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.

  • Article
  • Published:

Myeloid-specific KDM6B inhibition sensitizes glioblastoma to PD1 blockade

Nature Cancer volume 4, pages 1455–1473 (2023)Cite this article

Subjects

Abstract

Glioblastoma (GBM) tumors are enriched in immune-suppressive myeloid cells and are refractory to immune checkpoint therapy (ICT). Targeting epigenetic pathways to reprogram the functional phenotype of immune-suppressive myeloid cells to overcome resistance to ICT remains unexplored. Single-cell and spatial transcriptomic analyses of human GBM tumors demonstrated high expression of an epigenetic enzyme—histone 3 lysine 27 demethylase (KDM6B)—in intratumoral immune-suppressive myeloid cell subsets. Importantly, myeloid cell-specific Kdm6b deletion enhanced proinflammatory pathways and improved survival in GBM tumor-bearing mice. Mechanistic studies showed that the absence of Kdm6b enhances antigen presentation, interferon response and phagocytosis in myeloid cells by inhibition of mediators of immune suppression including Mafb, Socs3 and Sirpa. Further, pharmacological inhibition of KDM6B mirrored the functional phenotype of Kdm6b-deleted myeloid cells and enhanced anti-PD1 efficacy. This study thus identified KDM6B as an epigenetic regulator of the functional phenotype of myeloid cell subsets and a potential therapeutic target for enhanced response to ICT.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Human GBM tumor-associated myeloid cells express KDM6B.
Fig. 2: KDM6Bhigh intratumoral myeloid cells are immune suppressive.
Fig. 3: Myeloid-specific Kdm6b deletion improves survival in mice.
Fig. 4: Kdm6b deletion alters intratumoral monocytes and macrophages.
Fig. 5: Kdm6b regulates the chromatin landscape of myeloid cells.
Fig. 6: Kdm6b regulates genes associated with inflammatory response.
Fig. 7: Pharmacological inhibition of KDM6B improves anti-PD1 response.

Similar content being viewed by others

Data availability

ChIP–seq, scATAC-seq, scRNA-seq and spatial transcriptomics (Visium) data that support the findings of this study have been deposited in the European Genome Phenome Archive under accession code EGAS00001007002. Mass cytometry data have been deposited in the ImmPort repository (accession no. SDY2295). All requests for data should be made to the corresponding authors (S.G. and P. Sharma), following verification of any intellectual property or confidentiality obligations. Human GBM scRNA-seq data are available in Sequence Read Archive with accession no. PRJNA588461, the GEO dataset with accession no. GSE131928 and at https://www.brainimmuneatlas.org/. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The scRNA-seq, scATAC-seq and ChIP–seq analyses presented in the manuscript were performed with open-source algorithms as described in Methods. Further details will be made available by the authors on request.

References

  1. Locati, M., Curtale, G. & Mantovani, A. Diversity, mechanisms, and significance of macrophage plasticity. Annu. Rev. Pathol. 15, 123–147 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Broz, M. L. & Krummel, M. F. The emerging understanding of myeloid cells as partners and targets in tumor rejection. Cancer Immunol. Res. 3, 313–319 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  5. Mantovani, A. & Allavena, P. The interaction of anticancer therapies with tumor-associated macrophages. J. Exp. Med. 212, 435–445 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Wang, T. et al. BRAF inhibition stimulates melanoma-associated macrophages to drive tumor growth. Clin. Cancer Res. 21, 1652–1664 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Engblom, C., Pfirschke, C. & Pittet, M. J. The role of myeloid cells in cancer therapies. Nat. Rev. Cancer 16, 447–462 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Ruffell, B. & Coussens, L. M. Macrophages and therapeutic resistance in cancer. Cancer Cell 27, 462–472 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Goswami, S., Anandhan, S., Raychaudhuri, D. & Sharma, P. Myeloid cell-targeted therapies for solid tumours. Nat. Rev. Immunol. 23, 106–120 (2022).

    Article  PubMed  Google Scholar 

  11. Cassetta, L. & Pollard, J. W. Targeting macrophages: therapeutic approaches in cancer. Nat. Rev. Drug Discov. 17, 887–904 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Lopez-Yrigoyen, M., Cassetta, L. & Pollard, J. W. Macrophage targeting in cancer. Ann. N. Y. Acad. Sci. 1499, 18–41 (2021).

    Article  PubMed  Google Scholar 

  13. Pathria, P., Louis, T. L. & Varner, J. A. Targeting tumor-associated macrophages in cancer. Trends Immunol. 40, 310–327 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Beatty, G. L. et al. CD40 agonists alter tumor stroma and show efficacy against pancreatic carcinoma in mice and humans. Science 331, 1612–1616 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Cannarile, M. A. et al. Colony-stimulating factor 1 receptor (CSF1R) inhibitors in cancer therapy. J. Immunother. Cancer 5, 53 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  16. Chaib, M., Chauhan, S. C. & Makowski, L. Friend or foe? Recent strategies to target myeloid cells in cancer. Front. Cell Dev. Biol. 8, 351 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  17. Hu, J. et al. Regulation of tumor immune suppression and cancer cell survival by CXCL1/2 elevation in glioblastoma multiforme. Sci. Adv. 7, eabc2511 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Logtenberg, M. E. W., Scheeren, F. A. & Schumacher, T. N. The CD47-SIRPalpha immune checkpoint. Immunity 52, 742–752 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Veillette, A. & Chen, J. SIRPalpha-CD47 immune checkpoint blockade in anticancer therapy. Trends Immunol. 39, 173–184 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Barry, S. T., Gabrilovich, D. I., Sansom, O. J., Campbell, A. D. & Morton, J. P. Therapeutic targeting of tumour myeloid cells. Nat. Rev. Cancer 23, 216–237 (2023).

    Article  CAS  PubMed  Google Scholar 

  22. Alvarez-Errico, D., Vento-Tormo, R., Sieweke, M. & Ballestar, E. Epigenetic control of myeloid cell differentiation, identity and function. Nat. Rev. Immunol. 15, 7–17 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. De Leo, A., Ugolini, A. & Veglia, F. Myeloid cells in glioblastoma microenvironment. Cells 10, 18 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  25. Locarno, C. V. et al. Role of myeloid cells in the immunosuppressive microenvironment in gliomas. Immunobiology 225, 151853 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Preusser, M., Lim, M., Hafler, D. A., Reardon, D. A. & Sampson, J. H. Prospects of immune checkpoint modulators in the treatment of glioblastoma. Nat. Rev. Neurol. 11, 504–514 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Andersen, B. M. et al. Glial and myeloid heterogeneity in the brain tumour microenvironment. Nat. Rev. Cancer 21, 786–802 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Goswami, S. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat. Med. 26, 39–46 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Hong, S. et al. Identification of JmjC domain-containing UTX and JMJD3 as histone H3 lysine 27 demethylases. Proc. Natl Acad. Sci. USA 104, 18439–18444 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Croker, B. A. et al. SOCS3 negatively regulates IL-6 signaling in vivo. Nat. Immunol. 4, 540–545 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Kim, H. & Seed, B. The transcription factor MafB antagonizes antiviral responses by blocking recruitment of coactivators to the transcription factor IRF3. Nat. Immunol. 11, 743–750 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Zhang, X. et al. Macrophage/microglial Ezh2 facilitates autoimmune inflammation through inhibition of Socs3. J. Exp. Med. 215, 1365–1382 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Pombo Antunes, A. R. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat. Neurosci. 24, 595–610 (2021).

    Article  CAS  PubMed  Google Scholar 

  35. Wei, R. et al. Spatial charting of single-cell transcriptomes in tissues. Nat. Biotechnol. 40, 1190–1199 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Abram, C. L., Roberge, G. L., Hu, Y. & Lowell, C. A. Comparative analysis of the efficiency and specificity of myeloid-Cre deleting strains using ROSA-EYFP reporter mice. J. Immunol. Methods 408, 89–100 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Shi, J., Hua, L., Harmer, D., Li, P. & Ren, G. Cre driver mice targeting macrophages. Methods Mol. Biol. 1784, 263–275 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Swiecki, M. & Colonna, M. The multifaceted biology of plasmacytoid dendritic cells. Nat. Rev. Immunol. 15, 471–485 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Kedl, R. M. et al. Migratory dendritic cells acquire and present lymphatic endothelial cell-archived antigens during lymph node contraction. Nat. Commun. 8, 2034 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  41. Salmon, H. et al. Expansion and activation of CD103(+) dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition. Immunity 44, 924–938 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Xiang, Y. et al. JMJD3 is a histone H3K27 demethylase. Cell Res. 17, 850–857 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Mehrotra, P. & Ravichandran, K. S. Drugging the efferocytosis process: concepts and opportunities. Nat. Rev. Drug Discov. 21, 601–620 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Hashizume, R. et al. Pharmacologic inhibition of histone demethylation as a therapy for pediatric brainstem glioma. Nat. Med. 20, 1394–1396 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Li, Y. et al. Therapeutic potential of GSK-J4, a histone demethylase KDM6B/JMJD3 inhibitor, for acute myeloid leukemia. J. Cancer Res. Clin. Oncol. 144, 1065–1077 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Sui, A. et al. The pharmacological role of histone demethylase JMJD3 inhibitor GSK-J4 on glioma cells. Oncotarget 8, 68591–68598 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  48. Liu, T. M., Wang, H., Zhang, D. N. & Zhu, G. Z. Transcription factor MafB suppresses type I interferon production by CD14(+) monocytes in patients with chronic hepatitis C. Front. Microbiol. 10, 1814 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  49. Motohashi, H. & Igarashi, K. MafB as a type I interferon rheostat. Nat. Immunol. 11, 695–696 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Kershaw, N. J. et al. SOCS3 binds specific receptor-JAK complexes to control cytokine signaling by direct kinase inhibition. Nat. Struct. Mol. Biol. 20, 469–476 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Donahoe, R. M. & Huang, K. Y. Interferon preparations enhance phagocytosis in vivo. Infect. Immun. 13, 1250–1257 (1976).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Gottfried-Blackmore, A. et al. Acute in vivo exposure to interferon-gamma enables resident brain dendritic cells to become effective antigen presenting cells. Proc. Natl Acad. Sci. USA 106, 20918–20923 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Wong, G. H., Clark-Lewis, I., McKimm-Breschkin, L., Harris, A. W. & Schrader, J. W. Interferon-gamma induces enhanced expression of Ia and H-2 antigens on B lymphoid, macrophage, and myeloid cell lines. J. Immunol. 131, 788–793 (1983).

    Article  CAS  PubMed  Google Scholar 

  54. Barrat, F. J., Crow, M. K. & Ivashkiv, L. B. Interferon target-gene expression and epigenomic signatures in health and disease. Nat. Immunol. 20, 1574–1583 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Feng, M. et al. Phagocytosis checkpoints as new targets for cancer immunotherapy. Nat. Rev. Cancer 19, 568–586 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Liu, X. et al. CD47 blockade triggers T cell-mediated destruction of immunogenic tumors. Nat. Med. 21, 1209–1215 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Du, L. et al. KDM6B regulates M2 polarization of macrophages by modulating the stability of nuclear beta-catenin. Biochim. Biophys. Acta Mol. Basis Dis. 1869, 166611 (2023).

    Article  CAS  PubMed  Google Scholar 

  58. Bassler, K., Schulte-Schrepping, J., Warnat-Herresthal, S., Aschenbrenner, A. C. & Schultze, J. L. The myeloid cell compartment-cell by cell. Annu. Rev. Immunol. 37, 269–293 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Clausen, B. E., Burkhardt, C., Reith, W., Renkawitz, R. & Forster, I. Conditional gene targeting in macrophages and granulocytes using LysMcre mice. Transgenic Res. 8, 265–277 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Omuro, A. et al. Radiotherapy combined with nivolumab or temozolomide for newly diagnosed glioblastoma with unmethylated MGMT promoter: an international randomized phase III trial. Neuro Oncol. 25, 123–134 (2023).

    Article  CAS  PubMed  Google Scholar 

  61. Reardon, D. A. et al. Effect of nivolumab vs bevacizumab in patients with recurrent glioblastoma: the checkMate 143 Phase 3 randomized clinical trial. JAMA Oncol. 6, 1003–1010 (2020).

    Article  PubMed  Google Scholar 

  62. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  66. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Li, H. et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  69. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  71. 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).

    Article  CAS  PubMed  Google Scholar 

  72. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed Central  PubMed  Google Scholar 

  73. Shao, Z., Zhang, Y., Yuan, G. C., Orkin, S. H. & Waxman, D. J. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biol. 13, R16 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  75. Lerdrup, M., Johansen, J. V., Agrawal-Singh, S. & Hansen, K. An interactive environment for agile analysis and visualization of ChIP-sequencing data. Nat. Struct. Mol. Biol. 23, 349–357 (2016).

    Article  CAS  PubMed  Google Scholar 

  76. Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. 6, 748 (2017).

    Article  PubMed  Google Scholar 

  77. Gulhati, P. et al. Targeting T cell checkpoints 41BB and LAG3 and myeloid cell CXCR1/CXCR2 results in antitumor immunity and durable response in pancreatic cancer. Nat. Cancer 4, 62–80 (2023).

    CAS  PubMed  Google Scholar 

  78. Qiu, J. et al. Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction. Nat. Cancer 4, 43–61 (2023).

    CAS  PubMed  Google Scholar 

  79. van Hooren, L. et al. CD103(+) regulatory T cells underlie resistance to radio-immunotherapy and impair CD8(+) T cell activation in glioblastoma.Nat. Cancer 4, 665–681 (2023).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

This research is supported by the MD Anderson Physician Scientist Award (S.G.), Khalifa Physician Scientist Award (S.G.), Andrew Sabin Family Foundation Fellows Award (S.G.) and Clinic and Laboratory Integration Program Award (S.G.). We thank L. Xiong, B. Guan, D. N. Tang, S. Kemp and A. Jung for technical assistance. We thank the CATALYST-working group at MD Anderson Cancer Center for human GBM tumor samples. CATALYST is supported by the MD Anderson GBM Moon Sho. P. Sharma is a member of the Parker Institute for Cancer Immunotherapy. P. Sharma and S.G. are members of the James P. Allison Institute. S.G. and P. Sharma are members of the BreakThrough Cancer–GBM Extended Leadership Team.

Author information

Author notes

  1. These authors contributed equally: Sangeeta Goswami, Deblina Raychaudhuri, Pratishtha Singh, Seanu Meena Natarajan.

Authors and Affiliations

  1. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Sangeeta Goswami, Deblina Raychaudhuri, Pratishtha Singh, Seanu Meena Natarajan, Mercedes Hennessey, Aminah J. Tannir & Padmanee Sharma

  2. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Sangeeta Goswami, Jan Zhang, Swetha Anandhan & Padmanee Sharma

  3. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Sangeeta Goswami & Padmanee Sharma

  4. Immunotherapy Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yulong Chen, Marc D. Macaluso, Zhong He, Sonali Jindal, Sreyashi Basu & Padmanee Sharma

  5. Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Candice Poon, Brittany Parker Kerrigan & Frederick F. Lang

Authors
  1. Sangeeta Goswami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deblina Raychaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pratishtha Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seanu Meena Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yulong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Candice Poon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mercedes Hennessey
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aminah J. Tannir
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Swetha Anandhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Brittany Parker Kerrigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Marc D. Macaluso
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sonali Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Frederick F. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Sreyashi Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Padmanee Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.G. developed the project, designed the experiments, analyzed data, wrote the manuscript and acquired funding. D.R., S.M.N. and P. Singh performed experiments, analyzed data and wrote the manuscript. P. Singh and Y.C. performed bioinformatics analyses. J.Z., M.H., A.J.T. and S.A. helped with murine experiments. B.P.K., C.P. and F.F.L. provided human GBM tumor samples. M.D.M. and S.J. performed H&E, IHC and immunofluorescence staining of human GBM samples. S.B. and Z.H. helped with human scRNA-seq and Visium analysis. P. Sharma provided scientific input, edited the manuscript and acquired funding.

Corresponding authors

Correspondence to Sangeeta Goswami or Padmanee Sharma.

Ethics declarations

Competing interests

P. Sharma reports consulting, advisory roles and/or stocks/ownership for Achelois, Apricity Health, BioAlta, Codiak BioSciences, Constellation, Dragonfly Therapeutics, Forty-Seven, Inc., Hummingbird, ImaginAb, Jounce Therapeutics, Lava Therapeutics, Lytix Biopharma, Marker Therapeutics, BioNTx, Oncolytics, Glympse, Infinity Pharma and Polaris; and owns a patent licensed to Jounce Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Jason Miska, Michael Platten and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 KDM6B is enriched in intratumoral myeloid cells in human GBM.

a, Feature plots of scRNA seq data demonstrating the expression of indicated genes encoding epigenetic enzymes, in intratumoral myeloid cell clusters from Fig. 1c. b, UMAP plot of scRNA seq data showing CD45+ immune cell subsets in the GBM TME derived from patients with GBM (n = 4 patients). c, Violin plots demonstrating the expression level of indicated genes in the different immune cell clusters. d, UMAP plot of scRNA seq data depicting CD45+ immune cell subsets in the GBM TME derived from patients with GBM (n = 20 patients). e, Violin plots representing the expression level of indicated genes in the different immune cell clusters. f, Violin plot demonstrating the expression level of KDM6B in the different myeloid cell clusters in the TME of patients with GBM (n = 5 patients) from Fig. 1c. The dotted lines represent the cutoff KDM6B expression values used to define KDM6Bhigh and KDM6Blow cells. g,h, Volcano plots representing differentially expressed genes between KDM6Bhigh and KDM6Blow myeloid cells in the TME of patients with GBM (g) n = 4 patients (h) n = 13 patients. Volcano Plots show the fold change (log2FC) plotted against the Absolute Confidence log10 adjusted p value (-log10(p)). Two-tailed Poisson’s test was performed using the Seurat R package.

Extended Data Fig. 2 KDM6Bhigh myeloid cell enriched areas have paucity of CD8 T cells in human GBM tumors.

a, Hematoxylin and Eosin stained GBM tumor sections (n = 3 patients). Each section represent one patient. Scale included in the images. b, Representative figures showing immunohistochemical staining for KDM6B in GBM tissue samples (n = 3 patients). c,d, Multiplex immunofluorescence staining shows distribution of CD3 (white), KDM6B (blue), CD68 (green) GFAP (yellow) and CD163 (red) in GBM tumor samples. Arrow marks show the colocalization of KDM6B protein expression with CD68 and CD163. Data is representative of two independent experiments. e,i, UMAP plots of matched scRNA seq data showing CD45+ immune cell subsets in the GBM TME used to embed single cells to their spatial coordinates in tissue sections by applying CellTrek. f,j, Gene expression data of all the different immune cell clusters from matched patients plotted on spatial coordinates. g,h,k,l, Visium spatial gene expression score plots for the CD68 + KDM6B + CD163 + KLF2 + CXCL8 expressing myeloid cell clusters (highlighted in teal) and CD3E + CD8A + GZMB + GZMK + ICOS T cell clusters (highlighted in red) in human GBM tumors to assess spatial distribution of KDM6B expressing myeloid cells and activated CD8 T cells.

Extended Data Fig. 3 Immune cell clusters expressing KDM6B are enriched in immune-suppressive markers.

a–c, Dotplots showing the average gene activity score of genes of interest as well as percentage of cells in the cluster expressing the gene in the indicated clusters (a shown in Fig. 2e, and b and c shown in Extended Data Fig. 2e, i).

Extended Data Fig. 4 Myeloid cell specific deletion of Kdm6b does not affect myeloid cell abundance.

a, Schematic representation demonstrating generation of the LysMcreKDM6Bfl/fl genetic murine model. b, Representative image of an agarose gel showing bands depicting PCR amplified DNA from Kdm6b deleted homozygous mice (single 400 bp band), Kdm6b deleted heterozygous mice (both 368 and 400 bp bands), and control homozygous mice (single 368 bp band). Data is representative of two independent experiments. c, t-SNE plots and box and whisker plots depicting the identity and abundance of different immune cell populations present in the indicated anatomical locations in control and LysMcreKDM6Bfl/fl mice as determined from CyTOF analysis. n = 3 female mice/group. Two-tailed Student’s t test was performed (ns= non-significant). Whiskers represent minimum to maximum values.

Source data

Extended Data Fig. 5 Myeloid specific deletion of Kdm6b alters the abundance of intratumoral immune cells.

a, Heatmap showing the expression of genes of interest in the different Cd45+ immune cell clusters (shown in Fig. 3c). b, Bar graphs depicting the frequencies of the different intratumoral immune cell subsets in control and LysMcreKDM6Bfl/fl mice. c, Bar graph representing the ratio of intratumoral CTLs and Tregs in control and LysMcreKDM6Bfl/fl mice as determined from scRNA seq. d, Violin plots depicting the expression level of Lyz2 in different immune cell clusters in control and LysMcreKDM6Bfl/fl mice. Data representative of two independent scRNA seq experiments. e–g, Box and whisker plots representing the flow cytometry analysis of abundance of different immune cell populations present in PBMC (e), lymph nodes (f) and spleen (g) derived from control and LysMcreKDM6Bfl/fl GBM tumor bearing female mice (n = 3 biologically independent samples/group for (E), n = 4 biologically independent samples/group for f and g. Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values.

Source data

Extended Data Fig. 6 Myeloid specific deletion of Kdm6b skews TME to a pro-inflammatory phenotype.

a, Heatmap showing the expression of protein markers of interest in the indicated immune cell clusters from Fig. 3g determined by mass cytometry. b, Heatmap showing the expression of genes of interest in the different myeloid cell clusters (shown in Fig. 4A-right panel). c, Bar plots representing the frequencies of intratumoral myeloid clusters from control and LysMcreKDM6Bfl/fl mice. Data representative of two independent experiments.

Extended Data Fig. 7 Kdm6b deletion alters the chromatin landscape of intratumoral myeloid cells.

a, UMAP demonstrating the CTL and Treg clusters in the GBM (GL261) TME of control and LysMcreKDM6Bfl/fl mice determined by scATAC seq (as shown in Fig. 5a, b). Bar graphs depicting the frequencies and ratio of intratumoral CTLs and Tregs in control and LysMcreKDM6Bfl/fl mice as determined from scATAC seq. b, Coverage plots depicting the chromatin accessibility of the indicated genes in the CTL and Treg clusters. c, Heatmap showing the expression of genes of interest in the indicated myeloid cell clusters (shown in Fig. 5d). d,e, Coverage plots depicting accessibility of indicated chromatin regions (peaks) in genes of interest.

