Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

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.

References

  1. 1.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019).

    Article  Google Scholar 

  2. 2.

    Gan, L. et al. Epigenetic regulation of cancer progression by EZH2: from biological insights to therapeutic potential. Biomark Res. 6, 10 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Varambally, S. et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 419, 624–629 (2002).

    CAS  PubMed  Google Scholar 

  4. 4.

    Koh, C. M. et al. Myc enforces overexpression of EZH2 in early prostatic neoplasia via transcriptional and post-transcriptional mechanisms. Oncotarget 2, 669–683 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Peng, D. et al. Epigenetic silencing of TH1-type chemokines shapes tumour immunity and immunotherapy. Nature 527, 249–253 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ennishi, D. et al. Molecular and genetic characterization of MHC deficiency identifies EZH2 as therapeutic target for enhancing immune recognition. Cancer Discov. 9, 546–563 (2019).

    PubMed  Google Scholar 

  7. 7.

    Burr, M. L. et al. An evolutionarily conserved function of polycomb silences the MHC class I antigen presentation pathway and enables immune evasion in cancer. Cancer Cell 36, 385–401 e388 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Nagarsheth, N. et al. PRC2 epigenetically silences Th1-type chemokines to suppress effector T-cell trafficking in colon cancer. Cancer Res. 76, 275–282 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    Sheng, W. et al. LSD1 ablation stimulates anti-tumor immunity and enables checkpoint blockade. Cell 174, 549–563 e519 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Stone, M. L. et al. Epigenetic therapy activates type I interferon signaling in murine ovarian cancer to reduce immunosuppression and tumor burden. Proc. Natl Acad. Sci. USA 114, E10981–E10990 (2017).

    CAS  PubMed  Google Scholar 

  12. 12.

    Zingg, D. et al. The histone methyltransferase Ezh2 controls mechanisms of adaptive resistance to tumor immunotherapy. Cell Rep. 20, 854–867 (2017).

    CAS  PubMed  Google Scholar 

  13. 13.

    Adeegbe, D. O. et al. Synergistic immunostimulatory effects and therapeutic benefit of combined histone deacetylase and bromodomain inhibition in non-small cell lung cancer. Cancer Discov. 7, 852–867 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Hogg, S. J. et al. BET-bromodomain inhibitors engage the host immune system and regulate expression of the immune checkpoint ligand PD-L1. Cell Rep. 18, 2162–2174 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Wee, Z. N. et al. EZH2-mediated inactivation of IFN-gamma-JAK-STAT1 signaling is an effective therapeutic target in MYC-driven prostate cancer. Cell Rep. 8, 204–216 (2014).

    CAS  PubMed  Google Scholar 

  16. 16.

    Johnson, M. L. et al. Preliminary results of ENCORE 601, a phase 1b/2, open-label study of entinostat (ENT) in combination with pembrolizumab (PEMBRO) in patients with non-small cell lung cancer (NSCLC). J. Clin. Oncol. 34, e20659–e20659 (2016).

    Google Scholar 

  17. 17.

    Ellis, L. et al. Generation of a C57BL/6 MYC-driven mouse model and cell line of prostate cancer. Prostate 76, 1192–1202 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Xie, H. et al. Polycomb repressive complex 2 regulates normal hematopoietic stem cell function in a developmental-stage-specific manner. Cell Stem Cell 14, 68–80 (2014).

    CAS  PubMed  Google Scholar 

  19. 19.

    Ratnacaram, C. K. et al. Temporally controlled ablation of PTEN in adult mouse prostate epithelium generates a model of invasive prostatic adenocarcinoma. Proc. Natl Acad. Sci. USA 105, 2521–2526 (2008).

    CAS  PubMed  Google Scholar 

  20. 20.

    Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, 328rv324 (2016).

    Google Scholar 

  21. 21.

    Yu, J. et al. A polycomb repression signature in metastatic prostate cancer predicts cancer outcome. Cancer Res. 67, 10657–10663 (2007).

    CAS  PubMed  Google Scholar 

  22. 22.

    Jones, P. A., Ohtani, H., Chakravarthy, A. & De Carvalho, D. D. Epigenetic therapy in immune-oncology. Nat. Rev. Cancer 19, 151–161 (2019).

    CAS  PubMed  Google Scholar 

  23. 23.

    Canadas, I. et al. Tumor innate immunity primed by specific interferon-stimulated endogenous retroviruses. Nat. Med. 24, 1143–1150 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Li, H. et al. Immune regulation by low doses of the DNA methyltransferase inhibitor 5-azacitidine in common human epithelial cancers. Oncotarget 5, 587–598 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 164, 1073 (2016).

    CAS  PubMed  Google Scholar 

  26. 26.

    Kim, J. et al. Polycomb- and methylation-independent roles of EZH2 as a transcription activator. Cell Rep. 25, 2808–2820 e2804 (2018).

  27. 27.

    Pomerantz, M. M. et al. The androgen receptor cistrome is extensively reprogrammed in human prostate tumorigenesis. Nat. Genet. 47, 1346–1351 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Pomerantz, M. M. et al. Prostate cancer reactivates developmental epigenomic programs during metastatic progression. Nat. Genet. 52, 790–799 (2020).

    CAS  PubMed  Google Scholar 

  29. 29.

    Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Ayers, M. et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

    CAS  PubMed  Google Scholar 

  32. 32.

    Su, W. et al. The polycomb repressor complex 1 drives double-negative prostate cancer metastasis by coordinating stemness and immune suppression. Cancer Cell https://doi.org/10.1016/j.ccell.2019.06.009 (2019).

