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Intratumoral dendritic cell–CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma

Nature Medicine volume 29, pages 1389–1399 (2023)Cite this article

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Abstract

Despite no apparent defects in T cell priming and recruitment to tumors, a large subset of T cell rich tumors fail to respond to immune checkpoint blockade (ICB). We leveraged a neoadjuvant anti-PD-1 trial in patients with hepatocellular carcinoma (HCC), as well as additional samples collected from patients treated off-label, to explore correlates of response to ICB within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells (“CXCL13+ TH”) and Granzyme K+ PD-1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells dominated in nonresponders. CD4+ and CD8+ T cell clones that expanded post-treatment were found in pretreatment biopsies. Notably, PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones mainly with effector-like cells in responders or terminally exhausted cells in nonresponders, suggesting that local CD8+ T cell differentiation occurs upon ICB. We found that these Progenitor CD8+ T cells interact with CXCL13+ TH within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or “mregDC”. These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ TH control the differentiation of tumor-specific Progenitor exhasuted CD8+ T cells following ICB.

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Fig. 1: A subset of T cell rich tumors failed to respond to PD-1 blockade.
Fig. 2: Responders are characterized by a distinct molecular phenotype of CD8+ and CD4+ T cells clonally expanded in a tumor-specific manner.
Fig. 3: Local expansion of CD4+ and CD8+ T cells in the tumor upon PD-1 blockade.
Fig. 4: Cellular triads of mregDC, Progenitor CD8 and CXCL13+ TH producing IL-21 and CH25H associate with response to PD-1 blockade.

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Data availability

Human sequencing and MERFISH data will be available at time of publication on GEO (GSE206325) and Zenodo (https://doi.org/10.5281/zenodo.7758080) without restrictions. There are no patient confidentiality-related restrictions.

Code availability

Custom code was used for TCR analysis. Requests for code can be directed to the corresponding authors, and the code will be provided within 30 days and without restrictions.

References

  1. Tabrizian, P., Jibara, G., Shrager, B., Schwartz, M. & Roayaie, S. Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann. Surg. 261, 947–955 (2015).

    Article  PubMed  Google Scholar 

  2. Shindoh, J. et al. Risk factors of post-operative recurrence and adequate surgical approach to improve long-term outcomes of hepatocellular carcinoma. HPB (Oxford) 15, 31–39 (2013).

    Article  PubMed  Google Scholar 

  3. Chalabi, M. et al. Neoadjuvant immunotherapy leads to pathological responses in MMR-proficient and MMR-deficient early-stage colon cancers. Nat. Med. 26, 566–576 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Forde, P. M. et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N. Engl. J. Med. 378, 1976–1986 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med. 25, 454–461 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Marron, T. U. et al. Neoadjuvant cemiplimab for resectable hepatocellular carcinoma: a single-arm, open-label, phase 2 trial. Lancet Gastroenterol. Hepatol. 7, 219–229 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Marron, T. U. et al. Neoadjuvant clinical trials provide a window of opportunity for cancer drug discovery. Nat. Med. 28, 626–629 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li, F. et al. The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: a systematic review and meta-analysis. EClinicalMedicine 41, 101134 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Hegde, P. S., Karanikas, V. & Evers, S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin. Cancer Res. 22, 1865–1874 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Galon, J. & Bruni, D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat. Rev. Drug Discov. 18, 197–218 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Liu, B. et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat. Cancer 3, 108–121 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. 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 

  16. 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 

  17. Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Helmink, B. A. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Maier, B. et al. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580, 257–262 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gruosso, T. et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J. Clin. Invest. 129, 1785–1800 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ruiz de Galarreta, M. et al. β-Catenin activation promotes immune escape and resistance to anti–PD-1 therapy in hepatocellular carcinoma. Cancer Discov. 9, 1124–1141 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. 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 

  23. Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Leader, A. M. et al. Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell 39, 1594–1609.e12 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hanada, K. et al. A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers. Cancer Cell 40, 479–498 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Eberhardt, C. S. et al. Functional HPV-specific PD-1+ stem-like CD8 T cells in head and neck cancer. Nature 597, 279–284 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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 

  30. van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20, 218–232 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Crotty, S. T follicular helper cell biology: a decade of discovery and diseases. Immunity 50, 1132–1148 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Veatch, J. R. et al. Neoantigen-specific CD4+ T cells in human melanoma have diverse differentiation states and correlate with CD8+ T cell, macrophage, and B cell function. Cancer Cell 40, 393–409.e9 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).

