Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Lymph-node-derived stem-like but not tumor-tissue-resident CD8+ T cells fuel anticancer immunity

Nature Immunology volume 26, pages 1367–1383 (2025)Cite this article

Subjects

Abstract

CD8+ T cell-mediated tumor control and efficacy of immune checkpoint blockade (ICB) are associated with both precursors of exhausted T (TPEX) cells and tissue-resident memory T cells. Their relationships and relative contribution to tumor control, however, are insufficiently understood. Using single-cell RNA sequencing and genetic mouse models, we systematically dissected the heterogeneity and function of cytotoxic T cells in tumors and tumor-draining lymph nodes (tdLNs). We found that intratumoral TCF1+ TPEX cells and their progeny acquired a tissue-residency program that limits their contribution to tumor control and ICB response. By contrast, MYB-dependent stem-like TPEX cells residing in tdLNs sustained CD8+ T cell infiltration into tumors and mediated ICB response. The cytokine TGFβ was the central factor that enforced residency of intratumoral CD8+ T cells and limited the abundance of stem-like TPEX cells in tdLNs, thereby restraining tumor control. A similar network of TGFβ-constrained intratumoral and extratumoral CD8+ T cells with precursor and residency characteristics was found in human cancer.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Identification of CD8+ T cells with residency, exhaustion and precursor features in tumors.
Fig. 2: Precursor and exhausted CD8+ T cells in tumors exhibit TRM-like features.
Fig. 3: Tissue-resident T cells are dispensable for tumor control and ICB response.
Fig. 4: Tumor-draining lymph node TPEX cells mediate tumor control and response to checkpoint inhibition.
Fig. 5: MYB is required for lymph node stem-like CD8+ T cells and antitumor immunity.
Fig. 6: TGFβ limits development and differentiation capacity of stem-like TPEX cells in lymph nodes.
Fig. 7: TGFβ enforces residency and limits effector function of intratumoral CD8+ T cells.
Fig. 8: TGFβ induces residency and inhibits stemness and effector function in human cancer.

Similar content being viewed by others

Data availability

Single-cell RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE253205. Bulk RNA-seq data have been deposited in GEO under accession code GSE253487. Raw RNA-seq data related to Fig. 8g can be downloaded from Zenodo at https://doi.org/10.5281/zenodo.10589058 (ref. 77).

Code availability

Codes used to analyze high-dimensional flow cytometry are freely available on GitHub at https://github.com/luglilab/Cytophenograph. Codes related to human data can be found at GitHub at https://github.com/luglilab/Lymph-node-derived-stem-like-but-not-tumor-tissue-resident-CD8-T-cells-fuel-anti-cancer-immunity. All other codes related to this paper will be made publicly available upon publication.

References

  1. Philip, M. & Schietinger, A. CD8+ T cell differentiation and dysfunction in cancer. Nat. Rev. Immunol. 22, 209–223 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Giles, J. R., Globig, A. M., Kaech, S. M. & Wherry, E. J. CD8+ T cells in the cancer-immunity cycle. Immunity 56, 2231–2253 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rausch, L. & Kallies, A. Molecular mechanisms governing CD8 T cell differentiation and checkpoint inhibitor response in cancer. Annu Rev. Immunol. 43, 515–543 (2025).

    Article  CAS  PubMed  Google Scholar 

  4. 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 

  5. 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 

  6. Leong, Y. A. et al. CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. 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 

  8. Lugli, E., Galletti, G., Boi, S. K. & Youngblood, B. A. Stem, effector, and hybrid states of memory CD8(+) T cells. Trends Immunol. 41, 17–28 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Kallies, A., Zehn, D. & Utzschneider, D. T. Precursor exhausted T cells: key to successful immunotherapy? Nat. Rev. Immunol. 20, 128–136 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. 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 e110 (2019).

  11. Brummelman, J. et al. High-dimensional single cell analysis identifies stem-like cytotoxic CD8+ T cells infiltrating human tumors. J. Exp. Med. 215, 2520–2535 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 176, 404 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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 

  15. 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 

  16. Yates, K. B. et al. Epigenetic scars of CD8+ T cell exhaustion persist after cure of chronic infection in humans. Nat. Immunol. 22, 1020–1029 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Utzschneider, D. T. et al. Early precursor T cells establish and propagate T cell exhaustion in chronic infection. Nat. Immunol. 21, 1256–1266 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tsui, C. et al. MYB orchestrates T cell exhaustion and response to checkpoint inhibition. Nature 609, 354–360 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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.e1044 (2019).

  22. Zander, R. et al. CD4+ T cell help is required for the formation of a cytolytic CD8+ T cell subset that protects against chronic infection and cancer. Immunity 51, 1028–1042.e1024 (2019).

  23. Yamauchi, T. et al. T-cell CX3CR1 expression as a dynamic blood-based biomarker of response to immune checkpoint inhibitors. Nat. Commun. 12, 1402 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Milner, J. J. et al. Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature 552, 253–257 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kumar, B. V. et al. Human tissue-resident memory T cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell Rep. 20, 2921–2934 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hombrink, P. et al. Programs for the persistence, vigilance and control of human CD8+ lung-resident memory T cells. Nat. Immunol. 17, 1467–1478 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Masopust, D. & Soerens, A. G. Tissue-resident T cells and other resident leukocytes. Annu. Rev. Immunol. 37, 521–546 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Park, S. L. et al. Tissue-resident memory CD8+ T cells promote melanoma-immune equilibrium in skin. Nature 565, 366–371 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Duhen, T. et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat. Commun. 9, 2724 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Virassamy, B. et al. Intratumoral CD8+ T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer. Cancer Cell 41, 585–601.e588 (2023).

    Article  CAS  PubMed  Google Scholar 

  33. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Edwards, J. et al. CD103+ tumor-resident CD8+ T cells are associated with improved survival in immunotherapy-naive melanoma patients and expand significantly during anti-PD-1 treatment. Clin. Cancer Res. 24, 3036–3045 (2018).