  33. 33.

    Rexer, H., Graefen, M., Merseburger, A. & AUO. Phase II study of pembrolizumab (MK-3475) in patients with metastatic castration-resistant prostate cancer (KEYNOTE-199)-study AP 93/16 of the AUO. (In German) Urologe A 56, 1471–1472 (2017).

  34. 34.

    Rodrigues, D. N. et al. Immunogenomic analyses associate immunological alterations with mismatch repair defects in prostate cancer. J. Clin. Invest. 128, 5185 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wu, Y. M. et al. Inactivation of CDK12 delineates a distinct immunogenic class of advanced prostate cancer. Cell 173, 1770–1782.e1714 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Zhang, J. et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance. Nature 553, 91–95 (2018).

    CAS  PubMed  Google Scholar 

  37. 37.

    Calcinotto, A. et al. IL-23 secreted by myeloid cells drives castration-resistant prostate cancer. Nature 559, 363–369 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wang, D. et al. Targeting EZH2 reprograms intratumoral regulatory T cells to enhance cancer immunity. Cell Rep. 23, 3262–3274 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tumes, D. J. et al. The polycomb protein Ezh2 regulates differentiation and plasticity of CD4+ T helper type 1 and type 2 cells. Immunity 39, 819–832 (2013).

    CAS  PubMed  Google Scholar 

  40. 40.

    Soshnev, A. A., Josefowicz, S. Z. & Allis, C. D. Greater than the sum of parts: complexity of the dynamic epigenome. Mol. Cell 69, 533 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Goswami, S. et al. Modulation of EZH2 expression in T cells improves efficacy of anti-CTLA-4 therapy. J. Clin. Invest. 128, 3813–3818 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    de Groot, A. E. & Pienta, K. J. Epigenetic control of macrophage polarization: implications for targeting tumor-associated macrophages. Oncotarget 9, 20908–20927 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Wang, S. et al. Prostate-specific deletion of the murine Pten tumor suppressor gene leads to metastatic prostate cancer. Cancer Cell 4, 209–221 (2003).

    CAS  PubMed  Google Scholar 

  44. 44.

    Wu, X. et al. Generation of a prostate epithelial cell-specific Cre transgenic mouse model for tissue-specific gene ablation. Mech. Dev. 101, 61–69 (2001).

    CAS  PubMed  Google Scholar 

  45. 45.

    Shen, X. et al. EZH1 mediates methylation on histone H3 lysine 27 and complements EZH2 in maintaining stem cell identity and executing pluripotency. Mol. Cell 32, 491–502 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ellwood-Yen, K. et al. Myc-driven murine prostate cancer shares molecular features with human prostate tumors. Cancer Cell 4, 223–238 (2003).

    CAS  PubMed  Google Scholar 

  47. 47.

    Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Ku, S. Y. et al. Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 355, 78–83 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Pantelidou, C. et al. PARP inhibitor efficacy depends on CD8+ T-cell recruitment via intratumoral STING pathway activation in BRCA-deficient models of triple-negative breast cancer. Cancer Discov. 9, 722–737 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Google Scholar 

  52. 52.

    Lamprecht, M. R., Sabatini, D. M. & Carpenter, A. E. CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 42, 71–75 (2007).

    CAS  PubMed  Google Scholar 

  53. 53.

    Calagua, C. et al. Expression of PD-L1 in hormone-naive and treated prostate cancer patients receiving neoadjuvant abiraterone acetate plus prednisone and leuprolide. Clin. Cancer Res. 23, 6812–6822 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).

    CAS  PubMed  Google Scholar 

  55. 55.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Bourgey, M. et al. GenPipes: an open-source framework for distributed and scalable genomic analyses. Gigascience 8, https://doi.org/10.1093/gigascience/giz037 (2019).

  62. 62.

    Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    CAS  Google Scholar 

  64. 64.

    Lefebvre, C. et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol. 6, 377 (2010).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  PubMed  Google Scholar 

  66. 66.

    Beltran, H. et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets. Cancer Discov. 1, 487–495 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Abida, W. et al. Genomic correlates of clinical outcome in advanced prostate cancer. Proc. Natl Acad. Sci. USA 116, 11428–11436 (2019).

    CAS  PubMed  Google Scholar 

Download references

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

Affiliations

Authors

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.

Ethics declarations

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.

Source data

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).

Source data

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).

Source data

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.

Source data

Supplementary information

Reporting Summary

Supplementary Table

Excel spreadsheet with six independent tables including supporting data.

Source data

Source Data Fig. 1

Numerical source data for Fig. 1.

Source Data Fig. 2

Numerical source data for Fig. 2.

Source Data Fig. 2

Flow cytometry gating.

Source Data Fig. 4

Numerical source data for Fig. 4.

Source Data Fig. 4

Flow cytometry gating.

Source Data Fig. 4

Unprocessed western blot.

Source Data Fig. 5

Numerical source data for Fig. 5.

Source Data Fig. 5

Flow cytometry gating.

Source Data Fig. 6

Numerical source data for Fig. 6.

Source Data Fig. 6

Flow cytometry gating.

Source Data Fig. 6

Unprocessed cytokine array blots.

Source Data Fig. 7

Numerical source data for Fig. 7.

Source Data Extended Data Fig. 1

Numerical source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Numerical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 5

Numerical source data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Numerical source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 6

Unprocessed cytokine array blots.

Source Data Extended Data Fig. 7

Numerical source data for Extended Data Fig. 7.

Source Data Extended Data Fig. 7

Flow cytometry gating.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing