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In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer

Nature Immunology volume 23, pages 1495–1506 (2022)Cite this article

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Abstract

The immune system can eliminate tumors, but checkpoints enable immune escape. Here, we identify immune evasion mechanisms using genome-scale in vivo CRISPR screens across cancer models treated with immune checkpoint blockade (ICB). We identify immune evasion genes and important immune inhibitory checkpoints conserved across cancers, including the non-classical major histocompatibility complex class I (MHC class I) molecule Qa-1b/HLA-E. Surprisingly, loss of tumor interferon-γ (IFNγ) signaling sensitizes many models to immunity. The immune inhibitory effects of tumor IFN sensing are mediated through two mechanisms. First, tumor upregulation of classical MHC class I inhibits natural killer cells. Second, IFN-induced expression of Qa-1b inhibits CD8+ T cells via the NKG2A/CD94 receptor, which is induced by ICB. Finally, we show that strong IFN signatures are associated with poor response to ICB in individuals with renal cell carcinoma or melanoma. This study reveals that IFN-mediated upregulation of classical and non-classical MHC class I inhibitory checkpoints can facilitate immune escape.

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Fig. 1: In vivo genome-scale screens reveal mechanisms of immunotherapy resistance and sensitization.
Fig. 2: Loss of IFN signaling sensitizes tumors to ICB.
Fig. 3: IFN-mediated inhibition of antitumor immunity is dependent on MHC class I presentation.
Fig. 4: ICB activates CD4+ T cells and NK cells to eliminate IFN sensing-deficient tumors.
Fig. 5: Qa-1b/NKG2A is an ICB-induced immune checkpoint for CD8+ T cells.
Fig. 6: IFN inflammation is associated with immunotherapy resistance in melanoma.

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Data availability

Bulk in vitro RNA-seq and in vivo 10x scRNA-seq data have been deposited in Gene Expression Omnibus under the accession codes GSE206443 and GSE206668, respectively. The mouse reference genome GRCm38 (mm10) was used for sgRNA design and RNA-seq analyses. RNA-seq expression data from the Riaz44, Liu42 and Braun28 studies were obtained from their respective manuscripts. Raw RNA-seq data from the Riaz44 study are available under BioProject accession number PRJNA356761 or SRA SRP094781. Raw RNA-seq data from the Liu42 study are available in dbGAP under accession number phs000452.v3.p1. Raw RNA-seq data from the Gide43 study are available in the European Nucleotide Archive under accession number PRJEB23709. Source data are provided with this paper.

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Acknowledgements

We thank all members of the Manguso lab, E. Van Allen and N. Vokes of the Dana-Farber Cancer Institute, T. Golub, T. Sundberg and J. Growney of the Broad Institute and D. Stokoe and A. Firestone of Calico Life Sciences for discussions and feedback. We also thank A. Kohlgruber, D. Sen, B. Miller, R. Jenkins and G. Griffin for discussion and feedback. We thank P. Rogers and Broad Flow Cytometry staff for assistance with cell sorting. We thank J. Stathopoulos, T. Caron and Broad vivarium staff for assistance with animal studies.

Author information

Author notes

  1. Juan Dubrot

    Present address: Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain

  2. Peter P. Du

    Present address: Stanford University School of Medicine, Stanford, CA, USA

  3. W. Nicholas Haining

    Present address: ArsenalBio, South San Francisco, CA, USA

  4. These authors contributed equally: Juan Dubrot, Peter P. Du.

  5. These authors jointly supervised this work: Kathleen B. Yates, Robert T. Manguso.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Juan Dubrot, Peter P. Du, Sarah Kate Lane-Reticker, Emily A. Kessler, Audrey J. Muscato, Arnav Mehta, Samuel S. Freeman, Peter M. Allen, Kira E. Olander, Kyle M. Ockerman, Clara H. Wolfe, Fabius Wiesmann, Nelson H. Knudsen, Hsiao-Wei Tsao, Arvin Iracheta-Vellve, Emily M. Schneider, Andrea N. Rivera-Rosario, Ian C. Kohnle, Hans W. Pope, Austin Ayer, Gargi Mishra, Margaret D. Zimmer, Sarah Y. Kim, Animesh Mahapatra, Hakimeh Ebrahimi-Nik, W. Nicholas Haining, David E. Root, John G. Doench, Nir Hacohen, Kathleen B. Yates & Robert T. Manguso

  2. Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA

    Juan Dubrot, Arnav Mehta, Samuel S. Freeman, Nelson H. Knudsen, Dennie T. Frederick, Genevieve M. Boland, Nir Hacohen, Kathleen B. Yates & Robert T. Manguso

  3. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Juan Dubrot, Arnav Mehta, Samuel S. Freeman, Nelson H. Knudsen, Dennie T. Frederick, Genevieve M. Boland, Nir Hacohen, Kathleen B. Yates & Robert T. Manguso

  4. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Dennie T. Frederick & Genevieve M. Boland

  5. Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    W. Nicholas Haining

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Contributions

Study design: J.D., P.P.D., W.N.H., K.B.Y and R.T.M. Design and execution of in vivo screens: J.D., P.P.D., S.K.L.-R., E.A.K., K.E.O., K.M.O., C.H.W., I.C.K., H.W.P., A.A., G.M., M.D.Z., A. Mahapatra, J.G.D., K.B.Y. and R.T.M. Analysis and interpretation of data: P.P.D., J.D., S.S.F., S.Y.K., D.E.R., J.G.D., K.B.Y. and R.T.M. Execution of in vitro and in vivo validation studies: J.D., S.K.L.-R., E.A.K., A.J.M., P.M.A., K.E.O., K.M.O., F.W., N.H.K., H.-W.T., C.H.W., A.N.R.-R., I.C.K., A.I.-V., H.E.-N., E.M.S., H.W.P., A.A., G.M., M.D.Z. and A. Mahapatra. Human serum protein analysis: N.H., A. Mehta, G.M.B. and D.T.F. Manuscript writing and revision: J.D., P.P.D., S.K.L.-R., E.A.K., K.B.Y. and R.T.M.

Corresponding authors

Correspondence to Kathleen B. Yates or Robert T. Manguso.

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Competing interests

K.B.Y. and R.T.M. receive funding from Calico Life Sciences, LLC. P.P.D. consults for and owns equity in Related Sciences. J.G.D. consults for Agios, Foghorn Therapeutics, Maze Therapeutics, Merck and Pfizer; J.G.D. consults for and has equity in Tango Therapeutics. A.I.-V. is a current employee of AstraZeneca. W.N.H. is an employee of ArsenalBio and has equity in Merck & Co., Tango Therapeutics and ArsenalBio. H.W.P. is an employee of ArsenalBio. D.E.R. receives research funding from members of the Functional Genomics Consortium (Abbvie, BMS Jannsen, Merck, Vir) and is a director of Addgene, Inc. A. Mehta has consulted/advised Third Rock Ventures, Asher Biotherapeutics, Abata Therapeutics, venBio Partners, BioNTech, Rheos Medicines and Checkmate Pharmaceuticals and is an equity holder in Asher Biotherapeutics and Abata Therapeutics. N.H. owns equity in BioNTech and owns equity and advises Danger Bio. R.T.M. has served as a consultant for Bristol Myers Squibb. The remaining authors declare no competing interests.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. N. Bernard was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Tumor growth kinetics and treatment responses in genome-scale screens.

Tumor volume over time for in vivo genome-scale screening groups: cells injected into NSG, WT, or WT IBC-treated mice as indicated (n ≥ 20 mice/group were measured for tumor volume graphs). Data are represented as mean ± SEM from one pool.

Extended Data Fig. 2 Genome-scale screens: quality control metrics.

a, In vitro and in vivo library recovery. Dotted lines represent abundance at 5th and 95th percentile for each library. b, Replicate autocorrelation analysis. Pearson’s correlations are calculated for the library distribution in one 10-tumor replicate versus any other replicate, two averaged replicates versus any other two, and so on. The mean of all possible combinations is plotted. c, Effect size model for sgRNAs. A natural cubic spline (solid line) was fit between NSG and WT or ICB sgRNA abundances, and the effect size is calculated as the residual from the spline. d, Normalized effect size distributions for the WT versus NSG and ICB versus NSG comparisons; distributions for control sgRNAs are shown in dotted lines, distributions for gene-targeting sgRNAs are shown in solid lines.

Extended Data Fig. 3 Genome-scale screens: additional analysis of tumor-immune dependencies.

a, b, Genes ranked by (a) WT vs NSG or (b) ICB vs WT normalized fold change with circle size corresponding to -log10 FDR. The top genes with FDR < 0.25 at each end are listed and arranged by statistical significance. c, Comparison of the number of depleted gene deletions with FDR < 0.25 in the ICB vs NSG comparison that scored across combinations of models. d, Comparison of the number of enriched versus depleted genes recovered at FDR < 0.25 for ICB vs NSG or WT vs NSG comparisons. e, Paired fold change for all genes that were enriched or depleted with FDR < 0.25 in the ICB vs NSG or WT vs NSG comparisons. Median values are plotted by the center line. The upper and lower hinges correspond to the first and third quartiles. The upper and lower whiskers extend to no further than 1.5 * the interquartile range of their respective hinges. (for enriched and depleted, B16: n = 58, 222; CT26: n = 7, 14; KPC: n = 3, 32; LLC: n = 56, 36; MC38: n = 63, 93; Panc02: n = 31; Renca: n = 30; YUMMER: n = 10, 40) (for enriched and depleted, B16: p = 4.741 × 10−10, 6.728 × 10−41; CT26: p = 0.00212, 0.0236; KPC: p = 0.199, 1.504 × 10−9; LLC: p = 4.635 × 10−20, 3.079 × 10−13; MC38: p = 0.00875, 0.187; Panc02: p = 0.000661; Renca: p = 0.0235; YUMMER: p = 0.000338, 0.000622). *p < 0.05, **p < 0.01, ***p < 0.001 [paired, one-sided Student’s t-test].

