Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade
Open accessAbstract
Interferon-gamma (IFNG) augments immune function yet promotes T cell exhaustion through PDL1. How these opposing effects are integrated to impact immune checkpoint blockade (ICB) is unclear. We show that while inhibiting tumor IFNG signaling decreases interferon-stimulated genes (ISGs) in cancer cells, it increases ISGs in immune cells by enhancing IFNG produced by exhausted T cells (T EX). In tumors with favorable antigenicity, these T EX mediate rejection. In tumors with neoantigen or MHC-I loss, T EX instead utilize IFNG to drive maturation of innate immune cells, including a PD1+TRAIL+ ILC1 population. By disabling an inhibitory circuit impacting PD1 and TRAIL, blocking tumor IFNG signaling promotes innate immune killing. Thus, interferon signaling in cancer cells and immune cells oppose each other to establish a regulatory relationship that limits both adaptive and innate immune killing. In melanoma and lung cancer patients, perturbation of this relationship is associated with ICB response independent of tumor mutational burden.
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Several genes, including C10orf54 (VISTA), LGALS9, and TNFRSF14, were broadly expressed.
“Several genes, including C10orf54 (VISTA), LGALS9, and TNFRSF14, known to inhibit T cells (Benci et al., 2019, Chen and Flies, 2013) were broadly expressed.”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Benci et al., 2019
The Discussion (page unavailable) identifies HVEM and LGALS9 as possible inhibitory ligands involved in T-cell exhaustion, supporting the cited functional description for TNFRSF14 (HVEM) and LGALS9. Our copy does not identify C10orf54 (VISTA) in that role. The citation is attached to “known to inhibit T cells”; the sentence’s observation that the genes were broadly expressed is not attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIFN-γ has been proposed to play both stimulatory and suppressive roles in cancer, and type I IFNs are used both as an immunosuppressant to treat multiple sclerosis and as immunostimulatory adjuvant treatments for cancer and chronic viral infection.
“For example, IFN-γ has been proposed to play both stimulatory and suppressive roles in cancer, and type I IFNs are used both as an immunosuppressant to treat multiple sclerosis and as immunostimulatory adjuvant treatments for cancer (for example melanoma) and chronic viral infection (for example HCV)48–50.”
From Causal identification of single-cell experimental perturbation effects with CINEMA-OT (2023) · cites this paper as [48]
In solid tumors, a tumor IFN-stimulated gene signature termed ISG.RS is associated with a multigenic program of T-cell exhaustion and worse outcomes after immune checkpoint blockade.
“In solid tumors, a tumor IFN–stimulated gene signature termed ISG.RS is associated with a multigenic program of T-cell exhaustion and worse outcomes after immune checkpoint blockade.”
From Tumor interferon signaling and suppressive myeloid cells are associated with CAR T-cell failure in large B-cell lymphoma (2021) · cites this paper as [22]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989 · see citing works
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 12,116 · see citing works
Self-citationMutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works
Self-citationCancer immunotherapy using checkpoint blockade
Ribas, A., & Wolchok, J. D. (2018) · Science · doi:10.1126/science.aar4060 · Cited by 6,791 · see citing works
Self-citationPD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609 · see citing works
Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response
Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0136-1 · Cited by 5,179 · see citing works
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944 · see citing works
Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma
Snyder, A., Makarov, V., Merghoub, T., Yuan, J., Zaretsky, J. M., Desrichard, A., Walsh, L. A., Postow, M. A., Wong, P., Ho, T. S., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1406498 · Cited by 4,286 · see citing works
Self-citationIFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788 · 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
Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade
McGranahan, N., Furness, A. J. S., Rosenthal, R., Ramskov, S., Lyngaa, R., Saini, S. K., Jamal-Hanjani, M., Wilson, G. A., Birkbak, N. J., Hiley, C. T., et al. (2016) · Science · doi:10.1126/science.aaf1490 · Cited by 3,065 · see citing works
Self-citationRadiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer
Twyman-Saint Victor, C., Rech, A. J., Maity, A., Rengan, R., Pauken, K. E., Stelekati, E., Benci, J. L., Xu, B., Dada, H., Odorizzi, P. M., et al. (2015) · Nature · doi:10.1038/nature14292 · Cited by 2,434 · see citing works
Self-citationTumor and Microenvironment Evolution during Immunotherapy with Nivolumab
Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401 · see citing works
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303 · see citing works
Taube, J. M., Anders, R. A., Young, G. D., Xu, H., Sharma, R., McMiller, T. L., Chen, S., Klein, A. P., Pardoll, D. M., Topalian, S. L., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003689 · Cited by 2,157 · see citing works
Miller, B. C., Sen, D. R., Al Abosy, R., Bi, K., Virkud, Y. V., LaFleur, M. W., Yates, K. B., Lako, A., Felt, K., Naik, G. S., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0312-6 · Cited by 2,087 · see citing works
T-cell invigoration to tumour burden ratio associated with anti-PD-1 response
Huang, A. C., Postow, M. A., Orlowski, R. J., Mick, R., Bengsch, B., Manne, S., Xu, W., Harmon, S., Giles, J. R., Wenz, B., et al. (2017) · Nature · doi:10.1038/nature22079 · Cited by 1,664 · see citing works
Self-citationSimpson, T. R., Li, F., Montalvo-Ortiz, W., Sepulveda, M. A., Bergerhoff, K., Arce, F., Roddie, C., Henry, J. Y., Yagita, H., Wolchok, J. D., et al. (2013) · Journal of Experimental Medicine · doi:10.1084/jem.20130579 · Cited by 1,472 · see citing works
Self-citationBystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates
Simoni, Y., Becht, E., Fehlings, M., Loh, C. Y., Koo, S.-L., Teng, K. W. W., Yeong, J. P. S., Nahar, R., Zhang, T., Kared, H., et al. (2018) · Nature · doi:10.1038/s41586-018-0130-2 · Cited by 1,370 · see citing works
Rizvi, H., Sanchez-Vega, F., La, K., Chatila, W., Jonsson, P., Halpenny, D., Plodkowski, A., Long, N., Sauter, J. L., Rekhtman, N., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2017.75.3384 · Cited by 1,367 · see citing works
Self-citationPrimary Resistance to PD-1 Blockade Mediated by <i>JAK1/2</i> Mutations
Shin, D. S., Zaretsky, J. M., Escuin-Ordinas, H., Garcia-Diaz, A., Hu-Lieskovan, S., Kalbasi, A., Grasso, C. S., Hugo, W., Sandoval, S., Torrejon, D. Y., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-1223 · Cited by 1,337 · see citing works
Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy
Gao, J., Shi, L. Z., Zhao, H., Chen, J., Xiong, L., He, Q., Chen, T., Roszik, J., Bernatchez, C., Woodman, S. E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.069 · Cited by 1,300 · cited 2× · see citing works
