Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion.
CD58 Antigens
/ genetics
CRISPR-Cas Systems
Coculture Techniques
Computational Biology
/ methods
Drug Resistance, Neoplasm
/ drug effects
Epitopes
/ genetics
Gene Knockout Techniques
Humans
Immune Checkpoint Inhibitors
/ pharmacology
Interferon-gamma
/ immunology
Lymphocytes, Tumor-Infiltrating
/ pathology
Melanoma
/ drug therapy
Sequence Analysis, RNA
Single-Cell Analysis
/ methods
Tumor Escape
/ genetics
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
13
08
2020
accepted:
04
01
2021
pubmed:
3
3
2021
medline:
10
4
2021
entrez:
2
3
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33649592
doi: 10.1038/s41588-021-00779-1
pii: 10.1038/s41588-021-00779-1
pmc: PMC8376399
mid: NIHMS1699873
doi:
Substances chimiques
CD58 Antigens
0
Epitopes
0
Immune Checkpoint Inhibitors
0
Interferon-gamma
82115-62-6
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
332-341Subventions
Organisme : NCI NIH HHS
ID : U54 CA225088
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA238039
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007367
Pays : United States
Organisme : NCI NIH HHS
ID : K08 CA222663
Pays : United States
Organisme : NIAID NIH HHS
ID : F32 AI138458
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI133524
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA013696
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA163222
Pays : United States
Références
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
doi: 10.1016/j.cell.2016.11.038
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).
doi: 10.1016/j.cell.2016.11.048
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
doi: 10.1038/nmeth.4177
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).
doi: 10.1016/j.cell.2016.11.039
Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).
doi: 10.1038/s41592-019-0392-0
Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).
doi: 10.1126/science.aaa8172
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
doi: 10.1056/NEJMoa1604958
Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).
doi: 10.1038/s41467-017-01062-w
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018).
doi: 10.1016/j.cell.2018.09.006
Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
doi: 10.1038/nature23477
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).
doi: 10.1126/science.aao1710
Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).
doi: 10.1038/nature23270
Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).
doi: 10.1158/2159-8290.CD-15-0283
Mbofung, R. M. et al. HSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes. Nat. Commun. 8, 451 (2017).
doi: 10.1038/s41467-017-00449-z
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).
doi: 10.1093/jnci/djx257
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).
doi: 10.1158/1078-0432.CCR-17-2483
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
doi: 10.1038/nmeth.4380
Veillette, A. & Chen, J. SIRPα–CD47 immune checkpoint blockade in anticancer therapy. Trends Immunol. 39, 173–184 (2018).
doi: 10.1016/j.it.2017.12.005
Myers, L. M. et al. A functional subset of CD8
doi: 10.1038/s41467-019-08637-9
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).
doi: 10.1073/pnas.90.24.11613
Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).
doi: 10.1038/nrc3239
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).
doi: 10.1038/nri3191
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).
doi: 10.1038/ni.3558
Agrawal, S. & Kandimalla, E. R. Intratumoural immunotherapy: activation of nucleic acid sensing pattern recognition receptors. Immunooncol. Technol. 3, 15–23 (2019).
doi: 10.1016/j.iotech.2019.10.001
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).
doi: 10.1016/j.cell.2016.08.069
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).
doi: 10.1016/j.ccr.2011.11.006
Leitner, J., Herndler-Brandstetter, D., Zlabinger, G. J., Grubeck-Loebenstein, B. & Steinberger, P. CD58/CD2 is the primary costimulatory pathway in human CD28
doi: 10.4049/jimmunol.1401917
Strioga, M., Pasukoniene, V. & Characiejus, D. CD8
doi: 10.1111/j.1365-2567.2011.03470.x
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).
doi: 10.1186/s13059-015-0843-6
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).
doi: 10.1038/s41592-020-0905-x
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
doi: 10.1186/s13059-017-1382-0
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).
doi: 10.1214/12-AOS1018