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EZH2 inhibition activates a dsRNA–STING–interferon stress axis that potentiates response to PD-1 checkpoint blockade in prostate cancer

Nature Cancer volume 2, pages 444–456 (2021)Cite this article

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Abstract

Prostate cancers are considered to be immunologically ‘cold’ tumors given the very few patients who respond to checkpoint inhibitor (CPI) therapy. Recently, enrichment of interferon-stimulated genes (ISGs) predicted a favorable response to CPI across various disease sites. The enhancer of zeste homolog-2 (EZH2) is overexpressed in prostate cancer and known to negatively regulate ISGs. In the present study, we demonstrate that EZH2 inhibition in prostate cancer models activates a double-stranded RNA–STING–ISG stress response upregulating genes involved in antigen presentation, Th1 chemokine signaling and interferon response, including programmed cell death protein 1 (PD-L1) that is dependent on STING activation. EZH2 inhibition substantially increased intratumoral trafficking of activated CD8+ T cells and increased M1 tumor-associated macrophages, overall reversing resistance to PD-1 CPI. Our study identifies EZH2 as a potent inhibitor of antitumor immunity and responsiveness to CPI. These data suggest EZH2 inhibition as a therapeutic direction to enhance prostate cancer response to PD-1 CPI.

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Fig. 1: EZH2 negatively regulates type I/II ISGs in PCa.
Fig. 2: EZH2 inhibition derepresses endogenous dsRNA.
Fig. 3: ISGs are poised for activation by EZH2 inhibition.
Fig. 4: Activation of ISGs is STING dependent.
Fig. 5: EZH2 inhibition sensitizes murine prostate tumors to PD-1 CPI and is dependent on tumor PD-L1 activation.
Fig. 6: EZH2 inhibition increases T-cell infiltration and induces PCa cell Th1 chemokine expression.
Fig. 7: EZH2 inhibitor combination with PD-1 CPI alters the immunosuppressive tumor microenvironment.

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Data availability

ChIP–seq, ATAC–seq and RNA-seq data used to support the present study have been deposited in the Gene Expression Omnibus under accession nos. GSE130408, GSE107780, GSE146617 and GSE146076. Further information and requests for resources and reagents should be directed to the lead author, L.E. (Leigh.Ellis@cshs.org). Gene expression data for LNCaP cell lines treated with EZH2 inhibitor were obtained under accession no. GSE107780. Raw and normalized expression data for 550 TCGA PCa samples were obtained from the NCI Genomic Data Commons Data Portal. Some 102 samples were excluded based on pathological criteria provided by S. Tyekucheva and M. Loda, and the remaining 448 samples (40 normal samples and 408 tumor samples) were included in subsequent analyses. The NCI data were provided by A. Sowalsky. Human PCas have been described previously66 and were obtained from DbGaP (study accession no. phs000909). Normalized counts from the Stand Up 2 Cancer dataset were obtained from cBioPortal67. Any code used in this manuscript will be made available upon request. Source data are provided with this paper.

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Acknowledgements

The present study was supported by Dana-Farber Cancer Institute Faculty Start-Up Funds (to L.E.), a Prostate Cancer Foundation Young Investigator Award (to L.E., D.P.L., S.W. and A.G.S.) and the Intramural Research Program of the National Institutes of Health, National Cancer Institute (NCI; to A.G.S.). B.M.O was supported by Emory University Faculty Start-Up funds. D.P.L. is a Lewis Katz recipient of a Scholarship for the Next Generation of Scientists from the Cancer Research Society, and is also a Research Scholar, Junior 1 of the Fonds de la recherche du Québec-Santé. This research project was supported in part by the Emory University School of Medicine Flow Cytometry Core (to B.M.O.). We thank Epizyme Pharmaceuticals for supplying EPZ0011989. The results shown here are in whole or part based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Author information