“Accordingly, loss-of-function mutations and genomic alterations in the IFN signaling pathway have been associated with clinical ICB resistance and/or relapse (Gao et al., 2016; Shin et al., 2017; Zaretsky et al., 2016), and unbiased genetic screens have identified this same pathway as being important for immunotherapy…”
BackgroundTumor Interferon Signaling Regulates a Multigenic Resistance Program to Immune Checkpoint Blockade
Benci, J. L., Xu, B., Qiu, Y., Wu, T. J., Dada, H., Twyman-Saint Victor, C., Cucolo, L., Lee, D. S. M., Pauken, K. E., Huang, A. C., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.022 · Cited by 1,216 · see citing works
Self-citationFuertes, M. B., Kacha, A. K., Kline, J., Woo, S.-R., Kranz, D. M., Murphy, K. M., & Gajewski, T. F. (2011) · Journal of Experimental Medicine · doi:10.1084/jem.20101159 · Cited by 1,192 · 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
Zhang, Q., Bi, J., Zheng, X., Chen, Y., Wang, H., Wu, W., Wang, Z., Wu, Q., Peng, H. C., Wei, H., et al. (2018) · Nature Immunology · doi:10.1038/s41590-018-0132-0 · Cited by 1,122 · see citing works
Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment
Harlin, H., Meng, Y., Peterson, A. C., Zha, Y., Tretiakova, M., Slingluff, C., McKee, M., & Gajewski, T. F. (2009) · Cancer Research · doi:10.1158/0008-5472.can-08-2281 · Cited by 1,121 · see citing works
Li, H., van der Leun, A. M., Yofe, I., Lubling, Y., Gelbard-Solodkin, D., van Akkooi, A. C. J., van den Braber, M., Rozeman, E. A., Haanen, J. B. A. G., Blank, C. U., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.043 · Cited by 1,101 · see citing works
Hellmann, M. D., Nathanson, T., Rizvi, H., Creelan, B. C., Sanchez-Vega, F., Ahuja, A., Ni, A., Novik, J. B., Mangarin, L. M. B., Abu-Akeel, M., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.018 · Cited by 1,095 · see citing works
Self-citationType I interferon is selectively required by dendritic cells for immune rejection of tumors
Diamond, M. S., Kinder, M., Matsushita, H., Mashayekhi, M., Dunn, G. P., Archambault, J. M., Lee, H., Arthur, C. D., White, J. M., Kalinke, U., et al. (2011) · Journal of Experimental Medicine · doi:10.1084/jem.20101158 · Cited by 1,081 · see citing works
Resistance to checkpoint blockade therapy through inactivation of antigen presentation
Sade-Feldman, M., Jiao, Y. J., Chen, J. H., Rooney, M. S., Barzily-Rokni, M., Eliane, J.-P., Bjorgaard, S. L., Hammond, M. R., Vitzthum, H., Blackmon, S. M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01062-w · Cited by 1,005 · see citing works
Random forests for genomic data analysis
Chen, X., & Ishwaran, H. (2012) · Genomics · doi:10.1016/j.ygeno.2012.04.003 · Cited by 956 · see citing works
Self-citationProgenitor and Terminal Subsets of CD8 + T Cells Cooperate to Contain Chronic Viral Infection
Paley, M., Kroy, D. C., Odorizzi, P. M., Johnnidis, J. B., Dolfi, D. V., Barnett, B. E., Bikoff, E. K., Robertson, E. J., Lauer, G. M., Reiner, S. L., et al. (2012) · Science · doi:10.1126/science.1229620 · Cited by 903 · see citing works
Self-citationMaturation of mouse NK cells is a 4-stage developmental program
Chiossone, L., Chaix, J., Fuséri, N., Roth, C., Vivier, É., & Walzer, T. (2009) · Blood · doi:10.1182/blood-2008-10-187179 · Cited by 778 · see citing works
Persistent LCMV infection is controlled by blockade of type I interferon signaling
Teijaro, J. R., Ng, C., Lee, A. M., Sullivan, B. M., Sheehan, K. C. F., Welch, M., Schreiber, R. D., Carlos de la Torre, J., & Oldstone, M. B. A. (2013) · Science · doi:10.1126/science.1235214 · Cited by 755 · see citing works
SAVER: gene expression recovery for single-cell RNA sequencing
Huang, M. C., Wang, J., Torre, E. A., Dueck, H., Shaffer, S. M., Bonasio, R., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0033-z · Cited by 725 · see citing works
Self-citationBlockade of chronic type I interferon signaling to control persistent LCMV infection
Wilson, E. B., Yamada, D. H., Elsaesser, H., Herskovitz, J., Deng, J., Cheng, G., Aronow, B. J., Karp, C. L., & Brooks, D. G. (2013) · Science · doi:10.1126/science.1235208 · Cited by 709 · see citing works
Identification of CMTM6 and CMTM4 as PD-L1 protein regulators
Mezzadra, R., Sun, C., Jae, L. T., Gomez-Eerland, R., de Vries, E., Wu, W., Logtenberg, M. E. W., Slagter, M., Rozeman, E. A., Hofland, I., et al. (2017) · Nature · doi:10.1038/nature23669 · Cited by 697 · see citing works
Dighe, A. S., Richards, E., Old, L. J., & Schreiber, R. D. (1994) · Immunity · doi:10.1016/1074-7613(94)90087-6 · Cited by 663 · see citing works
Tumor immunoevasion by the conversion of effector NK cells into type 1 innate lymphoid cells
Gao, Y., Souza-Fonseca-Guimarães, F., Bald, T., Ng, S. S., Young, A., Ngiow, S. F., Rautela, J., Straube, J., Waddell, N., Blake, S. J., et al. (2017) · Nature Immunology · doi:10.1038/ni.3800 · Cited by 654 · see citing works
Weichselbaum, R. R., Ishwaran, H., Yoon, T., Nuyten, D. S. A., Baker, S. W., Khodarev, N. N., Su, A. W., Shaikh, A. Y., Roach, P., Kreike, B., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0809242105 · Cited by 618 · see citing works
Self-citationRomano, E., Kusio-Kobialka, M., Foukas, P. G., Baumgaertner, P., Meyer, C., Ballabeni, P., Michielin, O., Weide, B., Romero, P., & Speiser, D. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1417320112 · Cited by 601 · see citing works
Fc Effector Function Contributes to the Activity of Human Anti-CTLA-4 Antibodies
Arce Vargas, F., Furness, A. J. S., Litchfield, K., Joshi, K., Rosenthal, R., Ghorani, E., Solomon, I., Lesko, M. H., Ruef, N., Roddie, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.02.010 · Cited by 592 · see citing works
Rodig, S. J., Gusenleitner, D., Jackson, D. G., Gjini, E., Giobbie-Hurder, A., Jin, C., Chang, H., Lovitch, S. B., Horak, C., Weber, J. S., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aar3342 · Cited by 579 · see citing works
Self-citationCombination Cancer Therapy with Immune Checkpoint Blockade: Mechanisms and Strategies
Patel, S. A., & Minn, A. J. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.007 · Cited by 574 · see citing works
Self-citationType I Interferon in Chronic Virus Infection and Cancer
Snell, L. M., McGaha, T. L., & Brooks, D. G. (2017) · Trends in Immunology · doi:10.1016/j.it.2017.05.005 · Cited by 490 · see citing works
NK cells and type 1 innate lymphoid cells: partners in host defense
Spits, H., Bernink, J. H., & Lanier, L. L. (2016) · Nature Immunology · doi:10.1038/ni.3482 · Cited by 444 · see citing works
High-Dimensional Variable Selection for Survival Data
Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J., & Lauer, M. S. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08622 · Cited by 439 · see citing works
Self-citationCancer Immunosurveillance by Tissue-Resident Innate Lymphoid Cells and Innate-like T Cells
Dadi, S., Chhangawala, S., Whitlock, B. M., Franklin, R. A., Luo, C. T., Oh, S. A., Toure, A., Pritykin, Y., Huse, M., Leslie, C. S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.01.002 · Cited by 359 · see citing works
Cortez, V. S., Ulland, T. K., Cervantes‐Barragán, L., Bando, J. K., Robinette, M. L., Wang, Q., White, A. J., Gilfillan, S., Cella, M., & Colonna, M. (2017) · Nature Immunology · doi:10.1038/ni.3809 · Cited by 335 · see citing works
Virus-specific memory T cells populate tumors and can be repurposed for tumor immunotherapy
Rosato, P. C., Wijeyesinghe, S., Stolley, J. M., Nelson, C. E., Davis, R. L., Manlove, L. S., Pennell, C. A., Blazar, B. R., Chen, C. C., Geller, M. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08534-1 · Cited by 304 · see citing works
Ahlenstiel, G., Titerence, R. H., Koh, C., Edlich, B., Feld, J. J., Rotman, Y., Ghany, M. G., Hoofnagle, J. H., Liang, T. J., Heller, T., et al. (2009) · Gastroenterology · doi:10.1053/j.gastro.2009.08.066 · Cited by 269 · see citing works
Jiang, Y., Qiu, Y. L., Minn, A. J., & Zhang, N. R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1522203113 · Cited by 221 · see citing works
Self-citationBlocking type I interferon signaling enhances T cell recovery and reduces HIV-1 reservoirs
Cheng, L., Ma, J., Li, J., Li, D., Li, G., Li, F., Zhang, Q., Yu, H., Yasui, F., Ye, C., et al. (2016) · Journal of Clinical Investigation · doi:10.1172/jci90745 · Cited by 179 · see citing works
Rejection of immunogenic tumor clones is limited by clonal fraction
Gejman, R. S., Chang, A. Y., Jones, H. F., DiKun, K. M., Hakimi, A. A., Schietinger, A., & Scheinberg, D. A. (2018) · eLife · doi:10.7554/elife.41090 · Cited by 127 · see citing works
Interferon-γ mediates chemokine-dependent recruitment of natural killer cells during viral infection
Pak-Wittel, M. A., Yang, L., Sojka, D. K., Rivenbark, J. G., & Yokoyama, W. M. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220456110 · Cited by 95 · see citing works
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Roles of IFN-γ in tumor progression and regression: A review
Jorgovanovic, D., Song, M., Wang, L., & Zhang, Y. (2020) · Biomarker Research · doi:10.1186/s40364-020-00228-x · Cited by 1,234 · see citing works
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117 · see citing works
“Several genes, including C10orf54 (VISTA), LGALS9, and TNFRSF14, known to inhibit T cells (Benci et al., 2019; Chen and Flies, 2013) were broadly expressed.”
BackgroundPan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
Kinker, G. S., Greenwald, A. C., Tal, R., Orlova, Z., Cuoco, M. S., McFarland, J. M., Warren, A., Rodman, C., Roth, J. A., Bender, S. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00726-6 · Cited by 445 · see citing works
Exploring immunotherapy in colorectal cancer
Weng, J., Li, S., Zhu, Z., Liu, Q., Zhang, R., Yang, Y., & Li, X. (2022) · Journal of Hematology & Oncology · doi:10.1186/s13045-022-01294-4 · Cited by 355 · see citing works
Vernieri, C., Fucà, G., Ligorio, F., Huber, V., Vingiani, A., Iannelli, F., Raimondi, A., Rinchai, D., Frigè, G., Belfiore, A., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0030 · Cited by 319 · see citing works
Jain, M. D., Zhao, H., Wang, X., Atkins, R., Menges, M., Reid, K., Spitler, K., Faramand, R., Bachmeier, C., Dean, E. A., et al. (2021) · Blood · doi:10.1182/blood.2020007445 · Cited by 297 · 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
Targeting MHC-I molecules for cancer: function, mechanism, and therapeutic prospects
Wu, X., Li, T., Jiang, R., Yang, X., Guo, H., & Yang, R. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01899-4 · Cited by 223 · see citing works
Yang, Y., Wu, M., Cao, D., Yang, C., Jin, J., Wu, L., Hong, X., Li, W., Lu, L., Li, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf6290 · Cited by 187 · see citing works
Overcoming Genetically Based Resistance Mechanisms to PD-1 Blockade
