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Progesterone receptor modulates ERα action in breast cancer


A Corrigendum to this article was published on 05 August 2015

This article has been updated


Progesterone receptor (PR) expression is used as a biomarker of oestrogen receptor-α (ERα) function and breast cancer prognosis. Here we show that PR is not merely an ERα-induced gene target, but is also an ERα-associated protein that modulates its behaviour. In the presence of agonist ligands, PR associates with ERα to direct ERα chromatin binding events within breast cancer cells, resulting in a unique gene expression programme that is associated with good clinical outcome. Progesterone inhibited oestrogen-mediated growth of ERα+ cell line xenografts and primary ERα+ breast tumour explants, and had increased anti-proliferative effects when coupled with an ERα antagonist. Copy number loss of PGR, the gene coding for PR, is a common feature in ERα+ breast cancers, explaining lower PR levels in a subset of cases. Our findings indicate that PR functions as a molecular rheostat to control ERα chromatin binding and transcriptional activity, which has important implications for prognosis and therapeutic interventions.

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Figure 1: PR is a novel ERα interacting protein following progesterone treatment.
Figure 2: Progesterone redirects oestrogen-stimulated ERα binding events to novel chromatin loci and transcriptional targets.
Figure 3: Progesterone treatment inhibits ERα+ tumour progression.
Figure 4: The PGR genomic locus undergoes copy number loss in ERα+ breast cancer.

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Primary accessions

Gene Expression Omnibus

Data deposits

All microarray and ChIP-seq data are deposited in GEO with the accession number GSE68359. All proteomic data are deposited with the PRIDE database with the accession number PXD002104.

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  1. Chlebowski, R. T. et al. Estrogen plus progestin and breast cancer incidence and mortality in postmenopausal women. J. Am. Med. Assoc. 304, 1684–1692 (2010)

    Article  CAS  Google Scholar 

  2. Bentel, J. M. et al. Androgen receptor agonist activity of the synthetic progestin, medroxyprogesterone acetate, in human breast cancer cells. Mol. Cell. Endocrinol. 154, 11–20 (1999)

    Article  CAS  Google Scholar 

  3. Fournier, A., Berrino, F. & Clavel-Chapelon, F. Unequal risks for breast cancer associated with different hormone replacement therapies: results from the E3N cohort study. Breast Cancer Res. Treat. 107, 103–111 (2008)

    Article  CAS  Google Scholar 

  4. Blows, F. M. et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 7, e1000279 (2010)

    Article  Google Scholar 

  5. Vignon, F., Bardon, S., Chalbos, D. & Rochefort, H. Antiestrogenic effect of R5020, a synthetic progestin in human breast cancer cells in culture. J. Clin. Endocrinol. Metab. 56, 1124–1130 (1983)

    Article  CAS  Google Scholar 

  6. Musgrove, E. A., Swarbrick, A., Lee, C. S., Cornish, A. L. & Sutherland, R. L. Mechanisms of cyclin-dependent kinase inactivation by progestins. Mol. Cell. Biol. 18, 1812–1825 (1998)

    Article  CAS  Google Scholar 

  7. Chen, C. C., Hardy, D. B. & Mendelson, C. R. Progesterone receptor inhibits proliferation of human breast cancer cells via induction of MAPK phosphatase 1 (MKP-1/DUSP1). J. Biol. Chem. 286, 43091–43102 (2011)

    Article  CAS  Google Scholar 

  8. Kabos, P. et al. Patient-derived luminal breast cancer xenografts retain hormone receptor heterogeneity and help define unique estrogen-dependent gene signatures. Breast Cancer Res. Treat. 135, 415–432 (2012)

    Article  CAS  Google Scholar 

  9. Zheng, Z. Y., Bay, B. H., Aw, S. E. & Lin, V. C. A novel antiestrogenic mechanism in progesterone receptor-transfected breast cancer cells. J. Biol. Chem. 280, 17480–17487 (2005)

    Article  CAS  Google Scholar 

  10. Bardou, V. J., Arpino, G., Elledge, R. M., Osborne, C. K. & Clark, G. M. Progesterone receptor status significantly improves outcome prediction over estrogen receptor status alone for adjuvant endocrine therapy in two large breast cancer databases. J. Clin. Oncol. 21, 1973–1979 (2003)

    Article  CAS  Google Scholar 

  11. Pichon, M. F., Pallud, C., Brunet, M. & Milgrom, E. Relationship of presence of progesterone receptors to prognosis in early breast cancer. Cancer Res. 40, 3357–3360 (1980)

    CAS  PubMed  Google Scholar 

  12. Badwe, R. et al. Single-injection depot progesterone before surgery and survival in women with operable breast cancer: a randomized controlled trial. J. Clin. Oncol. 29, 2845–2851 (2011)

    Article  CAS  Google Scholar 

  13. Bines, J. et al. Activity of megestrol acetate in postmenopausal women with advanced breast cancer after nonsteroidal aromatase inhibitor failure: a phase II trial. Ann. Oncol. 25, 831–836 (2014)

    Article  CAS  Google Scholar 

  14. Lee, Y. J. & Gorski, J. Estrogen-induced transcription of the progesterone receptor gene does not parallel estrogen receptor occupancy. Proc. Natl Acad. Sci. USA 93, 15180–15184 (1996)

    Article  CAS  ADS  Google Scholar 

  15. Dowsett, M. et al. Retrospective analysis of time to recurrence in the ATAC trial according to hormone receptor status: an hypothesis-generating study. J. Clin. Oncol. 23, 7512–7517 (2005)

    Article  CAS  Google Scholar 

  16. The Breast International Group (BIG) 1-98 Collaborative Group. A comparison of letrozole and tamoxifen in postmenopausal women with early breast cancer. N. Engl. J. Med. 353, 2747–2757 (2005)

  17. Viale, G. et al. Prognostic and predictive value of centrally reviewed expression of estrogen and progesterone receptors in a randomized trial comparing letrozole and tamoxifen adjuvant therapy for postmenopausal early breast cancer: BIG 1–98. J. Clin. Oncol. 25, 3846–3852 (2007)

    Article  Google Scholar 

  18. Cui, X., Schiff, R., Arpino, G., Osborne, C. K. & Lee, A. V. Biology of progesterone receptor loss in breast cancer and its implications for endocrine therapy. J. Clin. Oncol. 23, 7721–7735 (2005)

    Article  CAS  Google Scholar 

  19. Hurtado, A., Holmes, K. A., Ross-Innes, C. S., Schmidt, D. & Carroll, J. S. FOXA1 is a key determinant of estrogen receptor function and endocrine response. Nature Genet. 43, 27–33 (2011)

    Article  CAS  Google Scholar 

  20. Read, L. D., Snider, C. E., Miller, J. S., Greene, G. L. & Katzenellenbogen, B. S. Ligand-modulated regulation of progesterone receptor messenger ribonucleic acid and protein in human breast cancer cell lines. Mol. Endocrinol. 2, 263–271 (1988)

    Article  CAS  Google Scholar 

  21. Mohammed, H. et al. Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor. Cell Rep. 3, 342–349 (2013)

    Article  CAS  Google Scholar 

  22. Ballaré, C. et al. Two domains of the progesterone receptor interact with the estrogen receptor and are required for progesterone activation of the c-Src/Erk pathway in mammalian cells. Mol. Cell. Biol. 23, 1994–2008 (2003)

    Article  Google Scholar 

  23. Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009)

    Article  CAS  ADS  Google Scholar 

  24. Stark, R. & Brown, G. D. DiffBind: differential binding analysis of ChIP-Seq peak data. Bioconductor (2011)

  25. Clarke, C. L. & Graham, J. D. Non-overlapping progesterone receptor cistromes contribute to cell-specific transcriptional outcomes. PLoS ONE 7, e35859 (2012)

    Article  CAS  ADS  Google Scholar 

  26. Liang, Y., Besch-Williford, C., Brekken, R. A. & Hyder, S. M. Progestin-dependent progression of human breast tumor xenografts: a novel model for evaluating antitumor therapeutics. Cancer Res. 67, 9929–9936 (2007)

    Article  CAS  Google Scholar 

  27. Centenera, M. M. et al. Evidence for efficacy of new Hsp90 inhibitors revealed by ex vivo culture of human prostate tumors. Clin. Cancer Res. 18, 3562–3570 (2012)

    Article  CAS  Google Scholar 

  28. Dean, J. L. et al. Therapeutic response to CDK4/6 inhibition in breast cancer defined by ex vivo analyses of human tumors. Cell Cycle 11, 2756–2761 (2012)

    Article  CAS  Google Scholar 

  29. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012)

    Article  CAS  Google Scholar 

  30. Tomlinson, I. P., Nicolai, H., Solomon, E. & Bodmer, W. F. The frequency and mechanism of loss of heterozygosity on chromosome 11q in breast cancer. J. Pathol. 180, 38–43 (1996)

    Article  CAS  Google Scholar 

  31. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401–404 (2012)

    Article  Google Scholar 

  32. Schmidt, D. et al. ChIP-seq: Using high-throughput sequencing to discover protein-DNA interactions. Methods 48, 240–248 (2009)

    Article  CAS  Google Scholar 

  33. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009)

    Article  CAS  Google Scholar 

  34. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  CAS  Google Scholar 

  35. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

    Article  CAS  ADS  Google Scholar 

  36. Venet, D., Dumont, J. E. & Detours, V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLOS Comput. Biol. 7, e1002240 (2011)

    Article  CAS  ADS  Google Scholar 

  37. Stark, R. & Norden, J. Checking gene expression signatures against random and known signatures with SigCheck. Bioconductor

  38. Tilley, W. D. et al. Detection of discrete androgen receptor epitopes in prostate cancer by immunostaining: measurement by color video image analysis. Cancer Res. 54, 4096–4102 (1994)

    CAS  PubMed  Google Scholar 

  39. Bengtsson, H., Wirapati, P. & Speed, T. P. A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6. Bioinformatics 25, 2149–2156 (2009)

    Article  CAS  Google Scholar 

  40. Venkatraman, E. S. & Olshen, A. B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007)

    Article  CAS  Google Scholar 

  41. Dunning, M. J., Smith, M. L., Ritchie, M. E. & Tavare, S. beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 23, 2183–2184 (2007)

    Article  CAS  Google Scholar 

  42. Cairns, J. M., Dunning, M. J., Ritchie, M. E., Russell, R. & Lynch, A. G. BASH: a tool for managing BeadArray spatial artefacts. Bioinformatics 24, 2921–2922 (2008)

    Article  CAS  Google Scholar 

  43. Barbosa-Morais, N. L. et al. A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res. 38, e17 (2010)

    Article  Google Scholar 

  44. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009)

    Article  Google Scholar 

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The authors would like to thank S. Leigh-Brown, the staff in the genomic core facility, S. Halim, the proteomic core facility and the bioinformatic core facility at Cancer Research UK. We acknowledge S. Jindal for pathology review, N. Ryan for technical assistance and S. Edwards for statistical analysis with ex vivo culture. The MCF7-LucYFP cells were a kind gift from N. Benaich. We thank H. Gronemeyer for the PR-A and PR-B expressing vectors. We would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. Research reported in this manuscript was supported by the National Cancer Institute of the National Institutes of Health under award number 5P30CA142543 (to University of Texas Southwestern) and Department of Defense grants W81XWH-12-1-0288-03 (GVR). W.D.T. is supported by grants from the National Health and Medical Research Council of Australia (ID 1008349; ID 1084416) and Cancer Australia (ID 627229) T.E.H. held a Fellowship Award from the US Department of Defense Breast Cancer Research Program (BCRP; W81XWH-11-1-0592) and currently is supported by a Florey Fellowship from the Royal Adelaide Hospital Research Foundation. J.S.C. is supported by an ERC starting grant and an EMBO Young investigator award.

