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PD-L1 expression by dendritic cells is a key regulator of T-cell immunity in cancer

Nature Cancer volume 1, pages 681–691 (2020)Cite this article

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Abstract

Inhibiting the programmed death-1 (PD-1) pathway is one of the most effective approaches to cancer immunotherapy, but its mechanistic basis remains incompletely understood. Binding of PD-1 to its ligand PD-L1 suppresses T-cell function in part by inhibiting CD28 signaling. Tumor cells and infiltrating myeloid cells can express PD-L1, with myeloid cells being of particular interest as they also express B7-1, a ligand for CD28 and PD-L1. Here we demonstrate that dendritic cells (DCs) represent a critical source of PD-L1, despite being vastly outnumbered by PD-L1+ macrophages. Deletion of PD-L1 in DCs, but not macrophages, greatly restricted tumor growth and led to enhanced antitumor CD8+ T-cell responses. Our data identify a unique role for DCs in the PD-L1–PD-1 regulatory axis and have implications for understanding the therapeutic mechanism of checkpoint blockade, which has long been assumed to reflect the reversal of T-cell exhaustion induced by PD-L1+ tumor cells.

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Fig. 1: Disruption of cis interactions between PD-L1 and B7-1 accelerates tumor growth.
Fig. 2: Macrophages are the dominant source of PD-L1 in tumors.
Fig. 3: Dendritic cell expression of PD-L1 is a critical checkpoint in the regulation of antitumor immunity.
Fig. 4: Antibody blockade of remaining PD-L1 in PD-L1ΔDC mice does not improve tumor control.
Fig. 5: Elimination of PD-L1 on DCs enhances T-cell responses.

Data availability

Single-cell RNA-seq data that support the findings of this study have been deposited on NCBI with BioProject ID PRJNA609924. Source data for Figs. 1–5 and Extended Data Figs. 1–9 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Human scRNAseq clustering analyses used scripts adapted from Martin et al.67, available at https://github.com/effiken/martin_et_al_cell_2019. Scripts for generating Fig. 2d–g are available at https://github.com/leaderam/Oh_et_al_Nature_Cancer_2020.

References

  1. Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Sharpe, A. H. & Pauken, K. E. The diverse functions of the PD1 inhibitory pathway. Nat. Rev. Immunol. 18, 153–167 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Barber, D. L. et al. Restoring function in exhausted CD8+ T cells during chronic viral infection. Nature 439, 682–687 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Kamphorst, A. O. & Ahmed, R. Manipulating the PD-1 pathway to improve immunity. Curr. Opin. Immunol. 25, 381–388 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pauken, K. E. & Wherry, E. J. Overcoming T cell exhaustion in infection and cancer. Trends Immunol. 36, 265–276 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Daud, A. I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lau, J. et al. Tumour and host cell PD-L1 is required to mediate suppression of anti-tumour immunity in mice. Nat. Commun. 8, 14572 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Juneja, V. R. et al. PD-L1 on tumor cells is sufficient for immune evasion in immunogenic tumors and inhibits CD8+ T cell cytotoxicity. J. Exp. Med. 214, 895–904 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lin, H. et al. Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression. J. Clin. Invest. 128, 805–815 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tang, H. et al. PD-L1 on host cells is essential for PD-L1 blockade-mediated tumor regression. J. Clin. Invest. 128, 580–588 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Powles, T. et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 515, 558–562 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Hui, E. et al. T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition. Science 355, 1428–1433 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kamphorst, A. O. et al. Rescue of exhausted CD8 T cells by PD-1-targeted therapies is CD28-dependent. Science 355, 1423–1427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Butte, M. J., Keir, M. E., Phamduy, T. B., Sharpe, A. H. & Freeman, G. J. Programmed death-1 ligand 1 interacts specifically with the B7-1 costimulatory molecule to inhibit T cell responses. Immunity 27, 111–122 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Paterson, A. M. et al. The programmed death-1 ligand 1:B7-1 pathway restrains diabetogenic effector T cells in vivo. J. Immunol. 187, 1097–1105 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Yang, J. et al. The novel costimulatory programmed death ligand 1/B7.1 pathway is functional in inhibiting alloimmune responses in vivo. J. Immunol. 187, 1113–1119 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Park, J. J. et al. B7-H1/CD80 interaction is required for the induction and maintenance of peripheral T-cell tolerance. Blood 116, 1291–1298 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Deng, R. et al. B7H1/CD80 interaction augments PD-1-dependent T cell apoptosis and ameliorates graft-versus-host disease. J. Immunol. 194, 560–574 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Chaudhri, A. et al. PD-L1 binds to B7-1 only in cis on the same cell surface. Cancer Immunol. Res. 6, 921–929 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sugiura, D. et al. Restriction of PD-1 function by. Science 364, 558–566 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Zhao, Y. et al. PD-L1:CD80 cis-heterodimer triggers the co-stimulatory receptor CD28 while repressing the inhibitory PD-1 and CTLA-4 pathways. Immunity 51, 1059–1073 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bedoui, S. et al. Cross-presentation of viral and self antigens by skin-derived CD103+ dendritic cells. Nat. Immunol. 10, 488–495 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Ginhoux, F. et al. The origin and development of nonlymphoid tissue CD103+ DCs. J. Exp. Med. 206, 3115–3130 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. del Rio, M. L., Bernhardt, G., Rodriguez-Barbosa, J. I. & Forster, R. Development and functional specialization of CD103+ dendritic cells. Immunol. Rev. 234, 268–281 (2010).

    Article  PubMed  Google Scholar 

  27. Edelson, B. T. et al. Peripheral CD103+ dendritic cells form a unified subset developmentally related to CD8α+ conventional dendritic cells. J. Exp. Med. 207, 823–836 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Spranger, S., Dai, D., Horton, B. & Gajewski, T. F. Tumor-residing Batf3 dendritic cells are required for effector T cell trafficking and adoptive T cell therapy. Cancer Cell 31, 711–723 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Salmon, H. et al. Expansion and activation of CD103(+) dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition. Immunity 44, 924–938 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Barry, K. C. et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24, 1178–1191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bottcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schraml, B. U. et al. Genetic tracing via DNGR-1 expression history defines dendritic cells as a hematopoietic lineage. Cell 154, 843–858 (2013).

    Article  CAS  PubMed  Google Scholar 

  35. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8(+) T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1(−)CD8(+) tumor-infiltrating T Cells. Immunity 50, 181–194 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Siddiqui, I. et al. Intratumoral Tcf1(+)PD-1(+)CD8(+) T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Martin-Orozco, N., Wang, Y. H., Yagita, H. & Dong, C. Cutting edge: programmed death (PD) ligand-1/PD-1 interaction is required for CD8+ T cell tolerance to tissue antigens. J. Immunol. 177, 8291–8295 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Goldberg, M. V. et al. Role of PD-1 and its ligand, B7-H1, in early fate decisions of CD8 T cells. Blood 110, 186–192 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tsushima, F. et al. Interaction between B7-H1 and PD-1 determines initiation and reversal of T-cell anergy. Blood 110, 180–185 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ahn, E. et al. Role of PD-1 during effector CD8 T cell differentiation. Proc. Natl Acad. Sci. USA 115, 4749–4754 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Capietto, A-H. et al. Mutation position is an important determinant for predicting cancer neoantigens. J. Exp. Med. 217, e20190179 (2020).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  45. Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Simoni, Y. et al. Bystander CD8. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Xiong, H. et al. Coexpression of inhibitory receptors enriches for activated and functional CD8. Cancer Immunol. Res. 7, 963–976 (2019).

    Article  PubMed  CAS  Google Scholar 

  49. Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Khan, O. et al. TOX transcriptionally and epigenetically programs CD8. Nature 571, 211–218 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51, 840–855 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fransen, M. et al. Tumor-draining lymph nodes are pivotal in PD-1/PD-L1 checkpoint therapy. JCI Insight 3, e124507 (2018).

    Article  PubMed Central  Google Scholar 

  55. Wu, T. D. et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579, 274–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Garris, C. S. et al. Successful anti-PD-1 cancer immunotherapy requires T cell-dendritic cell crosstalk involving the cytokines IFN-λ and IL-12. Immunity 49, 1148–1161 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Jansen, C. S. et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature 576, 465–470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).

    Article  PubMed  CAS  Google Scholar 

  60. Hughes E. D. & Saunders T. L. Gene Targeting in Embryonic Stem Cells. In: Pease S., Saunders T. (eds) Advanced Protocols for Animal Transgenesis. (Springer Protocols Handbooks. Springer, Berlin, Heidelberg, 2011).

