Abstract
Tiragolumab, an anti-TIGIT antibody with an active IgG1κ Fc, demonstrated improved outcomes in the phase 2 CITYSCAPE trial (ClinicalTrials.gov: NCT03563716) when combined with atezolizumab (anti-PD-L1) versus atezolizumab alone1. However, there remains little consensus on the mechanism(s) of response with this combination2. Here we find that a high baseline of intratumoural macrophages and regulatory T cells is associated with better outcomes in patients treated with atezolizumab plus tiragolumab but not with atezolizumab alone. Serum sample analysis revealed that macrophage activation is associated with a clinical benefit in patients who received the combination treatment. In mouse tumour models, tiragolumab surrogate antibodies inflamed tumour-associated macrophages, monocytes and dendritic cells through Fcγ receptors (FcγR), in turn driving anti-tumour CD8+ T cells from an exhausted effector-like state to a more memory-like state. These results reveal a mechanism of action through which TIGIT checkpoint inhibitors can remodel immunosuppressive tumour microenvironments, and suggest that FcγR engagement is an important consideration in anti-TIGIT antibody development.
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Data availability
The sequencing data generated in this study will be deposited once anonymized. Up-to-date details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents are available online (https://go.roche.com/data_sharing). Anonymized records for individual patients across more than one data source external to Roche cannot, and should not, be linked due to a potential increase in risk of patient re-identification. Source data of preclinical study data are provided with this paper, and source data of clinical study data will be deposited once anonymized. Source data are provided with this paper.
Code availability
All packages used in this study are publicly available. This study does not report original codes.
Change history
13 March 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41586-024-07280-9
References
Cho, B. C. et al. Tiragolumab plus atezolizumab versus placebo plus atezolizumab as a first-line treatment for PD-L1-selected non-small-cell lung cancer (CITYSCAPE): primary and follow-up analyses of a randomised, double-blind, phase 2 study. Lancet Oncol. 23, 781–792 (2022).
Dolgin, E. Antibody engineers seek optimal drug targeting TIGIT checkpoint. Nat. Biotechnol. 38, 1007–1009 (2020).
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
Gong, J., Le, T. Q., Massarelli, E., Hendifar, A. E. & Tuli, R. Radiation therapy and PD-1/PD-L1 blockade: the clinical development of an evolving anticancer combination. J. Immunother. Cancer 6, 46 (2018).
Wang, M., Herbst, R. S. & Boshoff, C. Toward personalized treatment approaches for non-small-cell lung cancer. Nat. Med. 27, 1345–1356 (2021).
Chiang, E. Y. & Mellman, I. TIGIT-CD226-PVR axis: advancing immune checkpoint blockade for cancer immunotherapy. J. Immunother. Cancer 10, e004711 (2022).
Herbst, R. S. et al. Atezolizumab for first-line treatment of PD-L1-selected patients with NSCLC. N. Engl. J. Med. 383, 1328–1339 (2020).
Felip, E. et al. Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB-IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet 398, 1344–1357 (2021).
Johnston, R. J. et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8+ T cell effector function. Cancer Cell 26, 923–937 (2014).
Banta, K. L. et al. Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8+ T cell responses. Immunity 55, 512–526 (2022).
Johnston, R. J., Lee, P. S., Strop, P. & Smyth, M. J. Cancer immunotherapy and the nectin family. Annu. Rev. Cancer Biol. 5, 203–219 (2021).
Waight, J. D. et al. Selective FcɣR co-engagement on APCs modulates the activity of therapeutic antibodies targeting T cell antigens. Cancer Cell 33, 1033–1047 (2018).
Han, J. H. et al. Effective anti-tumor response by TIGIT blockade associated with FcγR engagement and myeloid cell activation. Front. Immunol. 11, 573405 (2020).
Preillon, J. et al. Restoration of T-cell effector function, depletion of Tregs, and direct killing of tumor cells: the multiple mechanisms of action of a-TIGIT antagonist antibodies. Mol. Cancer Ther. 20, 121–131 (2021).
Yu, X. et al. The surface protein TIGIT suppresses T cell activation by promoting the generation of mature immunoregulatory dendritic cells. Nat. Immunol. 10, 48–57 (2008).
Stanietsky, N. et al. The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity. Proc. Natl Acad. Sci. USA 106, 17858–17863 (2009).
Patil, N. S. et al. Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer. Cancer Cell 40, 289–300 (2022).
Rittmeyer, A. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 389, 255–265 (2017).
Dige, A. et al. Soluble CD163, a specific macrophage activation marker, is decreased by anti-TNF-α antibody treatment in active inflammatory bowel disease. Scand. J. Immunol. 80, 417–423 (2014).
Davis, B. H. & Zarev, P. V. Human monocyte CD163 expression inversely correlates with soluble CD163 plasma levels. Cytometry B. Clin. Cytom. 63, 16–22 (2005).
Bendell, J. C. et al. Phase Ia/Ib dose-escalation study of the anti-TIGIT antibody tiragolumab as a single agent and in combination with atezolizumab in patients with advanced solid tumors. Cancer Res. 80, CT302 (2020).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) Hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).
Bar, N. et al. Differential effects of PD-L1 versus PD-1 blockade on myeloid inflammation in human cancer. JCI Insight 5, e129353 (2020).
Pello, O. M. et al. Role of c-MYC in alternative activation of human macrophages and tumor-associated macrophage biology. Blood 119, 411–421 (2012).
Lo, M. et al. Effector-attenuating substitutions that maintain antibody stability and reduce toxicity in mice. J. Biol. Chem. 292, 3900–3908 (2017).
Nimmerjahn, F. & Ravetch, J. V. Fcγ receptors: old friends and new family members. Immunity 24, 19–28 (2006).
Huang, A. Y. et al. The immunodominant major histocompatibility complex class I-restricted antigen of a murine colon tumor derives from an endogenous retroviral gene product. Proc. Natl Acad. Sci. USA 93, 9730–9735 (1996).
DeNardo, D. G. et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov. 1, 54–67 (2011).
Ries, C. H. et al. Targeting tumor-associated macrophages with anti-CSF-1R antibody reveals a strategy for cancer therapy. Cancer Cell 25, 846–859 (2014).
O’Brien, S. A. et al. Activity of tumor-associated macrophage depletion by CSF1R blockade is highly dependent on the tumor model and timing of treatment. Cancer Immunol. Immunother. 70, 2401–2410 (2021).
Cannarile, M. A. et al. Colony-stimulating factor 1 receptor (CSF1R) inhibitors in cancer therapy. J. Immunother. Cancer 5, 53 (2017).
Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).
Morad, G., Helmink, B. A., Sharma, P. & Wargo, J. A. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 184, 5309–5337 (2021).
Yofe, I. et al. Anti-CTLA-4 antibodies drive myeloid activation and reprogram the tumor microenvironment through FcγR engagement and type I interferon signaling. Nat. Cancer 3, 1336–1350 (2022).
Casanova-Acebes, M. et al. Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. Nature 595, 578–584 (2021).
Nielsen, S. R. & Schmid, M. C. Macrophages as key drivers of cancer progression and metastasis. Mediators Inflamm. 2017, 9624760 (2017).
Haas, L. & Obenauf, A. C. Allies or enemies—the multifaceted role of myeloid cells in the tumor microenvironment. Front. Immunol. 10, 2746 (2019).
Lindau, D., Gielen, P., Kroesen, M., Wesseling, P. & Adema, G. J. The immunosuppressive tumour network: myeloid-derived suppressor cells, regulatory T cells and natural killer T cells. Immunology 138, 105–115 (2013).
Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334 (2019).
Bournazos, S. et al. Signaling by antibodies: recent progress. Annu. Rev. Immunol. 35, 285–311 (2017).
Bruderer, R. et al. Analysis of 1508 plasma samples by capillary-flow data-independent acquisition profiles proteomics of weight loss and maintenance. Mol. Cell. Proteom. 18, 1242–1254 (2019).
Callister, S. J. et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5, 277–286 (2006).
Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).
Richie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Oh, S. A. et al. PD-L1 expression by dendritic cells is a key regulator of T-cell immunity in cancer. Nat. Cancer 1, 681–691 (2020).
Mily, A. et al. Polarization of M1 and M2 human monocyte-derived cells and analysis with flow cytometry upon Mycobacterium tuberculosis infection. J. Vis. Exp. 163, e61807 (2020).
