Targeting TBK1 to overcome resistance to cancer immunotherapy

Nature volume 615, pages 158–167 (2023)Cite this article

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Despite the success of PD-1 blockade in melanoma and other cancers, effective treatment strategies to overcome resistance to cancer immunotherapy are lacking1,2. Here we identify the innate immune kinase TANK-binding kinase 1 (TBK1)3 as a candidate immune-evasion gene in a pooled genetic screen4. Using a suite of genetic and pharmacological tools across multiple experimental model systems, we confirm a role for TBK1 as an immune-evasion gene. Targeting TBK1 enhances responses to PD-1 blockade by decreasing the cytotoxicity threshold to effector cytokines (TNF and IFNγ). TBK1 inhibition in combination with PD-1 blockade also demonstrated efficacy using patient-derived tumour models, with concordant findings in matched patient-derived organotypic tumour spheroids and matched patient-derived organoids. Tumour cells lacking TBK1 are primed to undergo RIPK- and caspase-dependent cell death in response to TNF and IFNγ in a JAK–STAT-dependent manner. Taken together, our results demonstrate that targeting TBK1 is an effective strategy to overcome resistance to cancer immunotherapy.

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The datasets generated and analysed in this study are included in the Article and its Supplementary Information. In vivo scRNA-seq data have been deposited at the GEO under the accession codes GSE217160 (in vivo TBK1i study) and GSE217274 (in vivo TBK1 CRISPR–Cas9 study) and are available on request. Descriptions of the analyses are provided in the Methods and Reporting summary.

Jenkins, R. W., Barbie, D. A. & Flaherty, K. T. Mechanisms of resistance to immune checkpoint inhibitors. Br. J. Cancer 118, 9–16 (2018).

Article CAS PubMed PubMed Central Google Scholar

Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

Article CAS PubMed PubMed Central Google Scholar

Zhou, R., Zhang, Q. & Xu, P. TBK1, a central kinase in innate immune sensing of nucleic acids and beyond. Acta Biochim. Biophys. Sin. 52, 757–767 (2020).

Article CAS PubMed Google Scholar

Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

Article CAS PubMed PubMed Central Google Scholar

Tang, J., Shalabi, A. & Hubbard-Lucey, V. M. Comprehensive analysis of the clinical immuno-oncology landscape. Ann. Oncol. 29, 84–91 (2018).

Article CAS PubMed Google Scholar

Gogas, H. et al. Cobimetinib plus atezolizumab in BRAFV600 wild-type melanoma: primary results from the randomized phase III IMspire170 study. Ann. Oncol. 32, 384–394 (2021).

Article CAS PubMed Google Scholar

Long, G. V. et al. Epacadostat plus pembrolizumab versus placebo plus pembrolizumab in patients with unresectable or metastatic melanoma (ECHO-301/KEYNOTE-252): a phase 3, randomised, double-blind study. Lancet Oncol. 20, 1083–1097 (2019).

Article CAS PubMed Google Scholar

Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).

Article ADS CAS PubMed Google Scholar

Lawson, K. A. et al. Functional genomic landscape of cancer-intrinsic evasion of killing by T cells. Nature 586, 120–126 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018).

Article ADS CAS PubMed PubMed Central Google Scholar

Vredevoogd, D. W. et al. Augmenting immunotherapy impact by lowering tumor TNF cytotoxicity threshold. Cell 178, 585–599 (2019).

Article CAS PubMed Google Scholar

Zhao, C. & Zhao, W. TANK-binding kinase 1 as a novel therapeutic target for viral diseases. Expert Opin. Ther. Targets 23, 437–446 (2019).

Article CAS PubMed Google Scholar

Kwon, J. & Bakhoum, S. F. The cytosolic DNA-sensing cGAS-STING pathway in cancer. Cancer Discov. 10, 26–39 (2020).

Article CAS PubMed Google Scholar

Jenkins, R. W. et al. Ex vivo profiling of PD-1 blockade using organotypic tumor spheroids. Cancer Discov. 8, 196–215 (2018).

Article CAS PubMed Google Scholar

Zhu, L. et al. TBKBP1 and TBK1 form a growth factor signalling axis mediating immunosuppression and tumourigenesis. Nat. Cell Biol. 21, 1604–1614 (2019).

Article CAS PubMed PubMed Central Google Scholar

Hasan, M. & Yan, N. Therapeutic potential of targeting TBK1 in autoimmune diseases and interferonopathies. Pharmacol. Res. 111, 336–342 (2016).

Article CAS PubMed PubMed Central Google Scholar

Lo, J. A. et al. Epitope spreading toward wild-type melanocyte-lineage antigens rescues suboptimal immune checkpoint blockade responses. Sci. Transl. Med. 13, eabd8636 (2021).

Article CAS PubMed PubMed Central Google Scholar

Aref, A. R. et al. 3D microfluidic ex vivo culture of organotypic tumor spheroids to model immune checkpoint blockade. Lab Chip 18, 3129–3143 (2018).

Article CAS PubMed PubMed Central Google Scholar

Voabil, P. et al. An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer. Nat. Med. 27, 1250–1261 (2021).

Article CAS PubMed Google Scholar

Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).

Article Google Scholar

Yu, J. et al. Regulation of T-cell activation and migration by the kinase TBK1 during neuroinflammation. Nat. Commun. 6, 6074 (2015).

Article ADS CAS PubMed Google Scholar

Lee, M. S. J. et al. B cell-intrinsic TBK1 is essential for germinal center formation during infection and vaccination in mice. J. Exp. Med. 219, e20211336 (2022).

Article CAS PubMed Google Scholar

Jin, J. et al. The kinase TBK1 controls IgA class switching by negatively regulating noncanonical NF-κB signaling. Nat. Immunol. 13, 1101–1109 (2012).

Article CAS PubMed PubMed Central Google Scholar

Xiao, Y. et al. The kinase TBK1 functions in dendritic cells to regulate T cell homeostasis, autoimmunity, and antitumor immunity. J. Exp. Med. 214, 1493–1507 (2017).

Article CAS PubMed PubMed Central Google Scholar

Gao, T. et al. Myeloid cell TBK1 restricts inflammatory responses. Proc. Natl Acad. Sci. USA 119, e2107742119 (2022).

Article CAS PubMed PubMed Central Google Scholar

Hagan, R. S., Torres-Castillo, J. & Doerschuk, C. M. Myeloid TBK1 signaling contributes to the immune response to influenza. Am. J. Respir. Cell Mol. Biol. 60, 335–345 (2019).

Article Google Scholar

Barth Jr, R. J., Mule, J. J., Spiess, P. J. & Rosenberg, S. A. Interferon gamma and tumor necrosis factor have a role in tumor regressions mediated by murine CD8+ tumor-infiltrating lymphocytes. J. Exp. Med. 173, 647–658 (1991).

Article CAS PubMed Google Scholar

Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 (2016).

Article CAS PubMed PubMed Central Google Scholar

Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).

Article PubMed Google Scholar

Kearney, C. J. et al. PD-L1 and IAPs co-operate to protect tumors from cytotoxic lymphocyte-derived TNF. Cell Death Differ. 24, 1705–1716 (2017).

Article CAS PubMed PubMed Central Google Scholar

Hafner, M., Niepel, M., Chung, M. & Sorger, P. K. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat. Methods 13, 521–527 (2016).

Article CAS PubMed PubMed Central Google Scholar

Clark, K. et al. Novel cross-talk within the IKK family controls innate immunity. Biochem. J. 434, 93–104 (2011).

Article CAS PubMed Google Scholar

Thomson, D. W. et al. Discovery of GSK8612, a highly selective and potent TBK1 inhibitor. ACS Med. Chem. Lett. 10, 780–785 (2019).

Article CAS PubMed PubMed Central Google Scholar

Crew, A. P. et al. Identification and characterization of Von Hippel-Lindau-recruiting proteolysis targeting chimeras (PROTACs) of TANK-binding kinase 1. J. Med. Chem. 61, 583–598 (2018).

Article CAS PubMed Google Scholar

Lafont, E. et al. TBK1 and IKKε prevent TNF-induced cell death by RIPK1 phosphorylation. Nat. Cell Biol. 20, 1389–1399 (2018).

Article CAS PubMed PubMed Central Google Scholar

Xu, D. et al. TBK1 suppresses RIPK1-driven apoptosis and inflammation during development and in aging. Cell 174, 1477–1491 (2018).

Article CAS PubMed PubMed Central Google Scholar

Weinlich, R., Oberst, A., Beere, H. M. & Green, D. R. Necroptosis in development, inflammation and disease. Nat. Rev. Mol. Cell Biol. 18, 127–136 (2017).

Article CAS PubMed Google Scholar

Park, H.-H. et al. HS-1371, a novel kinase inhibitor of RIP3-mediated necroptosis. Exp. Mol. Med. 50, 1–15 (2018).

PubMed PubMed Central Google Scholar

Hildebrand, J. M. et al. Activation of the pseudokinase MLKL unleashes the four-helix bundle domain to induce membrane localization and necroptotic cell death. Proc. Natl Acad. Sci. USA 111, 15072–15077 (2014).

Article ADS CAS PubMed PubMed Central Google Scholar

Knuth, A.-K. et al. Interferons transcriptionally up-regulate MLKL expression in cancer cells. Neoplasia 21, 74–81 (2019).

Article CAS PubMed Google Scholar

Montero, J. et al. Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy. Cell 160, 977–989 (2015).