Author notes

  1. These authors contributed equally: Katherine L. Morel, Anjali V. Sheahan.

Authors and Affiliations

  1. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

    Katherine L. Morel, Anjali V. Sheahan, Deborah L. Burkhart & Leigh Ellis

  2. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Sylvan C. Baca, Xintao Qiu, Constantia Pantelidou, Anis A. Hamid, David A. Barbie, Atish D. Choudhury, Mark M. Pomerantz, Christopher J. Sweeney, Henry W. Long, Geoffrey I. Shapiro & Matthew L. Freedman

  3. Cancer Research Program, Research Institute of the McGill University Health Centre, Montréal, Canada

    Nadia Boufaied & David P. Labbé

  4. Department of Medical Biophysics, University of Toronto and Princess Margaret Cancer Center University Health Network, Toronto, Canada

    Yin Liu & Housheng Hansen He

  5. Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, PA, USA

    Israel Cañadas

  6. Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA

    Kevin Roehle, Max Heckler & Stephanie K. Dougan

  7. Department of Medicine, Hematology-Oncology Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Carla Calagua

  8. Department of Pathology, University of California at Los Angeles, Los Angeles, CA, USA

    Huihui Ye

  9. Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Phillip Galbo

  10. Department of Health Informatics, Rutgers School of Health Professionals, Newark, NJ, USA

    Sukanya Panja & Antonina Mitrofanova

  11. Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Antonina Mitrofanova

  12. Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Scott Wilkinson, Nichelle C. Whitlock, Shana Y. Trostel & Adam G. Sowalsky

  13. Department of Urology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Adam S. Kibel

  14. Hematology and Oncology Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

    David J. Einstein & Steven P. Balk

  15. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA

    Massimo Loda

  16. Division of Urology, Department of Surgery, McGill University, Montréal, Canada

    David P. Labbé

  17. Departments of Hematology and Medical Oncology and Urology, Emory University School of Medicine, Atlanta, GA, USA

    Brian M. Olson

  18. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Leigh Ellis

  19. Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Leigh Ellis

  20. Division of Medical Oncology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Leigh Ellis

  21. Cedars-Sinai Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, USA

    Leigh Ellis

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  1. Katherine L. Morel
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Contributions

K.L.M., A.V.S., D.L.B. and B.M.O. performed research, analyzed data and wrote the manuscript. S.C.B., D.P.L., C.C., H.Y., P.G., S.P., A.M., A.A.H., C.J.S. and M.L. performed research. K.R., M.H., S.K.D., A.D.C., N.B., Y.L., H.H.H., A.G.S., M.M.P., M.L.F., S.W., N.C.W., S.Y.T., A.G.S., C.P., G.I.S., X.Q. and H.W.L. provided vital reagents. A.D.C., D.P.L., H.Y., M.L.F., S.C.B., C.J.S., D.J.E., S.K.D., A.G.S., S.P.B. and B.M.O. assisted with editing the final manuscript. L.E. designed experiments, analyzed data and wrote the manuscript.

Corresponding author

Correspondence to Leigh Ellis.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Mark A. Rubin, Weiping Zou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Generation of an EZH2 Activity Gene Signature.

(a) Three-dimensional PCa organoids generated from EM mice (without PSACreERT2) alleles. When treated with tamoxifen, no loss of H3K27me3 or EDU staining is indicated - demonstrating specificity of tamoxifen-PSACreERT2 mediated deletion of the Ezh2 set domain in Fig. 1. P-values were generated using a two-tailed unpaired T-test with Welch’s correction. Data was generated from three (n = 3) independent experiments and displayed as mean ±SEM (b) Principal component analysis (PCA) following chemical (n = 3 independent organoid cultures per treatment group) and genetic (n = 2 independent organoid cultures per treatment group) inhibition of Ezh2 catalytic function results in significant changes in gene expression. (c) A 29-gene signature derived from Fig. 1c (DZNep data) was used to generate signature scores for each patient within four independent human prostate cancer RNA-seq datasets. Patients were ranked highest score to lowest score and subject to quartile separation. First (blue) and fourth (red) quartiles were analyzed by supervised clustering to demonstrate expression differences within patients with lowest EZH2 activity and highest EZH2 activity. Sample numbers used for TCGA (n = 408 samples), SU2C (n = 118 samples), Beltran Adenocarcinomas (n = 33 samples), and NCI Primary Adeno (n = 41 samples). (d) Our 29-gene signature derived from demonstrates complete independence from a previously published polycomb repression signature. (e) Our 29 gene signature demonstrates significant correlation with a previously published polycomb repression signature in 2 independent human PCa gene expression datasets. (f) EZH2 activity is not determined by EZH2 mRNA expression. Pearson correlation coefficient analysis was utilized to generate data for E-F.