Wu, T. D., Reeder, J., Lawrence, M., Becker, G. & Brauer, M. J. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, Accuracy, and Functionality. Methods Mol. Biol. 1418, 283–334 (2016).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).
Laboratory for Functional Epigenetics, gbiomed.kuleuven.be/english/cme/research/laboratories/54213024/scRNAseq-NSCLC (KU Leuven, 2023).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).
Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020).
Bagaev, A. et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 39, 845–865 (2021).
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Sergushichev, A. A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Acknowledgements
We thank the patients who provided tumour samples for this study, as well as the investigators and staff involved in the CITYSCAPE study. Editorial assistance for the development of this Article, under the direction of the authors, was provided by A. Robertson and D. Christofi of Ashfield MedComms, an Inizio company, and funded by F. Hoffmann-La Roche. We also thank the staff at Biognosys for processing and generating the raw mass spectrometry data files for analysis; the staff at Abiosciences for generating mouse scRNA-seq raw data files; the staff at Immunai for generating and processing human PBMC scRNA-seq data; and C. Bais, T. Pham, R. Greathouse and A. Rapaport for reading the manuscript and providing technical assistance.
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X.G. and R. Hu are co-first authors; Y.C. and S.S. are co-second authors; and R.J.J. and N.S.P. are co-senior authors. N.S.P. and R.J.J. conceived the project. R. Hu, S.S., J.S., L.M., R. Hendricks, K.N., K.L.B., E.D., P.S.C., J.H. and S. Mittman performed experiments. N.S.P., R.J.J., X.G., R. Hu, Y.C., S.S., B.Y.N., L.M. and E.D analysed data. N.S.P., S.S., E.Y.C., L.M., A.D., S. Mariathasan, R.M., D.S.S., I.M. and R.J.J. guided data analysis. W.C., N.M. and P.D. coordinated clinical sample management and analysis. M.J., D.R.A., B.C.C., A.I., I.G.-B., E.F. and R.M. guided clinical trial and data management. N.S.P., R.J.J. and X.G. wrote the manuscript with input from all of the authors. All of the authors contributed to data interpretation, discussion of results and commented on the manuscript.
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X.G., R. Hu, Y.C., S.S., B.Y.N., J.S., L.M., R. Hendricks, K.N., K.L.B., E.D., A.D., P.S.C., J.H., S. Mittman, N.M., P.D., W.C., I.M., S. Mariathasan, D.S.S., R.M., E.Y.C., R.J.J. and N.S.P. are employees and stockholders of Roche/Genentech. M.J. declares research funding (paid to institution) from AbbVie, Acerta, Adaptimmune, Amgen, Apexigen, Arcus Biosciences, Array BioPharma, ArriVent BioPharma, Artios Pharma, AstraZeneca, Atreca, BeiGene, BerGenBio, BioAtla, Black Diamond, Boehringer Ingelheim, Bristol-Myers Squibb, Calithera Biosciences, Carisma Therapeutics, Checkpoint Therapeutics, City of Hope National Medical Center, Corvus Pharmaceuticals, Curis, CytomX, Daiichi Sankyo, Dracen Pharmaceuticals, Dynavax, Lilly, Eikon Therapeutics, Elicio Therapeutics, EMD Serono, EQRx, Erasca, Exelixis, Fate Therapeutics, Genentech/Roche, Genmab, Genocea Biosciences, GlaxoSmithKline, Gritstone Oncology, Guardant Health, Harpoon, Helsinn Healthcare SA, Hengrui Therapeutics, Hutchison MediPharma, IDEAYA Biosciences, IGM Biosciences, Immunitas Therapeutics, Immunocore, Incyte, Janssen, Jounce Therapeutics, Kadmon Pharmaceuticals, Kartos Therapeutics, LockBody Therapeutics, Loxo Oncology, Lycera, Memorial Sloan-Kettering, Merck, Merus, Mirati Therapeutics, Mythic Therapeutics, NeoImmune Tech, Neovia Oncology, Novartis, Numab Therapeutics, Nuvalent, OncoMed Pharmaceuticals, Palleon Pharmaceuticals, Pfizer, PMV