    Article  PubMed  Google Scholar 

  34. Zander, R. et al. Tfh-cell-derived interleukin 21 sustains effector CD8+ T cell responses during chronic viral infection. Immunity 55, 475–493 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 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).

    Article  CAS  PubMed  Google Scholar 

  36. Liu, B., Zhang, Y., Wang, D., Hu, X. & Zhang, Z. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade. Nat. Cancer 3, 1123–1136 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Noël, G. et al. Functional Th1-oriented T follicular helper cells that infiltrate human breast cancer promote effective adaptive immunity. J. Clin. Invest. 131, e139905 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hudson, W. H. et al. Proliferating transitory T cells with an effector-like transcriptional signature emerge from PD-1+ stem-like CD8+ T cells during chronic infection. Immunity 51, 1043–1058.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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.e10 (2019).

    Article  CAS  PubMed  Google Scholar 

  41. Tversky, A. Features of similarity. Psychol. Rev. 84, 327–352 (1977).

    Article  Google Scholar 

  42. Frenkel, N. C. et al. Liver lymphatic drainage patterns follow segmental anatomy in a murine model. Sci. Rep. 10, 21808 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Huang, Q. et al. The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell 185, 4049–4066.e25 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Prokhnevska, N. et al. CD8+ T cell activation in cancer comprises an initial activation phase in lymph nodes followed by effector differentiation within the tumor. Immunity 56, 107–124.e5 (2023).

    Article  CAS  PubMed  Google Scholar 

  45. Cohen, M. et al. The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response. Nat. Cancer 3, 303–317 (2022).

    Article  CAS  PubMed  Google Scholar 

  46. Martínez‐López, M., Iborra, S., Conde‐Garrosa, R. & Sancho, D. Batf3‐dependent CD103+ dendritic cells are major producers of IL‐12 that drive local Th1 immunity against Leishmania major infection in mice. Eur. J. Immunol. 45, 119–129 (2015).

    Article  PubMed  Google Scholar 

  47. Mitchell, D. M., Ravkov, E. V. & Williams, M. A. Distinct roles for IL-2 and IL-15 in the differentiation and survival of CD8+ effector and memory T cells. J. Immunol. 184, 6719–6730 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Hannedouche, S. et al. Oxysterols direct immune cell migration via EBI2. Nature 475, 524–527 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tang, H., Zhu, M., Qiao, J. & Fu, Y.-X. Lymphotoxin signalling in tertiary lymphoid structures and immunotherapy. Cell. Mol. Immunol. 14, 809–818 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ferris, S. T. et al. cDC1 prime and are licensed by CD4+ T cells to induce anti-tumour immunity. Nature 584, 624–629 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dorner, B. G. et al. Selective expression of the chemokine receptor XCR1 on cross-presenting dendritic cells determines cooperation with CD8+ T cells. Immunity 31, 823–833 (2009).

    Article  CAS  PubMed  Google Scholar 

  52. Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Workel, H. H. et al. A transcriptionally distinct CXCL13+CD103+CD8+ T-cell population is associated with B-cell recruitment and neoantigen load in human cancer. Cancer Immunol. Res. 7, 784–796 (2019).

    Article  CAS  PubMed  Google Scholar 

  54. Galletti, G. et al. Two subsets of stem-like CD8+ memory T cell progenitors with distinct fate commitments in humans. Nat. Immunol. 21, 1552–1562 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Cabeza-Cabrerizo, M., Cardoso, A., Minutti, C. M., Pereira da Costa, M. & Reis e Sousa, C. Dendritic cells revisited. Annu. Rev. Immunol. 39, 131–166 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Duraiswamy, J. et al. Myeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation. Cancer Cell 39, 1623–1642.e20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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 

  58. 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 

  59. Di Pilato, M. et al. CXCR6 positions cytotoxic T cells to receive critical survival signals in the tumor microenvironment. Cell 184, 4512–4530.e22 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. 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 

  61. van Buggenum, J. A. G. L. et al. A covalent and cleavable antibody–DNA conjugation strategy for sensitive protein detection via immuno-PCR. Sci. Rep. 6, 22675 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Remark, R. et al. In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide. Sci. Immunol. 1, aaf6925 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    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.e20 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mulè, M. P., Martins, A. J. & Tsang, J. S. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nat. Commun. 13, 2099 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  CAS  PubMed  Google Scholar 

  70. 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 

  71. Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by Regeneron Inc. We thank members of the Merad and Brown laboratories at the Marc and Jennifer Lipschultz Precision Immunology Institute at Mount Sinai and the Tisch Cancer Institute for insightful discussions and feedback; and the Mount Sinai Flow Cytometry Core, the Human Immune Monitoring Center and Biorepository and Pathology CoRE Laboratory of the Icahn School of Medicine at Mount Sinai for support. We thank the patients and their families for participating in the clinical trials. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. S.G. was partially supported by National Institutes of Health (NIH) grants CA224319, DK124165, CA263705 and CA196521. A.O.K. and T.U.M. were supported in part by the Tisch Cancer Institute Cancer Center Support Grant (P30 CA196521). A.O.K. and E.H. were supported in part by R01 AI153363. M.M. was partially supported by NIH grants CA257195, CA254104 and CA154947.

Author information

Author notes

  1. These authors contributed equally: Assaf Magen, Pauline Hamon, Thomas U. Marron, Gavin Thurston, Alice O. Kamphorst, Miriam Merad.

  2. These authors jointly supervised this work: Myron Schwartz, Miriam Merad.

Authors and Affiliations

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

    Assaf Magen, Pauline Hamon, Brian Y. Soong, Matthew D. Park, Raphaël Mattiuz, Etienne Humblin, Leanna Troncoso, Joel Kim, Steven Hamel, Mark Buckup, Christie Chang, Alexandra Tabachnikova, Hara Schwartz, Nausicaa Malissen, Yonit Lavin, Alessandra Soares-Schanoski, Samarth Hegde, Giorgio Ioannou, Edgar Gonzalez-Kozlova, Clotilde Hennequin, Jessica Le Berichel, Seunghee Kim-Schulze, Sacha Gnjatic, Ephraim Kenigsberg, Myron Schwartz, Thomas U. Marron, Alice O. Kamphorst & Miriam Merad

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

    Assaf Magen, Pauline Hamon, Brian Y. Soong, Matthew D. Park, Raphaël Mattiuz, Etienne Humblin, Leanna Troncoso, Joel Kim, Steven Hamel, Mark Buckup, Christie Chang, Alexandra Tabachnikova, Hara Schwartz, Nausicaa Malissen, Yonit Lavin, Alessandra Soares-Schanoski, Samarth Hegde, Giorgio Ioannou, Edgar Gonzalez-Kozlova, Clotilde Hennequin, Jessica Le Berichel, Seunghee Kim-Schulze, Sacha Gnjatic, Myron Schwartz, Thomas U. Marron, Alice O. Kamphorst & Miriam Merad

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

    Assaf Magen, Pauline Hamon, Brian Y. Soong, Matthew D. Park, Raphaël Mattiuz, Etienne Humblin, Leanna Troncoso, Joel Kim, Steven Hamel, Mark Buckup, Christie Chang, Alexandra Tabachnikova, Hara Schwartz, Nausicaa Malissen, Yonit Lavin, Alessandra Soares-Schanoski, Samarth Hegde, Giorgio Ioannou, Edgar Gonzalez-Kozlova, Clotilde Hennequin, Jessica Le Berichel, Seunghee Kim-Schulze, Sacha Gnjatic, Alice O. Kamphorst & Miriam Merad

  4. Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA

    Nathalie Fiaschi, Baijun Kou, Michael Dobosz, Lianjie Li, Raquel P. Deering & Gavin Thurston

  5. Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Darwin D’souza, Travis Dawson, Adeeb Rahman, Seunghee Kim-Schulze & Miriam Merad

  6. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruno Giotti, Alexander M. Tsankov & Ephraim Kenigsberg

  7. The Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Zhen Zhao, Stephen C. Ward & Isabel Fiel

  8. Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA

    Christina Adler, Min Ni, Yi Wei, Wei Wang, Gurinder S. Atwal & Namita T. Gupta

  9. VI NEXT, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA

    Kunal Kundu & Kamil J. Cygan

  10. Vizgen Inc., Cambridge, MA, USA

    Colles Price, Nicolas Fernandez & Jiang He

  11. Division of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Myron Schwartz & Thomas U. Marron

  12. Institute for Thoracic Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Miriam Merad

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  49. Myron Schwartz
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  50. Thomas U. Marron
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  51. Gavin Thurston
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Contributions

M.M., T.U.M., G.T. and M.S. conceived the project. M.M., A.O.K., S.G., A.R., E.K., T.U.M. and G.T. designed the experimental framework. T.U.M. and M.S. led the clinical trial. A.M., M.M. and A.O.K. wrote the manuscript, with contributions from P.H., T.U.M. and G.T. N. Fiaschi and P.H. performed multiplex imaging experiments with help from L.T., H.S., S.T., J.L.B., Z.Z., S.C.W., I.F., C.P., B.K., M.D., G.I. and S.G. C.H. coordinated the clinical and research teams. P.H. performed molecular profiling experiments with help from T.D., S.K.-S., M.B., C.C., N.M., A.S-S., J.L.B., C.A., M.N., Y.W., G.S.A. and L.L. Y.L., E.H., S. Hedge, R.M., J.H., K.J.C., N.T.G., R.P.D., A.T., S.G. and M.S. provided intellectual input. A.M. performed computational molecular and spatial analyses, with help from B.Y.S., M.D.P., D.D., J.K., S. Hamel, B.G., E.G.-K., N. Fernandez, W.W., K.K., N.T.G. and E.G.-K.

Corresponding authors

Correspondence to Myron Schwartz, Thomas U. Marron, Gavin Thurston, Alice O. Kamphorst or Miriam Merad.

Ethics declarations

Competing interests

M.M. serves on the scientific advisory board and holds stock from Compugen Inc., Myeloid Therapeutics Inc., Morphic Therapeutic Inc., Asher Bio Inc., Dren Bio Inc., Nirogy Inc., Oncoresponse Inc., Owkin Inc., DEMBIO and Larkspur Inc. M.M. serves on the scientific advisory board of Innate Pharma Inc., DBV Inc., Pionyr Inc., OSE Inc. and Genenta Inc. M.M. receives funding for contracted research from Regeneron Inc. and Boerhinger Ingelheim Inc. S.G. reports past consultancy or advisory roles for Merck and OncoMed; research funding from Regeneron Pharmaceuticals related to the current study, and research funding from Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Genentech, EMD Serono, Pfizer and Takeda, unrelated to the current work. S.G. is a named coinventor on an issued patent (US20190120845A1) for multiplex immunohistochemistry to characterize tumors and treatment responses. The technology is filed through Icahn School of Medicine at Mount Sinai (ISMMS) and is currently unlicensed. This technology was used to evaluate tissue in this study and the results could impact the value of this technology. N. Fiaschi, B.K., M.D., L.L., C.A., M.N., Y.W., W.W., N.T.G., G.S.A., K.K., K.J.C., R.P.D. and G.T. are employees and shareholders of Regeneron Pharmaceuticals Inc. C.P., N. Fernandez and J.H. are employees and shareholders of Vizgen Inc. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Golnaz Vahedi, Matteo Iannacone and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Joao Monteiro and Saheli Sadanand, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Characterization of T cell rich HCC lesions in response to PD-1 blockade.

Surgically resected HCC lesions were isolated after two or more doses of PD-1 blockade and analyzed by H&E (N = 20 biologically independent samples) and single-cell RNA sequencing (scRNAseq N = 29 biologically independent samples). (A) Distribution of responders and non-responders across HCC etiologies (Hep B: Hepatitis B; Hep C: Hepatitis C; NASH: Non-alcoholic steatohepatitis; ASH: Alcoholic steatohepatitis). (B) Quantification of immune aggregate areas and numbers stratified by response and T cell infiltration pattern (Two sided T test). (C) Expression of cluster-defining genes by scRNAseq of key immune populations, showing number of UMI per cell. (D) Differences of cluster frequencies between tumor and adjacent tissue (Two-sided T test, adjusted for multiple-hypotheses, Benjamini–Hochberg correction). Dots represent individual study subjects. The box plot center line represents the median; box limits represent the interquartile range (IQR); whiskers represent the minimum and maximum observations greater and lesser than the IQR plus 1.5×IQR, respectively.