Author information

Authors and Affiliations



Experimental work was conducted by H.M., I.A.R., T.E.H., G.A.T., A.A.A.S., A.B., A.S., C.D., J.L.L.R., R.L. and G.S. Computational analysis was conducted by R.S., O.M.R., S.M. and G.D.B. Clinical samples, information and support was provided by S.N.B., G.V.R., C.M.P. and C.C. In vivo work was conducted by J.S. Genomic work was conducted by J.H. and M.P. All experiments were overseen by W.D.T. and J.S.C. The manuscript was written by H.M., I.A.R., T.E.H., W.D.T. and J.S.C. with help from the other authors.

Corresponding authors

Correspondence to Wayne D. Tilley or Jason S. Carroll.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Protein purification of ERα and PR interacting proteins, using RIME, following treatment with a synthetic progestin.

T-47D and MCF-7 breast cancer cells were grown in SILAC-isotope containing media and treated with either vehicle control or R5020, a synthetic progestin for 3 h. PR (a) or ERα (b) RIME was conducted and the proteins that were quantitatively enriched in both cell lines are shown. Only proteins that were enriched with a FDR < 1% were included. c, Peptide coverage of the PR protein following ERα RIME in T-47D cells. The identified peptides are highlighted and one of the peptides covers the ‘Bus’ region representing the PR-B isoform. d, Comparison of binding at different time points and treatment of MCF-7 breast cancer cells with progesterone. ERα ChIP-seq at 3 h and 24 h results in comparable binding. Correlation between progesterone (PG) and R5020 (RO) at 3 and 24 h. e, MCF-7 cells were grown in oestrogen rich complete media and treated with progesterone or vehicle control for 3 h. ERα ChIP was conducted and peaks that occurred in at least two of three independent replicates were considered. Venn diagram showing the changes in ERα binding following progesterone treatment of MCF-7 cells. f, Uncropped western blots from Fig. 1c.

Extended Data Figure 2 Clustering of ERα, PR, and p300 ChIP-seq experiments in two ERα+ cell lines.

For each experiment, all binding sites identified as overlapping in at least two samples are merged and retained, and normalized read counts computed at each site for each sample. a, Clustering correlation heat maps, based on Pearson correlations read scores (with replicate numbers in the labels), show good reproducibility between replicates and similarity of natural and synthetic hormone treatments. b, PCA plots showing the two most significant principal components (with samples labelled with treatment type: ‘C’ for full-media control conditions, ‘P’ for progesterone, and ‘R’ for R5020). The data from the two cell lines is shown.

Extended Data Figure 3 ERα binding in single hormone conditions.

a, b, T47-D (a) or MCF-7 (b) cells were hormone-deprived and treated with vehicle control, oestrogen alone or progesterone alone. ERα ChIP-seq was conducted and we assessed the binding at the regions previously shown to be reprogrammed by oestrogen plus progesterone. The ERα reprogramming data under both oestrogen and progesterone conditions in the T-47D cells is from Fig. 2b. In the absence of oestrogen, progesterone does not induce ERα binding. In the absence of progesterone, oestrogen does not induce ERα binding to the locations shown to acquire reprogrammed ER binding events under dual hormone conditions.

