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Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity

Abstract

Epigenetic dysregulation is a defining feature of tumorigenesis that is implicated in immune escape1,2. Here, to identify factors that modulate the immune sensitivity of cancer cells, we performed in vivo CRISPR–Cas9 screens targeting 936 chromatin regulators in mouse tumour models treated with immune checkpoint blockade. We identified the H3K9 methyltransferase SETDB1 and other members of the HUSH and KAP1 complexes as mediators of immune escape3,4,5. We also found that amplification of SETDB1 (1q21.3) in human tumours is associated with immune exclusion and resistance to immune checkpoint blockade. SETDB1 represses broad domains, primarily within the open genome compartment. These domains are enriched for transposable elements (TEs) and immune clusters associated with segmental duplication events, a central mechanism of genome evolution6. SETDB1 loss derepresses latent TE-derived regulatory elements, immunostimulatory genes, and TE-encoded retroviral antigens in these regions, and triggers TE-specific cytotoxic T cell responses in vivo. Our study establishes SETDB1 as an epigenetic checkpoint that suppresses tumour-intrinsic immunogenicity, and thus represents a candidate target for immunotherapy.

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Fig. 1: In vivo chromatin regulator screens.
Fig. 2: SETDB1 (1q21.3) amplification in human tumours.
Fig. 3: SETDB1 targets evolving genomic loci.
Fig. 4: SETDB1 loss induces TE-encoded viral antigens.

Data availability

All genomic sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus database with the accession code GSE155972. The original mass spectra for all experiments, and the protein sequence database used for searches have been deposited in the public proteomics repository MassIVE (https://massive.ucsd.edu) and are accessible at ftp://massive.ucsd.edu/MSV000086580/. Source data are provided with this paper.

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Acknowledgements

This work was supported by funds from Calico Life Sciences, the Gene Regulation Observatory at the Broad Institute of MIT and Harvard, the Damon-Runyon Cancer Research Foundation (to G.K.G. and J.W.), the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (NIH/NCI U24-CA210986 and NIH/NCI U01 CA214125 to S.A.C), the Wong Family Award (to B.C.M.), and the NCI/NIH Director’s Fund (DP1CA216873 to B.E.B.). B.E.B. is the Bernard and Mildred Kayden Endowed MGH Research Institute Chair and an American Cancer Society Research Professor. The authors thank all members of the Bernstein and Manguso laboratories at MGH and the Broad Institute, E. Van Allen, N. Vokes, A. Sharpe, G. Poncet-Montange, D. McKinney, T. Sundberg, J. Growney, D. Stokoe and A. Firestone for thoughtful discussions and feedback. Graphics in Fig. 1a were created with Biorender.com using a paid license.

Author information

Authors and Affiliations

Authors

Contributions

Conception and experimental design: G.K.G., J.W., A.I.-V., J.G.D., J.D.J., S.A.C., W.N.H., K.B.Y., R.T.M. and B.E.B. Methodology and data acquisition: G.K.G., J.W., A.I.-V., J.C.P., J.H., D.D.-O., A.H., S.K., N.H.K., B.C.M., T.H.N., K.E.O., M.P., S.R., E.J.R., E.M.S., M.D.Y., M.D.Z., J.D.J., K.B.Y., R.T.M. and B.E.B. Analysis and interpretation of data: G.K.G., J.W., A.I.-V., J.C.P., J.H., T.D., D.D.-O., A.G.H., P.P.D., J.J.I., S.Y.K., S.K., N.H.K., B.C.M., T.H.N., K.E.O., M.P., S.R., E.J.R., M.D.Y., M.D.Z., J.D.J., W.N.H., K.B.Y., R.T.M. and B.E.B. Manuscript writing and revision: G.K.G., K.B.Y., R.T.M. and B.E.B. In addition to the equally contributing first authors, co-authors J.C.P., J.H. and T.D. each made critical and equal contributions to experimental and computational aspects of this study.