Article CAS PubMed PubMed Central Google Scholar

Heijink, A. M. et al. BRCA2 deficiency instigates cGAS-mediated inflammatory signaling and confers sensitivity to tumor necrosis factor-alpha-mediated cytotoxicity. Nat. Commun. 10, 100 (2019).

Article ADS PubMed PubMed Central Google Scholar

Sivick, K. E. et al. Magnitude of therapeutic sting activation determines CD8 T cell-mediated anti-tumor immunity. Cell Rep. 29, 785–789 (2019).

Knelson, E. H. et al. Activation of tumor-cell STING primes NK-cell therapy. Cancer Immunol. Res. 10, 947–961 (2022).

Article CAS PubMed PubMed Central Google Scholar

Griffin, G. K. et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature 595, 309–314 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Zhu, Y. et al. STING: a master regulator in the cancer-immunity cycle. Mol. Cancer 18, 152 (2019).

Article PubMed PubMed Central Google Scholar

Wang, H. et al. cGAS is essential for the antitumor effect of immune checkpoint blockade. Proc. Natl Acad. Sci. USA 114, 1637–1642 (2017).

Article ADS CAS PubMed PubMed Central Google Scholar

Woo, S. R. et al. STING-dependent cytosolic DNA sensing mediates innate immune recognition of immunogenic tumors. Immunity 41, 830–842 (2014).

Article CAS PubMed PubMed Central Google Scholar

Taft, J. et al. Human TBK1 deficiency leads to autoinflammation driven by TNF-induced cell death. Cell 184, 4447–4463 (2021).

Article CAS PubMed PubMed Central Google Scholar

Karki, R. et al. Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell 184, 149–168 (2021).

Article CAS PubMed Google Scholar

Hegde, P. S. & Chen, D. S. Top 10 challenges in cancer immunotherapy. Immunity 52, 17–35 (2020).

Article CAS PubMed Google Scholar

Deng, R. et al. Preclinical pharmacokinetics, pharmacodynamics, tissue distribution, and tumor penetration of anti-PD-L1 monoclonal antibody, an immune checkpoint inhibitor. MAbs 8, 593–603 (2016).

Article CAS PubMed PubMed Central Google Scholar

Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

Article PubMed PubMed Central Google Scholar

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

Article CAS PubMed PubMed Central Google Scholar

Ntranos, V., Yi, L., Melsted, P. & Pachter, L. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat. Methods 16, 163–166 (2019).

Article CAS PubMed Google Scholar

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

Article ADS CAS PubMed PubMed Central Google Scholar

Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

Article CAS PubMed PubMed Central Google Scholar

Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

Article CAS PubMed PubMed Central Google Scholar

Lu, H. et al. PAK signalling drives acquired drug resistance to MAPK inhibitors in BRAF-mutant melanomas. Nature 550, 133–136 (2017).

Article ADS CAS PubMed PubMed Central Google Scholar

van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

Article PubMed PubMed Central Google Scholar

Fraser, C., Ryan, J. & Sarosiek, K. BH3 profiling: a functional assay to measure apoptotic priming and dependencies. Methods Mol. Biol. 1877, 61–76 (2019).

Article CAS PubMed PubMed Central Google Scholar

Filbin, M. R. et al. Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep. Med. 2, 100287 (2021).

Article PubMed PubMed Central Google Scholar

Conant, D. et al. Inference of CRISPR edits from Sanger trace data. CRISPR J. 5, 123–130 (2022).

Article CAS PubMed Google Scholar

Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

Article PubMed PubMed Central Google Scholar

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This work was supported by NIH K08CA226391 (to R.W.J.), P01CA24023 (to S.I.P.), K99CA259511 (to K.P.), T32CA207021 (to J.H.C.), 5R01AR072304 (to D.E.F.), 5P01CA163222 (to D.E.F.), 5R01AR043369 (to D.E.F.) and 5R01CA222871 (to D.E.F.). Additional support was provided by the Melanoma Research Alliance Young Investigator Award (https://doi.org/10.48050/pc.gr.86371, to R.W.J.), a Karin Grunebaum Cancer Research Foundation Faculty Research Fellowship (to R.W.J.), Termeer Early Career Fellowship in Systems Pharmacology (to R.W.J.) and a gift from S. B. and J. W. Belkin. K.P. acknowledges support from the German Research Foundation (DFG), Stand Up to Cancer Peggy Prescott Early Career Scientist Award PA-6146, Stand Up to Cancer Phillip A. Sharp Award SU2C-AACR-PS-32; D.J. acknowledges the Susan Eid Tumor Heterogeneity Initiative; and D.E.F. acknowledges grant support from the Dr Miriam and Sheldon G. Adelson Medical Research Foundation. The funding bodies had no role in the design of the study, and the collection, analysis and interpretation of the data, or in writing the manuscript. We thank all members of the Manguso and Jenkins laboratories at MGH, HMS and the Broad Institute. Graphics in Figs. 2a and 5i were created using BioRender.