  61. Clausen, B. E., Burkhardt, C., Reith, W., Renkawitz, R. & Förster, I. Conditional gene targeting in macrophages and granulocytes using LysMcre mice. Transgenic Res. 8, 265–277 (1999).

    Article  CAS  PubMed  Google Scholar 

  62. Liang, H. Modeling antitumor activity in xenograft tumor treatment. Biom. J. 47, 358–368 (2005).

    Article  PubMed  Google Scholar 

  63. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Software https://doi.org/10.18637/jss.v036.i03 (2010).

  64. Nakagawa, S., Noble, D. W., Senior, A. M. & Lagisz, M. Meta-evaluation of meta-analysis: ten appraisal questions for biologists. BMC Biol. 15, 18 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).

    Article  PubMed  Google Scholar 

  66. DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control Clin. Trials 7, 177–188 (1986).

    Article  CAS  PubMed  Google Scholar 

  67. Martin, J. C. et al. Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Cell 178, 1493–1508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lo, M. et al. Effector-attenuating substitutions that maintain antibody stability and reduce toxicity in mice. J. Biol. Chem. 292, 3900–3908 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank E. Hui (Section of Cell and Developmental Biology, Division of Biological Sciences, University of California) for the gift of Jurkat PD-1 cells. We thank Y. Chestnut, M. Singh, S. Madireddi, T. Delfino and A. Seki for technical assistance with experiments. We thank B. Alicke and B. Forrest for support with statistical analysis. We thank A. Shaw for valuable discussions. We thank B. Halpenny, F. Gallardo-Chang, M. Dempsey and other members of the vivarium team for assistance with the animal colonies. This work was supported by National Institutes of Health (NIH) grants R01 CA190400 and U24 AI118644 (to M.M.) and 5T32CA078207 (to A.M.L.).

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Author notes

  1. Dai-Chen Wu

    Present address: Atreca, Inc., Redwood City, CA, USA

  2. Jeong M. Kim

    Present address: NGM Biopharmaceuticals, South San Francisco, CA, USA

Authors and Affiliations

  1. Genentech, Inc., South San Francisco, CA, USA

    Soyoung A. Oh, Dai-Chen Wu, Jeanne Cheung, Armando Navarro, Huizhong Xiong, Rafael Cubas, Klara Totpal, Henry Chiu, Yan Wu, Laetitia Comps-Agrar, Merone Roose-Germa, Soren Warming, Minhong Yan, Jeong M. Kim, Sascha Rutz & Ira Mellman

  2. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew M. Leader & Miriam Merad

  3. The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew M. Leader & Miriam Merad

  4. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew M. Leader & Miriam Merad

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Contributions

S.A.O., J.M.K. and I.M. conceived and designed the studies. S.A.O., D.-C.W., J.C., A.N., H.X., R.C. and L.C.-A. performed experiments and analyzed data. H.C. and Y.W. performed and directed antibody generation and characterization. A.M.L. and M.M. conceived, performed and analyzed human tumor single-cell RNA-seq studies. M.Y., M.R.-G. and S.W. generated mouse models. K.T., J.M.K., S.R. and I.M. supervised studies. S.A.O., S.R. and I.M. prepared the manuscript with input from co-authors.

Corresponding author

Correspondence to Ira Mellman.

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Competing interests

S.A.O., D.-C.W., J.C., A.N., H.X., R.C., K.T., H.C., Y.W., L.C.-A., M.R., S.W., M.Y., J.M.K., S.R. and I.M. are current or former employees of Genentech Inc., a member of the Roche group. A.M.L. and M.M. declare no competing interests.

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Extended data

Extended Data Fig. 1 PD-L1 and B7-1 undergo cis, not trans, interactions.

a, NFAT-dependent luciferase reporter signaling in Jurkat cells expressing chimeric PD-1 and human CD3ζ receptors (Jurkat-PD1-CD3) cultured alone, or in the presence of K562 cells expressing only PD-L1, only B7-1, or both proteins. b, NFAT-dependent luciferase reporter signaling in Jurkat-PD1-CD3 cells cultured with PD-L1-expressing K562 cells and increasing numbers of B7-1-expressing K562 cells. c, NFAT-dependent luciferase reporter signaling in Jurkat cells expressing chimeric PD-L1 and human CD3ζ receptors cultured alone, or in the presence of K562 cells expressing PD-1, B7-1, or neither protein. d, Schematic of culture system using CD28 wild-type or knockout Jurkat cells engineered to express human PD-1 with K562 cells as surrogate antigen presenting cells. e, IL-2 production by CD28 wildtype (left) and knockout (right) Jurkat cells cultured with K562 cells in the presence of anti-CD3 antibody. a-e, Circles represent individual cell culture replicates and bars represent mean values. Data are representative of two (b,c) or three (a,e) independent experiments. In a, data with K562 cell expressing only B7-1 is from a single experiment, all other conditions were run in triplicates.