Source data

Extended Data Fig. 2 Low EZH2 Activity is Associated with Enrichment in IFN Signaling and dsRNA Sensory Machinery.

(a) Genes representing IFN signaling (STAT1, IRF9), Th1 chemokines (CXCL10, CXCL11), and MHC Class I molecules (B2M, HLA-A) were shown to be enriched in PCa patients with low EZH2 activity. (b) Genes representing intracellular sensors of dsRNA (TLR3, MAVs, RIG-I, MDA5) were shown to be enriched in PCa patients with low EZH2 activity. (c) Genes from Canadas et al. (2018) described as ‘SPARCs’ regulated by STAT1 and EZH2 that house endogenous retroviral sequences important for inducing an innate immune response, were shown to be enriched in PCa patients with low EZH2 activity. Sample numbers used for TCGA (n = 408 samples), SU2C (n = 118 samples), Beltran Adenocarcinomas (n = 33 samples), and NCI Primary Adeno (n = 41 samples). All data was generated by using Pearson correlation coefficient analysis.

Extended Data Fig. 3 EZH2 Inhibition Regulates Innate Immune Signaling in Prostate Cancer.

(a) Overlay of five independent differentially expressed IFNα and IFNɣ gene lists from mouse and human RNA-seq data provided a merged gene list of 97 ISGs. (b) String analysis of the generated 97 type I/II IFN gene list reveals significant enrichment of biological processes including innate immune response, defense response, and type I interferon signaling pathway. Moreover, molecular function terms including double-stranded RNA binding, peptide antigen binding were also significantly enriched. (c) Mouse RNA-seq data was queried to demonstrate that our 97 IFN gene signature is upregulated in response to loss of EZH2 catalytic activity. Data was generated by performing two (n = 2) independent (genetic inhibition) or three (n = 3) independent experiments and displayed as the mean or mean ±SEM respectively. Statistical data was generated by performing a two-tailed unpaired T-test with Welch’s correction. (d) LNCaP RNA-seq data was queried to demonstrate that upon EZH2 genetic (left) or chemical (right) inhibition results in enrichment of IFNα/γ gene sets. Data was generated from previously published RNA-seq data – GSE107780. Triplicate, n = 3/treatment group RNA-seq experiments where available for analysis. (e) Heatmaps of normalized ATAC signal intensities of 97 ISGs from were analyzed in 5 independent patient prostatectomy samples. Each patient sample was analyzed once.

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Extended Data Fig. 4 Activation of interferon stimulated genes is STING dependent cont.

Full statistical comparisons for flow cytometry analysis of PD-L1, MHC-I and dsRNA expression in B6MYC-CaP and Pten-/- cells with and without STING inhibition (chemical by C-176 or genetic by sgSTING) and treated with DMSO, or EZH2 inhibitors DZNep or EPZ. These statistical data are partnered with Fig. 4c,d and was generated by performing a two-tailed unpaired T-test with Welch’s correction. Data was generated using flow cytometry analysis from three (n = 3) independent experiments and displayed as mean ±SEM. P-values can be seen in Extended Data Fig. 4 and were generated using a two-tailed unpaired T-test with Welch’s correction. Note: Data from Pten KO cells in Fig. 4c treated with C176 to inhibit STING was generated from two (n = 2) independent experiments and displayed as the mean value.

Extended Data Fig. 5 Low EZH2 activity is associated with increased immune gene expression related to positive response to check-point inhibition.

(a) Analysis of human RNA-seq datasets reveal immune signatures related to check-point blockade positive response are significantly enriched in PCa patients with low EZH2 activity. Sample numbers used for TCGA (n = 408 samples), SU2C (n = 118 samples), and Beltran Adenocarcinomas (n = 33 samples). (b) Normalized weights of mice (n = 10 mice/treatment group) indicate that no significant weight loss (ie: toxicity) was observed following therapy with indicated treatment cohorts. (c-d) Tumor measurements of individual tumors by waterfall or spider plots validate significant anti-tumor activity of EZH2 inhibition combined with PD-1 blockade. N = 10 mice/tumors per treatment group. (e) Mouse and human prostate cancer organoids (Pten-/- and human mCRPC organoids), and human LNCaP 2D cell lines treated with indicated EZH2 inhibitors for 96 hours demonstrate upregulation of PD-L1 mRNA. Data was generated for Pten-/- organoids with n = 5 independent experiments for parental and DMSO treatment groups and n = 3 independent experiments for DZ and EPZ treatment groups and displayed as the mean ±SEM. For LNCaP and human CRPC organoids, a n = 3 independent experiment was used to generate data and displayed as the mean ±SEM. Statistical data was generated by performing a two-tailed unpaired T-test with Welch’s correction of DMSO verse DZ or EPZ treatment groups. (f) Human PCa gene expression data was queried to demonstrate that increased PD-L1 gene up-regulation is significantly correlated with low EZH2 activity. Sample numbers used for TCGA (n = 408 samples), NCI (n = 41 samples), SU2C (n = 118 samples), and Beltran Adenocarcinomas (n = 33 samples).

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Extended Data Fig. 6 Low EZH2 activity is associated with positive association of inflammatory immune genes.

(a) B6MYC-CaP and Pten-/- 2D cell lines that express Cas9 were stably infected with gRNA towards Pd-l1 (Cd274). Treatment with IFNɣ validates the inhibition of Pd-l1 expression in KO cell lines. Data was generated by n = 2 independent qRT-PCR experiments. (b) Murine Pten KO prostate cancer cells treated with EZH2 inhibitors increase expression of Th-1 cytokines. Data for A-B was generated by performing two (n = 2) independent experiments and displayed as the mean. (c) Human prostate cancer patient correlation analysis between EZH2 repression score (X-axis) and Th1, Th2, or Th17 gene expression profiles (Y-axis). Sample numbers used for TCGA (n = 408 samples), NCI (n = 41 samples), SU2C (n = 118 samples), and Beltran Adenocarcinomas (n = 33 samples).

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Extended Data Fig. 7 Effects on the tumor microenvironment post EZH2 inhibition.

(a) Representative in vivo tumor analysis indicates that EZH2 inhibition and combination significantly reduce tumor H3K27me3 expression. (b) Frequency of Foxp3 + T-reg cells was determined by flow cytometry. No significant change was observed following treatment. (c) PD-1 protein expression on CD4 + and CD8 + T-cells was analyzed by flow cytometry. Only CD8 + T-cells were observed to express lower PD-1 protein following EZH2 inhibition. Box plots are displayed as min to max distribution. (d) Frequency of Mo-MDSC and Gr-MDSC cells was determined by flow cytometry. No significant change was observed following treatment. Data was generated by analysis of (A) ten (n = 10) independent mice or (B-D) four (n = 4) independent mice per treatment group. Statistical data was generated by performing a two-tailed unpaired T-test with Welch’s correction.

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Flow cytometry gating.

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Flow cytometry gating.

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Morel, K.L., Sheahan, A.V., Burkhart, D.L. et al. EZH2 inhibition activates a dsRNA–STING–interferon stress axis that potentiates response to PD-1 checkpoint blockade in prostate cancer. Nat Cancer 2, 444–456 (2021). https://doi.org/10.1038/s43018-021-00185-w

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