These authors jointly supervised this work: Robert T. Manguso, Russell W. Jenkins

Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Yi Sun, Or-yam Revach, Amina Fu, Xiang Ma, Jia Gwee, Princy Sindurakar, Jun Tian, Arnav Mehta, Moshe Sade-Feldman, Thomas LaSalle, Hongyan Xie, Rodrigo Saad-Beretta, Kathleen B. Yates, Angelina M. Cicerchia, Martin Q. Rasmussen, Samuel J. Klempner, Dejan Juric, David M. Miller, Jonathan H. Chen, Karin Pelka, Dennie T. Frederick, Debattama R. Sen, Ryan B. Corcoran, Nir Hacohen, Keith T. Flaherty, Robert T. Manguso & Russell W. Jenkins

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

Seth Anderson, Emily A. Kessler, Clara H. Wolfe, Emily J. Robitschek, Thomas G. R. Davis, Sarah Kim, Payal Tiwari, Peter P. Du, Arnav Mehta, Alexis M. Schneider, Moshe Sade-Feldman, Kathleen B. Yates, Arvin Iracheta-Vellve, Jonathan H. Chen, Karin Pelka, Nir Hacohen, Genevieve M. Boland, Robert T. Manguso & Russell W. Jenkins

Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Sciences, Harvard Medical School, Boston, MA, USA

Anne Jenney, Caitlin E. Mills, Shuming Liu, Johan K. E. Spetz, Xingping Qin, Kristopher A. Sarosiek, Peter K. Sorger & Russell W. Jenkins

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Arnav Mehta, Elena Ivanova, Amir R. Aref, Cloud P. Paweletz & David A. Barbie

Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Alexis M. Schneider

Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Institute of Technology, Technion, Haifa, Israel

Keren Yizhak

Division of Surgical Oncology, Department of Surgery, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA

Tatyana Sharova, William A. Michaud, Sara I. Pai & Genevieve M. Boland

Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, USA

Johan K. E. Spetz, Xingping Qin & Kristopher A. Sarosiek

John B. Little Center for Radiation Sciences, Harvard School of Public Health, Boston, MA, USA

Johan K. E. Spetz, Xingping Qin & Kristopher A. Sarosiek

Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA

Gao Zhang

Preston Robert Tisch Brain Tumor Center, Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA

Gao Zhang

Preston Robert Tisch Brain Tumor Center, Department of Pathology, Duke University School of Medicine, Durham, NC, USA

Gao Zhang

Moores Cancer Center, UC San Diego, La Jolla, CA, USA

Jong Wook Kim

Center for Novel Therapeutics, UC San Diego, La Jolla, CA, USA

Jong Wook Kim

Department of Medicine, UC San Diego, La Jolla, CA, USA

Jong Wook Kim

Cutaneous Biology Research Center, Department of Dermatology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Mack Y. Su & David E. Fisher

Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA

Sara I. Pai

Department of Dermatology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

David M. Miller

Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

Anita Giobbie-Hurder

Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

Jonathan H. Chen

Gilead Sciences, Foster City, CA, USA

Susanna Stinson

Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA

Elena Ivanova, Amir R. Aref, Cloud P. Paweletz & David A. Barbie

Xsphera Biosciences, Boston, MA, USA

Amir R. Aref

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Conception and experimental design: Y.S., O.-y.R., S.A., C.E.M., P.T., K.B.Y., A.I.-V., R.T.M. and R.W.J. Methodology and data acquisition: Y.S., O.-y.R., S.A., E.A.K., C.H.W., A.J., C.E.M., E.J.R., A.F., X.M., J.G., P.T., A.M.C., M.Q.R., P.S., J.T., A.M., H.X., T.S., S.L., W.A.M., R.S.-B., J.K.E.S., X.Q., G.Z., M.Y.S., J.W.K., S.J.K., D.T.F., D.J., S.I.P., D.M.M., S.S., E.I., A.R.A., C.P.P., D.R.S. and G.M.B. Analysis and interpretation of data: Y.S., O.-y.R., S.A., C.E.M., P.T., T.G.R.D., S.K., P.P.D., J.T., A.M., A.M.S., K.Y., M.S.-F., T.L., J.W.K., K.A.S., A.G.-H., J.H.C., K.P., D.A.B., D.E.F., R.B.C., N.H., P.K.S., K.T.F., G.M.B., R.T.M. and R.W.J. Manuscript writing and revision: Y.S., O.-y.R., S.A., C.E.M., H.X., D.J., D.A.B., R.T.M. and R.W.J.

Correspondence to Russell W. Jenkins.

R.W.J. is a member of the advisory board for and has a financial interest in Xsphera Biosciences, a company focused on using ex vivo profiling technology to deliver functional, precision immune-oncology solutions for patients, providers and drug development companies. A.M. is a consultant for Third Rock Ventures, Asher Biotherapeutics and Abata Therapeutics; and holds equity in Asher Biotherapeutics and Abata Therapeutics. S.I.P. has received consultancy payments from Abbvie, Astrazeneca/MedImmune, Cue Biopharma, Fusion Pharmaceuticals, MSD/Merck, Newlink Genetics, Oncolys Biopharma, Replimmune, Scopus Biopharma and Sensei Biopharma; she has received grants/research support from Abbvie, Astrazeneca/MedImmune, Cue Biopharma, Merck and Tesaro. S.J.K. has served a consultant/advisory role for Eli Lilly, Merck, BMS, Astellas, Daiichi-Sankyo, Pieris and Natera; and owns stock in Turning Point Therapeutics. D.J. received consulting fees from Novartis, Genentech, Syros, Eisai, Vibliome, Mapkure and Relay Therapeutics; conducted contracted research with Novartis, Genentech, Syros, Pfizer, Eisai, Takeda, Pfizer, Ribon Therapeutics, Infinity, InventisBio and Arvinas; and has ownership interest in Relay Therapeutics and PIC Therapeutics. D.M.M. has received honoraria for participating on advisory boards for Checkpoint Therapeutics, EMD Serono, Castle Biosciences, Pfizer, Merck, Regeneron and Sanofi Genzyme; and owns stock in Checkpoint Therapeutics. D.E.F. has a financial interest in Soltego, a company developing salt-inducible kinase inhibitors for topical skin-darkening treatments that might be used for a broad set of human applications. R.T.M. consults for Bristol Myers Squibb. M.S.-F. receives research funding from Bristol-Meyers Squib.

Nature thanks Robert Bradley, Toshiro Sato and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

a, Relative depletion/enrichment of Ikbke sgRNAs from a pool of sgRNAs targeting 2,368 genes expressed by Cas9-expressing B16 melanoma cells (n = 4 independent guides targeting each gene; false discovery rate (FDR) was calculated using the STARS algorithm v1.3, as previously described6,7). b, TBK1 and β-actin protein levels in control and Tbk1-null B16 cells. Results are representative of three independent experiments. c, Proliferation of Tbk1-null and control B16 tumour cells following at 1-4 days of in vitro culture (n = 9 per condition from three independent experiments). d, Tumour volume of control (grey), Tbk1-null (light red) B16 tumours in NSG mice (n = 5 mice per group). Mean tumour volumes (solid circles) are shown +/− s.e.m. (shaded region). 2-way ANOVA with Sidak's multiple comparisons test. e, Spider plots for tumour volume analysis for control sgRNA-1 (black), sgRNA-2 (grey), Tbk1 sgRNA-1 (pink), and Tbk1 sgRNA-2 (red) B16 tumours in anti-PD-1-treated wild-type C57BL/6 mice (see Fig. 1c). f-g, Spider plots for tumour volume analysis (f) and survival (g) for control (black), anti-PD-1 (grey), TBK1i (pink), and anti-PD-1+TBK1i (red) B16 tumours in C57BL/6 mice (see Fig. 1d). For survival analysis (g), pairwise testing was performed using the log-rank (Mantel-Cox) test for survival (g); n = 10 mice per treatment group, ***P < 0.001; ns, not significant, compared to control group. h, body weight of mice bearing B16-ova tumours on Day 14 of indicated treatment. Means (bars) and individual values (open circles) are shown (n = 10 mice per group, 1-way ANOVA with Tukey's multiple comparisons test; ns, not significant). i, Viability assessment of CT26 MDOTS with indicated treatments. Means (bars) and individual values (open circles) are shown (n = 3, biological replicates, one-way ANOVA with Tukey's multiple comparisons test; *P < 0.05; **P < 0.01; ***P < 0.001; **** P < 0.0001). j–k, Tumour volume analyses of mice bearing MC38 (j) and MB49 (k) tumours treated with TBK1i, anti-PD-L1, or combination compared to control (IgG + vehicle); n = 10 mice per treatment group. Mean tumour volumes (solid circles) are shown +/− s.e.m. (shaded region). 2-way ANOVA with Tukey's multiple comparisons test ***P < 0.001; compared to control group.

a, Tumour type, tissue source (location), clinical response data, PDOTS response data, and associated tumour mutation profile for specimens used for PDOTS profiling (samples ordered by ex vivo PDOTS response to combined anti-PD-1+TBK1i). PDOTS response parameters defined as follows: responder (reduction >30% compared to control), partial responder (<30% reduction and <20% growth compared to control), and non-responder (>20% growth compared to control). Red border around grey rectangle indicates presence of alteration in indicated gene. b, effect of IgG4 control monoclonal Ab on viability of PDOTS from a patient with melanoma. Means (bars) and individual values (open circles) are shown (n = 3, biological replicates, 2-sided unpaired t-test).

a–b, tSNE plot of 11 clusters of CD45+ cells (a) from patients with metastatic melanoma responsive (R) or non-responsive (NR) to immune checkpoint blockade (ref. Sade-Feldman et al. 2018), and t-SNE plots of RNA-sequenced single cells with colouring of CD3E (T cells), CD14 (myeloid cells), and CD19 (B cells) TBK1 and IKBKE expression (b). c–d, broad cluster proportions (c) and percent cells per cluster across indicated treatment groups (d). e–f, UMAP (c) and density (d) plots of reclustered lymphoid (T/NK) cells. g, cluster proportions of lymphoid (T/NK) cells. Means (bars) and individual values (circles) are shown +/− s.e.m (error bars). Multiple unpaired t-test, *P < 0.05; **P < 0.01; ***P < 0.001; **** P < 0.0001; ns, not significant. h, percentage of activated (CD69+CD25+) mouse CD8+ splenocytes pre-treated with TBK1i (1 μM) or DMSO (0.1%) with/without restimulation; n = 3 biologically independent samples, 2-way ANOVA, Sidak's multiple comparisons test; *P < 0.05; ***P < 0.001. i–k, intracellular cytokine staining for TNF (i), IL-2 (j), and IFNγ (k) of mouse CD3+CD8+ splenocytes pre-treated with TBK1i (1 μM) or DMSO (0.1%) with/without restimulation with data shown as % CD69+CD25+ cells and MFI); n = 3 biologically independent samples, 2-way ANOVA, Sidak's multiple comparisons test; **P < 0.01; **** P < 0.0001; ns, not significant.

a, Flow cytometry of immune populations from control and Tbk1-null B16 tumours treated with anti-PD-1 (n = 4 per group). Means (bars) and individual values (open circles) are shown (n = 4 biologically independent samples, 2-sided unpaired t-test). b-c, UMAP (b) and density (c) plots of 31,810 RNA-sequenced single cells from control and Tbk1-null B16 tumours following anti-PD-1 treatment (DC, dendritic cells; Tregs, regulatory T cells; MDSC, myeloid-derived suppressor cell; NK, natural killer cells; M1, M1 macrophages; M2, M2 macrophages). d, percent of cells in each lineage-defined cluster. Means (bars) and individual values (open circles) are shown (n = 4 biologically independent samples, 2-way ANOVA, Sidak's multiple comparisons test; P values shown for M1 macrophages and CD8 T cells that did not reach statistical significance). e, UMAP plot of RNA-sequenced single cells with colouring of Tbk1 and Ikbke expression with cell types referenced (b). f, bubble plot indicating Tbk1 and Ikbke expression across UMAP-defined cell clusters.

a, UMAP plot of RNA-sequenced single cells with colouring of Ifng and Tnf expression with cell types referenced (right). b, log-fold change of Ifng (light red) and Tnf (light blue) expression across lineage-defined cell clusters (Tbk1-null/control).

a, volcano plot depicting relative sgRNAs gene depletion/enrichment. Top 5 depleted sgRNAs indicated. b, scatter plot of gene essentiality from in vitro CRISPR screen (control and Tbk1-null B16 cells). c, TBK1 expression and cell viability (control vs. TNF/IFNγ;) for single cell clones derived from polyclonal control and Tbk1-null B16 cells. Western blot is representative of three independent experiments. Means (bars) and individual values (open circles) are shown (n = 6 across two independent experiments, 2-way ANOVA, Sidak's multiple comparisons test; **** P < 0.0001; ns, not significant). d, TBK1 indel spectrum from control sgRNA and Tbk1 sgRNA B16 single cell clones. e, Viability assessment (Cell Titer Glo) of B16-ova cells in standard 2D culture after 24 h treatment with TNF (160 ng ml−1) + IFNγ (40 ng ml−1) compared to unstimulated cells (n = 6, 2 independent experiments, 1-way ANOVA, Holm-Sidak's multiple comparisons test). f, Viability assessment (Hoechst/propidium iodide) of B16 tumour spheroids (lacking immune cells) in 3D microfluidic culture after 96 h treatment with TNF (10 ng ml−1) + IFNγ (10 ng ml−1) compared to unstimulated cells (n = 6, 2 independent experiments, 1-way ANOVA, Holm-Sidak's multiple comparisons test). g, Cell viability assessment of B16 cells after 24 h treatment with TNF (200 ng ml−1) + IFNγ (40 ng ml−1) compared to unstimulated cells treated with increasing concentrations of MRT67307 (n = 9, 3 independent experiments 2-way ANOVA, Sidak's multiple comparisons test). h, Cell viability assessment of B16 cells in standard 2D culture after 24 h treatment with TNF (200 ng ml−1) + IFNγ (40 ng ml−1) compared to unstimulated cells treated with increasing concentrations of GSK8612 (n = 3, 1 independent experiment, 2-way ANOVA, Sidak's multiple comparisons test). i, Cell viability assessment of B16 cells in standard 2D culture after 24 h treatment with TNF (200 ng ml−1) + IFNγ (40 ng ml−1) with increasing concentrations of TBK1 PROTAC 3i (n = 6, 2 independent experiments 2-way ANOVA, Sidak's multiple comparisons test). **** P < 0.0001; ns, not significant.

a, GR values for 9-point inhibitor titration of TBK1i in parental, control sgRNA (polyclonal and monoclonal), and Tbk1 sgRNA (polyclonal and monoclonal) B16 cells (2 independent experiments; representative data from single experiment with 6 technical replicates per condition). Means (solid circles) are shown +/− s.e.m (error bars). b–c, evaluation of TBK1i potency (b; half-maximal effect, GEC50) and overall efficacy (c; area over the GR curve, GRAOC) d–e, Heatmap of GR values for parental (d) and BRAF/MEK inhibitor resistant (e) A375 human melanoma cells treated with increasing concentrations of TNF and IFNγ for 24, 24, and 72 h with 0, 0.25, and 1.0 μM TBK1i (n = 3).

a–b, Cell viability assessment (Cell Titer Glo) in control and Tbk1-null B16 cells pre-treated with RIPK1 inhibitor (Nec-1s, 10 μM) and the pan-caspase inhibitor Q-VD-OPh (10 μM) +/− TNF/IFNγ (n=3, 1 independent experiment: 2-way ANOVA, Dunnett's multiple comparisons test). b, cell viability assessment (Cell Titer Glo) in control and Tbk1-null B16 cells pre-treated with RIPK1 inhibitor (Nec-1s, 10 μM) and the pan-caspase inhibitor z-VAD-fmk (20 μM) +/− TNF/IFNγ (n = 3-6, 1-2 independent experiments: 2-way ANOVA, Dunnett's multiple comparisons test). c, cell viability assessment in Tbk1-null B16 cells pre-treated with RIPK1 inhibitor (Nec-1s, 10 μM) and the caspase 8 inhibitor z-IETD-fmk (2.5 μM) +/− TNF/IFNγ (n = 6, 2 independent experiments; 2-way ANOVA, Dunnett's multiple comparisons test). d, cell viability assessment in Tbk1-null B16 cells pre-treated with RIPK3 inhibitor (HS-1371, 2 μM) and the pan-caspase inhibitor Q-VD-OPh (20 μM) +/− TNF/IFNγ (n = 6, 2 independent experiments: 2-way ANOVA, Dunnett's multiple comparisons test). e, cell viability assessment in Tbk1-null B16 cells pre-treated with MLKL inhibitor (GW806742X, 5 μM) and the pan-caspase inhibitor Q-VD-OPh (20 μM) +/− TNF/IFNγ (n = 6, 2 independent experiments: 2-way ANOVA, Dunnett's multiple comparisons test). f-h, Clonogenic assay of B16 cells treated with TNF (10 ng ml−1), IFNγ (10 ng ml−1), or TNF + IFNγ with control (0.1% DMSO), Q-VD-OPh (20 μM) with/without the RIPK1 inhibitor Nec-1s (10 μM, f), RIPK3 inhibitor HS-1371 (2 μM, g), and MLKL inhibitor GW806742X (2 μM, h) (representative images shown; n = 3). i, normalized expression of selected genes in B16 cells treated with TNF (10 ng ml−1), IFNγ (100 ng ml−1), or both, compared to control cells (source data for bulk RNA-seq – Manguso et al. 2017). j, normalized expression of Mlkl and Ripk3 in control and Tbk1-null B16 cells with/without TNF/IFNγ treatment (18 h) determined by qRT-PCR (n = 3; 2-way ANOVA, Sidak's multiple comparison test). *P < 0.05; **P < 0.01; ***P < 0.001; **** P < 0.0001; ns, not significant. k, Western blot of indicated proteins in Tbk1-null B16 cell lysates following 2-hour pre-treatment with vehicle control (0.1%DMSO), Q-VD-OPh (20 μM), Nec-1s (10 μM), or Q-VD-OPh plus Nec-1s, or Q-VD-OPh plus birinapant (1 μM) followed by 10 h treatment with TNF (160 ng ml−1) and IFNγ (40 ng ml−1) or unstimulated (PBS) control. Data are representative of three independent experiments.

a, heatmap of % cytochrome C (cyt C) release for in vitro BH3 profiling of unstimulated control (sg1 and sg2) and Tbk1-null (sg1 and sg2) B16 cells. Mean values shown; n=3 biologically independent samples; 2-way ANOVA, Dunnett's multiple comparisons test. b, heatmap of % cytochrome C (cyt C) release for in vitro BH3 profiling of control sgRNA and Tbk1 sgRNA B16 cells. Mean values shown; n = 3 biologically independent samples; 2-way ANOVA, Tukey's multiple comparisons test. No statistically significant differences observed between control sgRNA and Tbk1 sgRNA B16 cells at any time point. c, Viability assessment (Cell Titer Glo) of B16 cells in standard 2D culture after 24 h treatment with indicated concentrations of staurosporine (STS) in control and Tbk1-null B16 cells. Means (bars) and individual values (open circles) are shown (n = 6, 2 independent experiments, 2-way ANOVA, Sidak's multiple comparisons test). d, Viability assessment (Hoechst/propidium iodide) of B16 tumour spheroids (lacking immune cells) in 3D microfluidic culture after 48 h treatment indicated concentrations of staurosporine (STS) compared to unstimulated cells Means (bars) and individual values (open circles) are shown (n = 6, 2 independent experiments, 1-way ANOVA, Holm-Sidak's multiple comparisons test). e, Western blot for STING, IRF3, TBK1, and β-actin in B16 cells with single CRISPR cell lines with single-guide RNAs targeting Tmem173, Irf3, and Tbk1 compared to control sgRNA. Data are representative of three independent experiments. f, Western blot for STING, IRF3, TBK1, and β-actin in double CRISPR B16 cells with indicated sgRNA pairs. Data are representative of three independent experiments. g, Viability assessment (Cell Titer Glo) of indicated sgRNA B16 cells after 48 h treatment with TNF (160 ng ml−1) + IFNγ (40 ng ml−1) compared to unstimulated cells. Means (bars) and individual values (open circles) are shown (n = 4 biological replicates, 2-way ANOVA, Sidak's multiple comparisons test, **P < 0.01; **** P < 0.0001; ns, not significant). h, PDOTS viability assessment from patients (n = 2) with cutaneous melanoma with indicated treatments. Means (bars) and individual values (open circles) are shown (n = 6 biological replicates, 2 independent specimens; one-way ANOVA with Dunn's multiple comparisons test, **P < 0.01; **** P < 0.0001; ns, not significant). i, heatmap of secreted cytokine profiles (L2FC) of conditioned media from PDOTS in response to indicated treatments (n = 2). Mean values shown. **P < 0.01; **** P < 0.0001; ns, not significant.

a, Frequency histograms of enrichment (z-score) for all sgRNAs per target in a Tbk1-null B16 cells +/− in vitro stimulation with TNF (10 ng ml−1) and IFNγ (10 ng ml−1). b, scatter plot depicting relative depletion of sgRNAs targeting 19,674 genes in a Cas9+ B16 control and Tbk1 sgRNA cell line +/− in vitro stimulation with TNF (10 ng ml−1) and IFNγ (10 ng ml−1). c, Western blot of control sgRNA and Tbk1-null B16 cells treated with TNF (160 ng ml−1) and IFNγ (40 ng ml−1) for the indicates times. Data are representative of three independent experiments. d, cell viability assessment in parental B16 cells pre-treated with TBK1i (1 μM) +/− JAK 1/2 inhibitor (ruxolitinib, 0.5 μM) +/− TNF/IFNγ for 48 h compared to unstimulated controls. Means (bars) and individual values (open circles) are shown (n=3, 1 independent experiment; 2-way ANOVA, Dunnett's multiple comparisons test; *P < 0.05; ***P < 0.001; **** P < 0.0001; ns, not significant). e, Western blot of indicated proteins in Tbk1-null B16 cell lysates following 2-hour pre-treatment with vehicle control (0.1%DMSO), ruxolitinib (1 μM), Q-VD-OPh (20 μM), Nec-1s (10 μM), or Q-VD-OPh plus Nec-1s followed by 10-hour treatment with TNF (160 ng ml−1) and IFNγ (40 ng ml−1) or unstimulated (PBS) control. Data are representative of three independent experiments. f, GR values for 9-point inhibitor titration of ruxolitinib (JAK1/2i) in parental, control sgRNA (monoclonal), and Tbk1 sgRNA (monoclonal) B16 cells (2 independent experiments; representative data from single experiment with 6 technical replicates per condition). Means (solid circles) are shown +/− s.e.m (error bars).

Supplementary Figs. 1–13. Gene expression matrices for scRNA-seq, gating strategy for flow cytometry and uncropped images for western blot analyses.

PDOTS clinicopathological data.

sgRNA depletion and enrichment data for in vitro CRISPR screens in B16 cells.

Supporting data for the main figures.

Supporting data for the extended data figures.

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Sun, Y., Revach, Oy., Anderson, S. et al. Targeting TBK1 to overcome resistance to cancer immunotherapy. Nature 615, 158–167 (2023). https://doi.org/10.1038/s41586-023-05704-6

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Received: 16 July 2021

Accepted: 04 January 2023

Published: 12 January 2023

Issue Date: 02 March 2023

DOI: https://doi.org/10.1038/s41586-023-05704-6

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Targeting TBK1 to overcome resistance to cancer immunotherapy