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Enhancer reprogramming driven by high-order assemblies of transcription factors promotes phenotypic plasticity and breast cancer endocrine resistance

Nature Cell Biology volume 22, pages 701–715 (2020)Cite this article

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Abstract

Acquired therapy resistance is a major problem for anticancer treatment, yet the underlying molecular mechanisms remain unclear. Using an established breast cancer cellular model, we show that endocrine resistance is associated with enhanced phenotypic plasticity, indicated by a general downregulation of luminal/epithelial differentiation markers and upregulation of basal/mesenchymal invasive markers. Consistently, similar gene expression changes are found in clinical breast tumours and patient-derived xenograft samples that are resistant to endocrine therapies. Mechanistically, the differential interactions between oestrogen receptor α and other oncogenic transcription factors, exemplified by GATA3 and AP1, drive global enhancer gain/loss reprogramming, profoundly altering breast cancer transcriptional programs. Our functional studies in multiple culture and xenograft models reveal a coordinated role of GATA3 and AP1 in re-organizing enhancer landscapes and regulating cancer phenotypes. Collectively, our study suggests that differential high-order assemblies of transcription factors on enhancers trigger genome-wide enhancer reprogramming, resulting in transcriptional transitions that promote tumour phenotypic plasticity and therapy resistance.

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Fig. 1: Genomic analyses identify phenotypic plasticity-related transcriptional changes in breast cancer cells with endocrine resistance.
Fig. 2: Analyses using patient tumour tissues and PDX samples reveal phenotypic plasticity-enhancing transcriptional changes associated with therapy resistance.
Fig. 3: Endocrine resistance accompanies global enhancer reprogramming, which drives plasticity-related gene transcription.
Fig. 4: High-order enhancer component assemblies mediated by differential TF–TF and TF–enhancer interactions correspond with endocrine resistance-associated enhancer reprogramming.
Fig. 5: GATA3 is required for maintenance of LOSS enhancers and expression of epithelial makers.
Fig. 6: AP1-mediated GAIN enhancer activation promotes the endocrine resistance-associated gene program and phenotypes.
Fig. 7: GATA3 and AP1 function coordinately to promote TamR-associated enhancer reprogramming and gene expression.
Fig. 8: GATA3 and AP1 cooperate to regulate endocrine resistance and tumour growth in vitro and in vivo.

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Data availability

All deep sequencing raw data supporting the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE128460. All BioID proteomic data from MCF7P and TamR cell lines have been deposited in the Proteomexchange repository under the accession no. PXD014015. Previous published DNA methylation data for different cell lines including MCF7, TamR, MCF7X and FASR were retrieved from GEO website (GSE69118)36. Previous published microarray dataset for paired PDX models including HBCx22 and HBCx22 TamR were under GEO number GSE5556122. Source data for Figs. 1–8 and Extended Data Figs. 1, 2 and 4–7 are presented with the paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Z. Liu is a CPRIT Scholar in Cancer Research. This work was supported by grants to Z. Liu via a CPRIT award (RR160017), V Foundation’s V Scholar Award (V2016-017) and V Scholar Plus Award (DVP2019-018), Max and Minnie Tomerlin Voelcker Fund Young Investigator Award, a Susan G. Komen CCR award (CCR17483391), a National Cancer Institute grant (U54 CA217297/PRJ001) and a UT Rising STARs Award. Research reported in this publication was also supported by the NIGMS of the NIH under Award Number R01GM137009 to Z. Liu. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. All deep sequencing data were generated in the Genome Sequencing Facility at Greehey Children’s Cancer Research Institute in the UT Health San Antonio, which is supported by NIH-NCI P30 CA054174 (NIH Cancer Center at UT Health San Antonio), an NIH Shared Instrument grant 1S10OD021805-01 (S10 grant) and a CPRIT Core Facility Award (RP160732). BASiC, where pyrosequencing assay experiments were carried out, was supported by the CPRIT award (RP150600) and funding from the Office of the Vice President of Research, UTHSCSA. R.S. and X.F. were supported by NIH SPORE grants (P50 CA058183 and CA186784 to R.S.), Cancer Center grants (P30 CA125123 to R.S. and X.F.), the Breast Cancer Research Foundation (BCRF-17-143, 18-145 and 19-145 to R.S.), CPRIT grant no. RP190398 (R.S. and X.F.) and a DOD grant (W81XWH-14-1-0326 to X.F.).

