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.

KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements

Abstract

Tumours use various strategies to evade immune surveillance1,2. Immunotherapies targeting tumour immune evasion such as immune checkpoint blockade have shown considerable efficacy on multiple cancers3,4 but are ineffective for most patients due to primary or acquired resistance5,6,7. Recent studies showed that some epigenetic regulators suppress anti-tumour immunity2,8,9,10,11,12, suggesting that epigenetic therapies could boost anti-tumour immune responses and overcome resistance to current immunotherapies. Here we show that, in mouse melanoma models, depletion of KDM5B—an H3K4 demethylase that is critical for melanoma maintenance and drug resistance13,14,15—induces robust adaptive immune responses and enhances responses to immune checkpoint blockade. Mechanistically, KDM5B recruits the H3K9 methyltransferase SETDB1 to repress endogenous retroelements such as MMVL30 in a demethylase-independent manner. Derepression of these retroelements activates cytosolic RNA-sensing and DNA-sensing pathways and the subsequent type-I interferon response, leading to tumour rejection and induction of immune memory. Our results demonstrate that KDM5B suppresses anti-tumour immunity by epigenetic silencing of retroelements. We therefore reveal roles of KDM5B in heterochromatin regulation and immune evasion in melanoma, opening new paths for the development of KDM5B-targeting and SETDB1-targeting therapies to enhance tumour immunogenicity and overcome immunotherapy resistance.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Depletion of KDM5B induces anti-tumour immunity in a manner dependent on the adaptive immune system.
Fig. 2: Deletion of Kdm5b activates the type-I interferon response through both cytosolic RNA-sensing and DNA-sensing pathways.
Fig. 3: Deletion of Kdm5b derepresses endogenous retroelements, which can be reverse transcribed into cDNAs.
Fig. 4: KDM5B suppresses the expression of retroelements by recruiting the H3K9 methyltransferase SETDB1.

Data availability

All epigenomic sequencing data of this study have been deposited at the Gene Expression Omnibus database under accession number GSE161065Source data are provided with this paper.

Code availability

Custom codes have been deposited in GitHub (https://github.com/wesleylcai/zhang_nature_2021).

References

  1. Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer-immune set point. Nature 541, 321–330 (2017).

    ADS  CAS  Article  Google Scholar 

  2. Cao, J. & Yan, Q. Cancer epigenetics, tumor immunity, and immunotherapy. Trends Cancer 6, 580–592 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    ADS  CAS  PubMed  Article  Google Scholar 

  8. Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 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  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).

    ADS  CAS  PubMed  Article  Google Scholar 

  13. Roesch, A. et al. A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth. Cell 141, 583–594 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Roesch, A. et al. Overcoming intrinsic multidrug resistance in melanoma by blocking the mitochondrial respiratory chain of slow-cycling JARID1Bhigh cells. Cancer Cell 23, 811–825 (2013).

    CAS  PubMed  Article  Google Scholar 

  15. Liu, X. et al. KDM5B promotes drug resistance by regulating melanoma-propagating cell subpopulations. Mol. Cancer Ther. 18, 706–717 (2019).

    CAS  PubMed  Article  Google Scholar 

  16. Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).

  17. Trujillo, J. A., Sweis, R. F., Bao, R. & Luke, J. J. T cell-inflamed versus non-T cell-inflamed tumors: a conceptual framework for cancer immunotherapy drug development and combination therapy selection. Cancer Immunol. Res. 6, 990–1000 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Wang, J. et al. UV-induced somatic mutations elicit a functional T cell response in the YUMMER1.7 mouse melanoma model. Pigment Cell Melanoma Res. 30, 428–435 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Held, M. A. et al. Characterization of melanoma cells capable of propagating tumors from a single cell. Cancer Res. 70, 388–397 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Kalbasi, A. et al. Uncoupling interferon signaling and antigen presentation to overcome immunotherapy resistance due to JAK1 loss in melanoma. Sci. Transl. Med. 12, eabb0152 (2020).

  21. King, D. P. & Jones, P. P. Induction of Ia and H-2 antigens on a macrophage cell line by immune interferon. J. Immunol. 131, 315–318 (1983).