Extended Data Fig. 8 Absence of Kdm6b inhibits acquisition of an immune-suppressive phenotype in BMDMs.

a, Representative gating strategy on FlowJo for analysis of flow cytometry data showing GL261 phagocytosis by BMDMs (shown in Fig. 6j). b, Box and Whisker plots showing the concentration of the indicated cytokines in 24 hour culture supernatants of M2, M1 and M0 BMDMs derived from control and LysMcreKDM6Bfl/fl male mice(n = 6 biologically independent samples/condition). Data is representative of 2 independent experiments. Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values. c, Relative average expression of indicated genes in M2 and M1 BMDMs derived from control and LysMcreKDM6Bfl/fl male mice (n = 3 biologically independent samples for control M1 BMDMs and n = 4 for LysMcreKDM6Bfl/fl M1 BMDMs). Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values. d,e, Concentration of IL-6 and IL-10 in the supernatants of BMDMs derived from control and LysMcreKDM6Bfl/fl male mice following efferocytosis and subsequent LPS stimulation (n = 4 biologically independent samples/condition). Two-tailed Student’s t-test was performed. Data is representative of two independent experiments. Whiskers represent minimum to maximum values. f,g, Box and Whisker plots demonstrating the relative gene expression of INOS and IL10 in BMDMs derived from control and LysMcreKDM6Bfl/fl male mice following efferocytosis and subsequent LPS stimulation (n = 3 biologically independent samples for control BMDMs and n = 4 LysMcreKDM6Bfl/fl BMDMs for (F), n = 4 biologically independent samples for control BMDMs and n = 3 LysMcreKDM6Bfl/fl BMDMs for G). Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values.

Source data

Extended Data Fig. 9 Pharmacological inhibition of KDM6B alters the intratumoral immune cell repertoire.

a, Heatmap representing the expression of genes of interest in the indicated Cd45+ immune cell clusters as determined by scRNA seq (shown in Fig. 7c).

Extended Data Fig. 10 Pharmacological inhibition of KDM6B attenuates tumor growth and causes pro-inflammatory skewing of the TME.

a, Representative axial MRI images of CT-2A tumor from vehicle treated mice (left panel) and GSK-J4 treated mice (right panel), taken on day 14 post tumor inoculation. b, Box and whisker plot showing the difference in CT-2A tumor volumes (determined from MRI) between vehicle and GSK-J4 treated mice (n = 10 female mice/group). Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values. c, Box and whisker plot depicting the difference in CT-2A tumor weight (harvested on day 22 post tumor inoculation) between vehicle and GSK-J4 treated mice (n = 10 female mice/group). Two-tailed Student’s t-test was performed. Whiskers represent minimum to maximum values. d, Heatmap demonstrating the expression of protein markers of interest in the indicated CD45+ immune cell clusters in the GBM (CT-2A) TME of vehicle & GSK-J4 treated mice as determined by mass cytometry. e, Heatmap showing the expression of protein markers of interest in the indicated CD45+ immune cell clusters as determined by mass cytometry (shown in Fig. 7j).

Source data

Supplementary information

Supplementary Information

Supplementary Table 1: patient characteristics. Supplementary Table 2: key resource table with list of antibodies and reagents.

Reporting Summary

Source data

Source Data Fig. 1

Raw agarose gel image for the representative image showing bands depicting PCR-amplified DNA from Kdm6b-deleted homozygous mice (single 400 bp band), from Kdm6b-deleted heterozygous mice (both 368 and 400 bp bands) and from control homozygous mice (single 368 bp band) in Extended Data Fig. 4b (area highlighted in red-colored box).

Source Data Figs. 3, 6 and 7 and Extended Data Figs. 4, 5, 8 and 10

A single file containing source data for Figs. 3, 6 and 7 and Extended Data Figs. 4, 5, 8 and 10.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goswami, S., Raychaudhuri, D., Singh, P. et al. Myeloid-specific KDM6B inhibition sensitizes glioblastoma to PD1 blockade. Nat Cancer 4, 1455–1473 (2023). https://doi.org/10.1038/s43018-023-00620-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00620-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer