Evidence from mouse chronic viral infection models suggests that CD8+ T cell subsets characterized by distinct expression levels of the receptor PD-1 diverge in their state of exhaustion and potential for reinvigoration by PD-1 blockade. However, it remains unknown whether T cells in human cancer adopt a similar spectrum of exhausted states based on PD-1 expression levels. We compared transcriptional, metabolic and functional signatures of intratumoral CD8+ T lymphocyte populations with high (PD-1T), intermediate (PD-1N) and no PD-1 expression (PD-1) from non-small-cell lung cancer patients. PD-1T T cells showed a markedly different transcriptional and metabolic profile from PD-1N and PD-1 lymphocytes, as well as an intrinsically high capacity for tumor recognition. Furthermore, while PD-1T lymphocytes were impaired in classical effector cytokine production, they produced CXCL13, which mediates immune cell recruitment to tertiary lymphoid structures. Strikingly, the presence of PD-1T cells was strongly predictive for both response and survival in a small cohort of non-small-cell lung cancer patients treated with PD-1 blockade. The characterization of a distinct state of tumor-reactive, PD-1-bright lymphocytes in human cancer, which only partially resembles that seen in chronic infection, provides potential avenues for therapeutic intervention.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081–1091 (2012).

  2. 2.

    Heemskerk, B., Kvistborg, P. & Schumacher, T. N. The cancer antigenome. EMBO J. 32, 194–203 (2013).

  3. 3.

    Mellman, I., Coukos, G. & Dranoff, G. Cancer immunotherapy comes of age. Nature 480, 480–489 (2011).

  4. 4.

    Schreiber, R. D., Old, L. J. & Smyth, M. J. Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science 331, 1565–1570 (2011).

  5. 5.

    Schietinger, A. & Greenberg, P. D. Tolerance and exhaustion: defining mechanisms of T cell dysfunction. Trends Immunol. 35, 51–60 (2014).

  6. 6.

    Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).

  7. 7.

    Zajac, A. J. et al. Viral immune evasion due to persistence of activated T cells without effector function. J. Exp. Med. 188, 2205–2213 (1998).

  8. 8.

    Wherry, E. J. T cell exhaustion. Nat. Immunol. 12, 492–499 (2011).

  9. 9.

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

  10. 10.

    Day, C. L. et al. PD-1 expression on HIV-specific T cells is associated with T-cell exhaustion and disease progression. Nature 443, 350–354 (2006).

  11. 11.

    Trautmann, L. et al. Upregulation of PD-1 expression on HIV-specific CD8+ T cells leads to reversible immune dysfunction. Nat. Med. 12, 1198–1202 (2006).

  12. 12.

    Golden-Mason, L. et al. Upregulation of PD-1 expression on circulating and intrahepatic hepatitis C virus-specific CD8+ T cells associated with reversible immune dysfunction. J. Virol. 81, 9249–9258 (2007).

  13. 13.

    Ahmadzadeh, M. et al. Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood 114, 1537–1544 (2009).

  14. 14.

    Thommen, D. S. et al. Progression of lung cancer is associated with increased dysfunction of T cells defined by coexpression of multiple inhibitory receptors. CancerImmunol. Res. 3, 1344–1355 (2015).

  15. 15.

    Schreiner, J. et al. Expression of inhibitory receptors on intratumoral T cells modulates the activity of a T cell-bispecific antibody targeting folate receptor. Oncoimmunology 5, e1062969 (2015).

  16. 16.

    Zippelius, A. et al. Effector function of human tumor-specific CD8 T cells in melanoma lesions: a state of local functional tolerance. Cancer Res. 64, 2865–2873 (2004).

  17. 17.

    Blackburn, S. D., Shin, H., Freeman, G. J. & Wherry, E. J. Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade. Proc. Natl Acad. Sci. USA 105, 15016–15021 (2008).

  18. 18.

    Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).

  19. 19.

    Grosso, J. F. et al. Functionally distinct LAG-3 and PD-1 subsets on activated and chronically stimulated CD8 T cells. J. Immunol. 182, 6659–6669 (2009).

  20. 20.

    Sakuishi, K. et al. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J. Exp. Med. 207, 2187–2194 (2010).

  21. 21.

    Kansy, B. A. et al. PD-1 status in CD8+ T cells associates with survival and anti-PD-1 therapeutic outcomes in head and neck cancer. Cancer Res. 77, 6353–6364 (2017).

  22. 22.

    Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).

  23. 23.

    Wolfl, M. et al. Activation-induced expression of CD137 permits detection, isolation, and expansion of the full repertoire of CD8+ T cells responding to antigen without requiring knowledge of epitope specificities. Blood 110, 201–210 (2007).

  24. 24.

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

  25. 25.

    Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016).