    CAS  PubMed  Google Scholar 

  22. Mu, X., Ahmad, S. & Hur, S. Endogenous retroelements and the host innate immune sensors. Adv. Immunol. 132, 47–69 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Ahmad, S. et al. Breaching self-tolerance to Alu duplex RNA underlies MDA5-mediated inflammation. Cell 172, 797–810 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Mehdipour, P. et al. Epigenetic therapy induces transcription of inverted SINEs and ADAR1 dependency. Nature 588, 169–173 (2020).

    ADS  CAS  PubMed  Article  Google Scholar 

  25. Kong, Y. et al. Transposable element expression in tumors is associated with immune infiltration and increased antigenicity. Nat. Commun. 10, 5228 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. Ye, X. et al. Endogenous retroviral proteins provide an immunodominant but not requisite antigen in a murine immunotherapy tumor model. Oncoimmunology 9, 1758602 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  27. Cao, J. et al. Histone demethylase RBP2 is critical for breast cancer progression and metastasis. Cell Rep. 6, 868–877 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Horton, J. R. et al. Structural basis for KDM5A histone lysine demethylase inhibition by diverse compounds. Cell Chem. Biol. 23, 769–781 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Griffin, G. K. et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature 595, 309–314 (2021).

    ADS  CAS  PubMed  Article  Google Scholar 

  30. Yin, M. et al. Potent BRD4 inhibitor suppresses cancer cell-macrophage interaction. Nat. Commun. 11, 1833 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Kluger, H. M. et al. PD-L1 studies across tumor types, its differential expression and predictive value in patients treated with immune checkpoint inhibitors. Clin. Cancer Res. 23, 4270–4279 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Kluger, H. M. et al. Characterization of PD-L1 expression and associated T-cell infiltrates in metastatic melanoma samples from variable anatomic sites. Clin. Cancer Res. 21, 3052–3060 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Blenman, K. R. M. & Bosenberg, M. W. Immune cell and cell cluster phenotyping, quantitation, and visualization using in silico multiplexed images and tissue cytometry. Cytometry A 95, 399–410 (2019).

    CAS  PubMed  Article  Google Scholar 

  34. Treger, R. S. et al. Human APOBEC3G prevents emergence of infectious endogenous retrovirus in mice. J. Virol. 93, e00728-19 (2019).

  35. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  37. Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Article  Google Scholar 

  39. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. Storer, J., Hubley, R., Rosen, J., Wheeler, T. J. & Smit, A. F. The Dfam community resource of transposable element families, sequence models, and genome annotations. Mob. DNA 12, 2 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Tjeldnes, H. & Labun, K. ORFik: open reading frames in genomics (2021); https://github.com/Roleren/ORFik

  43. Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016).

    CAS  PubMed  Article  Google Scholar 

  44. Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Krueger, F. Trim Galore (2019).

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    CAS  Article  Google Scholar 

  51. Pohl, A. & Beato, M. bwtool: a tool for bigWig files. Bioinformatics 30, 1618–1619 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Wei, J. et al. Genome-wide CRISPR screens reveal host factors critical for SARS-CoV-2 infection. Cell 184, 76–91 (2021).

    CAS  PubMed  Article  Google Scholar 

  54. Tokuyama, M. et al. ERVmap analysis reveals genome-wide transcription of human endogenous retroviruses. Proc. Natl Acad. Sci. USA 115, 12565–12572 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Herquel, B. et al. Trim24-repressed VL30 retrotransposons regulate gene expression by producing noncoding RNA. Nat. Struct. Mol. Biol. 20, 339–346 (2013).

