa to estradiol (E2) in MCF7 (ER+) cells, as well as ERα signaling in both primary and metastatic breast cancer, has been well studied, and the epigenomic landscape of ERα+ BrCa is well documented. The differentially expressed genes (DEGs) identified under treatment with the hypoxia mimetic dimethyloxalylglycine (DMOG) revealed a subset of ERα-dependent hypoxic response …

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   Referee #1

   Evidence, reproducibility and clarity

   In this manuscript, Malcom et al. present evidence that, under hypoxic conditions, hypoxia-inducible factors (HIFs) alter the estrogen receptor alpha (ERα) epigenomic landscape in a model of estrogen receptor-positive (ER+) breast cancer (BrCa). The response of ER+ BrCa to estradiol (E2) in MCF7 (ER+) cells, as well as ERα signaling in both primary and metastatic breast cancer, has been well studied, and the epigenomic landscape of ERα+ BrCa is well documented. The differentially expressed genes (DEGs) identified under treatment with the hypoxia mimetic dimethyloxalylglycine (DMOG) revealed a subset of ERα-dependent hypoxic response (EDHR) genes. The outcome was a reprogramming of the basal ERα cistrome, coinciding with sites enriched for estrogen response elements (EREs) and co-transcription factor binding motifs for ERα, including FOXA1 and AP-1. This was demonstrated by ERα ChIP-seq (i.e. DMOG) and ATAC-seq (i.e. 1% O2) performed under different hypoxic conditions. The transcripts identified following DMOG treatment were leveraged and compared to publicly available RNA-seq datasets from various breast cancer subtypes exposed to 1% hypoxic oxygen. Although the comparison methods varied, the results suggested that BrCa cell lines under 1% hypoxic oxygen conditions showed strong similarity to MCF7 cells treated with DMOG. Genes upregulated in response to DMOG correlated with poorer survival outcomes. To demonstrate the requirement for ERα in this model, MCF7 cells were treated with the selective estrogen receptor degrader (SERD) fulvestrant-the only FDA-approved SERD for ER+ BrCa-showing a dampening of the HIF response among EDHR genes. This suggests that ERα is necessary for the expression of DEGs under hypoxic conditions induced by DMOG. Finally, the sodium channel protein ENaC subunits (i.e., SCNN1B and SCNN1G) were further characterized as candidate EDHR genes. Analyses of publicly available datasets indicated that high mRNA expression levels of these subunits were associated with worse survival outcomes, supporting the clinical relevance of EDHR genes SCNN1B and SCNN1G. To further validate clinical relevance, utilize the Spatial Transcriptome in a small subset of ER+ BrCa.

   Major:

   1. Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.
   2. Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.
   3. Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.
   4. Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.
   5. Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG. The single-gene examples of DMOG effects shown in Fig. S1A are not significant.
   6. Figures S2 and S3: Fig. S2 lacks 1% O₂ conditions, and Fig. S3 lacks DMOG-induced HIF factor assessments.
   7. Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality. Hypoxic conditions for assessing ER status and appropriate controls are also lacking.
   8. Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.
   9. Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

   Minor:

   1. Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

   Significance

   In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer. Additionally, a lack of a through comparison between DMOG and or 1 %O2 induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript. The lack of considering estradiol exposure under hypoxic conditions with either 1%O2 and or DMOG also limits relevance to patients with ER+ BrCa. The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

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   Version published to 10.1101/2025.10.03.680339 on bioRxiv

   Oct 4, 2025