Torrejon, D. Y., Abril-Rodriguez, G., Champhekar, A. S., Tsoi, J., Campbell, K. M., Kalbasi, A., Parisi, G., Zaretsky, J. M., Garcia-Diaz, A., Puig-Saus, C., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1409 · Cited by 146 · see citing works
Chen, J., Harding, S. M., Natesan, R., Tian, L., Benci, J. L., Li, W., Minn, A. J., Asangani, I. A., & Greenberg, R. A. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108080 · Cited by 111 · see citing works
Self-citationArwert, E. N., Milford, E. L., Rullan, A., Derzsi, S., Hooper, S., Kato, T., Mansfield, D., Melcher, A., Harrington, K. J., & Sahai, E. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0527-7 · Cited by 90 · see citing works
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
Dong, M., Wang, B., Wei, J., de O. Fonseca, A. H., Perry, C. J., Frey, A., Ouerghi, F., Foxman, E. F., Ishizuka, J. J., Dhodapkar, R. M., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02040-5 · Cited by 94 · see citing works
Chen, J., Levy, A., Tian, A.-L., Huang, X., Cai, G., Fidelle, M., Rauber, C., Ly, P., Pizzato, E., Sitterle, L., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.02.010 · Cited by 67 · see citing works
Plattner, C., Lamberti, G., Blattmann, P., Kirchmair, A., Rieder, D., Loncova, Z., Sturm, G., Scheidl, S., Ijsselsteijn, M., Fotakis, G., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108399 · Cited by 27 · see citing works
- Authors
- Benci, J. L., Johnson, L. R., Choa, R., Xu, Y., Qiu, J., Zhou, Z., Xu, B., Ye, D., Nathanson, K. L., June, C. H., Wherry, E. J., Zhang, N. R., Ishwaran, H., Hellmann, M. D., Wolchok, J. D., Kambayashi, T., & Minn, A. J.
- Affiliations
- University of Pennsylvania (US)
- Parker Institute for Cancer Immunotherapy (US)
- University of Miami (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Minn, Andy J.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 178
- Issue
- 4
- Pages
- 933-948.e14
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6830508
- OpenAlex
- W2968188498
- Cited by
- 541
- References
- 58
- Citations by year
- 2026: 63 · 2025: 82 · 2024: 81 · 2023: 97 · 2022: 87 · 2021: 76 · 2020: 46 · 2019: 7 · 2018: 1
- FWCI
- 22.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- AstraZeneca
- Novartis Institutes for BioMedical Research
- Melanoma Research Alliance
- National Institutes of Health (1P01CA210944-01, 5-T32-HG-000046-16, F31CA228455)
- University of Pennsylvania
- Parker Institute for Cancer Immunotherapy
- Eli Lilly and Company
- Bristol-Myers Squibb
- Pfizer
- Merck
- Takeda Pharmaceuticals U.S.A.
- Breast Cancer Research Foundation
- Mark Foundation For Cancer Research (F31CA228455, 5-T32-HG-000046-16)
- Genentech (PD-1, CTLA-4)
- U.S. Department of Defense (W81XWH-17-1-0264)
- MedImmune
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › interferon and immune responses
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- interferon-gamma, immune checkpoint blockade, T cell exhaustion, PD-L1, interferon-stimulated genes, innate immune cells, TRAIL, PD-1, ILC1, melanoma, lung cancer, tumor mutational burden, cancer immunotherapy response
- MeSH terms
- Progression-Free Survival, RNA-Seq, Animals, Antineoplastic Agents, Female, Humans, Immunity, Innate, Interferon-gamma, Killer Cells, Natural, Lung Neoplasms, Melanoma, Mice, Inbred BALB C, Mice, Inbred C57BL, Transfection, Cohort Studies, Mice, Knockout, CD8-Positive T-Lymphocytes, Adoptive Transfer, Cell Line, Tumor, Mice, Gene Knockout Techniques, Adaptive Immunity, CTLA-4 Antigen, Programmed Cell Death 1 Receptor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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