Functional genomic landscape of cancer-intrinsic evasion of killing by T cells
Open accessAbstract
The genetic circuits that allow cancer cells to evade destruction by the host immune system remain poorly understood 1-3. Here, to identify a phenotypically robust core set of genes and pathways that enable cancer cells to evade killing mediated by cytotoxic T lymphocytes (CTLs), we performed genome-wide CRISPR screens across a panel of genetically diverse mouse cancer cell lines that were cultured in the presence of CTLs. We identify a core set of 182 genes across these mouse cancer models, the individual perturbation of which increases either the sensitivity or the resistance of cancer cells to CTL-mediated toxicity. Systematic exploration of our dataset using genetic co-similarity reveals the hierarchical and coordinated manner in which genes and pathways act in cancer cells to orchestrate their evasion of CTLs, and shows that discrete functional modules that control the interferon response and tumour necrosis factor (TNF)-induced cytotoxicity are dominant sub-phenotypes. Our data establish a central role for genes that were previously identified as negative regulators of the type-II interferon response (for example, Ptpn2, Socs1 and Adar1) in mediating CTL evasion, and show that the lipid-droplet-related gene Fitm2 is required for maintaining cell fitness after exposure to interferon-γ (IFNγ). In addition, we identify the autophagy pathway as a conserved mediator of the evasion of CTLs by cancer cells, and show that this pathway is required to resist cytotoxicity induced by the cytokines IFNγ and TNF. Through the mapping of cytokine- and CTL-based genetic interactions, together with in vivo CRISPR screens, we show how the pleiotropic effects of autophagy control cancer-cell-intrinsic evasion of killing by CTLs and we highlight the importance of these effects within the tumour microenvironment. Collectively, these data expand our knowledge of the genetic circuits that are involved in the evasion of the immune system by cancer cells, and highlight genetic interactions that contribute to phenotypes associated with escape from killing by CTLs.
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Pooled in vivo and in vitro CRISPR-Cas9 based screening have nominated several tumour intrinsic drivers of resistance to immunotherapy, but therapeutic applications of these findings remain limited.
“Pooled in vivo and in vitro CRISPR-Cas9 based screening have nominated several tumour intrinsic drivers of resistance to immunotherapy 4,8-11 , but therapeutic applications of these findings remain limited.”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [9]
IFNγ and TNFα are key effector cytokines that contribute to anti-tumour immune responses, and genes associated with IFN and TNF signaling pathways contribute to immune evasion.
“IFN γ and TNF α are key effector cytokines that contribute to anti-tumour immune responses 9,11,27-30 , and genes associated with IFN and TNF signaling pathways contribute to immune evasion 4,9,29 .”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [9]
TBK1 has been nominated as a candidate immune evasion gene.
“thus it is surprising that TBK1 has been nominated as a candidate immune evasion gene 4,9-11”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [9]
A CRISPR-Cas9 screen performed across multiple cell lines indicates that autophagy proficiency entails the inherent ability to evade immune detection.
“This result has been further reinforced in a CRISPR‐Cas9 screen performed across multiple cell lines, indicating that autophagy proficiency entails the inherent ability to evade immune detection (Lawson et al, 2020).”
From Autophagy in major human diseases (2021) · cites this paper as Lawson et al, 2020
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Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778 · see citing works
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865 · see citing works
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774 · see citing works
Understanding the tumor immune microenvironment (TIME) for effective therapy
Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., Coussens, L. M., Gabrilovich, D. I., Ostrand-Rosenberg, S., Hedrick, C. C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0014-x · Cited by 6,145 · see citing works
The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870 · see citing works
Self-citationElements of cancer immunity and the cancer–immune set point
Chen, D. S., & Mellman, I. (2017) · Nature · doi:10.1038/nature21349 · Cited by 5,352 · see citing works
Molecular and genetic properties of tumors associated with local immune cytolytic activity
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., & Hacohen, N. (2015) · Cell · doi:10.1016/j.cell.2014.12.033 · Cited by 3,825 · see citing works
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448 · see citing works
Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma
Zaretsky, J. M., Garcia-Diaz, A., Shin, D. S., Escuin-Ordinas, H., Hugo, W., Hu-Lieskovan, S., Torrejon, D. Y., Abril-Rodriguez, G., Sandoval, S., Barthly, L., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1604958 · Cited by 3,237 · see citing works
Top 10 Challenges in Cancer Immunotherapy
Hegde, P. S., & Chen, D. S. (2020) · Immunity · doi:10.1016/j.immuni.2019.12.011 · Cited by 2,238 · see citing works
A family of cytokine-inducible inhibitors of signalling
Starr, R., Willson, T. A., Viney, E. M., Murray, L. J. L., Rayner, J. R., Jenkins, B. J., Gonda, T. J., Alexander, W. S., Metcalf, D., Nicola, N. A., et al. (1997) · Nature · doi:10.1038/43206 · Cited by 2,066 · see citing works
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892 · see citing works
High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.
Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., Mis, M., Zimmermann, M., Fradet-Turcotte, A., Sun, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.015 · Cited by 1,571 · see citing works
Self-citationAssociation analysis identifies 65 new breast cancer risk loci
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemaçon, A., Soucy, P., Glubb, D. M., Rostamianfar, A., et al. (2017) · Nature · doi:10.1038/nature24284 · Cited by 1,380 · see citing works
Self-citationCORUM: the comprehensive resource of mammalian protein complexes—2009
Ruepp, A., Wägele, B., Lechner, M., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., & Mewes, H. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp914 · Cited by 1,320 · see citing works
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., Wang, W., Usaj, M., Hanchard, J., Lee, S. D., et al. (2016) · Science · doi:10.1126/science.aaf1420 · Cited by 1,240 · see citing works
In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target
Manguso, R. T., Pope, H. W., Zimmer, M. D., Brown, F. D., Yates, K. B., Miller, B. C., Collins, N. B., Bi, K., LaFleur, M. W., Juneja, V. R., et al. (2017) · Nature · doi:10.1038/nature23270 · Cited by 1,147 · see citing works
RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself
Liddicoat, B., Piskol, R., Chalk, A. M., Ramaswami, G., Higuchi, M., Hartner, J. C., Li, J. B., Seeburg, P. H., & Walkley, C. R. (2015) · Science · doi:10.1126/science.aac7049 · Cited by 963 · see citing works
Identification of essential genes for cancer immunotherapy
Patel, S. J., Sanjana, N. E., Kishton, R. J., Eidizadeh, A., Vodnala, S. K., Cam, M., Gartner, J. J., Jia, L., Steinberg, S. M., Yamamoto, T. N., et al. (2017) · Nature · doi:10.1038/nature23477 · Cited by 870 · see citing works
A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Pan, D., Kobayashi, A., Jiang, P., Ferrari de Andrade, L., Tay, R. E., Luoma, A. M., Tsoucas, D., Qiu, X., Lim, K., Rao, P., et al. (2018) · Science · doi:10.1126/science.aao1710 · Cited by 852 · see citing works
Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade
Ishizuka, J. J., Manguso, R. T., Cheruiyot, C. K., Bi, K., Panda, A., Iracheta-Vellve, A., Miller, B. C., Du, P. P., Yates, K. B., Dubrot, J., et al. (2019) · Nature · doi:10.1038/s41586-018-0768-9 · Cited by 697 · see citing works
A Probabilistic Functional Network of Yeast Genes
Lee, I., Date, S. V., Adai, A. T., & Marcotte, E. M. (2004) · Science · doi:10.1126/science.1099511 · Cited by 682 · see citing works
Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens
Hart, T., Tong, A. H. Y., Chan, K., Van Leeuwen, J., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A., et al. (2017) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.117.041277 · Cited by 599 · see citing works
Self-citationCORUM: the comprehensive resource of mammalian protein complexes—2019
Giurgiu, M., Reinhard, J., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., & Ruepp, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky973 · Cited by 595 · see citing works
CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions
Zimmermann, M., Murina, O., Reijns, M. A. M., Agathanggelou, A., Challis, R. C., Tarnauskaitė, Ž., Muir, M., Fluteau, A., Aregger, M., McEwan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0291-z · Cited by 405 · see citing works
Self-citationMeasuring error rates in genomic perturbation screens: gold standards for human functional genomics
Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. K., & Moffat, J. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145216 · Cited by 389 · see citing works
Self-citationTumor immune evasion arises through loss of TNF sensitivity
Kearney, C. J., Vervoort, S. J., Hogg, S. J., Ramsbottom, K. M., Freeman, A. J., Lalaoui, N., Pijpers, L., Michie, J., Brown, K. K., Knight, D. A., et al. (2018) · Science Immunology · doi:10.1126/sciimmunol.aar3451 · Cited by 381 · see citing works
Self-citationSeki, A., & Rutz, S. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20171626 · Cited by 369 · see citing works
Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma
Smith, C. C., Beckermann, K. E., Bortone, D. S., De Cubas, A. A., Bixby, L. M., Lee, S. J., Panda, A., Ganesan, S., Bhanot, G., Wallen, E. M., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci121476 · Cited by 303 · see citing works
BAGEL: a computational framework for identifying essential genes from pooled library screens
Hart, T., & Moffat, J. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1015-8 · Cited by 285 · see citing works
Self-citationThe multiple facets of mTOR in immunity
Weichhart, T., & Säemann, M. D. (2009) · Trends in Immunology · doi:10.1016/j.it.2009.02.002 · Cited by 260 · see citing works
Augmenting Immunotherapy Impact by Lowering Tumor TNF Cytotoxicity Threshold
Vredevoogd, D. W., Kuilman, T., Ligtenberg, M. A., Boshuizen, J., Stecker, K. E., de Bruijn, B., Krijgsman, O., Huang, X., Kenski, J. C. N., Lacroix, R., et al. (2019) · Cell · doi:10.1016/j.cell.2019.06.014 · Cited by 259 · see citing works
Identifying chemogenetic interactions from CRISPR screens with drugZ
Colic, M., Wang, G., Zimmermann, M., Mascall, K. S., McLaughlin, M. E., Bertolet, L. L., Lenoir, W. F., Moffat, J., Angers, S., Durocher, D., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0665-3 · Cited by 206 · see citing works
Self-citationA Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq
Li, B., Qing, T., Zhu, J., Wen, Z., Yu, Y., Fukumura, R., Zheng, Y., Gondo, Y., & Shi, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-04520-z · Cited by 184 · see citing works
An RNA-Seq atlas of gene expression in mouse and rat normal tissues
Söllner, J. F., Leparc, G., Hildebrandt, T., Klein, H., Thomas, L., Stupka, E., & Simon, E. (2017) · Scientific Data · doi:10.1038/sdata.2017.185 · Cited by 133 · see citing works
Mair, B., Tomic, J., Masud, S. N., Tonge, P. J., Weiss, A., Ušaj, M., Tong, A. H. Y., Kwan, J. J., Brown, K. R., Titus, E., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.041 · Cited by 123 · see citing works
Self-citationMHC class II presentation of endogenously expressed antigens by transfected dendritic cells
Diebold, S. S., Cotten, M. L., Koch, N., & Zenke, M. (2001) · Gene Therapy · doi:10.1038/sj.gt.3301433 · Cited by 117 · see citing works
Fat Storage-inducing Transmembrane Protein 2 Is Required for Normal Fat Storage in Adipose Tissue
Miranda, D. A., Kim, J.-H., Nguyen, L. N., Cheng, W., Tan, B. C., Goh, V. J., Tan, J. S. Y., Yaligar, J., KN, B. P., Velan, S. S., et al. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.547687 · Cited by 71 · see citing works
Phenotypic Identification of a Novel Autophagy Inhibitor Chemotype Targeting Lipid Kinase VPS34
Robke, L., Laraia, L., Corrales, M. A. C., Konstantinidis, G., Muroi, M., Richters, A., Winzker, M., Engbring, T., Tomassi, S., Watanabe, N., et al. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201703738 · Cited by 69 · see citing works
Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout ScreensPreprint
Hart, T., Tong, A. H. Y., Chan, K., van Leeuwen, J. S., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A. J. C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/117341 · Cited by 41 · see citing works
Self-citationFIT2 is a lipid phosphate phosphatase crucial for endoplasmic reticulum homeostasisPreprint
Becuwe, M., Bond, L. M., Mejhert, N., Boland, S., Elliott, S. D., Cicconet, M., Liu, X. N., Graham, M. M., Walther, T. C., & Farese, R. V. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/291765 · Cited by 16 · see citing works
An autophagy-independent role for ATG16L1: promoting lysosome-mediated plasma membrane repair
Tan, J. M. J., Mellouk, N., & Brumell, J. H. (2019) · Autophagy · doi:10.1080/15548627.2019.1586261 · Cited by 15 · see citing works
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Autophagy in major human diseases
Klionsky, D. J., Petroni, G., Amaravadi, R. K., Baehrecke, E. H., Ballabio, A., Boya, P., Bravo‐San Pedro, J. M., Cadwell, K., Cecconi, F., Choi, A. M. K., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2021108863 · Cited by 1,585 · see citing works
In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer
Dubrot, J., Du, P. P., Lane-Reticker, S. K., Kessler, E. A., Muscato, A. J., Mehta, A., Freeman, S. S., Allen, P. M., Olander, K. E., Ockerman, K. M., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01315-x · Cited by 218 · see citing works
Zhou, X., Singh, M., Sanz Santos, G., Guerlavais, V., Carvajal, L. A., Aivado, M., Zhan, Y., Oliveira, M. M. S., Westerberg, L. S., Annis, D. A., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1741 · Cited by 169 · see citing works
Targeting TBK1 to overcome resistance to cancer immunotherapy
Sun, Y., Revach, O.-Y., Anderson, S., Kessler, E. A., Wolfe, C. H., Jenney, A., Mills, C. E., Robitschek, E. J., Davis, T. G. R., Kim, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05704-6 · Cited by 174 · see citing works
Targeting autophagy as a therapeutic strategy against pancreatic cancer
Yamamoto, K., Iwadate, D., Kato, H., Nakai, Y., Tateishi, K., & Fujishiro, M. (2022) · Journal of Gastroenterology · doi:10.1007/s00535-022-01889-1 · Cited by 29 · see citing works
Wang, Z., Wang, Z., Ji, X., Zhao, L., Zheng, K., Yu, W., Zhang, H., Chang, H., & Liu, F. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08301-z
- Authors
- Lawson, K. A., Sousa, C. M., Zhang, X., Kim, E., Akthar, R., Caumanns, J. J., Yao, Y., Mikolajewicz, N., Ross, C., Brown, K. R., Zid, A. A., Fan, Z. P., Hui, S., Krall, J. A., Simons, D. M., Slater, C. J., De Jesus, V., Tang, L., Singh, R., Goldford, J. E., Martin, S., Huang, Q., Francis, E. A., Habsid, A., Climie, R., Tieu, D., Wei, J., Li, R., Tong, A. H. Y., Aregger, M., Chan, K. S., Han, H., Wang, X., Mero, P., Brumell, J. H., Finelli, A., Ailles, L., Bader, G., Smolen, G. A., Kingsbury, G. A., Hart, T., Kung, C., & Moffat, J.
- Affiliations
- University Health Network (CA)
- Agios Pharmaceuticals (United States) (US)
- University of Toronto (CA)
- The University of Texas MD Anderson Cancer Center (US)
- Hospital for Sick Children (CA)
- Ontario Institute for Cancer Research (CA)
- Corresponding author
- Moffat, Jason
- ORCID
- Lawson, Keith A.
- Sousa, Cristovão M.
- Zhang, Xiaoyu
- Kim, Eiru
- Mikolajewicz, Nicholas
- Brown, Kevin R.
- Fan, Zi Peng
- Hui, Shirley
- Krall, Jordan A.
- Simons, Donald M.
- Slater, Chloe J.
- Goldford, Joshua E.
- Huang, Qian
- Francis, Elizabeth A.
- Tieu, David
- Wei, Jiarun
- Tong, Amy Hin Yan
- Aregger, Michael
- Chan, Katherine S.
- Han, Hong
- Wang, Xiaowei
- Brumell, John H.
- Finelli, Antonio
- Ailles, Laurie
- Bader, Gary
- Smolen, Gromoslaw A.
- Kingsbury, Gillian A.
- Hart, Traver
- Moffat, Jason
- Venue
- Nature
- Published
- 2020-09-23
- Volume
- 586
- Issue
- 7827
- Pages
- 120-126
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9014559
- OpenAlex
- W3088246260
- Cited by
- 443
- References
- 44
- Citations by year
- 2026: 50 · 2025: 80 · 2024: 80 · 2023: 78 · 2022: 89 · 2021: 59 · 2020: 6
- FWCI
- 21.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (P30CA016672)
- National Institute of General Medical Sciences (R35GM130119)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Life Sciences › Immunology and Microbiology › Immunology › interferon and immune responses
- Keywords
- tumor immune evasion, cytotoxic T lymphocytes, CTL-mediated killing, interferon-gamma, TNF-α, autophagy, lipid droplets, PTPN2, SOCS1, ADAR1, tumor microenvironment, genetic interactions
- MeSH terms
- Animals, Autophagy, Female, Humans, Interferon-gamma, Male, Neoplasms, T-Lymphocytes, Cytotoxic, Reproducibility of Results, Signal Transduction, NF-kappa B, Genome, Tumor Escape, Genomics, Cell Line, Tumor, Mice, Genes, Neoplasm
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