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Author notes

  1. These authors contributed equally: Mingjun Bi, Zhao Zhang.

Authors and Affiliations

  1. Department of Molecular Medicine, Mays Cancer Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Mingjun Bi, Zhao Zhang, Pengya Xue, Hu Wang, Zhao Lai, Jianhua Ruan, Victor X. Jin, Tim Hui-Ming Huang, Lizhen Chen & Zhijie Liu

  2. Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Yi-Zhou Jiang, Yue Gong & Zhi-Ming Shao

  3. Department of Medicine, Department of Molecular and Cellular Biology, Lester & Sue Smith Breast Center, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA

    Xiaoyong Fu, Carmine De Angelis & Rachel Schiff

  4. Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA

    Zhen Gao & Jianhua Ruan

  5. Translational Research Department, Institut Curie, PSL University, Paris, France

    Elisabetta Marangoni & Elodie Montaudon

  6. Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California at San Diego, La Jolla, CA, USA

    Christopher K. Glass

  7. Department of Biological Chemistry, University of California, Irvine, Irvine, CA, USA

    Wei Li

  8. Barshop Institute for Longevity and Aging Studies, Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Lizhen Chen

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  1. Mingjun Bi
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Contributions

M.B. performed most of the experiments with assistance from P.X., H.W. and Z. Liu. Z.Z. performed the computational analyses for all next-generation sequencing assays. Z. Liu, L.C., M.B. and Z.Z. conceived the work and designed the study. Z. Liu supervised the research and oversaw the project. Z. Liu, L.C., M.B. and Z.Z. wrote the manuscript, with input from all authors. Z. Lai helped with all deep sequencing run services. Z.G., J.R., V.X.J., C.K.G., W.L. and T.H.-M.H. provided comments and participated in data analyses. E. Marangoni and E. Montaudon provided paired endocrine-sensitive versus endocrine-resistant human clinical PDX samples and microarray data. Y.-Z.J., Y.G. and Z.-M.S. collected biospecimens from breast cancer patients before and after chemoendocrine therapy and performed RNA-seq. X.F., C.D.A. and R.S. provided paired tamoxifen-sensitive and tamoxifen-resistant MCF7 and T47D cells for this study, provided comments and reviewed the manuscript.

Corresponding authors

Correspondence to Lizhen Chen or Zhijie Liu.

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Competing interests

R.S. has received research support from AstraZeneca, GlaxoSmithKline, Gilead and Puma Biotechnology, served as a consultant to Eli Lilly, and is a consultant/advisory board member for MacroGenics. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Genomic analyses identify phenotypic plasticity-related transcriptional changes in breast cancer cells with endocrine resistance.

a, Cell growth rate assays of MCF7P and TamR lines in the presence of 4-OHT showing the endocrine resistance of TamR line. P values were determined by two-sided t-tests. b, Brightfield images of MCF7P and TamR lines at ×100 magnification showing different morphology for these two lines. MCF7P displayed a typical epithelial cell-like morphology and grew in tightly packed cobblestone-like clusters. TamR began spreading as individual cells, a phenotype similar to mesenchymal cells. Scale bar, 100 μm. c, ERα protein levels in MCF7P and TamR cells detected by Western blots using a serial dilution of whole cell extract for semi quantitative purpose. GAPDH was used as a loading control. d, Structural diagram of ERα protein showing the positions of point mutations in the ligand-binding domain (LBD) that were reported in endocrine-resistant or metastatic ERα+ breast cancers before (left). No LBD point mutation was detected in this TamR cell line with Sanger sequencing (right). e, Genome browser snap images of the GRO-seq and RNA-seq signals at PGR and PRLR loci showing a significant downregulation of these two epithelial markers in TamR cells. f, Genome browser snap images of the GRO-seq and RNA-seq signals at gene body regions for S100P and FN1, showing a significant upregulation of these two cancer invasiveness-associated genes in TamR cells. g, h, RT-qPCR analyses of mRNA levels of selected epithelial markers (g) or invasive genes (h) in MCF7P and TamR cell lines. The epithelial markers are downregulated and invasiveness-associated genes are upregulated in TamR cells. For a, g and h, data are presented as mean ± s.d. from n = 3 independent experiments. b and c are representative of two independent experiments. Unprocessed immunoblots are shown in Source Data Extended Data Fig. 1. Statistical source data are available in Source Data Extended Data Fig. 1.

Source data

Extended Data Fig. 2 Analyses using patient tumor tissues and PDX samples revealed phenotypic plasticity-enhancing transcriptional changes associated with therapy resistance.

a, b, Genome browser snap images of the RNA-seq signals at gene body regions for KRT18 and GATA3 showing the significant downregulation of these two epithelial markers at post-treatment stage in the 8 randomly picked therapy-resistant patients. c, d, Genome browser snap images of the RNA-seq signals at gene body regions for EGFR and JUN showing the significant upregulation of these two invasive genes at post-treatment stage in the 8 randomly picked therapy-resistant patients. e, f, RT-qPCR analyses of mRNA levels of selected epithelial and invasive genes in paired parental (HBCx22) vs tamoxifen-resistant (HBCx22 TamR) PDX tumors (e), and in paired parental (HBCx124) vs estrogen deprivation derived resistant (HBCx124 ED) PDX tumors (f). The results show all of these epithelial markers are downregulated and all of these invasive genes are upregulated in endocrine-resistant PDX tumors. Data are presented as mean ± s.d. from n = 3 independent experiments. P values were determined by two-sided t-tests. Statistical source data are available in Source Data Extended Data Fig. 2.

Source data

Extended Data Fig. 3 Endocrine resistance accompanies global enhancer reprogramming that drives plasticity-related gene transcription.

a, Genomic annotations of the H3K27ac ChIP-seq signals in MCF7P and TamR cell lines. b, Volcano plots showing the changes of H3K27ac signals at promoter regions correlate well with the changes in gene expression detected by RNA-seq in TamR cells. n = 2 biologically independent experiments, and P values were determined by Wald test with Benjamini-Hochberg adjustment. c, Heatmap of H3K27ac, H3K4me1 and P300 ChIP-seq data for all identified lost, common and gained enhancers genome wide. Chromatin accessibility profiled by ATAC-seq at the corresponding genomic regions is also shown on the right. d, GSEA analyses on RNA-seq data showing the enrichment of oncogenic signatures from MSigDB database in MCF7P or TamR cells. The nominal P values were determined by empirical gene-based permutation test. e, Total super-enhancers (SEs) in MCF7P and TamR cell lines identified by the ROSE program ranked by H3K27ac signal intensities. f, Histograms of the log2(Fold Change) of genes nearest to the differential SEs showing that gained SEs correlate with gene upregulation and lost SEs correlate with gene downregulation. g, Genome browser snap images of lost SE at BCL2 locus and gained SE at CXCL8 locus. The SE gain/loss correlates well with gene upregulation and downregulation detected by GRO-seq.

Extended Data Fig. 4 High-order enhancer component assemblies mediated by differential TF-TF and TF-enhancer interactions correspond with endocrine resistance-associated enhancer reprogramming.

a, Schematic diagram of BioID (in vivo proximity-dependent biotin identification) approach for identification of ERα-interacting nuclear proteins including both TFs and other transcriptional cofactors in alive cells. This technology was used to explore the ERα-interacting (or in the close proximity) enhancer components in either endocrine-sensitive or -resistant cellular context. b, Western blot analyses of total JUN or phosphorylated JUN protein levels in MCF7P and TamR cells. Tubulin was used as a loading control. c–e, Genome browser snap images of ChIP-seq data showing the co-binding of GATA3, JUN, FOXA1 and ERα at the LOSS enhancer regions near BCL2 gene (c), the COMMON enhancer regions near TFF1 gene (d), and GAIN enhancer regions near CXCL8 gene (e) in both MCF7P and TamR cell lines. Immunoblots are representative of two independent experiments. Unprocessed immunoblots are shown in Source Data Extended Data Fig. 4.

Source data

Extended Data Fig. 5 GATA3 is required for maintenance of LOSS enhancers and expression of epithelial makers.

a, Our pyrosequencing analyses (bottom), and published DNA methylation data from three different endocrine-resistant MCF7-derived lines (TamR: tamoxifen-resistant, FASR: fulvestrant-resistant, MCF7X: estrogen deprivation-resistant) (top). DNA methylation level at GATA3 locus is significantly increased in endocrine-resistant lines. n = 3 independent experiments, two-sided t-tests. b, RT-qPCR showing transcript levels of GATA3 in MCF7P and TamR with or without 5-Aza treatment for 100 hours. n = 2 independent experiments. c, Heatmap generated by integrating TCGA data on GATA3 mRNA level, DNA methylation, and breast cancer subtype. High DNA methylation and low GATA3 expression are associated with invasive breast cancers (ER-/PR-/ HER2- and basal subtype). d, Aggregate plots of normalized GRO-seq tag density in MCF7P with shCtrl or shGATA3. e, Heatmap of ChIP-seq (bottom) showing that GATA3 OE in TamR can re-activate LOSS enhancers. Western blot confirms GATA3 overexpression (top). f, Heatmap depiction of the downregulation of epithelial genes after KD GATA3 in MCF7P. n = 2 independent experiments. g, Western blot of the indicated epithelial markers in MCF7P cells upon GATA3 KD. h, CCK8 assays with 4-OHT treatment for 5 days. Dox-induced GATA3 overexpression in TamR re-sensitizes them to 4-OHT. n = 3 independent experiments, mean ± s.d., two-sided t-tests. i, j, Tumor growth curves (i) and representative tumor images at end point (j) of orthotopic xenografts of manipulated TamR cells in nude mice (n = 4/group). After tumors reached ~200mm3, tumor sizes were measured once a week upon starting doxycycline water diet and subcutaneous injections of tamoxifen (1 mg/mouse, three times/week). Mean ± s.d., two-sided t-tests. k, Relapse free survival (RFS) curves generated from kmplot website according to BCL2 levels in patients receiving endocrine therapy. P values were determined by log-rank test. Immunoblots are representative of two independent experiments. Unprocessed immunoblots are shown in Source Data Extended Data Fig. 5. Statistical source data are available in Source Data Extended Data Fig. 5.

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Extended Data Fig. 6 AP1-mediated GAIN enhancer activation promotes endocrine resistance-associated gene program and phenotypes.

a, Heatmap depiction of the upregulation of indicated invasive genes after JUN overexpression in MCF7P cells. n = 2 biologically independent experiments. b, Western blot images of indicated invasive markers in MCF7P cells with or without JUN overexpression, showing that JUN overexpression is sufficient to activate the expression of these invasive markers. c, Aggregate plots of the normalized GRO-seq tag density at GAIN enhancers in TamR cells transduced with shCtrl or shJUN lentiviruses showing that knockdown of JUN greatly reduces eRNA transcription due to enhancer inactivation. The dashed and solid lines represent the minus and plus strands of eRNA respectively. d, Heatmap depiction of the downregulation of indicated invasive genes after JUN knockdown in TamR cells. n = 2 biologically independent experiments. e, Western blot analyses on indicated invasive markers in TamR cells transduced with a scramble control or two different lentiviral shRNAs for JUN, showing that JUN is required for the expression of these invasive markers. f, Knockdown of JUN in TamR cells re-sensitizes them to 4-OHT. TamR cells were stably knocked down with shJUN (a scramble shRNA was used as control) and CCK8 assays were used to check the relative cell viability of cells after treatment with indicated 4-OHT concentrations for 5 days. Data are presented as mean ± s.d. from n = 3 independent experiments. P values were determined by two-sided t-tests. g, Relapse free survival (RFS) curves according to FN1 and S100P gene expression levels in patients receiving endocrine therapy. The curves were generated using data from kmplot website. P values were determined by log-rank test. n numbers for different groups of patients were listed in the figure. Immunoblots are representative of two independent experiments. Unprocessed immunoblots are shown in Source Data Extended Data Fig. 6. Statistical source data are available in Source Data Extended Data Fig. 6.

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Extended Data Fig. 7 GATA3 and AP1 function coordinately to promote TamR-associated enhancer reprogramming and gene expression.

a, d, RT-qPCR analyses of selected epithelial markers and invasion-related genes in MCF7P (a) or T47D (d) cells with indicated manipulations, showing the coordinate gene regulation effects by GATA3 and JUN. Data are presented as mean ± s.d. P values were determined by two-sided t-tests. b, e, Western blot analyses of selected epithelial markers and invasion-related genes in MCF7P (b) and T47D (e) cells with indicated manipulations, showing the coordinate role of GATA3 and JUN in regulating gene expression. c, g, The aggregate plots of the normalized GRO-seq tag density at GAIN enhancers in MCF7P (c) and T47D (g) cells under indicated treatments. GATA3 KD and JUN OE demonstrate a synergistic effect on eRNA transcription. The dashed line represents the minus strand and solid line indicates the plus strand of eRNA. f, Box plots representation of gene expression in T47D cells. Simultaneously depleting GATA3 and overexpressing JUN (“both”) shows a more dramatic effect on the lost and gained gene expression in T47D cells compared to manipulating individual gene alone. P values were calculated by Wilcoxon signed rank test. The lower and upper hinges correspond to the first and third quartiles, and the midline represents the median. The upper and lower whiskers extend from the hinge up to 1.5 * IQR (inter-quartile range). Outlier points are indicated if they extend beyond this range. h, Heatmaps of H3K27ac ChIP-seq data at LOSS, COMMON and GAIN enhancers in ZR75-1 cells with the indicated treatments. For a and d, the data are from n = 3 independent experiments. Immunoblots are representative of two independent experiments. Unprocessed immunoblots are shown in Source Data Extended Data Fig. 7. Statistical source data are available in Source Data Extended Data Fig. 7.

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Extended Data Fig. 8 GATA3 and AP1 cooperate to regulate endocrine resistance and tumor growth in vitro and in vivo.

a, b, Representative brightfield pictures of MCF7P cells (a) and T47D cells (b) with indicated manipulations. The control cells display a typical epithelial cell-like morphology and grow in tightly packed clusters. Cells with both GATA3 knockdown and JUN overexpression have become more spread out (a phenotype of more invasive cancer cells) than the control and the cells with individual manipulation. Magnification, ×100. Scale bar, 100 μm. n = 2 independent experiments were performed with similar results. c, d, GSEA analyses of RNA-seq data for 34 different cancer types including breast cancer (BRCA) from TCGA database showing the correlation of GATA3 (c) and JUN (d) expression levels with the enrichment of cancer hallmark gene sets from MSigDb database. We found that high expression level of JUN was positively associated with the enrichment of EMT pathway in breast cancer, however high expression level of GATA3 was negatively correlated with EMT pathway in breast cancer. The circle size indicates significance level; and the color represents the normalized enrichment score (NES). The nominal P values were determined by empirical gene-based permutation test with Benjamini-Hochberg adjustment.

Supplementary information

Reporting Summary

Supplementary Tables 1–4.

Supplementary Table 1: List of oligonucleotides used. Supplementary Table 2: List of antibodies used. Supplementary Table 3: List of ERα BioID mass spectrometry data. Supplementary Table 4: Information on 21 human research participants.

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Bi, M., Zhang, Z., Jiang, YZ. et al. Enhancer reprogramming driven by high-order assemblies of transcription factors promotes phenotypic plasticity and breast cancer endocrine resistance. Nat Cell Biol 22, 701–715 (2020). https://doi.org/10.1038/s41556-020-0514-z

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