Extended Data Fig. 4 Sub-genome screens.

a, Pre-ranked GSEA of enriched and depleted sub-genome screen hits in the corresponding genome screen. Gene sets in sub-genome screens were defined with FDR < 0.25 threshold separately for enriched and depleted genes, and GSEA enrichment was performed by ranking genes by signed STARS score in the genome screen. Significance was assessed by the GSEA algorithm permutation test. b, Genes ranked by ICB vs NSG normalized fold change with circle size corresponding to -log10 FDR. The top genes with FDR < 0.25 at each end are listed and arranged by statistical significance.

Source data

Extended Data Fig. 5 MHC-I is a potential immune inhibitory ligand downstream of IFN.

a, Scatter plot of IFNγ-induced gene expression change (x-axis) by enrichment or depletion in WT vs NSG comparison across in vivo screens (y-axis) for all genes included in the genome-scale screening library. Dot size indicates aggregate screen score. b, Gene expression fold change of NK ligands, IFN signaling pathway, antigen processing and presentation machinery, classical and non-classical class I MHC, and immune inhibitory receptor genes in IFNγ or IFNβ stimulation compared to baseline expression. c, Histograms showing cell surface expression of H2-K, H-2D, Qa-1b or PD-L1 measured by flow cytometry on CT26 and KPC cells with (red) and without (grey) IFNβ stimulation. d, Histograms showing cell surface expression of H2-Db and H2-Kb on KPC Cas9 cells transduced with control, Tap1, or Jak1 sgRNA and cultured with or without IFNγ in vitro. e, Log fold change in the ratio of tumor cells with sgRNAs targeting Ifngr1 or Jak1 vs control sgRNA within CT26 tumors in a control (grey) or Tap1-null (purple) genetic background, normalized to the ratio for tumors implanted in NSG mice (Ifngr1 No Tx: p = 0.0014; Jak1 No Tx: p < 0.0001; anti-PD-1: p = 0.003). Experiments were conducted using n ≥ 5 mice/group from at least two independent experiments. Data in bar plots are presented as mean ± SEM. **p < 0.01, ****p < 0.0001 [unpaired, two-sided Student’s t-test].

Extended Data Fig. 6 6p21.3 loss in human cancers.

a, Frequency of 6p21.3 deletion across TCGA histopathologies and their overall survival hazard ratios as calculated by Cox proportional hazard models. P-values are calculated from z-statistics. Data are presented as hazard coefficients and 95% confidence intervals. b, Kaplan-Meier plots of overall survival in the Braun patient cohort, stratified by 6p21.3 copy number status. c, Univariate Cox proportional hazard models for the Braun data set, calculated for the nivolumab treated patient cohort (n = 48 patients for 6p21.3 disomy and 46 patients for 6p21.3 deletion). P-values are calculated from z-statistics. Data are presented as hazard coefficients and 95% confidence intervals.

Extended Data Fig. 7 scRNAseq analysis.

a, All cells labeled by cell type according to marker gene expression. b, Heatmap of differentially expressed marker genes used to label cell populations. c, Violin plots showing expression of NKG2 and Ly49 receptor family members in all cell populations. Y-axis represents log-transformed expression. d, Expression of Klrc1 in CD8+ T cell, NK cell, and innate lymphoid cell populations. Populations are ordered by mean expression, which is indicated by the solid line. e, UMAP density projections showing shifts in the innate lymphoid cell compartment in response to 100 μg anti-PD-1 and 100 μg CTLA-4 treatment (ICB) on days 6 and 9. f, Quantification of cell population changes in e. Each point represents immune infiltrate from a different tumor (n = 917 cells from untreated animals and 1123 cells from treated animals over 4 animals per group) (NK: p = 0.0319; ILC1: p = 0.00142). Data in bar plots are presented as mean ± SEM. ns p > 0.05, *p < 0.05, **p < 0.01 [unpaired, two-sided Student’s t-test].