    CAS  PubMed  Article  Google Scholar 

  58. West, A. P. et al. Mitochondrial DNA stress primes the antiviral innate immune response. Nature 520, 553–557 (2015).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of the Yan, Bosenberg and Iwasaki laboratories for their discussion and support; J. J. Ishizuka for discussions; R. Halaban for providing the human melanoma cell lines used in this study; Q. Xiao for help with some FACS analysis; F. J. Rauscher III from the Wistar Institute for providing pCMV.Flag-SETDB1 (wild-type and catalytically inactive mutant) plasmids; M. Zhong for performing the sequencing at Yale Stem Cell Center Genomics Core facility, which was supported by the Connecticut Regenerative Medicine Research Fund and the Li Ka Shing Foundation; staff at the Yale Center for Genome Analysis for performing part of the Illumina sequencing; and staff at the Comparative Pathology Research Core for Histology Service and Amos Brooks at Yale Pathology Tissue Services for histology and immunohistochemistry services. These studies were supported in part by grants from the NIH Yale SPORE in Skin Cancer, NCI P50 CA121974 (to M.W.B., H.M.K., M.S. and Q.Y.), P01 CA128814 (to M.W.B.), R01 CA237586 (to Q.Y.), R01 CA227473 and R01 CA216846 (to H.M.K.), Department of Defense W81XWH-13-1-0235 (to Q.Y.) and W81XWH-16-1-0306 and W81XWH-20-1-0360 (to S.-M.Z.), a pilot grant from Yale Cancer Center and NCI P30 CA016359 (to M.W.B., Q.Y. and A.I.), the Melanoma Research Foundation (to M.W.B. and Q.Y.), the Lung Cancer Research Foundation-LUNGevity and Melanoma Research Alliance Award 308721 (to L.B.J.) and the Melanoma Research Alliance Young Investigator Award (to K.R.M.B.).

Author information

Affiliations

Authors

Contributions

S.-M.Z., M.W.B. and Q.Y. conceived and designed the study. S.-M.Z. performed most of the experiments with help from X.L., W.L.C., L.H.C., M.K.M., J.L., S.J., Y.Z. and M.Y.; W.L.C. performed most of the bioinformatics analysis. D.T. and M.K.M. performed some RNA-seq analysis. K.R.M.B. performed capturing, quantification and analytical analysis for TMA. E.S. contributed to neoantigen analysis. G.M. performed some TCGA bioinformatics analysis. L.B.J. performed TMA staining. M.S. and H.M.K. provided clinical patient samples. C.J.B. performed histology analysis and captured immunohistochemistry images. W.D. and A.I. provided input on study design and data analysis. S.-M.Z., M.W.B. and Q.Y. wrote the manuscript.

Corresponding authors

Correspondence to Marcus W. Bosenberg or Qin Yan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Daniel De Carvalho, Lélia Delamarre and Didier Trono for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 KDM5B expression is negatively correlated with anti-tumor immunity, and depletion of KDM5B in mouse melanoma tumor cells induces strong anti-tumor immunity and enhances response to anti-PD-1 treatment.

a, Spearman correlation coefficient scores of selected genes negatively correlated with KDM5B gene expression from the melanoma TCGA dataset, categorized by their functions. b, Box plot showing expression levels of CD8A, CD8B, IFNG, TNF, CXCL9 and CXCL10 in samples from patients with melanoma from the TCGA dataset are higher in “KDM5B low” group (lowest quartile) than in “KDM5B high” (highest quartile) group. Statistical significance was computed with the unpaired two-sided Student’s t test. c, Immunofluorescence staining of pre-treatment melanoma samples with a melanoma cocktail (S100, HMB45 -green), KDM5B (red), and DAPI (blue). Images A-C are representative images from 3 independent non-responders to anti-PD-1 treatment; Images D-F are representative images from 3 independent responders. Cell-specific KDM5B expression levels of KDM5B and melanoma markers in the tumor microenvironment are displayed in 2D dot plots in non-responders (G) and responders (H). Each dot represents the mean fluorescent intensities of a single cell for KDM5B and the melanoma marker cocktail. Cells that are positive for both KDM5B and melanoma marks are in the upper right quadrants. (I) displays the frequency and distribution of expression levels of KDM5B in responders (green) and non-responders (red). MGLI = mean grey level intensity. Cutoff values for positive settings are denoted by the horizontal and vertical bars. Scale bar = 100 µm. d, Survival curves of C57BL/6 mice subcutaneously injected with 500,000 control (black) (n = 6) or Kdm5b−/− (Kdm5b sg) (red) (n = 6) YUMMER1.7 cells. Mice injected with Kdm5b−/− cells had completely regressed tumors, and were re-challenged with control tumor cells on day 45 after initial tumor injection. Data are representative of two independent experiments. Log-rank test for survival curves, **p = 0.004. e, Tumor growth curves of C57BL/6 mice injected with polyclonal YUMMER1.7 cells with stable lentiviral Ctl sg (n = 5), Kdm5b sg1 (n = 5) or Kdm5b sg2 (n = 5), with western blot analyses (insert) showing knockout efficacy of KDM5B. Data are representative of two independent experiments. Data are mean ± SEM. 2-way ANOVA analysis for comparison at day 18 after tumor injection, ****p<0.0001. For gel source data, see Supplementary Fig. 1. f, Representative images of immunofluorescent staining of CD3 (green), Melan A (red) and DAPI (blue) (left) and Quantification of CD3+ cells per view field (3-5 view fields per tumor; each dot represents the mean for each tumor; 5 tumors per group) (right) from Ctl, Kdm5b sg1 and Kdm5b sg2 YUMMER1.7 tumors harvested at day 14 after injection. scale bar = 200 µm. Quantification data are mean ± SD; Unpaired two-sided Student’s t-test, **p = 0.0081 for Ctl vs sg1; **p = 0.0063 for Ctl vs sg2. g, Quantitation of the percentages of CD4+ and CD8+ cells in total CD45+ immune cells using flow cytometry analysis of control and Kdm5b sgRNA tumors. Unpaired two-sided Student’s t-test for each comparison, *p = 0.036 for CD4+%; *p = 0.024 for CD8+%. Data are representative of two independent experiments. Data are mean± SD. h, Representative flow cytometry plots showing CD8+ T cells in Ctl and Kdm5b sg YUMMER1.7 tumors harvested at day 14 after tumor injection. i. Representative immunohistochemical staining of CD8 (CD8+ T cells) and cleaved caspase-3 in control and Kdm5b sg YUMMER1.7 tumors at day 14 after injection. scale bar = 100 µm. Data are representative of three independent experiments. j,k, Tumor growth curves (j) and survival curves (k) of C57BL/6 mice injected with single clones of Kdm5b−/− YUMMER1.7 cells, WT (n = 5), 1G8 (n = 5), 4B5 (n = 5), 4H1(n = 5) and 4H3(n = 5) are clone numbers. Statistical significance of tumor growth curves was analysed using two-way ANOVA, ****p<0.0001. Data are representative of two independent experiments. Data are mean± SEM. Statistical significance of tumor survival was analysed with log-rank (Mantel-Cox) test. **p = 0.0018. l, Tumor growth curves of wild-type C57BL/6J (B6) mice injected with control (Ctl) (n = 4) or Kdm5b−/− (Kdm5b sg) (n = 4) YUMMER1.7 cells or Rag1−/− mice injected with control (n = 5) or Kdm5b−/− YUMMER1.7 cells (n = 4). Data represent one experiment. Data are mean ± SEM. ****p<0.0001. m, Tumor growth curves of WT (n = 5) and Kdm5b−/− YUMM1.7 (n = 4) in Rag1−/− mice. Western blot of the cell lines is shown on the right. Data represent one experiment. Data are mean ± SEM. 2-way ANOVA analysis for comparison at day 25 after tumor injection, ****p<0.0001. For gel source data, see Supplementary Fig. 1. n, Tumor growth curves of C57BL/6J (B6) mice that rejected Kdm5b−/− YUMMER1.7 tumors (Challenged B6) rechallenged with YUMM1.7 (n = 6) or Kdm5b−/− YUMM1.7 (n = 5) cells compared with age matched Naïve B6 mice injected with YUMM1.7 (n = 5) or Kdm5b−/− YUMM1.7 (n = 5) cells. Data represent one experiment. Data are mean ± SEM. 2-way ANOVA analysis for comparison of tumor size at day 25. n.s., not significant; ****p<0.0001. o, Survival curves of C57BL/6J (B6) mice that rejected Kdm5b−/− YUMMER1.7 tumors (Challenged B6) rechallenged with Kdm5b−/− YUMM1.7 cells and treated with anti-CD8 antibody (n = 4) or IgG control (n = 4). Data represent one experiment. Statistical significance of tumor survival was analysed with log-rank (Mantel-Cox) test. **p = 0.0067. p, Tumor growth curves of C57BL/6 mice injected with wild-type YUMM1.7 cells treated with IgG control (n = 10) or anti-PD-1 antibody (n = 10); or Kdm5b−/− YUMM1.7 cells treated with IgG control (n = 9) or anti-PD-1 antibody (n = 10). Data are representative of two independent experiments. Data are mean ± SEM. 2-way ANOVA analysis for comparison at day 18. n.s., not significant; ****p<0.0001. q, Spider plots of tumor growth data shown in p.

Source data

Extended Data Fig. 2 Depletion of KDM5B in mouse melanoma cells activates the type I interferon response through both RNA and DNA sensing pathways.

a, Gene set enrichment analysis comparing RNA-seq data from Kdm5b−/− (Kdm5b sg) YUMMER1.7 cells with control YUMMER1.7 cells. Shown are selected top upregulated pathways (FDR<=0.05). b, Western blot analyses of DNA and RNA sensors, as well as signalling proteins involved in the type I IFN pathway. T-, total; p-, phosphorylated. For gel source data, see Supplementary Fig. 1. c, RT-qPCR analyses of ISGs in Ifngr1−/− YUMMER1.7 mouse melanoma cells with Ctl sg, Kdm5b sg1 and Kdm5b sg2. d, RT-qPCR analyses of ISGs (left) and western blot analyses (right) of YUMM1.7 mouse melanoma cells with Ctl sg and Kdm5b sg. For gel source data, see Supplementary Fig. 1. e, RT-qPCR analyses of ISGs in 1445 mouse melanoma cells with Ctl shRNA and Kdm5b shRNA. f, Western blot analyses of the indicated cell lines used in Figure 2b and Extended Data Fig. 2g. For gel source data, see Supplementary Fig. 1. g, RT-qPCR analyses of selected ISGs in the indicated YUMMER1.7 cells treated with 10 ng/ml IFNγ for 24h. Data are mean ± SD. h, Western blot analyses of the indicated cell lines, used in Figure 2c. For gel source data, see Supplementary Fig. 1. i, RT-qPCR analyses (left) and western blot analyses (right) of YUMM1.7-Kdm5b sg cells with Ctl sg, Sting1 sg, Ddx58 (RIG-I gene) sg, Mda5 sg, Mavs sg, or Cgas sg. For gel source data, see Supplementary Fig. 1. j, RT-qPCR analyses of IRF9 in1445-Kdm5b sg cells with Ctl sg, Sting1 sg, Ddx58 sg, Mda5 sg, Mavs sg, or Cgas sg. k, RT-qPCR analyses of ISGs from YUMM1.7-Kdm5b sg cells treated with Ctl siRNA, Sting1 siRNA or Mavs siRNA for 3 days. l, RT-qPCR analyses of ISGs from 1445 cells with Ctl shRNA treated with Ctl siRNA, 1445 cells with Kdm5b shRNA treated with Ctl siRNA, Sting1 siRNA, or Mavs siRNA cells for 3 days. Data are means ± SEM. m, Tumor growth curve (left) and survival curve (right) of C57BL/6 mice injected with YUMMER1.7 cells of the indicated genotypes, WT (n = 5), Kdm5b−/−-Mda5−/− Cgas−/−-C1 (n = 4), Kdm5b−/−-Mda5−/− Cgas−/−-C3 (n = 4), Kdm5b−/− (n = 5). C1 and C3 are clone numbers. Data represent one experiment. Data are mean ± SEM. 2-way ANOVA analysis for comparison at day 20. n.s., not significant; ****p<0.0001. Log-rank test for survival curves. **p = 0.004 for C1 vs Kdm5b−/−; **p = 0.005 for C3 vs Kdm5b−/−; *p = 0.023 for C3 vs WT. Unpaired two-sided Students’ t tests were performed for all RT-qPCR data (c-e,g, i-l). n.s., not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

Source data

Extended Data Fig. 3 Deletion of KDM5B in mouse melanoma cells increases MHC-I expression, and induces expression of genes involved in antigen processing/presentation and endogenous retroelements.

a–c, Flow cytometry analysis for MHC-I levels (left) and RT-qPCR analyses of MHC-I genes and genes involved in antigen processing and presentation (right) in YUMMER1.7 cells with Ctl sg, Kdm5b sg1 or sg2 (a), YUMM1.7 cells with Ctl sg, Kdm5b sg1 or sg2 (b), and YUMM3.3 cells with Ctl shRNA or Kdm5b shRNA (c). Data are mean ± SEM. d,e, RT-qPCR analyses of retroelements in YUMM1.7 cells (d) and 1445 cells (e) with Ctl shRNA or Kdm5b shRNA. Data are mean ± SEM. Statistical analysis was done with unpaired two-sided Student’s t test. *p<0.05, **p<0.01, *** p<0.001 and ****p<0.0001.

Extended Data Fig. 4 Deletion of KDM5B induces endogenous retroelement expression and forms dsRNA.

a,b, Immunofluorescence staining of dsRNA with J2 antibody (red) and DAPI (blue) in WT or Kdm5b−/− YUMMER1.7 cells, using a confocal microscope (a) (scale bar = 50 µm) and 1445 cells with Ctl shRNA or Kdm5b shRNA (b) (scale bar = 50 µm). c, Immunofluorescence staining of dsRNA with J2 antibody (red) and DAPI (blue) of WT or Kdm5b sg YUMMER1.7 tumors harvested 14 days after tumor injection (scale bar = 100 µm). d. Immunofluorescence staining (left) and quantification (right) of dsRNA with J2 antibody (red) and DAPI (blue) of representative patients as responder or non-responder to ICB (scale bar = 100 µm). MGLI= mean grey level intensity. e, Volcano plots comparing inverted repeats in YUMMER1.7 cells with Kdm5b sg vs YUMMER1.7 cells with Ctl sg. f, Western blot analyses of Kdm5b sg YUMMER1.7 cells treated with 20 µM of reverse transcriptase inhibitors (RTi) for 48h. For gel source data, see Supplementary Fig. 1. g, qPCR analyses of retroelements from DNA isolated from cytosolic (cyto) lysate versus from nuclear lysate. Data are mean ± SD. h, Relative copy number of mitochondrial (mito) DNA quantified by qPCR analyses with Dloop primers. Data are mean ± SD. i, RT-qPCR analyses of MMVL30-int, RLTR6_Mm and Isg15 in Kdm5b−/− cells with the indicated doxycycline (Dox) inducible shRNAs. Data are mean ± SEM. p values for g-i were calculated using unpaired two-sided Students’ t tests. n.s., not significant; **p<0.01; ***p<0.001; ****p<0.0001.

Extended Data Fig. 5 KDM5B represses retroelement expression in a demethylase-independent manner.

a,b, ChIP–qPCR analyses of WT and Kdm5b−/− (KO) YUMMER1.7 cells. Data are mean± SEM. c,d, Western blot (c) and RT-qPCR analyses of retroelements and ISGs (d) in YUMMER1.7 treated with 1 µM, 3 µM and 5 µM of KDM5i (CPI-48 and KDM5-C70) for 7 days. For gel source data, see Supplementary Fig. 1. e, RT-qPCR analyses of retroelements and ISGs (left) and western blot analyses (right) of YUMMER1.7 cells treated with DMSO or EZH2 inhibitors (5 µM EPZ6438, 5 µM GSK343) for 10 days. For gel source data, see Supplementary Fig. 1. f, RT-qPCR analyses (left) and western blot analyses (right) of YUMMER1.7 cells treated with DMSO or chaetocin (10, 20, and 30 nM), an inhibitor of H3K9me3 methyltransferases, for 3 days. g,h, ChIP–qPCR analyses of WT and Kdm5b−/− (KO) YUMMER1.7 cells. i,j, RT-qPCR analyses (i) and western blot analyses (j) of YUMMER1.7 cells with Ctl sg, G9a sg (1-3) or Suv39h1 sg (1-3). For gel source data, see Supplementary Fig. 1. k, RT-qPCR analyses of MMVL30 in YUMMER1.7 cells with Ctl sg, G9a sg (1-3), Suv39h1 sg (1-3), and Setdb1 sg (1-3). l, Western blot analyses of YUMMER1.7 cells with Ctl sg or Setdb1 sg (1-3). For gel source data, see Supplementary Fig. 1. m, ChIP–qPCR analyses of WT YUMMER1.7 cells with GFP, Kdm5b−/− YUMMER1.7 cells, with GFP control, or reconstituted expression of WT-KDM5B or catalytically inactive mutant (MT) KDM5B. For a,b, d-h, i,k,m, data are means ± SEM. Statistical analysis was done with unpaired two-sided Student’s t test. n.s. not significant, *p<0.05, **p<0.01, *** p<0.001 and ****p<0.0001.