Corresponding authors

Correspondence to Robert T. Manguso or Bradley E. Bernstein.

Ethics declarations

Competing interests

G.K.G. consults for Moderna Therapeutics. J.J.I. consults for Tango Therapeutics and Phenomic AI. B.C.M. consults for Rheos Medicines. J.G.D. consults for Agios, Foghorn Therapeutics, Maze Therapeutics, Merck and Pfizer; J.G.D. consults for and has equity in Tango Therapeutics. S.C.A. is a member of the scientific advisory boards of Kymera, PTM BioLabs and Seer and an ad hoc scientific advisor to Pfizer and Biogen. R.T.M. consults for Bristol Myers Squibb. B.E.B. declares outside interests in Fulcrum Therapeutics, Arsenal Biosciences, HiFiBio, Cell Signaling Technologies and Chroma Medicine. The remaining authors declare no competing interests.

Additional information

Peer review information Nature thanks Daniel De Carvalho, Lelia Delamarre and Didier Trono for their contribution to the peer review of this work.

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 Analysis of screening performance.

a, Tumour volumes (mean ± s.e.m.) of bilateral tumours (n = 25 mice, n = 50 individual tumours) in the LLC (top) and B16 (bottom) screens for the indicated treatment conditions on day 12 (LLC) and day 9 (B16) after tumour inoculation. Statistics by ANOVA with Tukey’s test for multiple comparisons. b, Saturation analysis of animal replicates from the three in vivo screening conditions for LLC (top) and B16 (bottom). Pearson’s correlations are calculated for the log2 guide abundance in one animal versus any other animal, then for two averaged animals versus any other two, and so on. Saturation approaches r = 0.95 for both screens. c, Depletion (negative ratios) or enrichment (positive ratios) of targeted chromatin regulator genes in ICB-treated WT versus NSG mice in the LLC (x-axis) and B16 (y-axis) screens. Circle sizes reflect the significance (−log10(P value)) of depletion in the higher scoring model. Selected genes that scored uniquely in B16 (left) or LLC (right) are highlighted and coloured according to their associated chromatin regulator complexes. d, RNA expression (FPKM) in LLC (x-axis) and B16 (y-axis) for the top 30 screening hits by STARS score in each cell line. Colours indicate whether the gene was depleted in LLC only (orange), B16 only (blue), or in both cell lines (red). One outlier value (x = 11.7, y = 248.7) for the B16-only hit, Cdk2 is excluded for ease of visualization but is included in the calculation of the correlation coefficient. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Source data

Extended Data Fig. 2 Tumour growth and survival data for Setdb1, Trim28, and HUSH complex KO.

a, Tumour growth (mean ± s.e.m.) in untreated WT mice (no ICB) inoculated with Setdb1 (n = 10) Tasor (n = 5), Mphosph8 (n = 5), or Trim28 (n = 5) KO LLC cells, or Setdb1 or Trim28 KO B16 cells. Data are representative of 3 (Setdb1), 1 (Tasor), 1 (Mphosph8), 1 (Trim28 in LLC), and 2 (Trim28 in B16) experiments. Statistics by two-sided Student’s t-test at the indicated time-points. b, Tumour growth (mean ± s.e.m.) in WT mice treated with ICB inoculated with Mphosph8 (n = 20) or Trim28 (n = 20) KO LLC cells. Data represent 1 experiment. Statistics by two-sided Student’s t-test at the indicated time-points. c, Overall survival for untreated (top) and ICB-treated (bottom) WT mice inoculated with B16 (left) or LLC (right) tumours and corresponding to Fig. 1d. Statistics by log-rank test. d, Tumour growth (top, mean ± s.e.m.) and overall survival (bottom) for untreated NSG mice (no ICB) inoculated with Setdb1 KO B16 (left, n = 20) or LLC (right, n = 15). Data represent 1 experiment. Statistics for tumour growth by two-sided Student’s t-test at the indicated time-points. Statistics for overall survival by log-rank test. *P < 0.05; ***P < 0.001; ****P < 0.0001.

Source data

Extended Data Fig. 3 SETDB1 (1q21.3) amplification in human TCGA and ICB-treated cohorts.

a, Running enrichment scores by GSEA for immune gene sets significantly (FDR <0.001) anti-correlated with SETDB1 expression by Pearson’s correlation across TCGA cohorts. b, Pearson’s correlation between SETDB1 expression and cytolytic score (geometric mean of PRF1 and GZMA expression) in TCGA cohorts. Circle size indicates statistical significance (−log10(P value)) of the Pearson’s correlation. Blue and red indicate negative and positive values, respectively. c, Bootstrap analysis plotting the rank of the correlation between cytolytic score and SETDB1 expression (red lines) in each TCGA cohort, compared to 408 randomly selected control genes (grey lines). d, Kaplan–Meier curves for patients with renal cell carcinoma treated with PD-1 blockade (left, nivolumab) or mTOR inhibitor (right, everolimus). Overall survival curves are stratified according to SETDB1 expression (top 50%, high expression; bottom 50%, low expression). Hazard ratios associated with SETDB1 high expression are listed. The number of patients-at-risk are indicated for each time point. Statistics by log-rank test. e, Bootstrap analysis showing the impact of GISTIC2-defined copy-number alterations (CNA) on overall survival in patients treated with mTOR inhibitor (left, everolimus) or PD-1 blockade (right, nivolumab). Positive values indicate a CNA that has a harmful impact on survival with ICB or mTOR inhibitor, and negative values indicate a CNA that has a beneficial effect. 1q21.3 amplification (red) is highlighted alongside chromosomal regions previously reported as predictors of ICB response in RCC, including 10q23.31 deletion (associated with improved response) and 9p21.3 deletion (associated with poor response).

Extended Data Fig. 4 Identification of SETDB1 domains.

a, Heat map of H3K9me3 peaks (rows, FPKM) in control and Setdb1 KO LLC (left) and B16 (right) cells. Peaks are separated based on whether they were lost (top) or retained (bottom) in Setdb1 KO cells, and annotated by whether they are located in the open compartment A of the genome. Statistics for compartment A enrichment by permutation testing. b, The number of 100-kb windows containing the indicated numbers of SETDB1-dependent H3K9me3 peaks in B16 (left) or LLC (right) cells, compared to random control peaks. Statistics by Chi-square test. c, Workflow for annotation of SETDB1-domains from H3K9me3 ChIP–seq data in LLC and B16 cells. *P < 0.05; ****P < 0.0001.

Extended Data Fig. 5 TE-encoded regulatory elements in Setdb1 KO LLC and B16 cells.

a, Proportion of chromatin accessible sites (ATAC-seq) gained in Setdb1 KO LLC or B16 cells that are located within (red) or outside (grey) SETDB1 domains. b, Proportion of ATAC-seq sites gained in Setdb1 KO LLC or B16 cells that coincide with promoters (light grey), distal TEs (red), or other promoter-distal sites (dark grey). Statistics by permutation testing. c, Proportion of gained ATAC-seq sites at distal TEs in Setdb1 KO B16 cells that also gain H3K27 acetylation and resemble active enhancers. d, Coordinate gain of chromatin accessibility and H3K27 acetylation at an example TE-site in Setdb1 KO B16 cells. e, Activation of genes near (<50 kb) gained ATAC-seq sites at distal TEs in Setdb1 KO LLC or B16 cells compared to control genes. Statistics by permutation testing. fg, Flow cytometry in control and Setdb1 KO cells showing gating strategy (f), cell-surface expression (g) (y-axis, median fluorescence intensity (MFI)) for ULBP1 and RAET1 ligands in LLC (left), and MHCI expression in LLC and B16 (right) with or without induction with IFNγ (10 ng ml−1, 24 h). Data are mean ± s.e.m. and reflect 2 independent experiments with 4 biological replicates. Statistics by two-sided Student’s t-test. *P < 0.05; **P < 0.01.

Extended Data Fig. 6 Gene and TE expression in Setdb1 KO LLC and B16 cells.

a, Distribution of TE types (top) and LTR subfamilies (bottom) induced in Setdb1 KO LLC or B16 cells by RNA-seq. b, Heat map showing RNA expression (row normalized) of canonical interferon-stimulated genes in untreated and poly(I:C) stimulated (500 ng ml−1, 48 h) control and Setdb1 KO LLC and B16 cells. c, Percentage of TEs induced in Setdb1 KO LLC or B16 cells that retain intact viral ORFs, compared to control TEs. Statistics by Fisher’s exact test. d, Flow cytometry for cell-surface expression of the MuLV envelope protein in Setdb1 KO LLC and B16 cells. Gating strategy (left) and histograms (right) with mode-normalized cell counts are shown. Data are representative of n = 3 and n = 2 experiments in LLC and B16, respectively. e, Differential protein expression in B16 cells by whole-cell mass spectrometry. Tryptic protein sequences derived from TEs (red) or canonical proteins (grey) are highlighted. Fold-change (x-axis) and statistical significance (y-axis) for proteins in Setdb1 KO versus control are shown. f, Venn diagrams showing the number of predicted, unique TE-encoded H2-Kb/H2-Db binding peptides in LLC and B16 cells by GRCm38 RNA-seq analysis. Diagrams show the total number of predicted, TE-encoded MHC Class I peptides in LLC and B16 cells (left), and subsets showing (1) high expression in control cells and further induction upon Setdb1 KO (middle), and (2) no detectable expression in control cells and strong induction only upon Setdb1 KO (right). Several MuLV-encoded peptides known to be presented by H2-Kb or H2-Db are highlighted. ***P < 0.001. ****P < 0.0001.

Extended Data Fig. 7 TE expression in SETDB1 KO A375 cells.

a, Distribution of TE types (top) and LTR subfamilies (bottom) induced in SETDB1 KO A375 cells by RNA-seq. b, Volcano plot of Hallmark IFN-alpha response genes in A375 cells by RNA-seq. Fold-change (x-axis) and statistical significance (y-axis) in SETDB1 KO versus control are shown. c, Percentage of TEs induced in Setdb1 KO A375 cells that retain intact viral ORFs compared to control TEs. Statistics by Fisher’s exact test. d, Diagram showing the total number of predicted, unique TE-encoded MHCI peptides induced in SETDB1 KO A375 cells by RNA-seq. Binding predictions are based on A375-specific HLA types (see Methods). Subsets of predicted TE-encoded MHCI peptides with (1) high expression in control cells and further induction upon SETDB1 KO, or (2) no detectable expression in control cells and strong induction only upon SETDB1 KO, are highlighted. ***P < 0.001. ****P < 0.0001.

Extended Data Fig. 8 Gene expression and scRNA-seq analysis of immune infiltration in LLC tumours.

ac, Transcriptional profiling with RNA-seq performed on bulk tumour tissue from control (n = 8 untreated and n = 6 ICB-treated) and Setdb1 KO (n = 10 untreated and n = 6 ICB-treated) LLC tumours. Data represent 1 experiment. a, Running enrichment scores by GSEA for immune gene sets significantly (FDR <0.01) upregulated in Setdb1 KO LLC tumours treated with ICB relative to controls. b, Volcano plot depicts expression fold-change (x-axis) and statistical significance (y-axis) of cytotoxicity genes (red) and all other genes (grey) in Setdb1 KO LLC tumours treated with ICB relative to controls. c, TCR repertoire profiling with targeted sequencing of alpha and beta-chain variable regions from Setdb1 KO LLC tumours (untreated and ICB-treated) relative to controls. Variation in clonotype abundance (skewing) is represented by the Gini index (left, higher number indicates greater skewness) and Shannon entropy (right, lower number indicates greater skewness). Data are mean ± s.e.m. Statistics by two-sided Student’s t-test. dh, scRNA-seq (3′) analysis of immune cells (CD45+ enrichment) from control (n = 3 untreated, n = 4 ICB-treated) or Setdb1 KO (n = 3 untreated, n = 4 ICB-treated) LLC tumours. Data are from 1 experiment. d, UMAP plots highlight 4,497 cells and associated clusters identified in the lymphoid compartment. e, Representative marker genes used to identify and annotate cell clusters in d. f, Changes in lymphoid populations in ICB-treated tumours (n = 4 control, n = 4 Setdb1 KO) as a proportion of the total lymphoid population. Data are mean ± s.e.m. Statistics by two-sided Student’s t-test. g, Ratio of NK-2 to NK-1 cells in ICB-treated samples. Data are mean ± s.e.m. Statistics by Mann–Whitney U test. h, Differentially expressed genes (log2(fold-change)) in NK-2 vs NK-1 cells. Circle sizes indicate statistical significance (FDR). *P < 0.05. ****P < 0.0001.

Extended Data Fig. 9 TCR profiling and scRNA-seq of p15E-specific T cells isolated from control and Setdb1 KO LLC tumours.

a, Unique CDR3 sequences (x-axis) identified from TCR sequencing of flow-sorted p15E-tetramer-positive CD8+ T cells isolated from control LLC tumours. High-confidence CDR3 sequences (n = 377) are highlighted by brackets and identified based on strong statistical enrichment (−log10(P value) > 46 cut-off indicated by dotted line, see Methods) within the p15E-tetramer-positive fraction. b, c, scRNA-seq (5′) of 24,860 lymphoid cells (CD4+CD8+ enrichment) isolated from control (n = 4 untreated, n = 4 ICB-treated) and Setdb1 KO (n = 3 untreated, n = 3 ICB-treated) LLC tumours. b, UMAP plot highlights cell populations identified among CD4+CD8+-enriched lymphoid cells. c, Representative marker genes used to identify and annotate cell clusters in b. d, Representative flow cytometry gating strategy for p15E-tetramer studies. Corresponds to Fig. 4e. *P < 0.05.

Extended Data Fig. 10 Survival data and functional studies evaluating MHCI ablation, CD8 depletion, and NK depletion in Setdb1 KO cells.

a, Overall survival for ICB-treated WT mice inoculated with control and Setdb1 KO B16 (left) or LLC (right) cells with intact (B2m WT) or deficient (B2m KO) MHCI, as detailed in Fig. 4f and Methods. Statistics by log-rank test. b, Overall survival for ICB-treated WT mice inoculated with control or Setdb1 KO B16 (left) or LLC (right) cells that received intraperitoneal injections with isotype (left), CD8-depleting (middle), or NK-depleting (right) antibodies starting on day −3 before tumour challenge and continuing every 3 days until day 18, as detailed in Fig. 4f and Methods. Statistics by log-rank test. *P < 0.05; ***P < 0.001.

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Reporting Summary

Supplementary Table 1

List of chromatin regulator genes included in the CRISPR screening library.

Supplementary Table 2

sgRNA depletion and enrichment data for in vivo CRISPR screens in LLC and B16.

Supplementary Table 3

Enrichments for transposable elements and segmental duplications within SETDB1 domains.

Supplementary Table 4

Gene-ontology enrichments for segmental duplications within SETDB1 domains.

Supplementary Table 5

H2-Kb peptides detected by immunopeptidomics in control and Setdb1 KO LLC cells.

Supplementary Table 6

High-confidence CDR3 regions identified by TCR sequencing of p15E-specific CD8+ T cells.

Supplementary Table 7

SRA accession numbers for public RNA-seq data of normal tissues.

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Griffin, G.K., Wu, J., Iracheta-Vellve, A. et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature 595, 309–314 (2021). https://doi.org/10.1038/s41586-021-03520-4

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