Source data

Extended Data Fig. 2 Development of domain-specific PD-L1 antibodies.

a-b, To assess PD-L1 blocking activity, an ELISA-based assay was used to test the ability of anti-PD-L1 antibodies to impair PD-L1 binding to either PD-1 or B7-1 as purified proteins. a, Anti-PD-L1 clones 6E11 and 27C11 blocked the binding of PD-L1 to PD-1 (IC50 = 0.31 nM and 1.85 nM, respectively), whereas clone 17H9 was inactive. b, Anti-PD-L1 clones 17H9 and 6E11 blocked the PD-L1 and B7-1 interaction in a dose-dependent manner (IC50 = 0.085 nM and 0.42 nM, respectively), whereas clone 27C11 had a negligible effect (IC50 > 100 nM). a-b, Data are representative of two independent experiments. c, Addition of anti-PD-L1 clones 6E11 and 27C11 to cultures of Jurkat cells expressing chimeric PD-1 and human CD3ζ receptors (Jurkat-PD1-CD3) and PD-L1-expressing K562 cells inhibits NFAT-dependent luciferase reporter signaling in a dose-dependent manner, whereas clone 17H9 has minimal effect. Data are representative of two independent experiments. d, Addition of anti-PD-L1 clones 17H9 and 6E11 to cultures of Jurkat cells expressing chimeric PD-1 and human CD3ζ receptors (Jurkat-PD1-CD3) and B7-1-expressing K562 cells inhibits NFAT-dependent luciferase reporter, whereas clone 27C11 has no effect. Data are from a single experiment. Circles represent individual technical replicates and bars represent mean values. e, TR-FRET analysis of cells co-expressing SNAP-tagged B7-1 and ACP-tagged PD-L1 in the presence or absence of the 17H9 anti-PD-L1 antibody. Data are representative of two independent experiments, each performed in triplicates.

Source data

Extended Data Fig. 3 Expression of CD28 ligands by dendritic cells and myeloid cells in tumors and draining lymph nodes.

a, Representative flow cytometry plots of CD11b and F4/80 expression among tumor-infiltrating CD11c + MHCII + cells (left), and CD135 and CD103 expression among tumor-infiltrating CD11b-F4/80-CD11c + MHCII + cells in control mice. Data are representative of three independent experiments. b, Flow cytometric analysis of B7-1 and PD-L1 expression on tumor-infiltrating myeloid cell subsets. Data are representative of two independent experiments. c, Frequencies of total PD-L1 + cells (gray bars, white circles) and B7-1+ cells among PD-L1 + cells (red bars, red circles) in draining lymph node migratory dendritic cells at days 3, 7, and 14 following tumor inoculations. Circles represent individual mice (n = 10), bars represent mean values, and error bars represent the mean ± standard deviation. d, Flow cytometric analysis of B7-1 and PD-L1 expression on migratory DCs in the draining lymph nodes of tumor-bearing mice e, Flow cytometric analysis of B7-2 and PD-L1 expression on migratory DCs in the draining lymph nodes and tumor-infiltrating myeloid cells of tumor-bearing mice. d-e, Data are representative of two independent experiments. f, Frequencies of total PD-L1 + cells (gray bars, white circles) and B7-2+ cells among PD-L1 + cells (red bars, red circles) in draining lymph node migratory dendritic cells and tumor-infiltrating myeloid cells at day 14 following tumor inoculations. Circles represent individual mice (n = 10 for all groups except tumor CD11b-CD64- n = 8), bars represent mean values, and error bars represent the mean ± standard deviation. g, Frequencies of myeloid cell subsets among all tumor-infiltrating immune cells expressing PD-L1. Circles represent individual mice (n = 10), bars represent mean values, and error bars represent the mean ± standard deviation. f-g, Data are representative of two independent experiments.

Source data

Extended Data Fig. 4 Analysis of PD-L1 and B7-1 expression after activation of BMDCs.

a, Flow cytometric analysis of B7-1 and PD-L1 expression by Flt3L- and GM-CSF-induced BMDCs following overnight activation with various stimuli. b, Representative pie charts of PD-L1 and B7-1 expressing subsets of BMDCs after overnight, 24, and 48 hours of activation. c-d, MFIs of c, PD-L1 and d, B7-1 on BMDCs after overnight, 24, and 48 hours of activation. e, Histograms of B7-2 expression on PD-L1 + B7-1+ and PD-L1 + B7-1 BMDCs. f, MFI of B7-2 on BMDCs after overnight, 24, and 48 hours of activation. c-d and f, Circles represent technical replicates, bars represent mean values. a-f, Data are representative of two independent experiments.

Source data

Extended Data Fig. 5 Characterization of dendritic cell (DC) PD-L1 deficient mice.

a-f, Characterization of control Cd274fl/fl and Clec9a.Cre Cd274fl/fl mice a, Representative histograms of PD-L1 expression on splenic CD11c + MHCII + subsets. b, Frequencies of PD-L1 expression in splenic CD11c + MHCII + subsets. c, Representative histograms of PD-L1 expression on splenic macrophages. d. Frequencies of PD-L1 expression in splenic macrophages. b and d, Circles represent individual mice, solid bars represent mean values, and error bars represent the mean ± standard deviation. n = 5 per group. Statistics were calculated using the two-tailed, unpaired Student’s t-test with Welch’s correction. * p ≤ 0.05, *** p ≤ 0.001, **** p ≤ 0.0001., ns = not significant. e, Representative histograms of PD-L1 expression on various subtypes of immune cells. f, Representative histograms of PD-L2 expression on CD11c + MHCII + cells. a-f, Pooled data from two experiments.

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Extended Data Fig. 6 Characterization of T cell phenotype in total and dendritic cell (DC) PD-L1 deficient mice.

a, Frequencies and b, numbers of total (left) and subsets (right) of splenic CD11c + MHCII + cells from control Cd274fl/fl and Clec9a.Cre Cd274fl/fl mice. Circles represent individual mice (n = 5 per group), solid bars represent mean values, and error bars represent the mean ± standard deviation. Pooled data from two experiments. Statistics were calculated using the two-tailed, unpaired Student’s t-test with Welch’s correction. c, Frequencies of CD62L + CD44- CD8 + T cells in lymph nodes and spleens. d-e, Frequencies of CD62L- and CD44-expressing subsets of CD8 + T cells in d, lymph nodes and e, spleens. f, Frequencies of CD62L + CD44- CD4 + Foxp3- T cells in lymph nodes and spleens. g-h, Frequencies of CD62L- and CD44-expressing subsets of CD4 + Foxp3- T cells in g, lymph nodes and h, spleens. i, Frequencies of Foxp3-expressing CD4 + T cells in lymph nodes and spleens. j-k, Frequencies of CD62L- and CD44-expressing subsets of CD4 + Foxp3 + T cells in j, lymph nodes and k, spleens. Circles represent individual mice (Control and total PD-L1 knockout n = 3, PD-L1ΔDC n = 2), bars represent mean values, and error bars represent the mean ± standard deviation. Data are from a single experiment.

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Extended Data Fig. 7 Tumor-infiltrating myeloid cells do not exhibit compensatory upregulation of PD-L2 in total and DC-specific PD-L1 knockout mice.

a, Frequencies of CD11b + F4/80 + , CD11b + F4/80-, and CD11b-F4/80- subsets among tumor-infiltrating CD11c + MHCII + cells. b, Representative flow cytometry plots of PD-L2 expression on tumor-infiltrating CD11c + MHCII + cells. Data are representative of two independent experiments. c, Frequencies of PD-L2-expressing cells among subsets of tumor-infiltrating CD11c + MHCII + cells. d, Mean fluorescence intensity of PD-L2 expression by tumor-infiltrating CD11c + MHCII + cells. a, c-d, Circles represent individual mice (Control n = 3, total PD-L1 knockout and PD-L1ΔDC n = 5), bars represent mean values, and error bars represent the mean ± standard deviation. Data are representative of two independent experiments. e, Frequencies of PD-L1 expression on macrophages, monocytes, and neutrophils from control Cd274fl/fl and LysM.Cre Cd274fl/fl mice. f, Frequencies of PD-L1 expression on splenic CD11c + MHCII + cells. e-f, Circles represent individual mice (Control and LysM.Cre Cd274fl/fl mice n = 3), bars represent mean values, and error bars represent the mean ± standard deviation. Data are representative of two independent experiments.

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Extended Data Fig. 8 Comparable control of PD-L1 sufficient tumors in total and DC-specific PD-L1 knockout mice.

a-b, Upper panels, Growth curves of (a) PDL1-sufficient MC-38 tumors or (b) PDL1-sufficient HEPA1-6.X1.1 tumors in control, total PD-L1 knockout, or DC PD-L1 knockout (Clec9a.Cre) mice (a-b, n = 10 per group). Data are representative of two independent experiments. a-b, Lower panels, Comparison of growth contrast (the difference in area under the curve-based growth rates) between reference (control group) and PD-L1-deficient mice. Circles represent growth contrast and lines represent 95% confidence intervals. Each circle represents an individual study, the red diamond represents the overall growth contrast and confidence interval calculated across all studies. A confidence interval that does not cross zero indicates statistical significance. LN units = natural log units. CI = confidence interval. c, Frequencies of PD-L1 expressing-cells among CD11b + CD64 + , CD11b + CD64-, and CD11b-CD64- subsets of HEPA1-6.X1.1 tumor-infiltrating CD11c + MHCII + cells. d, Frequencies of HEPA1-6.X1.1 tumor-infiltrating T cells. c-d, Circles represent individual mice (n = 10 per group), bars represent mean values, and error bars represent the mean ± standard deviation. Data are representative of two independent experiments. Statistics were calculated using the two-tailed, unpaired Student’s t-test with Welch’s correction (c, CD11b + subsets) or the Mann-Whitney test (c, CD11b-CD64- subsets and d). ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, ns = not significant.

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Extended Data Fig. 9 Dendritic cell-specific loss of PD-L1 results in enhanced T cell activation.

a, Frequency of MC38 neoantigen-specific CD8 + T cells in the draining lymph nodes of tumor-bearing mice at day 10 post-tumor inoculation. b, ELISPOT analysis of IFN-γ production by draining lymph node cells following stimulation with neoantigen peptides. Analysis is at day 10 post-tumor inoculation. c, Frequency of MC38 neoantigen-specific CD8 + T cells in tumors at day 10 post-tumor inoculation. a-c, Circles represent individual mice (Control and PD-L1ΔDC n = 10, total PD-L1 knockout n = 8), bars represent mean values, and error bars represent the mean ± SEM. Statistics were calculated using the Mann-Whitney test. ns = not significant. Data is from a single experiment. d, Representative flow cytometry plots of H2-Kb SIINFEKL Dextramer staining on splenic CD8 + T cells on day 7 following immunization with DEC-Ova and anti-CD40 antibody. e, Frequencies of SIINFEKL Dextramer positive cells among CD8 + T cells. n = number of mice (non-immunized n = 5, control n = 10, total PD-L1 knockout n = 9, PD-L1ΔDC n = 8). Statistics were calculated using the Mann-Whitney test. Pooled data from two experiments. f, Representative flow cytometry plots of CD62L and CD44 expression among SIINFEKL Dextramer-specific CD8 + T cells. g, Frequencies of CD62L-expressing (left) and CD44 + CD62L- (right) cells among SIINFEKL Dextramer-specific CD8 + T cells. h, Representative histograms of PD-1 expression on SIINFEKL Dextramer-specific CD8 + T cells. i, Frequencies of PD-1-expressing cells among SIINFEKL Dextramer-specific CD8 + T cells. j, Mean fluorescence intensity (MFI) of PD-1 expression on SIINFEKL Dextramer-specific CD8 + T cells. d-j, Data are representative of three independent experiments g, i-j, Circles represent individual mice (control n = 5, total PD-L1 knockout and PD-L1ΔDC n = 6) bars represent mean values, and error bars represent the mean ± standard deviation. Statistics were calculated using the two-tailed, unpaired Student’s t-test with Welch’s correction. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001.

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Oh, S.A., Wu, DC., Cheung, J. et al. PD-L1 expression by dendritic cells is a key regulator of T-cell immunity in cancer. Nat Cancer 1, 681–691 (2020). https://doi.org/10.1038/s43018-020-0075-x

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