  35. Jaiswal, A. et al. An activation to memory differentiation trajectory of tumor-infiltrating lymphocytes informs metastatic melanoma outcomes. Cancer Cell 40, 524–544.e525 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Banchereau, R. et al. Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade. J. Immunother. Cancer 9, e002231 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Connolly, K. A. et al. A reservoir of stem-like CD8+ T cells in the tumor-draining lymph node preserves the ongoing antitumor immune response. Sci. Immunol. 6, eabg7836 (2021).

  38. Dammeijer, F. et al. The PD-1/PD-L1-checkpoint restrains T cell immunity in tumor-draining lymph nodes. Cancer Cell 38, 685–700.e688 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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.e105 (2023).

  41. Schenkel, J. M. et al. Conventional type I dendritic cells maintain a reservoir of proliferative tumor-antigen specific TCF-1+ CD8+ T cells in tumor-draining lymph nodes. Immunity 54, 2338–2353.e2336 (2021).

  42. Rahim, M. K. et al. Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes. Cell 186, 1127–1143.e1118 (2023).

  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.e4025 (2022).

  44. Mackay, L. K. et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352, 459–463 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Gabriel, S. S. et al. Transforming growth factor-β-regulated mTOR activity preserves cellular metabolism to maintain long-term T cell responses in chronic infection. Immunity 54, 1698–1714.e1695 (2021).

  46. Christo, S. N. et al. Discrete tissue microenvironments instruct diversity in resident memory T cell function and plasticity. Nat. Immunol. 22, 1140–1151 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Milner, J. J. et al. Heterogenous populations of tissue-resident CD8+ T cells are generated in response to infection and malignancy. Immunity 52, 808–824.e807 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gago da Graca, C. et al. Stem-like memory and precursors of exhausted T cells share a common progenitor defined by ID3 expression. Sci. Immunol. 10, eadn1945 (2025).

    Article  CAS  PubMed  Google Scholar 

  49. 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 

  50. Chow, M. T. et al. Intratumoral activity of the CXCR3 chemokine system is required for the efficacy of anti-PD-1 therapy. Immunity 50, 1498–1512.e1495 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Peng, Q. et al. PD-L1 on dendritic cells attenuates T cell activation and regulates response to immune checkpoint blockade. Nat. Commun. 11, 4835 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Vanhersecke, L. et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat. Cancer 2, 794–802 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  54. Sautes-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W. H. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Verdegaal, E. M. et al. Neoantigen landscape dynamics during human melanoma–T cell interactions. Nature 536, 91–95 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tauriello, D. V. F. et al. TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature 554, 538–543 (2018).

    Article  CAS  PubMed  Google Scholar 

  58. Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Liu, M. et al. TGF-β suppresses type 2 immunity to cancer. Nature 587, 115–120 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gunderson, A. J. et al. TGFβ suppresses CD8+ T cell expression of CXCR3 and tumor trafficking. Nat. Commun. 11, 1749 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Tu, E. et al. T cell receptor-regulated TGF-β type I receptor expression determines T cell quiescence and activation. Immunity 48, 745–759 e746 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wischnewski, V. et al. Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation. Nat. Cancer 4, 908–924 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Losurdo, A. et al. Single-cell profiling defines the prognostic benefit of CD39high tissue resident memory CD8+ T cells in luminal-like breast cancer. Commun. Biol. 4, 1117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Leveen, P. et al. Induced disruption of the transforming growth factor beta type II receptor gene in mice causes a lethal inflammatory disorder that is transplantable. Blood 100, 560–568 (2002).

    Article  CAS  PubMed  Google Scholar 

  65. Steinke, F. C. et al. TCF-1 and LEF-1 act upstream of Th-POK to promote the CD4+ T cell fate and interact with Runx3 to silence Cd4 in CD8+ T cells. Nat. Immunol. 15, 646–656 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Voehringer, D., Liang, H. E. & Locksley, R. M. Homeostasis and effector function of lymphopenia-induced “memory-like” T cells in constitutively T cell-depleted mice. J. Immunol. 180, 4742–4753 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Maekawa, Y. et al. Notch2 integrates signaling by the transcription factors RBP-J and CREB1 to promote T cell cytotoxicity. Nat. Immunol. 9, 1140–1147 (2008).

    Article  CAS  PubMed  Google Scholar 

  68. Utzschneider, D. T. et al. T cells maintain an exhausted phenotype after antigen withdrawal and population reexpansion. Nat. Immunol. 14, 603–610 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47, e47 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  72. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

    Article  PubMed  Google Scholar 

  75. Puccio, S. et al. CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data. Nat. Commun. 14, 5102 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Alvisi, G. et al. IRF4 instructs effector Treg differentiation and immune suppression in human cancer. J. Clin. Invest. 130, 3137–3150 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Galletti, G., De Luca, M. & Lugli, E. Data related to human CD8+ Tpex cells with/without Tgfb experiment. Zenodo https://doi.org/10.5281/zenodo.10589058 (2024).

Download references

Acknowledgements

This study was supported by National Health and Medical Research Council (NHMRC) Investigator grants (2017420 to A.K., 1194482 to T.G. and 1195296 to N.D.H.), NHMRC Ideas and Project Grants (1147409 to A.K.; 2003934 to S.G. and C.T.; and 1185346 to A.K. and S.G.), German Research Foundation (to L.R.) and a Cancer Council Victoria grant (to A.K.). E.L. is a CRI Lloyd J. Old STAR (CRI award 3914) and is supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC; IG 2022 – ID 27391 and AIRC 5×1000 program UniCanVax 22757) and by intramural funding of the Humanitas Research Hospital. G.G. was supported by a fellowship from the Fondazione Italiana per la Ricerca sul Cancro-Associazione Italiana per la Ricerca sul Cancro (FIRC-AIRC). J.S. is supported by EMBO Postdoctoral Fellowship ALTF 663–2022. The generation of Hobit and IRF4 reporter mice used in this study was supported by Phenomics Australia and the Australian Government through the National Collaborative Research Infrastructure Strategy program.