  26. 26.

    Inozume, T. et al. Selection of CD8+PD-1+ lymphocytes in fresh human melanomas enriches for tumor-reactive T cells. J. Immunother. 33, 956–964 (2010).

  27. 27.

    Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e1316 (2017).

  28. 28.

    Henson, S. M. et al. KLRG1 signaling induces defective Akt (Ser473) phosphorylation and proliferative dysfunction of highly differentiated CD8+ T cells. Blood 113, 6619–6628 (2009).

  29. 29.

    Crawford, A. et al. Molecular and transcriptional basis of CD4+ T cell dysfunction during chronic infection. Immunity 40, 289–302 (2014).

  30. 30.

    Schietinger, A. et al. Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity 45, 389–401 (2016).

  31. 31.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  32. 32.

    Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511.e1509 (2016).

  33. 33.

    Gupta, P. K. et al. CD39 expression identifies terminally exhausted CD8+ T cells. PLoS Pathog. 11, e1005177 (2015).

  34. 34.

    Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).

  35. 35.

    Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

  36. 36.

    Scharping, N. E. et al. The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction. Immunity 45, 701–703 (2016).

  37. 37.

    Chang, C. H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).

  38. 38.

    Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).

  39. 39.

    Sena, L. A. et al. Mitochondria are required for antigen-specific T cell activation through reactive oxygen species signaling. Immunity 38, 225–236 (2013).

  40. 40.

    MacIver, N. J., Michalek, R. D. & Rathmell, J. C. Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259–283 (2013).

  41. 41.

    van der Windt, G. J. et al. CD8 memory T cells have a bioenergetic advantage that underlies their rapid recall ability. Proc. Natl Acad. Sci. USA 110, 14336–14341 (2013).

  42. 42.

    Schurich, A. et al. Distinct metabolic requirements of exhausted and functional virus-specific CD8 T cells in the same host. Cell Rep. 16, 1243–1252 (2016).

  43. 43.

    Sukumar, M. et al. Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function. J. Clin. Invest. 123, 4479–4488 (2013).

  44. 44.

    Wang, Y. et al. Autocrine complement inhibits IL10-dependent T-cell-mediated antitumor immunity to promote tumor progression. Cancer Discov. 6, 1022–1035 (2016).

  45. 45.

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

  46. 46.

    He, R. et al. Follicular CXCR5- expressing CD8+ T cells curtail chronic viral infection. Nature 537, 412–428 (2016).

  47. 47.

    Ansel, K. M. et al. A chemokine-driven positive feedback loop organizes lymphoid follicles. Nature 406, 309–314 (2000).

  48. 48.

    Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, 328rv4 (2016).

  49. 49.

    Gu-Trantien, C. et al. CXCL13-producing TFH cells link immune suppression and adaptive memory in human breast cancer. JCI Insight 2, 91487 (2017).

  50. 50.

    Gunnlaugsdottir, B., Maggadottir, S. M. & Ludviksson, B. R. Anti-CD28-induced co-stimulation and TCR avidity regulates the differential effect of TGF-beta1 on CD4+ and CD8+ naïve human T-cells. Int. Immunol. 17, 35–44 (2005).

  51. 51.

    Allen, C. D. et al. Germinal center dark and light zone organization is mediated by CXCR4 and CXCR5. Nat. Immunol. 5, 943–952 (2004).

  52. 52.

    Wu, T. D., Reeder, J., Lawrence, M., Becker, G. & Brauer, M. J. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. Methods Mol. Biol. 1418, 283–334 (2016).

  53. 53.

    Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

  54. 54.

    Becker, R. A., Chambers, J. M. & Wilks, A. R. The New S Language (Wadsworth & Brooks/Cole, Monterey, CA, USA, 1988).

  55. 55.

    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

  56. 56.

    Nazarov, V. I. et al. tcR: an R package for T cell receptor repertoire advanced data analysis. BMC Bioinformatics 16, 175 (2015).

Download references


We thank D. Labes and E. Traunecker for exemplary technical assistance with cell sorting, F. Franco and T. Chao for performing electron microscopy analysis, L. Tietze (Ortenau Klinikum, Germany) for contribution of tumor samples, B. Dolder-Schlienger for technical assistance, and F. Uhlenbrock and D. Pinschewer for discussions and critical reading of the manuscript. This work was supported by grants from the Swiss National Science Foundation (P300PB_164755 to D.S.T., 320030_162575 to A.Z. and 31003A_163204 to P.C.H.), the Research Funds University of Basel (D.S.T.), the Lichtenstein-Stiftung (D.S.T.), the FAG-Basel (D.S.T.), the Dutch Cancer Society Queen Wilhelmina Award NKI 2013-6122 (T.N.S.) and ERC grant SENSIT (T.N.S.).