Extended Data Fig. 6 KDM5B directly binds to the retroelements induced by Kdm5b loss.

a, Genomic annotations of Kdm5b binding peaks from KDM5B ChIP–seq in YUMMER1.7 cells. b, Heatmaps of input-subtracted KDM5B, SETDB1 and H3K9me3 ChIP–seq signal averaged across all loci per retroelement in YUMMER1.7 cells, sorted by KDM5B binding intensity. Top 10 significantly increased retroelements plus “MMERVK10C-int, RLTR6-int, and RLTR6B_Mm“ are labeled. c, Violin plots of RNA-seq analysis showing differential expressed endogenous retroelements categorized in classes (three classes of ERVs, LINEs and SINEs) in 1445 mouse melanoma cells with Kdm5b sg vs. Ctl sg. d, Input-subtracted KDM5B ChIP–seq signal averaged across all loci per retroelement in 1445 cells. KDM5B signal on retroelements were sorted by intensity. Top 10 significantly increased retroelements are labeled. e, Venn diagram showing overlapping ChIP–seq binding regions shared between KDM5B and SETDB1. KDM5B only peaks, n = 25,315; Shared peaks, n = 66,503; SETDB1 only peaks, n = 16,934. f, Box plot showing RNA-seq log2 Fold Change (FC) of (Kdm5b-/- vs WT) for four different categories of TE located within overlapping regions between KDM5B and SETDB1. g, Venn diagram showing overlapping ChIP–seq binding regions shared among KDM5B, SETDB1 and H3K9me3. h,i, Western blot analyses of KDM5B (h) or SETDB1 (i) immunoprecipitates from nuclear extracts of 1445 mouse melanoma cells, MC38 mouse colon cancer cells or YUMM1.7 mouse melanoma cells. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 7 ChIP–seq and ATAC-seq analyses of epigenomic changes induced by Kdm5b loss.

a. Aggregated RNA-seq, KDM5B, SETDB1, H3K9me3, H3K4me3 and H3K4me2 ChIP–seq signals of MMVL30-int loci in WT and Kdm5b-/- (KO) YUMMER1.7 cells. b-c. IGV (Integrative Genomic Viewer) screenshots of KDM5B, SETDB1, H3K9me3, H3K4me3, and H3K4me2 ChIP–seq signals from WT and Kdm5b−/− YUMMER1.7 cells. d. Volcano plot showing ATAC-seq signals from WT and Kdm5b−/− YUMMER1.7 cells; Peaks enriched significantly in Kdm5b−/− cells are in red and enriched significantly in WT cells are in blue (FDR < 0.05). Significant changed MMVL30-int loci are marked in black. e. Box plot showing ATAC-seq log2 Fold Change of (Kdm5b−/− vs WT YUMMER1.7) for four different categories of TE. f. Pie charts showing proportion of increased or unchanged ATAC peaks within KDM5B domains in Kdm5b−/− vs WT YUMMER1.7 cells. g. Comparison of ATAC-seq signals among baseline or IFNγ treated Kdm5b−/− (KO) and WT YUMMER1.7 cells. h-l, Motif sequences (left) and top 5 best matched transcription factor (TF) with corresponding p value (right) from de novo motif analysis of KDM5B binding regions (h), increased ATAC seq peaks in Kdm5b−/− (KO) compared to WT YUMMER1.7 cells (i), increased ATAC seq peaks in IFNγ treated Kdm5b−/− compared to WT YUMMER1.7 cells (j), retroelements within KDM5B binding regions, with increased ATAC seq peaks in Kdm5b−/− cells compared to WT YUMMER1.7 cells (k), and retroelements within KDM5B binding regions, with increased ATAC seq peaks in IFNγ treated Kdm5b−/− (KO) YUMMER1.7 compared to IFNγ treated WT YUMMER1.7 cells (l).

Extended Data Fig. 8 KDM5B regulates SETDB1 protein stability and is required for the recruitment of SETDB1 to retroelements, some of which encode potential antigens.

a, Western blot analyses of SETDB1 in cytosolic and nuclear extracts from Kdm5b−/− YUMMER1.7 cells treated with DMSO, 5 µM or 10 µM proteasome inhibitor MG132 for 21 h. For gel source data, see Supplementary Fig. 1. b, Western blot analyses of YUMMER1.7 cells with GFP, Kdm5b−/− YUMMER.17 cells with GFP, WT SETDB1, or MT SETDB1. For gel source data, see Supplementary Fig. 1. c, RT-qPCR analyses of retroelements and ISGs in Kdm5b−/− YUMMER1.7 cells with GFP, WT SETDB1, or MT SETDB1, in the absence or presence of 100 nM IFNγ for 24 h. d, H3K9me3 ChIP–qPCR analyses of Kdm5b−/− YUMMER1.7 cells with GFP, SETDB1, WT KDM5B or MT KDM5B. c-d, Data are means ± SEM. Statistical analysis was done with unpaired two-sided Student’s t test. P values for c are from comparison to the GFP control with the same IFNγ treatment status. n.s. not significant; *p<0.05; **p<0.01; ***p<0.001 and ****p<0.0001. e,f, Kaplan–Meier survival curves (e) and spider plots of tumor growth (f) of C57BL/6 mice injected with Kdm5b−/− YUMMER1.7 cells with GFP (n = 6) or SETDB1 (n = 6). P values were determined using the log-rank test, n.s. not significant. g, Flow cytometry analysis for MuLV envelope protein (Env) in YUMMER1.7 cells with Ctl sg or Kdm5b sg. h, Quantification of flow cytometry analysis for P15E+CD8+ T cells in YUMMER1.7 tumors with Ctl sg (n = 3) or Kdm5b sg (n = 7). Data are means ± SEM. Statistical analysis was done with unpaired two-sided Student’s t test. n.s. not significant. i, Quantification showing the percentage of retroelement encoded peptides that were exchanged onto H2-Kb in vitro.

Source data

Extended Data Fig. 9 Knockdown of KDM5B in human melanoma cells induces ISGs and ERVs through RIG-I and MAVS.

a,b, RT-qPCR analyses of ERVs and ISGs in YURIF (a) or YUDOSO (b) with Ctl or KDM5B shRNA. c, RT-qPCR analyses of ISGs (left) and western blot analyses of DNA and RNA sensors and the type I IFN pathway (right) in YUDOSO human melanoma cells with KDM5B shRNA and Ctl sg, TLR3 sg, RIG-I (DDX58) sg, MDA5 sg, CGAS sg, MAVS sg, or STING1 sg. For gel source data, see Supplementary Fig. 1. Data are means ± SEM. Statistical analysis was done with unpaired two-sided Student’s t test. **p<0.01, ***p<0.001 and ****p<0.0001.

Extended Data Fig. 10 Correlation analysis of human ERVs with KDM5B expression and selected epigenetic regulators with patient response to anti-PD-1 treatment, and the working model summarizing the mechanistic findings.

a, Heatmap showing highly differential expressed human ERVs in patient biopsies prior to anti-PD-1 treatment. Samples were ranked by KDM5B expression, with patient responses to anti-PD-1 treatment annotated (PD, progressive disease; PR, partial response; CR, complete response). b, ERVmap_2637 expression is inversely correlated with KDM5B expression in patient cohort described in panel a. Gray zone represents 95% confidence interval for the linear model prediction. c-k, Box plot showing expression of ERVmap_2637 (c), KDM5A (d), KDM5C (e), KDM5D (f), EZH2 (g), KDM1A (h), DNMT1 (i), DNMT3B (j) and SETDB1 (k) in patient cohort described in panel a. Statistical analysis was done with unpaired two-sided Student’s t test. l, The working model showing that KDM5B suppresses anti-tumor responses by recruiting SETDB1 to inhibit endogenous retroelements and type I interferon response. This panel is created with BioRender.com.

Supplementary information

Supplementary Figs. 1 and 2

Supplementary Fig. 1 shows gel source data. Supplementary Fig. 2 shows the gating strategies used.

Reporting Summary

Peer Review File

Supplementary Tables 1–18

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, SM., Cai, W.L., Liu, X. et al. KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements. Nature 598, 682–687 (2021). https://doi.org/10.1038/s41586-021-03994-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-03994-2

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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