Author information

Author notes

  1. Giovanni Galletti

    Present address: School of Biological Sciences, Department of Molecular Biology, University of California, San Diego, San Diego, CA, USA

  2. These authors contributed equally: Sharanya K. M. Wijesinghe, Lisa Rausch.

Authors and Affiliations

  1. Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Queensland, Australia

    Sharanya K. M. Wijesinghe, Lisa Rausch, Sarah S. Gabriel, Lei Qin, Lifen Wen, Carlson Tsui, Kevin Man, Leonie Heyden, Teisha Mason, Lewis D. Newland, Thomas Gebhardt, Daniel T. Utzschneider, Jan Schroeder & Axel Kallies

  2. IRCCS Humanitas Research Hospital, Rozzano, Italy

    Giovanni Galletti, Marco De Luca, Julian Swatler, Emanuele Voulaz, Giuseppe Marulli, Valentina Errico, Agnese Losurdo & Enrico Lugli

  3. Institute of Experimental Oncology (IEO), Medical Faculty, University Hospital Bonn, University of Bonn, Bonn, Germany

    Lewis D. Newland

  4. The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

    Andrew Kueh & Marco J. Herold

  5. Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia

    Andrew Kueh, Yang Liao, David Chisanga, Marco J. Herold & Wei Shi

  6. School of Cancer Medicine, La Trobe University, Heidelberg, Victoria, Australia

    Andrew Kueh, Yang Liao, David Chisanga, Marco J. Herold & Wei Shi

  7. Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia

    Andrew Kueh & Marco J. Herold

  8. Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

    Yang Liao, Nicholas D. Huntington & Wei Shi

  9. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy

    Emanuele Voulaz & Giuseppe Marulli

  10. Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia

    Gustavo R. Rossi & Fernando Souza-Fonseca-Guimaraes

  11. oNKo-Innate, Melbourne, Victoria, Australia

    Nicholas D. Huntington

  12. Institute of Molecular Medicine & Experimental Immunology, University Hospital Bonn, Bonn, Germany

    Axel Kallies

Authors
  1. Sharanya K. M. Wijesinghe
    View author publications

    Search author on:PubMed Google Scholar

  2. Lisa Rausch
    View author publications

    Search author on:PubMed Google Scholar

  3. Sarah S. Gabriel
    View author publications

    Search author on:PubMed Google Scholar

  4. Giovanni Galletti
    View author publications

    Search author on:PubMed Google Scholar

  5. Marco De Luca
    View author publications

    Search author on:PubMed Google Scholar

  6. Lei Qin
    View author publications

    Search author on:PubMed Google Scholar

  7. Lifen Wen
    View author publications

    Search author on:PubMed Google Scholar

  8. Carlson Tsui
    View author publications

    Search author on:PubMed Google Scholar

  9. Kevin Man
    View author publications

    Search author on:PubMed Google Scholar

  10. Leonie Heyden
    View author publications

    Search author on:PubMed Google Scholar

  11. Teisha Mason
    View author publications

    Search author on:PubMed Google Scholar

  12. Lewis D. Newland
    View author publications

    Search author on:PubMed Google Scholar

  13. Andrew Kueh
    View author publications

    Search author on:PubMed Google Scholar

  14. Yang Liao
    View author publications

    Search author on:PubMed Google Scholar

  15. David Chisanga
    View author publications

    Search author on:PubMed Google Scholar

  16. Julian Swatler
    View author publications

    Search author on:PubMed Google Scholar

  17. Emanuele Voulaz
    View author publications

    Search author on:PubMed Google Scholar

  18. Giuseppe Marulli
    View author publications

    Search author on:PubMed Google Scholar

  19. Valentina Errico
    View author publications

    Search author on:PubMed Google Scholar

  20. Agnese Losurdo
    View author publications

    Search author on:PubMed Google Scholar

  21. Gustavo R. Rossi
    View author publications

    Search author on:PubMed Google Scholar

  22. Fernando Souza-Fonseca-Guimaraes
    View author publications

    Search author on:PubMed Google Scholar

  23. Nicholas D. Huntington
    View author publications

    Search author on:PubMed Google Scholar

  24. Thomas Gebhardt
    View author publications

    Search author on:PubMed Google Scholar

  25. Daniel T. Utzschneider
    View author publications

    Search author on:PubMed Google Scholar

  26. Marco J. Herold
    View author publications

    Search author on:PubMed Google Scholar

  27. Wei Shi
    View author publications

    Search author on:PubMed Google Scholar

  28. Jan Schroeder
    View author publications

    Search author on:PubMed Google Scholar

  29. Enrico Lugli
    View author publications

    Search author on:PubMed Google Scholar

  30. Axel Kallies
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.W., L.R., S.G, E.L. and A.K. conceived the study, designed the experiments, interpreted the results and wrote the manuscript; G.G., J.S. and M.D.L. performed the human tumor analyses; V.E., E.V. and A.L. provided human samples; L.Q. and J.S. performed scRNA-seq analyses; L.W. performed the scRNA-seq experiment; C.T., K.M., D.U., T.G. and N.D.H. were involved in conceptualization and supervision of the study; L.H. and T.M. provided critical technical help; L.N., G.R.R. and F.F.G. performed experiments; A. Kueh and M.J.H. created the reporter mouse models; Y.L., D.C. and W.S. performed bulk RNA-seq analyses; and E.L. is responsible for the human data.

Corresponding author

Correspondence to Axel Kallies.

Ethics declarations

Competing interests

A.K. receives research support from Pfizer. N.D.H. is a founder and shareholder in oNKo-Innate and serves on advisory boards for Bristol Myers Squibb and Syena. T.G. was a scientific advisory board member of oNKo-Innate and received research funding from Merck Healthcare KGaA. E.L. is listed as the inventor on a patent on TSCM cells and received royalties related to that patent, received research funding from Bristol Myers Squibb on a topic unrelated to this paper and reports consulting fees from BD Biosciences, BioLegend, Swarm Oncology, Pfizer, Menarini and Astra Zeneca. All other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.

Additional information

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

Extended data

Extended Data Fig. 1 Quantification of TRM-like, TEX and TPEX cells in multiple tumor models.

(a-c) C57BL/6 mice were subcutaneously inoculated with AT3-OVA (n = 5) (a), MC38-OVA (n = 5) (b) or B16-OVA (n = 5) (c) tumor cells and tumors were analyzed when tumors were established. Flow cytometry plots and frequencies of CD44+CD69+ CD103+ TRM-like (left) and PD-1+ TCF1+ or PD-1+TIM-3+ (right) cells within CD44+ CD8+ tumor-infiltrating cells is shown. (d,e) Gating strategy for identification of tumor-infiltrating CD8+ T cells (d) or CD8+ T cells within tdLNs (e) by flow cytometry. Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means ± SEM. Data are representative of two independent experiments. P values are from two-tailed unpaired t-tests. P > 0.05, not significant (n.s.).

Extended Data Fig. 2 Single-cell RNA sequencing analysis of CD8 T cells isolated from AT3-OVA tumor bearing mice, and generation and validation of the HobitTomatoCre reporter mouse model.

(a,b) CD44+ CD8+ T cells were sorted from tumors and PD-1+ CD8+ T cells from tdLN of AT3-OVA-tumor-bearing mice and subjected to scRNA sequencing. (a) UMAP analyses showing expression of exhaustion (Tox, Nr4a2), TEX/effector cell (Gzmb, Prf1), TPEX cell (Slamf6) and TRM cell down (Klf2, S1pr1) signature genes. (b) Violin plots showing normalized expression of TRM cell signature44, TGF-β-induced signaling signature45 and Itgae expression within TEX (top) and TPEX (bottom) tumor-specific clusters. (c) Schematic illustrating the generation of the HobitTomatoCre (HobitTomCre) allele. A tdTomato reporter cassette and an IRES followed by a Cre recombinase sequence were inserted downstream of exon5 of the Hobit (Znf683) gene locus (mouse chromosome 4). After translation tdTomato is cleaved from the Hobit protein. (d,e) HobitTomCre P14 T cells were transferred into congenically marked naive recipient mice, which were infected with LCMV Armstrong (n = 3). CD44+CD8+ P14 TRM cells at day 30 post-infection were analyzed. (d) Contour plots (left) and quantification (right) showing Hobit versus CD69 expression of CD44+ P14 T cells in indicated organs. (e) Contour plots (left) and quantification (right) showing Hobit vs CD103 expression of CD44+ P14 T cells in indicated organs. (f) HobitTomCre reporter mice were epicutaneously inoculated with B16-gB tumors for analysis of CD8+ T cells at tumor endpoint. Contour plots of CD103+CD69+ cells in CD44+ CD8+ T cells in indicated organs (upper). Contour plots and quantification of Hobit versus CD103 (middle) and Hobit versus CD69 within the tumor (n = 12), peritumoral skin (n = 14) and tdLN (n = 13). (g,h) HobitTomCre reporter mice were orthotopically inoculated with AT3-OVA into the fourth MFP and were treated with ICB on days 11, 13, 16, and 19. (g) Schematic experimental setup. (h) CD44+PD-1− CD8+ cells at day 50 post-inoculation were analyzed to study TRMs. Contour plots of CD103+CD69+ cells in CD44+ CD8+ T cells in indicated organs (top). Contour plots (left) and quantification (right) of Hobit versus CD103 (top), Hobit versus CD69 (middle) and Hobit versus CD49a (bottom) within MFP (n = 6) and spleen (n = 6). Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ± SEM, respectively. Data in (d-h) are representative of at least two independent experiments. P values are from one-way ANOVA with Tukey’s multiple comparisons test (d-f) and from two-tailed unpaired t-tests (h).

Extended Data Fig. 3 Characterization of Hobit+ cells in established AT3-OVA tumors.

(a-d) Subcutaneously inoculated, AT3-OVA tumor-bearing HobitTomCre mice were analyzed at tumor endpoint. (a) Quantification of Hobit expression within indicated CD45+ immune cell populations from indicated organs (n = 7). (b) Proportion (left) and numbers per mg (right) of indicated immune cell subsets within CD45+ Hobit+ cells within tumors (n = 7). (c) Histograms (right) and quantification of CD103, CD69 and CD49a in Hobit+ and Hobit− (n = 8) pre-gated on CD44+ (middle) and OVA tetramer+ (right) CD8+ T cells. (d) Histograms (right) and quantification of TOX, TCF1 and TIM-3 in Hobit+ and Hobit− (n = 9) pre-gated on CD44+ (middle) and OVA tetramer+ (right) CD8+ T cells. (e,f) HobitTomCreId3GFP reporter mice were inoculated with AT3-OVA tumors for analysis of CD8+ T cells in tumors at endpoint. (e) Contour plot of Hobit-Tomato vs ID3-GFP among PD-1+ CD8+ T cells (left) and frequencies (right, n = 16) of specified subsets. (f) Frequencies of TCF1+(n = 5), TIM-3+(n = 8), PD-1+(n = 8), CD103+ (n = 7) and CD49a+ (n = 7) among OVA tetramer+ (Tet+)-specific CD8+ T cells from AT3-OVA tumors. (g,h) ID3 SP, DP, Hobit SP and DN CD8+ T cells were sorted from AT3 tumors-bearing HobitTomCreId3GFP reporter mice and subjected to bulk RNA-seq. Heatmaps show expression of TPEX and TEX cell signature genes17 (g). Barcode plots show enrichment of the TPEX signature in ID3/Hobit DP vs ID3 SP (left) and the TRM signature in ID3/Hobit DP vs Hobit+ ID3− cells (right) derived from tumors (h). Upregulated genes in red and downregulated genes in blue. (i,j) HobitTomCreId3GFP reporter mice were orthotopically inoculated with AT3-OVA into the fourth MFP. (i) Contour plots and frequencies show the expression of ID3-GFP and Hobit-Tomato among CD44+ CD8+ T cells from tumor (n = 7). (j) Frequencies of indicated markers within ID3+ Hobit− (ID3 SP), Hobit+ ID3+ (DP), Hobit+ ID3− (Hobit SP), Hobit− ID3−(DN) cells are also shown (n = 6). Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ± SEM, respectively. Data are representative of or are pooled from 2–3 independent experiments. P values are from one-way ANOVA with Tukey’s comparisons test (b,e-f,i-j), from two-tailed unpaired t-tests (c,d). P > 0.05, not significant (n.s.).

Extended Data Fig. 4 Characterization of intratumoral CD8+ cell populations in AT3 tumors using HobitTomCreId3GFP double reporter mice.

(a-d) HobitTomCreId3GFP reporter mice were inoculated with AT3-OVA tumors for analysis of CD8+ T cells in tumors. Histograms and quantification of CD69 (a); CD39 and TOX (b), Ly108 and CX3CR1 (c) in ID3+ Hobit− (ID3 SP), Hobit+ ID3+ (DP), Hobit+ ID3− (Hobit SP), Hobit− ID3− (DN) from AT3-OVA tumors, pre-gated on CD44+ (n = 7) or tetramer+ (n = 9) cells. (d) HobitTomCreId3GFP reporter mice were inoculated with AT3-OVA tumors for analysis of CD8+ T cells in tumors. Histograms and quantifications showing the expression of IFNγ (n = 5), TNF (n = 6) and GzmB (n = 6) within ID3 SP, DP, Hobit SP and DN CD44+ subsets after PMA and ionomycin restimulation. (e-h) AT3-OVA tumor-bearing HobitTomCreId3GFP mice were treated with anti-PDL-1 and anti-CTLA-4 (ICB) every 2–3 days and CD8+ T cells in tumors analyzed 2–3 days after last treatment. (e) Tumor volume in untreated (n = 8) or ICB-treated (n = 8) mice over time. Flow cytometry plots (left), frequencies (middle), and numbers (right) of tumor of PD-1+ (untreated n = 13, ICB-treated n = 12) (f) and GzmB+ PD-1+ (untreated n = 6, ICB-treated n = 8) (g) TCF1+ PD-1+ (untreated n = 9, ICB-treated n = 7) (h) CD8+ T cells in tumors in untreated (n = 9) or ICB-treated (n = 7) mice. Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ±SEM. Data are pooled from at least two independent experiments. P values are from one-way ANOVA with Tukey’s comparisons test (a-d) or from two-tailed unpaired t-tests (e-h). P > 0.05, not significant (n.s.).

Extended Data Fig. 5 Immune checkpoint inhibitor (ICB) response in AT3-OVA tumor-bearing HobitTomCreId3GFP double reporter, HobitTomCreTcf7fl/fl and Cd8CreTcf7fl/fl mice.

(a-h) HobitTomCreTcf7fl/fl and HobitTomCre control (Ctrl) mice were subcutaneously inoculated with AT3-OVA tumors and analyzed near tumor endpoint. (a) Frequencies of tumor of TCF1+ Hobit− (TCF1 SP), Hobit+ TCF1+ DP, Hobit+ TCF1− (Hobit SP), Hobit− TCF1− (DN) CD44+ CD8+ T cells within intratumoral OVA tetramer+ (Tetramer+) CD8+ T cells (n = 4). (b) Contour plots (left) and quantification of TCF1+ cells within CD44+ (middle) or PD-1 (right) CD8 + T cells in tdLN and spleen (n = 4). (c-f) Contour plots (left), quantification (middle) and numbers per mg of tumor (right) of PD-1 (HobitTomCreTcf7fl/fl (n = 16) or Ctrl (n = 17), TIM-3 (HobitTomCreTcf7fl/fl (n = 16) or Ctrl (n = 17)) (c), GzmB (HobitTomCreTcf7fl/fl (n = 12) or Ctrl (n = 12)) (d), CD103 (HobitTomCreTcf7fl/fl (n = 18) or Ctrl (n = 17)) (e) and CD69 (HobitTomCreTcf7fl/fl (n = 18) or Ctrl (n = 18)) (f) expressing CD44+ CD8+ T cells in tumor. (g-h) AT3-OVA tumor-bearing HobitTomCreTcf7fl/fl and HobitTomCre control (Ctrl) mice treated with anti-PDL-1 and anti-CTLA-4 (ICB). (g) Individual tumor volume curves of Ctrl (upper) and Tcf7fl/flHobitTomCre (lower) tumor-bearing mice treated with ICB (right) or left untreated (left) (untreated HobitTomCreTcf7fl/fl n = 7, ICB-treated HobitTomCreTcf7fl/fl n = 8, untreated Ctrl n = 6, ICB-treated Ctrl n = 6). (h) Numbers of CD44+ (left) PD-1+ (middle) and GzmB+ (right) CD8+ T cells within the tumor on day 20 post-ICB treatment (untreated HobitTomCreTcf7fl/fl n = 14, ICB-treated HobitTomCreTcf7fl/fl n = 11, untreated Ctrl=11, ICB-treated Ctrl=12). (i-k) AT3-OVA tumor-bearing Cd8CreTcf7fl/fl and Cd8Cre control mice treated with anti-PDL-1 and anti-CTLA-4 (ICB) (untreated Cd8CreTcf7fl/fl n = 9, ICB-treated Cd8CreTcf7fl/fl n = 8, untreated Cd8Cre n = 8, ICB-treated Cd8Cre n = 8). (i) Individual tumor volume curves of Ctrl (upper) and Cd8CreTcf7fl/fl (lower) tumor-bearing mice treated with ICB (right) or left untreated (left). (j) Average AT3-OVA tumor volume till day 40 post-inoculation. (k) Survival curves of Cd8CreTcf7fl/fl and Cd8Cre mice treated with ICB or left untreated. (l-m) HobitTomCreRosa26DTA (n = 8) mice and HobitTomCre control (Ctrl) (n = 7) P14 T cells were transferred into congenically marked naive recipient mice, which were infected with LCMV Armstrong. CD44+ CD8+ P14 TRM cells at day 30 post-infection were analyzed. (i) Experimental setup. (m) Representative contour plots (left) and numerical quantification (right) of GP33+ cells within the spleen and CD69+ GP33+ cells in the liver and IEL. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ±SEM. Flow cytometry plots are representative. Data are representative (a-b) or pooled from two independent experiments (c-m). P values are from two-tailed unpaired t-tests (a-f,m) or one-way ANOVA (h). P > 0.05, not significant (n.s.).

Extended Data Fig. 6 Intratumoral Hobit+ cells are dispensable for tumor control and checkpoint inhibitor response.

(a-d) AT3-OVA tumor-bearing HobitTomCreRosa26DTA mice and HobitTomCre control (Ctrl) mice were analyzed near tumor endpoint. (a) Numbers per mg of tumor of Hobit+ CD69+ cells and Hobit+ CD103+ cells within CD44+ CD8 + T cells in the tumor (HobitTomCreRosa26DTA n = 14 or Ctrl=17). (b) Numbers of indicated CD8+ T cell subsets within the tdLN (HobitTomCreRosa26DTA n = 5 or Ctrl n = 4). (c) Numbers of indicated immune subsets in tumors of HobitTomCreRosa26DTA (n = 5) mice and HobitTomCre control (Ctrl) (n = 4) mice. (d) Numbers of indicated immune subsets in tdLNs of HobitTomCreRosa26DTA (n = 5) mice and HobitTomCre control (Ctrl) (n = 4) mice. (e,f) AT3-OVA tumor-bearing HobitTomCreRosa26DTA mice and HobitTomCre control (Ctrl) mice were treated with ICB. (e) Individual tumor volume curves of Ctrl (upper) and HobitTomCreRosa26DTA mice (lower) tumor-bearing mice treated with ICB (right) or left untreated (left) (untreated HobitTomCreRosa26DTA n = 16, ICB-treated HobitTomCreRosa26DTA n = 15, untreated Ctrl=16, ICB-treated Ctrl=12). (f) Contour plots of TIM-3, PD-1 (left) and GzmB (right) expression by CD44+ CD8+ T cells within the tumor. (g-i) HobitTomCreRosa26DTA mice and HobitTomCre control (Ctrl) mice were orthotopically inoculated with AT3-OVA into the fourth MFP and were treated with ICB on days 11, 13, 16 and 19. CD44+ PD-1− CD8+ cells at day 50 post-inoculation were analyzed. (g) Contour plots of Hobit versus CD69 expression within the local MFP. (h) Numbers per gram of MFP of CD69, CD49a and CD103 expressing CD44+ CD8+ T cells in HobitTomCreRosa26DTA (n = 7) and Ctrl (n = 7) mice. (i) Average AT3-OVA tumor volumes till day 50 post-inoculation (left) of treated (n = 9-11) and untreated (n = 4) mice. Individual tumor volume curves of Ctrl (upper) and HobitTomCreRosa26DTA mice (lower) tumor-bearing mice treated with ICB (right) or left untreated (left). (j,k) MC38-OVA tumor-bearing HobitTomCreRosa26DTA mice and HobitTomCre control (Ctrl) mice were analyzed near tumor endpoint. (j) Contour plots (left) of Hobit versus CD69 (top, left) or CD103 (bottom left) and quantifications per mg of tumor (right) of CD69+ (top, right) and CD103+ (bottom, right) CD8+ CD44+ T cells within the tumor of HobitTomCreRosa26DTA (n = 4) and Ctrl (n = 4) mice. (k) Average MC38-OVA tumor volumes till day 24 post-inoculation in ICB-treated and untreated mice and individual tumor volume curves of Ctrl (upper) and HobitTomCreRosa26DTA mice (lower) tumor-bearing mice treated with ICB (right) or left untreated (left) (untreated HobitTomCreRosa26DTA n = 9, ICB-treated HobitTomCreRosa26DTA n = 10, untreated Ctrl=10, ICB-treated Ctrl=10). Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ±SEM. Data are pooled from two independent experiments or representative of two independent experiment (b-d,j). P values are from two-tailed unpaired t-tests (a-d,h,j) or 2-way ANOVA with Turkey’s comparison test (i,k). P > 0.05, not significant (n.s.).

Extended Data Fig. 7 Characterization of TPEX cells in the tumor-draining lymph nodes.