Author information

Author notes

  1. These authors contributed equally: Viktor H. Koelzer, Petra Herzig, Andreas Roller.

  2. These authors jointly directed this work: Ton N. Schumacher, Alfred Zippelius.


  1. Cancer Immunology, Department of Biomedicine, University Hospital Basel, Basel, Switzerland

    • Daniela S. Thommen
    • , Petra Herzig
    • , Marcel Trefny
    •  & Alfred Zippelius
  2. Division of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    • Daniela S. Thommen
    •  & Ton N. Schumacher
  3. Institute of Pathology, Cantonal Hospital Baselland, Liestal, Switzerland

    • Viktor H. Koelzer
    • , Jonathan Hanhart
    •  & Kirsten D. Mertz
  4. Molecular and Population Genetics Laboratory, University of Oxford, Oxford, UK

    • Viktor H. Koelzer
  5. Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland

    • Andreas Roller
    •  & Anna Kiialainen
  6. Immunobiology, Department of Biomedicine, University Hospital Basel, Basel, Switzerland

    • Sarah Dimeloe
    •  & Christoph Hess
  7. Oncology, Cantonal Hospital Baselland, Liestal, Switzerland

    • Catherine Schill
  8. Institute of Pathology, University Hospital Basel, Basel, Switzerland

    • Spasenija Savic Prince
  9. Department of Surgery, University Hospital Basel, Basel, Switzerland

    • Mark Wiese
    •  & Didier Lardinois
  10. University of Lausanne, Ludwig Center for Cancer Research, Epalinges, Switzerland

    • Ping-Chih Ho
  11. Roche Pharmaceutical Research and Early Development, Roche Innovation Center Zurich, Zurich, Switzerland

    • Christian Klein
    •  & Vaios Karanikas
  12. Medical Oncology, University Hospital Basel, Basel, Switzerland

    • Alfred Zippelius


  1. Search for Daniela S. Thommen in:

  2. Search for Viktor H. Koelzer in:

  3. Search for Petra Herzig in:

  4. Search for Andreas Roller in:

  5. Search for Marcel Trefny in:

  6. Search for Sarah Dimeloe in:

  7. Search for Anna Kiialainen in:

  8. Search for Jonathan Hanhart in:

  9. Search for Catherine Schill in:

  10. Search for Christoph Hess in:

  11. Search for Spasenija Savic Prince in:

  12. Search for Mark Wiese in:

  13. Search for Didier Lardinois in:

  14. Search for Ping-Chih Ho in:

  15. Search for Christian Klein in:

  16. Search for Vaios Karanikas in:

  17. Search for Kirsten D. Mertz in:

  18. Search for Ton N. Schumacher in:

  19. Search for Alfred Zippelius in:


D.S.T.: study design and supervision, design and execution of the experiments; data acquisition, analysis and interpretation; writing and revision of the manuscript; V.H.K.: execution of immunohistochemistry stainings, digital image analysis; contribution to manuscript drafting and revision; P.H.: execution of experiments; contribution to manuscript drafting and revision; A.R.: statistical analysis and interpretation, contribution to manuscript drafting; M.T.: execution of experiments; A.K.: RNA-seq analysis; S.D.: design and technical support with metabolism analysis; J.H.: execution of immunohistochemistry and digital image analysis; C.S.: collection and analysis of clinical data; C.H.: design of metabolism experiments, contribution to manuscript drafting; S.S.P.: collection and pathological characterization of patient samples; M.W. and D.L.: recruitment and characterization of patients; P.C.H.: execution of experiments, contribution to manuscript drafting; C.K. and V.K.: contribution to manuscript drafting; K.D.M.: execution of immunohistochemistry analysis; contribution to manuscript drafting; T.N.S.: study design and supervision; writing and revision of the manuscript; A.Z.: study design and supervision, writing and revision of the manuscript.

Competing interests

A.R., A.K., C.K., V.K. are employed by Roche. A.Z. received research funding from Roche. Part of the work described in this manuscript is the subject of a patent application co-owned by NKI-AVL and the University of Basel. Based on NKI-AVL and the University of Basel policy on management of intellectual property, D.S.T., V.H.K., K.D.M., A.Z. and T.N.S. would be entitled to a portion of the royalty income received.

Corresponding authors

Correspondence to Daniela S. Thommen or Alfred Zippelius.

Supplementary information

  1. Supplementary Figures

    Supplementary Figures 1–6

  2. Reporting Summary

  3. Supplementary Table 1

    TCR analysis data

  4. Supplementary Table 2

    Gene expression data

  5. Supplementary Table 3

    Tumor sample overview

  6. Supplementary Table 4

    Patient characteristics for predictive analysis

About this article

Publication history




Issue Date