Extended Data Figure 4 Validation of binding and gene expression changes.

a, Validation of dependence on PR for ERα binding and overlap between ERα binding and FOXA1 binding. T-47D cells were grown in full, oestrogen-rich media and transfected with siControl or siRNA to PR. ERα ChIP was conducted followed by qPCR of several novel ERα binding events only observed under progesterone treatment conditions. In the absence of PR, ERα is not able to associate with the progesterone-induced binding sites. The figure represents one biological replicate of three completed replicates and the error bars represent standard deviation of the technical ChIP-PCR replicates. b, Venn diagram showing the ERα binding events that were conserved in T-47D cells (that is, not altered by progesterone when compared to oestrogen alone) and the ERα binding events that were reprogrammed by progesterone treatment, when overlapped with FOXA1 ChIP-seq data from T-47D cells. The FOXA1 ChIP-seq data from T-47D cells was from ref. 19. c, Differential gene changes in MCF-7 and T-47D cells following treatment with progesterone or R5020 for 3 h. Heat map showing gene changes relative to matched controls. Eight replicates were included. d, Table showing the differentially regulated genes in the two cell lines and in the two treatment conditions. e, Overlap between genes regulated by progesterone (in both cell lines) and gene regulated by the synthetic progestin R5020 (in both cell lines).

Extended Data Figure 5 Analysis of gene expression changes and generation of gene signature.

a, RNA-seq was conducted after progesterone or R5020 treatment for 3 h. GSEA analysis was conducted on progesterone/R5020 repressed genes with lost ERα binding events observed in T47-D cells. The progesterone-decreased ERα binding regions correlate with progesterone downregulated genes. b, Kaplan–Meier survival curve in 1,959 breast cancer patients based on a gene signature derived from the progesterone regulated genes and progesterone regulated ERα binding events. c, For a gene to be considered it was differentially regulated by progesterone/progestin (as measured by RNA-seq) and the gene had a differentially regulated ERα binding event within 10 kb of the transcription start site. This resulted in 38 genes. d, Performance of progesterone induced gene signature at separating based on survival over 392 patients in top or bottom 10% of expression compared to null distribution of P values computed using 1,000 randomly selected 38-gene signatures. e, Copy number alterations on chromosome 11 in T-47D and MCF-7 cells. Green is copy number neutral, blue is copy number loss and red is copy number gain. T-47D cells have an amplification of the chromosome 11 region encompassing the PGR gene and MCF-7 cells have a copy number loss of this genomic region.

Extended Data Figure 6 PR inhibits cell line growth and progesterone inhibits T-47D xenograft growth.

a, MCF-7 cells were transfected with control vector, PR-A or PR-B expressing vectors. Western blotting confirmed the expression of the appropriate PR isoform. b, Growth was assessed following oestrogen plus progesterone treatment. The graph represents the average of three independent biological replicates and the error bars represent standard deviation. c, Assessment of MCF-7 xenograft tumour growth by physical measurement of tumour volume. Ten tumours for each condition (two in each of five mice per condition) were included. The data were analysed using a t-test and the error bars represent ±s.e.m. d, T-47D xenografts were established in NSG mice. Ten tumours for each condition (two in each of five mice per condition) were included. All were grown in the presence of oestrogen (E2) pellets and subsequently supplemented with vehicle, progesterone, tamoxifen or tamoxifen plus progesterone. Normalized tumour growth is shown. The data were analysed using a t-test and the error bars represent ±s.e.m. e, Final T-47D xenograft tumour volumes are shown. f, Final T-47D xenograft tumour volumes plotted graphically.

Extended Data Figure 7 Histological analysis of xenograft tumours and ChIP-seq from xenograft tumours in ovariectomized mice.

a, Histological analysis of MCF-7 xenograft tumours in untreated, oestrogen or oestrogen plus progesterone conditions. Tumours were taken from 25 day treated conditions. The human xenograft cells expressed GFP, permitting discrimination between human tumour cells and mouse host cells. MCF-7 xenograft experiment in ovariectomized mice. b, In order to map ERα binding events by ChIP-seq in MCF-7 xenograft tumours, we repeated the experiment in ovariectomized mice to eliminate any issues related to the endogenous mouse progesterone. Ten tumours for each condition (two in each of five mice per condition) were included. Growth of xenograft tumours under different hormonal conditions, Control, oestrogen alone (E2) and oestrogen plus progesterone (E2 + Prog). The data were analysed using a t-test and the error bars represent ±s.e.m. c, ChIP-seq for ERα and PR were conducted in six matched tumours from each hormonal condition. Also included were two tumours from no hormone conditions. Correlation heat map of all samples.