(a) CD8+ T cells from AT3-OVA tumor-bearing mice were analyzed. Contour plots of CD44 versus PD-1 expression pre-gated on CD8+ (left) or OVA-specific (Tet+) (left) from tdLNs or tumor, with quantification of PD-1+ and Tet+ among CD44+ on the right (n = 7). (b,c) C57BL/6 mice were subcutaneously inoculated with AT3-OVA tumors and tumors were analyzed at tumor endpoint. Contour plots (left) show TCF1 vs TIM-3 expression; quantification (right) (n = 5). (c) Histograms (left) and quantification (right) of expression of ID3 (n = 6) and Ly108 (n = 7) as in CD8+ T cells within the tdLN and tumor. (d) Irf4Tomato mice were subcutaneously inoculated with AT3-OVA tumors and tumors were analyzed at tumor endpoint. Histograms (left) and quantification (right) of expression of IFR4-Tomato (n = 7) and TOX (n = 8) in specified CD8+ T cell subsets in tdLN are shown. (e) UMAP representation of TPEX and TEX17 and TRM44 gene signatures projected onto shared and expanded CD8+ T cell clones within tdLN and tumors of ICB-treated AT3-OVA tumor bearing mice. (f,g) AT3-OVA tumor-bearing mice were treated with ICB (n = 5) on days 11, 13, 15 or left untreated (n = 7) and CD8+ T cells in tdLN were analyzed on day 17. (f) Flow cytometry plots (upper), frequencies and numbers per LN (lower) of OVA-specific (Tet+) cells among PD-1+ cells. (g) Flow cytometry plots (upper), frequencies, and numbers per LN (lower) of CX3CR1+ cells among Tet+ cells. (h-j) B16-GP33 tumor-bearing mice were treated with ICB (n = 11) when tumors became palpable, receiving three doses every three days, or left untreated (n = 12). CD8+ T cells in tdLN were analyzed two days after the last treatment. Frequencies (left) and numbers per LN (right) of CD44+ PD-1+ among CD8+ T cells (h), of CD62L+ and CD62L− TPEX cells among PD-1+ CD8+ T cells (i), and of CX3CR1+ among PD-1+ CD8+T cells (j). (k) Numbers of specified CD8+ T cell subsets per mg of AT3-OVA tumor in untreated or ICB ± FTY720 treated mice (untreated n = 11, ICB n = 7, ICB + FTY n = 8). GMFI, geometric mean fluorescence intensity. Dots in graphs represent individual mice; horizontal lines and error bars of bar graphs indicate means and ±SEM. Histograms and flow cytometry plots are representative. Data are representative of at least two or pooled from two independent experiments. P values are from two-tailed unpaired t-tests (a-j) or one-way ANOVA with Tukey’s comparisons test (k).

Extended Data Fig. 8 MYB expression is restricted to TPEX cells in tumor-draining lymph nodes and is essential for sustaining CD8⁺ T cell responses and checkpoint blockade efficacy.

(a,b) CD8+ T cells from tdLNs and tumors of B16-GP33 tumor-bearing MybGFP reporter mice or C57BL/6 (Ctrl) were analyzed. Contour plots show Myb-GFP+ CD62L+ cells within PD-1+ CD8+ T cells in the tdLN and tumor (n = 8) (a). Quantification of Myb-GFP expression in CD62L+ and CD62L− TPEX cells from tdLNs and CD62L+, CD62L− TPEX and TEX cells from tumors, normalized to naïve cells of B16-GP33 bearing MybGFP reporter mice (n = 8) tdLN: CD62L+ TPEX (minimum 5.4, maximum 7.3, median 6.7, 25% percentile 5.6, 75 percentile 7.0) CD62L− TPEX (minimum 3.5, maximum 5.6, median 4.8, 25% percentile 4.2, 75 percentile .45) Tumor: CD62L+ TPEX (minimum 1.9, maximum 2.6, median 2.1, 25% percentile 1.9, 75 percentile 2.1) CD62L− TPEX (minimum 1.0, maximum 1.5, median 1.3, 25% percentile 1.2, 75 percentile 1.4) TEX (minimum 1.6, maximum 3.4, median 2.0, 25% percentile 1.8, 75 percentile 2.8) (b). (c) Cd8CreMybfl/fl or Mybfl/fl (Ctrl) mice were injected with AT3-OVA or B16-GP33 and tumor growth was monitored over time. Individual tumor volume curves in AT3-OVA bearing (left, (Cd8CreMybfl/fl (n = 26) or Mybfl/fl (Ctrl, n = 20)) and B16-GP33-bearing (right, Cd8CreMybfl/fl (n = 12) or Mybfl/fl (Ctrl, n = 8)) are shown. (d) Frequencies (left), and numbers per LN (right) of CD62L+ and CD62L− TPEX cells among PD-1+CD8+ T cells in tdLNs from B16-GP33-bearing Cd8CreMybfl/fl (n = 7) or Mybfl/fl (Ctrl) (n = 7) mice. (e) Frequencies (left), and numbers per mg of tumor (right) of PD-1+ cells among CD8 + T cells (left) of TPEX cells (middle) and TEX cells (right) among PD-1+ CD8+ T cells from tumors of B16-GP33-bearing Cd8CreMybfl/fl (n = 7) or Mybfl/fl (Ctrl) (n = 7) mice. (f-h) AT3-OVA tumor-bearing mixed bone marrow chimeric mice containing congenically marked Cd4CreMybfl/fl and Mybfl/fl (Ctrl) CD8+ T cells (n = 10). (f) Contour plots (left) and quantification (right) showing CD8+ T cells within the tdLN and tumor of chimeric mice. (g) Contour plots and quantification showing CD62L+ cell among PD-1+ CD8+ T cells within tdLN. (h) Contour plots and quantification showing PD-1+ among CD8+ T cells within tumors. (i-k) AT3-OVA tumor-bearing mixed bone marrow chimeric mice containing congenically marked Cd8CreMybfl/fl and Mybfl/fl (Ctrl) CD8+ T cells were treated or left untreated (n = 11) with anti-PDL-1 and anti-CTLA-4 (n = 10) (three doses). Contour plots and quantification showing PD-1+ CD8+ T cells (i) or CX3CR1+ PD-1+ CD8+ T cells (j) within tdLN in treated and untreated mice. (k) Quantification showing PD-1+ CD8+ T cells (left) or TPEX, TEX (middle) and Gzmb+ (right) PD-1+ CD8+ T cells per mg of tumor in treated and untreated mice. Numbers are normalized to ratios of Cd8CreMybfl/fl and Mybfl/fl (Ctrl) naïve CD8+ T cells (i-k). Flow cytometry plots are representative. Data are representative of or pooled from two independent experiments. P values were calculated using two-tailed unpaired t-tests (a,d-h) or two-way ANOVA with Tukey’s comparisons test (b,i-k).