Extended Data Fig. 8 Tumor IFN sensing inhibits NK cell cytotoxicity via upregulation of classical MHC-I.

a, Log fold change in the ratio of control (grey) or Tap1-null (purple) CT26 tumor cells transduced with Jak1 or control sgRNAs (n = 5 mice/group) cultured for 48 hours with activated NK cells at effector:target ratios of 0:1, 4:1 (p = 0.0319 [unpaired, two-sided Student’s t-test]), and 8:1 (p = 0.0189 [unpaired, two-sided Student’s t-test]), normalized to the 0:1 ratio for each condition. Data in bar plots are presented as mean ± SEM. b, Scatter plot of in vivo CT26 screening data showing average fold change by gene for the ICB vs NSG sub-genome-scale screen (x-axis) against the ICB + anti-asialo GM1 vs NSG screen (y-axis). Circle size is scaled to FDR in the ICB + anti-asialo GM1 vs NSG screen. *p < 0.05 (a).

Extended Data Fig. 9 Qa-1b is an immune inhibitory ligand downstream of IFN.

a, Average normalized fold change by aggregate STARS score for the ICB vs NSG comparison. b, Average normalized fold change by aggregate STARS score for the WT vs NSG comparison. c, Tumor volume over time for KPC (left), Renca (mid) or YUMMER (right) cells transduced with control (grey lines) or H2-T23-targeting (red lines) sgRNA implanted into untreated WT mice (n = 5-10 mice/group; KPC, p = 0.711; Renca, p = 0.446, YUMMER, p = 0.611 [unpaired, two-sided Student’s t-test]). d, Relative transcript abundance of H2-T23 mRNA across cell lines treated with and without IFNγ, measured by RNAseq. e, Histograms showing cell surface expression of Qa-1b measured by flow cytometry on MC38 cells transduced with hCD19 or constitutive or IFNγ-inducible Qa-1b overexpression constructs. f, Tumor volume over time for MC38 cells overexpressing hCD19 or Qa-1b from a constitutive (EF1a) or interferon-inducible (Irf1) promoter implanted into untreated mice (n = 10 mice/group; EF1a, p = 0.145, Irf1, p = 0.232 [unpaired, two-sided Student’s t-test]). Data are represented as mean ± SEM.

Extended Data Fig. 10 The Qa-1b/NKG2A axis is an interferon-dependent T cell regulator.

a, Dot plot showing expression of NKG2 family and activation and exhaustion marker gene transcripts on subpopulations of CD8+ T cells. b, Gating strategy for flow cytometry analysis of NKG2A expression on CD8+ T cells and NK cells in tumor infiltrating lymphocytes from KPC tumors. c, Histograms showing cell surface staining for NKG2A measured by flow cytometry on isolated splenic CD8+ T cells stimulated with anti-CD8 and anti-CD28 and cultured in IL-2 alone (top) or IL-2 and IL-12 (bottom). d, Log fold change in the ratio of EF1a-Qa1 to CD19-overexpressing MC38 Ova tumor cells co-cultured for 48 h with activated OT-I T cells at indicated effector:target ratios. Three replicates of 5 × 104−8 × 104 tumor cells were used per condition, data are representative of two independent experiments. Data in bar plots are presented as mean ± SEM. e, Schematic for in vivo competitive OT-I transfer assay; created with BioRender.com. f, Log fold change in the ratio of MC38-Ova tumor cells overexpressing EF1a-Qa1 versus CD19 implanted in NSG mice that were injected intravenously with 3e6 activated OT-I CD8+ T cells on day 6 post-implantation (n = 8 OT-I-transferred mice; n = 3 control -no transfer- mice, p = 0.0203 [unpaired, two-sided Student’s t-test)]. Data in bar plots are presented as mean ± SEM. g, Histograms showing cell surface expression of PD-L1 or Qa-1b measured by flow cytometry on KPC cells transfected with Cas9 and sgRNA targeting Jak1 or control with and without IFNγ treatment. h, Contour plots showing representative populations of control KPC tumor cells and mCD19+ KPC tumor cells co-cultured for 72 hours with untransduced (UTD) T cells or mCD19 CAR-T cells at an effector to target ratio of 2:1. i, Serum protein expression in melanoma patients pre- and post-ICB. Heatmap showing normalized fold changes in serum protein expression of 50 ISGs in patients with melanoma 6 weeks post ICB treatment and GSEA enrichment of non-responders. *p < 0.05 (f).

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Supplementary Tables 1–20.

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Dubrot, J., Du, P.P., Lane-Reticker, S.K. et al. In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer. Nat Immunol 23, 1495–1506 (2022). https://doi.org/10.1038/s41590-022-01315-x

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