Resistance to immune checkpoint inhibitors (ICIs) is a key challenge in cancer therapy. To elucidate underlying mechanisms, we developed Perturb-CITE-sequencing (Perturb-CITE-seq), enabling pooled clustered regularly interspaced short palindromic repeat (CRISPR)–Cas9 perturbations with single-cell transcriptome and protein readouts. In patient-derived melanoma cells and autologous tumor-infiltrating lymphocyte (TIL) co-cultures, we profiled transcriptomes and 20 proteins in ~218,000 cells under ~750 perturbations associated with cancer cell-intrinsic ICI resistance (ICR). We recover known mechanisms of resistance, including defects in the interferon-γ (IFN-γ)–JAK/STAT and antigen-presentation pathways in RNA, protein and perturbation space, and new ones, including loss/downregulation of CD58. Loss of CD58 conferred immune evasion in multiple co-culture models and was downregulated in tumors of melanoma patients with ICR. CD58 protein expression was not induced by IFN-γ signaling, and CD58 loss conferred immune evasion without compromising major histocompatibility complex (MHC) expression, suggesting that it acts orthogonally to known mechanisms of ICR. This work provides a framework for the deciphering of complex mechanisms by large-scale perturbation screens with multimodal, single-cell readouts, and discovers potentially clinically relevant mechanisms of immune evasion.
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Processed data are available via the single-cell portal: https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion. Raw data are available on the Broad Data Use and Oversight System through the accession code DUOS-000124: https://duos.broadinstitute.org.
Code is available at https://github.com/klarman-cell-observatory/Perturb-CITE-Seq.
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).
Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).
Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018).
Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).
Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018).
Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).
Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).
Mbofung, R. M. et al. HSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes. Nat. Commun. 8, 451 (2017).
McKenzie, J. A. et al. The effect of topoisomerase I inhibitors on the efficacy of T-cell-based cancer immunotherapy. J. Natl Cancer Inst. 110, 777–786 (2018).
Huang, L. et al. The RNA-binding protein MEX3B mediates resistance to cancer immunotherapy by downregulating HLA-A expression. Clin. Cancer Res. 24, 3366–3376 (2018).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Veillette, A. & Chen, J. SIRPα–CD47 immune checkpoint blockade in anticancer therapy. Trends Immunol. 39, 173–184 (2018).
Myers, L. M. et al. A functional subset of CD8+ T cells during chronic exhaustion is defined by SIRPα expression. Nat. Commun. 10, 794 (2019).
Zhang, W. et al. Advances in anti-tumor treatments targeting the CD47/SIRPα Axis. Front. Immunol. 11, 18 (2020).
Arulanandam, A. R. et al. The CD58 (LFA-3) binding site is a localized and highly charged surface area on the AGFCC’C” face of the human CD2 adhesion domain. Proc. Natl Acad. Sci. USA 90, 11613–11617 (1993).
Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).
Restifo, N. P., Dudley, M. E. & Rosenberg, S. A. Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12, 269–281 (2012).
Chen, Q., Sun, L. & Chen, Z. J. Regulation and function of the cGAS-STING pathway of cytosolic DNA sensing. Nat. Immunol. 17, 1142–1149 (2016).
Agrawal, S. & Kandimalla, E. R. Intratumoural immunotherapy: activation of nucleic acid sensing pattern recognition receptors. Immunooncol. Technol. 3, 15–23 (2019).
Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to Anti-CTLA-4 therapy. Cell 167, 397–404 (2016).
Challa-Malladi, M. et al. Combined genetic inactivation of β2-microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer Cell 20, 728–740 (2011).
Leitner, J., Herndler-Brandstetter, D., Zlabinger, G. J., Grubeck-Loebenstein, B. & Steinberger, P. CD58/CD2 is the primary costimulatory pathway in human CD28–CD8+ T cells. J. Immunol. 195, 477–487 (2015).
Strioga, M., Pasukoniene, V. & Characiejus, D. CD8+CD28– and CD8+CD57+ T cells and their role in health and disease. Immunology 134, 17–32 (2011).
Boyeau, P. et al. Deep generative models for detecting differential expression in single cells. Preprint at bioRxiv https://doi.org/10.1101/794289 (2019).
Li, W. et al. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 16, 281 (2015).
Li, B. et al. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nat. Methods 17, 793–798 (2020).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Loh, P.-L. & Wainwright, M. J. High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity. Ann. Stat. 40, 1637–1664 (2012).
This work was supported by the Koch Institute-Dana-Farber/Harvard Cancer Center Bridge Project Grant (A.Regev, B.E.J. and B.I.); the Klarman Cell Observatory (A. Regev and O.R.-R.); HHMI (A. Regev); Center for Cell Circuits, NHGRI Center of Excellence in Genomic Science (A. Regev); National Institutes of Health (NIH) grant nos. K08CA222663 (B.I.) and U54CA225088 (B.I.); the Burroughs Wellcome Fund Career Award for Medical Scientists (B.I.); the Louis V. Gerstner, Jr Scholars Program (B.I.); and the Velocity Fellow Program (B.I.). P.I.T. was supported by NIH grant no. F32AI138458. P.H. was supported by NIH grant no. T32GM007367. L.J.-A. was supported by a CRI Irvington Fellowship, the Eric and Wendy Schmidt postdoctoral program and the Burroughs Wellcome Fund Career Award at the Scientific Interface. K.W.W. was supported by the Ludwig Center for Cancer Research at Harvard and NIH grant nos. R01CA238039 and P01CA163222. B.C. was supported by the Broad Fellows. A. Regev was an Investigator of the Howard Hughes Medical Institute. This research was funded in part through the NIH/NCI Cancer Center Support (grant no. P30CA013696) at Columbia University.
A. Regev is a cofounder and equity holder of Celsius Therapeutics, an equity holder in Immunitas and, until 31 July 2020, was an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. From 1 August 2020, A. Regev has been an employee of Genentech. B.I. is a consultant for Merck and Volastra Therapeutics. B.E.J. is on the scientific advisory board of Checkpoint Therapeutics. From 16 November 2020, K.G.S. and O.R.-R. have been employees of Genentech. A. Rotem is a consultant to eGenesis, a SAB member of NucleAI and an equity holder in Celsius Therapeutics. Since 31 August 2020, A. Rotem has been an employee of AstraZeneca. K.W.W. serves on the scientific advisory board of TCR2 Therapeutics, T-Scan Therapeutics, SQZ Biotech and Nextechinvest and receives sponsored research funding from Novartis. K.W.W. is a cofounder of Immunitas, a biotech company. These activities are not related to the research reported in this publication. C.B. is on the scientific advisory board of Myst Therapeutics. A. Regev, B.I., J.C.M., K.R.G.-S., P.I.T. and C.J.F. are inventors on a patent application related to this work.
Peer review information Nature Genetics thanks Dominic Grun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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a, Approach for imaging-based quantification of TIL-mediated killing of melanoma target cells. Plates were imaged at 0, 24, 48 and 72 hours, and viable cell counts were normalized to starting counts to quantify outgrowth of target cells. b-d, Sorting and gating strategy to isolate and expand TIL cultures prior to co-culture. b. TILs grown in IL2 or OKT3-stimulated for 72 hours and analyzed after 4 hours of PMA-I-stimulation. FMO, fluorescent-minus-one control b. TILs from 2686 retain ability to induce IFNγ and TNFa, and OKT-3 reactivation leads to an increase in Granzyme-B production compared to TILs grown in IL2 alone. c, MaMel-134 TILs produce IFNγ, TNFa, and Granzyme-B. d, MaMel-80 TILs produce IFNγ, TNFa, and Granzyme-B. e-h. Impact of time, dose, IFNγ pre-treatment, MHC-I blocking, and OKT3 on TIL-mediated killing in the co-culture system from patient 2686. Ratio of viable cancer cells (y axis, relative to t0) in co-cultures: (e) after different time points of co-culture at increasing TIL:cancer cell ratios (x axis), where TILs were restimulated with immobilized OKT3 for 72 h prior to co-culture; f, after 48 h of co-culture, where cancer cells were pre-treated with 1 ng/ml IFNγ for 16 hours (without prior OKT3-reactivation); g, after 48 hours of co-culture as in (f) but using OKT3-reactivated TILs. h, Specificity of IFNγ pre-treatment approach. Ratio of viable allogenic cancer cells (y axis, relative to t0) in different culture conditions with or without IFNγ pre-treatment (x axis) grown from 0 to 72 hours (color bars) with 2686 TILs with or without prior reactivation with OKT3. For e-h, we performed a one-way ANOVA with Tukey post hoc test. Error bars: Mean±SD. All experiments were performed in triplicates in each of at least two independent experiments.
Extended Data Fig. 2 Generation of Cas9 transgenic patient derived lines and sgDNA library titration.
a, High Cas9 activity in Cas9 transgenic line. Flow cytometry of EGFP levels in Cas9-expressing and parental melanoma cells from patient 2686 transduced with lentivirus encoding EGFP and an EGFP targeting sgRNA at MOI <1 and selected using puromycin. b-d, Transduction of sgDNA lentiviral library to Cas9 transgenic line. b, Proportion of mKATE2+ cells prior to selection (x axis) and survival after puromycin selection (y axis) in 2686 melanoma Cas9 transgenic cells transduced with the ICR library. Line: Linear regression, Pearson R2=0.90. c, Percentage of mKate2+ cells (y axis) in 2686 melanoma Cas9 transgenic cells transduced with the ICR library at virus dilutions (x axis). Red: Dilution used for the Perturb-CITE-seq screen. d. Proportion of cells estimated to be infected by one virus (y axis) at different dilutions of the ICR library (x axis). Red: Dilution used for the Perturb-CITE-seq screen.
a, Dose-response killing in the co-culture experiment validates target killing range. Percent of surviving cells relative to IFNγ pretreated target cells (y axis) in different co-culture conditions (x axis) from a plate run in parallel to the viability and Perturb-CITE screens, with triplicate wells for each condition. One-way ANOVA with Dunnet post-hoc test. Error bars: Mean±SD. b,c, Screen reproducibility across triplicates. Number of reads detected (x, y axis) for each sgDNA (dots) when comparing each pair within triplicate experiments (color legend) in pre-treated day 7 (b) or day 14 (c). Pearson correlation coefficients are noted in the color legend. d-f, Identification of essential genes and genes affecting resistance to TIL mediated killing. Relative depletion (log2(FC), x axis) for each individual sgDNA (red bar) of the top20 target genes by MAGeCK analysis (rows) (n = 3 sgDNAs/target gene, Methods) on day 7 (without TILs, to recover essential genes, d), day 17 of 2:1 TIL:cancer cells co-cultures, comparing control cells (e), or day 17 of IFNγ treated cells (f, no co-culture). Bold lettering indicates significantly enriched / depleted target.
Extended Data Fig. 4 Characterization of different immune pressures by single-cell RNA and protein profiles.
a,b, Removal of profiled T cells. a. UMAP embedding of single cell RNA-Seq profiles from the Perturb-CITE-seq screen, colored by unsupervised cluster assignment34 (Methods). b, A permutation test was used to score marker genes associated with each cluster shown in (a)35. Score (y axis, permutation test, Methods) of marker genes (x axis) associated with the distinct cluster marked by an arrow in (a), include canonical T cell markers. c, Cell complexity affects scRNA-Seq profiles. UMAP embedding of scRNA-Seq profiles of cancer cells only, colored by UMI count bins. d,e, CITE profiles of 20 cell surface proteins do not reflect cell cycle phases (d) or UMI count (e). UMAP embedding of cells (dots) by CITE-seq profiles (dots) colored by cell cycle phase, as scored from scRNA-Seq of the cells, and (e) UMAP of cells by count bins (indicated in legend). f-h, Limited relation between the cell cycle and immune pressure or phenotype. UMAP embedding of cells (dots) based only on RNA expression on cell cycle genes colored by (f) cell cycle phase based on the cell’s RNA profile; (g) MHC protein levels from the CITE signal of the cell; or (h) condition.
a-f, Identification of programs by jackstraw PCA in each condition. a-c. Explained variance (y axis) by each principal component (x axis) for PCA performed on control (a), IFNγ-treated (b), or co-culture (c) Perturb-CITE-seq data. d-f, Number of features (y axis) for each jackstraw program (x axis) for models learned on control (a), IFNγ-treated (b), or co-culture (c) Perturb-CITE-seq data. Dotted red line: cutoff for programs considered in further analysis. g, G2M program learned from control dataset. UMAP embedding of cells (dots) by scRNA-seq profiles, with cells colored by the gene set score (color bar) (Methods) of a G2M cell cycle control program (compare to Fig. 3f) (Methods). h, Identifying related programs across conditions. Jaccard index (color bar) for each pair of programs across all 30 programs (rows).
Extended Data Fig. 6 Addressing cell cycle and complexity covariates by the Perturb-CITE-seq model and impact of targeting vs. non-targeting guides.
a, Estimated Multiplicity of infection (MOI). Distribution of cells (%, y axis) at different estimated MOI (x axis) in each experimental condition (color legend) as determined from the guide dictionary (Methods). b,c, Improved model fit following accounting for cell state as a covariate. Pearson correlation (color bar) between the residuals from the linear model fit for each regulated feature (columns) from models learned without (b) or with (c) cell state covariates accounting for the cell cycle and cell complexity.
a, Validation of CRISPR/Cas9 KO (and unperturbed control) melanoma cells. a, Distribution of fluorescent intensity of CD58 (APC-CD58), B2M (APC-B2M), MHC-I (FITC-HLA-A,B,C), PD-L1 (APC-CD274) or staining with isotype control, without or with IFNg stimulation, in 2686 melanoma cells. b, Validation of CD58 KO MaMel-134, and c, MaMel-80 melanoma cells. d, Comparable growth of control and KO cells. Ratio of viable cells relative to timepoint 0 (y axis) for control and B2M KO, CD58 KO or CD274 KO melanoma cells from patient 2686 (x axis). e, Comparable induction of apoptosis in response to Staurosporin and resistance to DTIC in control and KO melanoma cells. Percent of cells inducing Caspase 3/7 (y axis) in control and B2M KO, CD58 KO or CD274 KO melanoma cells (color code) from patient 2686 in different treatment conditions (x axis). f, Co-culture experiments of three melanoma cells lines (2686, MaMel134 and MaMel80) at increasing ratios of NK cells and tumor cells with different genotypes (including CD58 KO, B2M KO in all models, and additionally CD274 KO in 2686). g-j, CD58 perturbation in co-culture does not affect B2M and HLA expression at the RNA and protein level but induces CD274. Distribution of fluorescent intensity by flow cytometry (corresponding to Fig. 5i-k) of MHC Class I and II, CD58, and PD-L1 in parental (control) and CD58 KO lines at baseline (g) and after 72 hours of stimulation with either 1 ng IFNγ (h) or 10 ng IFNγ (i), and summary of the impact of IFNγ on CD58 protein abundance after 72 hours. In all experiments, we used a one-way ANOVA with Tukey post hoc test. Error bars: Mean ±SD. All experiments were performed in triplicates in each of at least two independent experiments.
a, Schematic representation of interactions between T cell and cancer cells. At baseline, TCR stimulation via peptide-loaded MHC Class I and through CD58:CD2 co-stimulation results in production of cytokines (such as IFNγ, Granzymes), which lead to activation of the IFNγ-JAK/STAT-pathway that determine the cell fate and expression of surface proteins. b, In the absence of antigen presentation, either due to genetic defects or downregulation of the antigen presentation machinery, cancer cells survive. c, Defects in the JAK/STAT-pathway (such as deleterious mutations in JAK genes) result in poor response of cancer cells to T cell secreted IFNγ and promote cancer cell survival. d, Loss or downregulation of CD58 does not interfere with antigen presentation or JAK/STAT-signaling per se, but reduces T cell co-stimulation while simultaneously promoting increased co-inhibitory signaling via PD-L1, and therefore overall confers resistance to T cell mediated killing.
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Frangieh, C.J., Melms, J.C., Thakore, P.I. et al. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat Genet 53, 332–341 (2021). https://doi.org/10.1038/s41588-021-00779-1