Extended Data Fig. 9 TGF-β signaling suppresses stem-like TPEX differentiation in tumor-draining lymph nodes and restricts TRM-like cell antitumor immunity.

(a) Contour plots (left), frequencies (middle) and numbers per LN (right) of CD103+, CD62L+ (upper) and CD103+CD69+ (lower) cells among Tetramer (Tet)+ CD8+ T cells in tdLNs of AT3-OVA-bearing mice (n = 10). (b) Contour plots (upper), frequencies of ID3+ (SP), ID3+Hobit+(DP), Hobit+ (SP)and ID3−Hobit− (DN) cells among Tetramer (Tet)+ CD8+ T cells in the tdLNs of AT3-OVA-bearing HobitTomCreId3GFP reporter mice (n = 7). (c) Contour plots (right) and frequencies (left) of CX3CR1 expression among PD-1+ CD8+ T cells in tdLN from mixed bone marrow chimeric mice containing congenically marked Cd8CreTgfbr2fl/fl and Ctrl CD8+ T cells (n = 13). (d) Contour plots (right) and frequencies (left) showing the expression of PD-1+ CD8+ T cells in tumor (n = 13). (e,f) Mixed bone marrow chimeric mice bearing B16-GP33 tumors, containing congenically marked Cd8CreTgfbr2fl/fl and control Cd8Cre (Ctrl) CD8+ T cells, were analyzed. (e) Frequencies of PD-1+ cells among CD8+ T cell, CD62L+ TPEX and CX3CR1 among PD-1+ CD8+ T cells in tdLN from Cd8CreTgfbr2fl/fl and Ctrl CD8+ T cells (n = 10). (f) Frequencies of PD-1+ CD8+ T cells, GzmB+ and TPEX cells among PD-1+ CD8+ cells in tumors (n = 8). (g) Average and individual tumor volume curves of HobitTomCre (Ctrl, left, n = 8), HobitTomCreTgfbr2fl/fl (middle, n = 8) and HobitTomCreTgfbr2fl/fl CD8-depleted (right, n = 9) AT3-OVA tumor-bearing mice (h) Proportions (left) and numbers per mg of tumor (right) of indicated immune subsets in HobitTomCre (Ctrl, n = 6) and HobitTomCreTgfbr2fl/fl (n = 5) AT3-OVA-bearing mice. (i-m) AT3-OVA tumor-bearing mixed bone marrow chimeric mice containing congenically marked HobitTomCreTgfbr2fl/fl and Ctrl T cells, were analyzed at tumor endpoint (n = 10). (i) Schematic diagram of experimental setup. (j) Ratios of HobitTomCreTgfbr2fl/fl over Ctrl cells within the same mouse at endpoint, normalized to blood day 0 ratio. (k-m) Proportions showing the expression of CD69, Hobit, CD103, CD49a (k), IFNγ, TNF and PD-1+ GzmB+ (i) among CD44+ cells among HobitTomCreTgfbr2fl/fl and Ctrl CD8+ T cells in the tumor. (m) Proportions showing PD-1+ cells among HobitTomCreTgfbr2fl/fl and Ctrl CD8+ T cells in the tdLN. Flow cytometry plots are representative. Dots in graphs represent individual mice; horizontal lines and error bars in bar graphs indicate means and ±SEM. Data are pooled or representative of two independent experiments. P values are from two-tailed unpaired (a,h,j-m) or paired t-tests (c-f) or one-way (b) or two-way (g) ANOVA with Tukey’s comparisons test. P > 0.05, not significant (n.s.).

Extended Data Fig. 10 TGF-β inhibits stemness and imparts features of tissue residence on human tumor-derived TPEX cells.

(a) Pearson correlation between the signature of CD8+ TPEX cells12 and TGFB1 transcript abundance in different tumors using the GEPIA2 web tool. P values are indicated. Lung, lung adenocarcinoma; Breast, breast invasive carcinoma; Colon, colon adenocarcinoma; Pancreas, pancreatic adenocarcinoma; Prostate, prostate adenocarcinoma; Skin, skin cutaneous melanoma; TPM, transcript per million. (b) UMAP representation of the distribution of neoantigen-specific CD8+ single T cell clones from n = 3 NSCLC patients30 in paired tdLNs and tumors (left) and according to clusters obtained by scRNA-seq (right). Manually curated signature genes related to the clusters are indicated (all FDR < 0.01). (c) Flow cytometry gating strategy used for isolating TPEX and TEX cells from NSCLC tumors. (d) Flow cytometric analysis of selected markers expressed by TPEX and TEX cells isolated as in c following stimulation. Numbers refer to percent of positive cells and MFI of marker expression (between brackets). Unstimulated (Unstim): medium only; control: anti-CD3/CD28 + IL-2/IL-15; TGF-β: anti-CD3/28 + IL-2/IL-15/TGF-β (e) Quantification (means±SEM summary, n = 5) of data in (d). 2-way ANOVA with Tukey’s multiple comparisons test. Only significant P values of interest are shown for simplicity. (f) CD8+ TPEX cells were sorted from 5 NSCLC samples using the gating strategy in (b) and stimulated with anti-CD3/CD28 beads, IL-2 and IL-15 in the presence of TGF-β or not (control) for 5 days.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and legends.

Reporting Summary

Supplementary Table 1

Bulk_RNA-seq_log2FPKM.

Supplementary Table 2

Human data.

Supplementary Table 3

Human TPEX TGF versus control.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wijesinghe, S.K.M., Rausch, L., Gabriel, S.S. et al. Lymph-node-derived stem-like but not tumor-tissue-resident CD8+ T cells fuel anticancer immunity. Nat Immunol 26, 1367–1383 (2025). https://doi.org/10.1038/s41590-025-02219-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41590-025-02219-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer