Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers (original) (raw)

Nature Medicine volume 25, pages 89–94 (2019)Cite this article

Subjects

Abstract

Infiltration of human cancers by T cells is generally interpreted as a sign of immune recognition, and there is a growing effort to reactivate dysfunctional T cells at such tumor sites1. However, these efforts only have value if the intratumoral T cell receptor (TCR) repertoire of such cells is intrinsically tumor reactive, and this has not been established in an unbiased manner for most human cancers. To address this issue, we analyzed the intrinsic tumor reactivity of the intratumoral TCR repertoire of CD8+ T cells in ovarian and colorectal cancer—two tumor types for which T cell infiltrates form a positive prognostic marker2,3. Data obtained demonstrate that a capacity to recognize autologous tumor is limited to approximately 10% of intratumoral CD8+ T cells. Furthermore, in two of four patient samples tested, no tumor-reactive TCRs were identified, despite infiltration of their tumors by T cells. These data indicate that the intrinsic capacity of intratumoral T cells to recognize adjacent tumor tissue can be rare and variable, and suggest that clinical efforts to reactivate intratumoral T cells will benefit from approaches that simultaneously increase the quality of the intratumoral TCR repertoire.

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

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

209,00 € per year

only 17,42 € per issue

Buy this article

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

Additional access options:

Similar content being viewed by others

Data availability

DNA and RNA sequencing data have been deposited in the European Genome-phenome Archive under accession code EGAS00001003119 and are subject to a controlled Data Access Agreement. These data are available from the corresponding authors to any party able to comply with the associated Data Access Agreement.

References

  1. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).
    Article CAS Google Scholar
  2. 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 Google Scholar
  3. Zhang, L. et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N. Engl. J. Med. 348, 203–213 (2003).
    Article CAS Google Scholar
  4. Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).
    Article CAS Google Scholar
  5. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
    Article CAS Google Scholar
  6. Le, D. T. et al. PD-1 Blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
    Article CAS Google Scholar
  7. Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
    Article CAS Google Scholar
  8. Brahmer, J. R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455–2465 (2012).
    Article CAS Google Scholar
  9. Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer-immune set point. Nature 541, 321–330 (2017).
    Article CAS Google Scholar
  10. Andersen, R. S. et al. Dissection of T-cell antigen specificity in human melanoma. Cancer Res. 72, 1642–1650 (2012).
    Article CAS Google Scholar
  11. Bobisse, S. et al. Sensitive and frequent identification of high avidity neo-epitope specific CD8 (+) T cells in immunotherapy-naive ovarian cancer. Nat. Commun. 9, 1092 (2018).
    Article Google Scholar
  12. Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+T cell response. Sci. Transl. Med. 6, 254ra128 (2014).
    Article Google Scholar
  13. Kvistborg, P. et al. TIL therapy broadens the tumor-reactive CD8(+) T cell compartment in melanoma patients. Oncoimmunology 1, 409–418 (2012).
    Article Google Scholar
  14. Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015).
    Article CAS Google Scholar
  15. van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–e442 (2013).
    Article Google Scholar
  16. Simoni, Y. et al. Bystander CD8(+) T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).
    Article CAS Google Scholar
  17. Pasetto, A. et al. Tumor- and neoantigen-reactive T-cell receptors can be identified based on their frequency in fresh tumor. Cancer Immunol. Res. 4, 734–743 (2016).
    Article CAS Google Scholar
  18. 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 Google Scholar
  19. Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016).
    Article CAS Google Scholar
  20. Dahlin, A. M. et al. Colorectal cancer prognosis depends on T-cell infiltration and molecular characteristics of the tumor. Mod. Pathol. 24, 671–682 (2011).
    Article CAS Google Scholar
  21. Sato, E. et al. Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc. Natl Acad. Sci. USA 102, 18538–18543 (2005).
    Article CAS Google Scholar
  22. Ye, Q. et al. CD137 accurately identifies and enriches for naturally occurring tumor-reactive T cells in tumor. Clin. Cancer Res. 20, 44–55 (2014).
    Article CAS Google Scholar
  23. Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).
    Article CAS Google Scholar
  24. Verdegaal, E. M. et al. Neoantigen landscape dynamics during human melanoma–T cell interactions. Nature 536, 91–95 (2016).
    Article CAS Google Scholar
  25. McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).
    Article CAS Google Scholar
  26. Webb, J. R., Milne, K., Kroeger, D. R. & Nelson, B. H. PD-L1 expression is associated with tumor-infiltrating T cells and favorable prognosis in high-grade serous ovarian cancer. Gynecol. Oncol. 141, 293–302 (2016).
    Article CAS Google Scholar
  27. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).
    Article CAS Google Scholar
  28. Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535 (2010).
    Article CAS Google Scholar
  29. Bolotin, D. A. et al. MiTCR: software for T-cell receptor sequencing data analysis. Nat. Methods 10, 813–814 (2013).
    Article CAS Google Scholar
  30. Linnemann, C. et al. High-throughput identification of antigen-specific TCRs by TCR gene capture. Nat. Med. 19, 1534–1541 (2013).
    Article CAS Google Scholar
  31. Ochi, T. et al. Optimization of T-cell reactivity by exploiting TCR chain centricity for the purpose of safe and effective antitumor TCR gene therapy. Cancer Immunol. Res. 3, 1070–1081 (2015).
    Article CAS Google Scholar
  32. Kwakkenbos, M. J. et al. Generation of stable monoclonal antibody-producing B cell receptor-positive human memory B cells by genetic programming. Nat. Med. 16, 123–128 (2010).
    Article CAS Google Scholar
  33. Linnemann, C. et al. High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nat. Med. 21, 81–85 (2015).
    Article CAS Google Scholar
  34. Gelman, A. et al. Bayesian Data Analysis. (CRC Press, Boca Raton, 2014). .