Extended Data Fig. 2 Molecular profiling of expanded CD8+ and CD4+ T cell clones associated with response to PD-1 blockade.

Surgically resected HCC lesions were isolated after two or more doses of PD-1 blockade and analyzed by single-cell RNA and TCR sequencing (scRNAseq and scTCRseq, N = 29 and N = 21 biologically independent samples, respectively). (A, B) Expression of cluster-defining gene modules by scRNAseq for (A) CD8+ (B) CD4+ T cell clusters showing number of UMI per cell. (C, D) Cluster frequencies among (C) CD8+ and (D) CD4+ T cells in tumor and adjacent tissue, stratified by response and T cell infiltration pattern. (E) Cluster frequencies among PD-1lo CD8+ T cells in tumor among tumor-enriched clonal T cells and tumor singlet T cells, stratified by response and T cell infiltration pattern. (F) Cluster frequencies among CD4+ T cells in tumor among tumor-enriched clonal T cells and tumor singlet T cells, stratified by response and T cell infiltration pattern. Dots represent individual study subjects. The box plot center line represents the median; box limits represent the interquartile range (IQR); whiskers represent the minimum and maximum observations greater and lesser than the IQR plus 1.5 × IQR, respectively.

Extended Data Fig. 3 Phenotypic distribution among CD8+ T cell clones and clonality assessment in PBMC and tdLN.

(A–C) Phenotypic analysis of clonotype sharing using scTCRseq. (A) Phenotypic distribution of all CD8+ T cell clusters in individual tumor-enriched clones (top 10 per patient) in responders and T cell rich non-responders. (B) Highlight of PD-1hi CD8+ phenotypic distribution for selected CD8+ clones across remaining patients not shown in Fig. 3B. (C) Expression of PD-1hi CD8+ cluster-defining genes by scRNAseq for CD8+ T cells from the selected clones of Fig. 3B for a responder patient, showing number of UMI per cell. (D) BaseScope TCR imaging using DapB (negative control) and PPIB (positive control) in pre-treatment core biopsies, surgical resection and control human tonsils. (E-G) Dynamic TCRseq of tumor lesions, PBMC and tdLN in responders and non-responders. (E) Number of post-treatment tumor-enriched clones present in pre-treatment tumor lesions analyzed using bulk TCRseq, and in PBMC and tdLN analyzed using scTCRseq, across responders and T cell rich non-responders. (F) Percent of post-treatment tumor-enriched clones in PBMC and tdLN, across responders and T cell rich non-responders (N = 7 PBMC and N = 11 tdLN biologically independent samples). (G) Histograms of clone size (number of cells per clone) distribution per patient, stratified by response and T cell infiltration pattern, separately in PBMC and tdLN. Each bar represents an individual clone.

Extended Data Fig. 4 Spatial localization of CXCL13+ Th, Progenitor CD8+ T cells and mregDC.

(A, B) CITEseq antibody sequencing analysis of DCs. (A) Expression of DC cluster-defining proteins by showing number of UMI per cell. (B) Gating of CD141 (as a marker of DC1) and CD1c (as a marker of DC2) among resting DC (left) and mregDCs (middle and right). (C–I) Post treatment HCC tissue sections analyzed by MERFISH, multiplex IHC, IF and BaseScope for spatial distribution of T cell subsets and mregDC. (C) MERFISH factor analysis gene scores for selected factors, showing top 10 genes per factor. (D) Quantification of factor activation from (C), defined as average gene expression per cluster. (E) Densities of Progenitor (CD3+CD8+TCF1+ CD45RA-) and naive (CD3+CD8+TCF1+ CD45RA+) CD8+ T cells among T cell rich lesions (N = 12 biologically independent samples. Two-sided T test. Dots represent individual study subjects. The box plot center line represents the median; box limits represent the interquartile range (IQR); whiskers represent the minimum and maximum observations greater and lesser than the IQR plus 1.5 × IQR, respectively). (F) IHC (left) and BaseScope analysis (right) of a representative immune aggregate in responder patient showing CD8+ clones accumulation (N = 1). (G, H) Spatial distribution of mregDC (DCLAMP+), Effector CD8+ T cell (CD3+CD8+TCF1− CD45RA−), Effector CD4+ (CD3+CD8-TCF1− CD45RA−) and CXCL13+ Th (CD3+CD8-TCF1+ CD45RA−), and Progenitor CD8+ (CD3+CD8+TCF1+ CD45RA−) and CXCL13+ Th (CD3+CD8-TCF1+ CD45RA−) in two different responder patients, showing computational rendering of IF with density contour annotation for mregDC (DCLAMP+). (I) Distribution of Progenitor (CD3+CD8+TCF1+ CD45RA−) and Effector (CD3+CD8+TCF1− CD45RA−) CD8+ T cell proximities to mregDC (DCLAMP+), showing histograms of individual cells from a representative responder, vertical gray bars represent the median.

Extended Data Fig. 5 Spatial localization of CXCL13+ Th, Progenitor CD8+ T cells and mregDC.

(A–J) Multiplex IF analysis of treatment-naïve HCC lesions (N = 20 biologically independent samples), Surgically resected HCC lesions during treatment with PD-1 blockade (N = 13 biologically independent samples) and HCC tumor biopsies (N = 13 biologically independent samples). (A) Representative images of an mregDC niche, analyzed by IF for T cell markers, in a representative responder (left) and T cell rich non-responder (right). (B) Spatial proximity enrichment analysis of Progenitor CD8+ (CD3+CD8+TCF1+), CXCL13+ Th (CD3+CD8−CXCL13+) and mregDC (DCLAMP+) post-treatment (Two-sided Mann–Whitney U test). (C) Robustness analysis showing spatial proximity patterns across increasing distance thresholds, for pairs and triads post-treatment. (D) Representative images of a niche, analyzed by IF for DC subsets including DC1 (CLEC9A), DC2 (CD1c) and mregDC (DCLAMP). (E) Spatial proximity enrichment analysis showing contact densities (top) and relative enrichment (bottom) at distance of up to 50 µm between Progenitor CD8+ T cell (CD3+CD8+TCF1+) and CXCL13+ Th (CD3+CD8-TCF1+) triads with mregDC (DCLAMP+), DC1 (CLEC9A+) and DC2 (CD1C+), in 6 responders post-treatment. (F-G) Cellular densities of (F) Progenitor CD8+ T cells (CD3+CD8+TCF1+) and CXCL13+ Th (CD3+CD8-CXCL13+) and (G) mregDC (DCLAMP+), DC1 (CLEC9A+) and DC2 (CD1C+) across responders, T cell rich non-responders, pre and post-treatment (Two-sided Mann–Whitney U test). (H) Spatial proximity enrichment analysis of Progenitor CD8+ T cells (CD3+CD8+TCF1+) and mregDC (DCLAMP+) with CXCL13+ Th (CD3+CD8-CXCL13+) or TCF1+ CD4+ T cells (CD3+CD8-TCF1+) in pre-treatment biopsies (Two-sided Mann–Whitney U test). (I) Cellular densities of CXCL13+ Th (CD3+CD8-CXCL13+), TCF1+ CD4+ T cells (CD3+CD8-TCF1+) and Progenitor CD8+ T cells (CD3+CD8+TCF1+) in treatment-naive patients. (J) Spatial proximity enrichment analysis of Progenitor CD8+ T cells (CD3+CD8+TCF1+) and mregDC (DCLAMP+) with CXCL13+ Th (CD3+CD8-CXCL13+) or TCF1+ CD4+ T cells (CD3+CD8-TCF1+) in treatment-naive patients. Dots represent individual study subjects. The box plot center line represents the median; box limits represent the interquartile range (IQR); whiskers represent the minimum and maximum observations greater and lesser than the IQR plus 1.5×IQR, respectively.

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Magen, A., Hamon, P., Fiaschi, N. et al. Intratumoral dendritic cell–CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat Med 29, 1389–1399 (2023). https://doi.org/10.1038/s41591-023-02345-0

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