Extended Data Figure 8 Primary tumours cultivated as ex vivo explants shown response to progesterone.

Representative images of primary breast cancer explant tissue sections treated with vehicle, oestrogen (E2), the progestin R5020 or oestrogen plus progestin (E2 + R5020). a, b, These sections were probed with anti-Ki67 (brown) to label proliferating cells (a) or haematoxylin and eosin (b). Each image is of a single tissue segment from a selection of 3–4 sections per sample treatment. Scale bars, 100 μm. c, Confocal microscopy images (representative fields from each of the triplicate fragments) of a representative primary breast cancer explant tissue treated with vehicle, oestrogen (E2), the progestin R5020 (Progestin) or oestrogen plus progestin (E2 + R5020) and probed with anti-ERα (green), anti-PR (red) and anti-Ki67 to assess proliferating cells (blue).

Extended Data Figure 9 Analysis of PGR copy number loss in the METABRIC cohort.

a, Kaplan–Meier analysis of breast cancer specific survival within the METABRIC cohort. Only within luminal A tumours (based on PAM50 gene expression signature), tumours were stratified based on copy number loss of PGR or not. In total 19% of luminal A tumours contain a copy number loss of the PGR genomic locus and these patients have a poorer clinical outcome. b, All ERα+ cases were stratified based on PGR copy number status, showing tumours with heterozygous and homozygous deletions separately. c, Chromosome 11 in tumours with neutral or gained PGR versus those with copy number loss of the PGR gene (defined by line). d, Chromosome 11 copy number status between ERα positive and negative tumours. e, Visual representation of all ERα+ tumours with a copy number alteration at the PGR genomic locus, showing the copy number changes relative to the PGR gene (highlighted below) and the surrounding 2.2 Mb of genomic sequence.

Extended Data Figure 10 Validation of genomic copy number loss in the PGR gene in an independent data set.

a, TCGA ERα+ breast cancers were assessed for copy number changes in PGR. The number of tumours in each category, based on copy number changes. Only ERα+ breast cancers were included. b, Correlation between PR mRNA levels and copy number status in all luminal breast cancers within the TCGA cohort. The heterozygous and homozygous deletions are combined. c, Frequency of copy number alterations across entire genome in TCGA breast cancer cohort, stratified based on subtype using PAM50 signature. Chromosome 11, which encompasses PGR gene is highlighted and the frequency of copy number loss of the PGR genomic region is provided. d, Copy number changes on chromosome 11 within the METABRIC cohort, based on subtype stratification (PAM50 signature).

Supplementary information

Supplementary Table 1

This Supplementary table contains all RIME proteomic SILAC data from Figures 1a, Figure 1b and Extended data figure 1. (XLSX 2776 kb)

Supplementary Table 2

Peak numbers following ERα, PR and p300 ChIP-seq in T-47D and MCF-7 cell lines. The number of peaks for the different conditions are shown, these include estrogen, estrogen plus progesterone and estrogen plus R5020 treatment. Also included are the common peaks observed under estrogen plus progesterone and estrogen plus R5020 conditions. (XLSX 39 kb)

Supplementary Table 3

Enriched pathways based on the ERα binding events induced by progesterone and R5020. The enriched pathways that occur in both T-47D and MCF-7 cells are shown. The values represent the Odds ratio. (XLSX 47 kb)

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Mohammed, H., Russell, I., Stark, R. et al. Progesterone receptor modulates ERα action in breast cancer. Nature 523, 313–317 (2015).

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