Pharmaceuticals, Rain Therapeutics, RasCal Therapeutics, Regeneron Pharmaceuticals, Relay Therapeutics, Revolution Medicines, Ribon Therapeutics, Rubius Therapeutics, Sanofi, Seven and Eight Biopharmaceuticals/Birdie Biopharmaceuticals, Shattuck Labs, Silicon Therapeutics, Stem CentRx, Syndax Pharmaceuticals, Taiho Oncology, Takeda Pharmaceuticals, Tarveda, TCR2 Therapeutics, Tempest Therapeutics, Tizona Therapeutics, TMUNITY Therapeutics, Turning Point Therapeutics, University of Michigan, Vyriad, WindMIL Therapeutics and Y-mAbs Therapeutics; and consulting/advisory roles (paid to institution) for AbbVie, Amgen, Arcus Biosciences, Arrivent, Astellas, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Calithera Biosciences, D3 Bio Limited, Daiichi Sankyo, Fate Therapeutics, Genentech/Roche, Genmab, Genocea Biosciences, Gilead Sciences, GlaxoSmithKline, Gritstone Oncology, Hookipa Biotech, Immunocore, Janssen, Jazz Pharmaceuticals, Lilly, Merck, Mirati Therapeutics, Molecular Axiom, Normunity, Novartis, Novocure, Pfizer, Pyramid Biosciences, Revolution Medicines, Sanofi-Aventis, SeaGen, Synthekine, Takeda Pharmaceuticals and VBL Therapeutics. D.R.A. reports personal payment/honoraria from Roche, AstraZeneca, Bristol-Myers Squibb, Merck Sharp & Dohme, Eli Lilly, Pfizer, and Novartis; and institutional support for attending meetings or travel from Roche, Bristol-Myers Squibb, Merck Sharp & Dohme and Novartis. BCC declares royalties from Champions Oncology, Crown Bioscience, Imagen and PearlRiver Bio; grants/research support/funding from MOGAM Institute, LG Chem, Oscotec, Interpark Bio Convergence Corp, GIInnovation, GI-Cell, Abion, Abbvie, AstraZeneca, Bayer, Blueprint Medicines, Boehringer Ingelheim, Champions Oncology, CJ Bioscience, CJ Blossom Park, Cyrus, Dizal Pharma, Genexine, Janssen, Lilly, MSD, Novartis, Nuvalent, Oncternal, Ono, Regeneron, Dong-A ST, Bridgebio Therapeutics, Yuhan, ImmuneOncia, Illumina, Kanaph Therapeutics, Therapex, JINTSbio, Hanmi, CHA Bundang Medical Center and Vertical Bio AG; consultancy roles for Abion, BeiGene, Novartis, AstraZeneca, Boehringer-Ingelheim, Roche, BMS, CJ, CureLogen, Cyrus Therapeutics, Ono, Onegene Biotechnology, Yuhan, Pfizer, Eli Lilly, GI-Cell, Guardant, HK Inno-N, Imnewrun Biosciences, Janssen, Takeda, MSD, Medpacto, Blueprint medicines, RandBio and Hanmi; employment from Yonsei University Health System; participation on an advisory board for KANAPH Therapeutic, Bridgebio Therapeutics, Cyrus Therapeutics, Guardant Health, Oscotec, J INTS Bio, Therapex, Gliead and Amgen; speaker roles for ASCO, AstraZeneca, Guardant, Roche, ESMO, IASLC, Korean Cancer Association, Korean Society of Medical Onoclogy, Korean Society of Thyroid-Head and Neck Surgery, Korean Cancer Study Group, Novartis, MSD, The Chinese Thoracic Oncology Society and Pfizer; stocks/shares in TheraCanVac, Gencurix, Bridgebio Therapeutics, KANAPH Therapeutic, Cyrus Therapeutics, Interpark Bio Convergence and J INTS BIO; founder for DAAN Biotherapeutics; and member of the board of directors for J INTS BIO. A.I. declares grants and/or consulting fees from BMS, MSD, Roche, Bayer and AstraZeneca. I.G.-B. declares clinical investigator, advisory board, speaker and director of scientific meeting roles for Roche/Genentech; and financial support from Roche/Genentech. E.F. declares advisory board/speaker roles for Abbvie, Amgen, Astra Zeneca, Bayer, Beigene, Boehringer Ingelheim, Bristol Myers Squibb, Daiichi Sankyo, Eli Lilly, F. Hoffmann-La Roche, Genentech, Gilead, Glaxo Smith Kline, Janssen, Medscape, Merck Serono, Merck Sharp & Dohme, Novartis, Peervoice, Peptomyc, Pfizer, Regeneron, Sanofi, Takeda, Touch Oncology and Turning Point Therapeutics; and independent member of the board for Grifols.
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Extended data figures and tables
Extended Data Fig. 1 Intratumoural myeloid and Treg cell content correlates with tiragolumab plus atezolizumab outcome but not placebo plus atezolizumab.
a, Forest plot comparing tiragolumab plus atezolizumab versus placebo plus atezolizumab in patients with tumours expressing high or low gene levels (cutoff by median expression) of CD274, TIGIT, CD226, and PVR in CITYSCAPE. Hazard ratio and 95% confidence interval were determined using univariate Cox model. The dots represent the hazard ratio and the horizontal bars the 95% confidence interval. b–e, Kaplan–Meier curves comparing PFS in patients with tumours enriched (solid lines) or not enriched (dashed lines) for TAMs (b), Tregs (c), CD16-high monocytes (d), and CD8 + T effector cells (T-eff) (e). Enrichment or not was determined by the median cell type signature score cutoffs. f, g, Kaplan–Meier curves comparing the PFS (f) and OS (g) in PD-L1-positive patients from the phase 3 NSCLC OAK study who received atezolizumab monotherapy and had tumours enriched for TAMs. h, i, Kaplan–Meier curves comparing the PFS (h) and OS (i) in PD-L1-positive patients from the phase 3 NSCLC OAK study who received atezolizumab monotherapy and had tumours enriched for Tregs. f-i, Hazard ratio and 95% confidence interval were determined using univariate Cox model, and P values were estimated using the log-rank test.
Extended Data Fig. 2 Correlation of bulk RNA-seq-based cell type signature scores with multiplex immunofluorescence.
a, b, Correlation of TAM signature with CD68+ cells by mIF (a) and Treg signature with FoxP3+ cells by mIF (b). Two-tailed Pearson correlation; n = 27. mIF, multiplex immunofluorescence.
Extended Data Fig. 3 The proportion of proliferation cells and major cell types in PBMC.
a, Scatter plot showing the S and G2M cell cycle phase scores, coloured by cells in proliferating (red) or non-proliferating states (black). b, Bar plot showing the proportion of proliferating cells in each major cell type. c, Box plots showing the proportion of proliferating cells in CD4_non_naive, CD8_non_naive, and NK cells, across each timepoint. d, Box plots comparing the proportions of each cell type at on-treatment (C1D15, C2D1, and C4D1) versus baseline (C1D1). e, Box plots comparing the proportions of each cell type between responders and non-responders at baseline (C1D1) and on-treatment (C1D15, C2D1, and C4D1). c-e, Boxplot center line, median; box, interquartile range (IQR; the range between the 25th and 75th percentile); whiskers, 1.58 × IQR. c,d, Median values per time point are connected by solid black lines; samples from the same patient at different time points are connected by grey lines. c-d, P values shown were calculated by two-tailed paired Student’s t-test and BH-adjusted. e, Nominal P values derived from two-tailed unpaired Student t-test are shown and red asterisk represents significance levels where * P < 0.05. c-e, n = 16 patients.
Extended Data Fig. 4 Efficient tumour rejection by anti-PD-L1 and anti-TIGIT mAbs treatment depends on functional Fc-FcɣR interaction axis.
a, Plots depicting tumour volumes in each mouse over time; data are representative of one independent experiment. Wildtype BALB/c mice were implanted with CT26 tumours and then treated as described in the method. b, Plots depicting tumour volumes in each mouse over time; data are representative of one independent experiment. Wildtype (top) and FcɣR knockout (bottom) BALB/c mice were implanted with CT26 tumours and then treated as described in the method.
Extended Data Fig. 5 anti-TIGIT treatment modulation of tumour infiltrating immune cells and peripheral blood monocytes depends on the Fc region.
a, UMAP of single cells from tumour infiltrating T and NK cells (top, n = 21,407) and myeloid cells (bottom, n = 5,352) coloured by cell types. b, Bubble plots showing marker gene expression for T and NK cells (left) and myeloid cells (right) as shown in (a).c-e, Heatmaps showing the expression of selected genes across different treatments in tumour macrophages and monocytes combined (c), tumour CD8 + T cells combined (d), and tumour CD4+ Tregs (e). f, UMAP of single cells from the peripheral blood (n = 26,174) coloured by cell types. g, Bubble plots showing the marker gene expression of cell types as in (f). h, Heatmap displaying the scaled gene expression of marker genes distinguishing classical, non-classical, and intermediate monocytes, and the expression patterns of FcɣR. i, Heatmap showing the scaled gene expression of MHC and interferon response in non-classical monocytes across different treatments. a-i, Single cell RNA-seq was performed on intratumoural (a-e) and peripheral (f-i) CD45+ cells isolated at day 3 after treatment, and data are from one independent experiment with n = 5 mice in each group.
Extended Data Fig. 6 Annotation of single cells collected from mouse tumours.
Single cell RNA-seq was performed on intratumoural CD45+ cells isolated from tumours at day 3 after treatment, and data are from one independent experiment with n = 5 mice in each group. This is related to Fig. 4b–d. a, UMAP of tumour-infiltrating lymphocytes (top, n = 35,358) and myeloid (bottom, n = 4,261) cells coloured by cell types. b, Bubble plots showing marker gene expression for T and NK cells (left) and myeloid cells (right) as shown in (a).
Extended Data Fig. 7 The modulation effects of anti-PD-L1 + anti-TIGIT on peripheral blood monocytes depends on the anti-TIGIT mAb Fc region.
a, UMAP of single cells from the peripheral blood cells (n = 55,368) coloured by cell types. b, Bubble plot showing the marker gene expression of cell types as in (a). c, Heatmap displaying the scaled gene expression of marker genes distinguishing classical, non-classical, and intermediate monocytes, and the expression patterns of FcɣR. d, Box plots comparing cell proportions of different treatments versus IgG2a isotype control (B1). B2, aPD-L1; B3, aTIGIT-IgG2b; B4, aTIGIT-IgG2a; B5, aPD-L1 + aTIGIT-IgG2b; B6, aPD-L1 + aTIGIT-IgG2a. Boxplot center line, median; box, interquartile range (IQR; the range between the 25th and 75th percentile); whiskers, 1.58 × IQR. Normal P values by two-tailed unpaired Student’s t-test are shown in grey colour; adjusted P values by Dunnett’s multiple comparison were shown in black colour. e, f, Volcano plots showing the gene expression of anti-PD-L1 + anti-TIGIT IgG2a versus anti-PD-L1 (e), and anti-PD-L1 + anti-TIGIT-IgG2b versus anti-PD-L1 (f) in peripheral blood classical (left), intermediate (middle), and non-classical (right) monocytes. P values were calculated by two-tailed Wilcoxon rank-sum test. a-f, Single cell RNA-seq was performed on peripheral CD45+ cells isolated at day 3 after treatment, and data are from one independent experiment with n = 5 mice in each group.
Extended Data Fig. 8 Flow cytometry analysis of anti-TIGIT activity in tumour myeloid cells of E0771 model, and T cells of CT26 model.
a, Mean fluorescence intensity (MFI) of cell surface MHC-II on tumour-infiltrating dendritic cells (DC, left), macrophages (middle), and monocytes (right), normalized to their respective median MFI value following control treatment. Far right, histogram of representative surface MHC-II expression on tumour monocytes following various treatments. E0771-bearing C57BL/6 J mice were treated as indicated and data were collected at day 7 after treatment. Data are a composite of two independent experiments with n = 4 mice in each group; shown are mean +/− SEM with one-way ANOVA with Dunnett’s multiple comparisons, with the Control IgG group designated as the control group. b, Frequencies of tumour-infiltrating FoxP3- non-Treg CD4 + T cells (left), FoxP3+ Treg CD4 + T cells (middle), and CD8 + T cells (right) out of total CD45+ cells. c, Ratio of tumour CD8 + T cells to FoxP3+ Treg CD4 + T cells. d, e, Additional data related to Fig. 5e,f. Frequencies of TCF1 + TIM3+ memory-like T cells (d) and TOX+ terminally differentiated effector T cells (e) in CT26-tumour bearing mice treated with control and anti-PD-L1 plus anti-TIGIT mIgG2a-LALAPG or mIgG2a antibodies. b-e, Intratumoural CD45+ cells were analysed by flow cytometry at day 3 after treatment (b, c) and gp70 tetramer positive T cells at day 7 after treatment (d, e). Data are representative of one (b, c) or two (d, e) independent experiments with n = 5 mice in each group. b-e, Data in the dot plots are mean +/− SEM with one-way ANOVA with Dunnett’s multiple comparisons, with the anti-PD-L1 monotherapy group designated as the control group.
Extended Data Fig. 9 Tumour infiltrating leukocyte FACS and scRNA-seq analysis following treatment with anti-PD-L1, anti-TIGIT, and anti-CSF-1R.
a, Percentage of tumour macrophages (left) and representative FACS plots of tumour CD11b+ cell expression of F4/80 and CD86 following treatment (right). Data were collected at day 7 after treatment, and are representative of two independent experiments with n = 5 mice in each group. Left, data are mean +/ − SEM with one-way ANOVA with Tukey’s multiple comparisons. b, Growth of CT26 tumours in syngeneic BALB/c mice treated with anti-gp120 (left), anti-PD-L1 + anti-TIGIT IgG2a (middle), and anti-PD-L1 + anti-TIGIT mIgG2a + anti-CSF-1R (right). Data are representative of two experiments with n = 10 mice in each group. c, UMAP of tumour-infiltrating lymphocytes (top, n = 21, 575) and myeloid (bottom, n = 3, 734) cells coloured by cell types. d, Bubble plots showing marker gene expression for T and NK cells (left) and myeloid cells (right) as shown in (c). e, Volcano plots showing the gene expression of anti-PD-L1 + anti-TIGIT IgG2a versus control IgG2a (left), anti-PD-L1 + anti-TIGIT IgG2a + anti-CSF-1R versus control IgG2a (middle), and anti-PD-L1 + anti-TIGIT IgG2a + anti-CSF-1R versus anti-PD-L1 + anti-TIGIT IgG2a (right) in tumour macrophage and monocytes combined. f, g, Volcano plots showing the gene expression of anti-PD-L1 + anti-TIGIT IgG2a + anti-CSF-1R versus anti-PD-L1 + anti-TIGIT IgG2a in tumour CD8 + T cells combined (f) and CD4 Tregs (g). c-g, Single cell RNA-seq was performed on intratumoural CD45+ cells isolated from tumours at day 3 after treatment, and data are from one independent experiment with n = 5 mice in each group. In volcano plots, the broken y-axis was used to make the y-axis range comparable and for better comparison between treatments; P values were calculated by two-tailed Wilcoxon rank-sum test.
Extended Data Fig. 10 Graphic illustration showing the design of the current study.
Top, To understand the mechanism(s) of response with tiragolumab in combination with atezolizumab, we leveraged samples collected from CITYSCAPE (left; NSCLC, Ph2) including tumour pretreatment samples for bulk RNA-seq and multiplex immunofluorescence (mIF), and pretreatment and on-treatment serum samples for Mass Spec, GO30103 (middle; NSCLC, Ph1b) including pretreatment and on-treatment peripheral blood mononuclear cells (PMBC) for single cell RNA-seq, and preclinical models (right). Bottom, Anti-TIGIT antibody, in a Fc dependent manner, remodels immunosuppressive tumour microenvironments by leveraging myeloid cells and Tregs, which was further enhanced with the addition of anti-PD-(L)1 antibody. Created with BioRender.com.
Supplementary information
Supplementary Figures
Supplementary Figs. 1–7.
Supplementary Table 1
Baseline characteristics in CITYSCAPE (ITT population and the BEP).
Supplementary Table 2
Population demographics in patients whose tumors have high vs low cell types. P values were derived using Fisher’s exact test.
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Guan, X., Hu, R., Choi, Y. et al. Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells. Nature 627, 646–655 (2024). https://doi.org/10.1038/s41586-024-07121-9
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DOI: https://doi.org/10.1038/s41586-024-07121-9
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