Download references

Acknowledgements

We thank M. van Zon, N. Bakker, and N. van Rooij for handling of patient material; A. Pfauth for flow cytometric support; S. Reijm for technical assistance; D. Thommen for critical reading of the manuscript; R. van Kerkhoven and M. Nieuwland for support with next generation sequencing; B. Thijssen for helpful discussion on Bayesian analysis; L. Wessels for discussions; the NKI-AVL Core Facility Molecular Pathology & Biobanking for supplying NKI-AVL Biobank material and laboratory support; H. Spits and R. Schotte (AIMM Therapeutics) for sharing reagents for B cell immortalization; K. van de Vijver for histological support; and M. Kranendonk for critical reading of the manuscript and histological support. This work was supported by the Dutch Cancer Society Queen Wilhelmina Award NKI 2013−6122, EU H2020 grant 633592 (APERIM), and the K.G. Jebsen Foundation (T.N.S.), Krebsliga Beider Basel (C.H.), and BC Cancer Foundation (B.H.N.).

Author information

Author notes

  1. These authors contributed equally: W. Scheper, S. Kelderman.

Authors and Affiliations

  1. Division of Molecular Oncology & Immunology, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Wouter Scheper, Lorenzo F. Fanchi, Riccardo Mezzadra, Maarten Slagter & Ton N. Schumacher
  2. Division of Molecular Oncology & Immunology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Sander Kelderman, Carsten Linnemann, Gavin Bendle, Marije A. J. de Rooij, Krijn Dijkstra, Emile E. Voest & John B. A. G. Haanen
  3. Department of Biomedicine, University of Basel, Basel, Switzerland
    Christian Hirt
  4. Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Maarten Slagter
  5. Central Genomics Facility, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Roelof J. C. Kluin
  6. Division of Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Petur Snaebjornsson
  7. Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
    Katy Milne & Brad H. Nelson
  8. Department of Gynecologic Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Henry Zijlmans & Gemma Kenter
  9. Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
    Emile E. Voest & John B. A. G. Haanen

Authors

  1. Wouter Scheper
    You can also search for this author inPubMed Google Scholar
  2. Sander Kelderman
    You can also search for this author inPubMed Google Scholar
  3. Lorenzo F. Fanchi
    You can also search for this author inPubMed Google Scholar
  4. Carsten Linnemann
    You can also search for this author inPubMed Google Scholar
  5. Gavin Bendle
    You can also search for this author inPubMed Google Scholar
  6. Marije A. J. de Rooij
    You can also search for this author inPubMed Google Scholar
  7. Christian Hirt
    You can also search for this author inPubMed Google Scholar
  8. Riccardo Mezzadra
    You can also search for this author inPubMed Google Scholar
  9. Maarten Slagter
    You can also search for this author inPubMed Google Scholar
  10. Krijn Dijkstra
    You can also search for this author inPubMed Google Scholar
  11. Roelof J. C. Kluin
    You can also search for this author inPubMed Google Scholar
  12. Petur Snaebjornsson
    You can also search for this author inPubMed Google Scholar
  13. Katy Milne
    You can also search for this author inPubMed Google Scholar
  14. Brad H. Nelson
    You can also search for this author inPubMed Google Scholar
  15. Henry Zijlmans
    You can also search for this author inPubMed Google Scholar
  16. Gemma Kenter
    You can also search for this author inPubMed Google Scholar
  17. Emile E. Voest
    You can also search for this author inPubMed Google Scholar
  18. John B. A. G. Haanen
    You can also search for this author inPubMed Google Scholar
  19. Ton N. Schumacher
    You can also search for this author inPubMed Google Scholar

Contributions

W.S. and S.K. designed, performed, analyzed, and interpreted experiments and wrote the manuscript. L.F.F., M.A.J.d.R., and C.H. performed experiments. C.L. and G.B. helped develop the single-cell TCR sequencing protocol. M.S. performed the likelihood of tumor reactivity analysis. R.M. generated TCRα/β-deficient Jurkat cells. K.D. and E.E.V. provided essential assistance with the generation of tumor organoids. R.J.C.K. analyzed TCR sequencing data. P.S. and K.M. provided assistance with immunohistochemistry. B.H.N., H.Z., G.K., and J.B.A.G.H. provided patient material. T.N.S. supervised the project, designed and interpreted experiments, and wrote the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence toTon N. Schumacher.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

About this article

Cite this article

Scheper, W., Kelderman, S., Fanchi, L.F. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers.Nat Med 25, 89–94 (2019). https://doi.org/10.1038/s41591-018-0266-5

Download citation