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:

PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+CD38hi cells and anti-PD-1 resistance

Nature Immunology volume 20, pages 1231–1243 (2019)Cite this article

Subjects

An Author Correction to this article was published on 24 September 2019

This article has been updated

Abstract

Understanding resistance to antibody to programmed cell death protein 1 (PD-1; anti-PD-1) is crucial for the development of reversal strategies. In anti-PD-1-resistant models, simultaneous anti-PD-1 and vaccine therapy reversed resistance, while PD-1 blockade before antigen priming abolished therapeutic outcomes. This was due to induction of dysfunctional PD-1+CD38hi CD8+ cells by PD-1 blockade in suboptimally primed CD8 cell conditions induced by tumors. This results in erroneous T cell receptor signaling and unresponsiveness to antigenic restimulation. On the other hand, PD-1 blockade of optimally primed CD8 cells prevented the induction of dysfunctional CD8 cells, reversing resistance. Depleting PD-1+CD38hi CD8+ cells enhanced therapeutic outcomes. Furthermore, non-responding patients showed more PD-1+CD38+CD8+ cells in tumor and blood than responders. In conclusion, the status of CD8+ T cell priming is a major contributor to anti-PD-1 therapeutic resistance. PD-1 blockade in unprimed or suboptimally primed CD8 cells induces resistance through the induction of PD-1+CD38hi CD8+ cells that is reversed by optimal priming. PD-1+CD38hi CD8+ cells serve as a predictive and therapeutic biomarker for anti-PD-1 treatment. Sequencing of anti-PD-1 and vaccine is crucial for successful therapy.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Anti-PD-1 before antigenic stimulation abrogates the antitumor effects of Vax + αPD-1.
Fig. 2: Prior PD-1 blockade abrogates vaccine-induced tumor-specific immune responses early during the course of treatment.
Fig. 3: PD-1 blockade before antigenic stimulation induces PD-1+CD38hi CD8+ T cells.
Fig. 4: PD-1+CD38hi CD8+ T cells induced as a result of anti-PD-1 pretreatment are dysfunctional.
Fig. 5: Depletion of PD-1+CD38hi CD8+ T cells results in a strong antitumor response.
Fig. 6: PD-1 blockade without proper priming predisposes CD8+ T cells toward dysfunction and apoptosis-mediated cell death.
Fig. 7: PD-1 blockade in suboptimally primed CD8+ T cells induces dysfunctional PD-1+CD38hi CD8+ T cells.
Fig. 8: The frequency of PD-1+CD38+ CD8+ T cells is a pharmacodynamic and predictive biomarker of anti-PD-1 therapy.

Similar content being viewed by others

Data availability

For the clinical data, the cohorts were not collected specifically for this study and are already published. The references describing the participants of the human research and clinical data have been provided in this published article. In vitro, in vivo, flow cytometry and clinical data are included in this published article and its Supplementary Information. All other relevant data are available from the corresponding author upon reasonable request.

Change history

  • 24 September 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Kyi, C. & Postow, M. A. Checkpoint blocking antibodies in cancer immunotherapy. FEBS Lett. 588, 368–376 (2014).

    Article  CAS  Google Scholar 

  2. Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    Article  CAS  Google Scholar 

  3. Dong, H. et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat. Med. 8, 793–800 (2002).

    Article  CAS  Google Scholar 

  4. Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc. Natl Acad. Sci. USA 99, 12293–12297 (2002).

    Article  CAS  Google Scholar 

  5. Page, D. B., Postow, M. A., Callahan, M. K., Allison, J. P. & Wolchok, J. D. Immune modulation in cancer with antibodies. Annu. Rev. Med. 65, 185–202 (2014).

    Article  CAS  Google Scholar 

  6. 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).

    Article  Google Scholar 

  7. Kleponis, J., Skelton, R. & Zheng, L. Fueling the engine and releasing the break: combinational therapy of cancer vaccines and immune checkpoint inhibitors. Cancer Biol. Med. 12, 201–208 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  Google Scholar 

  9. Mkrtichyan, M. et al. Anti-PD-1 antibody significantly increases therapeutic efficacy of Listeria monocytogenes (Lm)-LLO immunotherapy. J. Immunother. Cancer 1, 15 (2013).

    Article  Google Scholar 

  10. Mkrtichyan, M. et al. Anti-PD-1 synergizes with cyclophosphamide to induce potent anti-tumor vaccine effects through novel mechanisms. Eur. J. Immunol. 41, 2977–2986 (2011).

    Article  CAS  Google Scholar 

  11. Mkrtichyan, M. et al. B7-DC-Ig enhances vaccine effect by a novel mechanism dependent on PD-1 expression level on T cell subsets. J. Immunol. 189, 2338–2347 (2012).

    Article  CAS  Google Scholar 

  12. Karyampudi, L. et al. Accumulation of memory precursor CD8 T cells in regressing tumors following combination therapy with vaccine and anti-PD-1 antibody. Cancer Res. 74, 2974–2985 (2014).

    Article  CAS  Google Scholar 

  13. Soares, K. C. et al. PD-1/PD-L1 blockade together with vaccine therapy facilitates effector T-cell infiltration into pancreatic tumors. J. Immunother. 38, 1–11 (2015).

    Article  CAS  Google Scholar 

  14. Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).

    Article  CAS  Google Scholar 

  15. Patsoukis, N. et al. Immunometabolic regulations mediated by coinhibitory receptors and their impact on T cell immune responses. Front. Immunol. 8, 330 (2017).

    Article  Google Scholar 

  16. Boussiotis, V. A. Molecular and biochemical aspects of the PD-1 checkpoint pathway. N. Engl. J. Med. 375, 1767–1778 (2016).

    Article  CAS  Google Scholar 

  17. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Shrimali, R. K. et al. Concurrent PD-1 blockade negates the effects of OX40 agonist antibody in combination immunotherapy through inducing T-cell apoptosis. Cancer Immunol. Res. 5, 755–766 (2017).

    Article  CAS  Google Scholar 

  20. 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).

    Article  CAS  Google Scholar 

  21. 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).

    Article  CAS  Google Scholar 

  22. Latchman, Y. et al. PD-L2 is a second ligand for PD-1 and inhibits T cell activation. Nat. Immunol. 2, 261–268 (2001).

    Article  CAS  Google Scholar 

  23. Refaeli, Y., Van Parijs, L., Alexander, S. I. & Abbas, A. K. Interferon γ is required for activation-induced death of T lymphocytes. J. Exp. Med. 196, 999–1005 (2002).

    Article  CAS  Google Scholar 

  24. Lohman, B. L. & Welsh, R. M. Apoptotic regulation of T cells and absence of immune deficiency in virus-infected gamma interferon receptor knockout mice. J. Virol. 72, 7815–7821 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Overwijk, W. W. et al. gp100/pmel 17 is a murine tumor rejection antigen: induction of “self”-reactive, tumoricidal T cells using high-affinity, altered peptide ligand. J. Exp. Med. 188, 277–286 (1998).

    Article  CAS  Google Scholar 

  26. Chen, L. & Flies, D. B. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat. Rev. Immunol. 13, 227–242 (2013).

    Article  Google Scholar 

  27. Rosette, C. et al. The impact of duration versus extent of TCR occupancy on T cell activation: a revision of the kinetic proofreading model. Immunity 15, 59–70 (2001).

    Article  CAS  Google Scholar 

  28. Vonderheide, R. H. The immune revolution: a case for priming, not checkpoint. Cancer Cell 33, 563–569 (2018).

    Article  CAS  Google Scholar 

  29. Vonderheide, R. H., Domchek, S. M. & Clark, A. S. Immunotherapy for breast cancer: what are we missing? Clin. Cancer Res. 23, 2640–2646 (2017).

    Article  Google Scholar 

  30. Sade-Feldman, M. et al. Defining T Cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 e20 (2018).

    Article  CAS  Google Scholar 

  31. Frey, A. B. & Monu, N. Signaling defects in anti-tumor T cells. Immunol. Rev. 222, 192–205 (2008).

    Article  CAS  Google Scholar 

  32. 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 

  33. Eroglu, Z. et al. High response rate to PD-1 blockade in desmoplastic melanomas. Nature 553, 347–350 (2018).

    Article  CAS  Google Scholar 

  34. Banerjea, A., Bustin, S. A. & Dorudi, S. The immunogenicity of colorectal cancers with high-degree microsatellite instability. World J. Surg. Oncol. 3, 26 (2005).

    Article  Google Scholar 

  35. Vandeven, N. A. & Nghiem, P. Merkel cell carcinoma: an unusually immunogenic cancer proves ripe for immune therapy. J. Oncol. Pract. 12, 649–650 (2016).

    Article  Google Scholar 

  36. Yokosuka, T. et al. Programmed cell death 1 forms negative costimulatory microclusters that directly inhibit T cell receptor signaling by recruiting phosphatase SHP2. J. Exp. Med. 209, 1201–1217 (2012).

    Article  CAS  Google Scholar 

  37. Acuto, O., Di Bartolo, V. & Michel, F. Tailoring T-cell receptor signals by proximal negative feedback mechanisms. Nat. Rev. Immunol. 8, 699–712 (2008).

    Article  CAS  Google Scholar 

  38. Poltorak, M. et al. TCR activation kinetics and feedback regulation in primary human T cells. Cell Commun. Signal. 11, 4 (2013).

    Article  Google Scholar 

  39. Pageon, S. V. et al. Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination. Proc. Natl Acad. Sci. USA 113, E5454–E5463 (2016).

    Article  CAS  Google Scholar 

  40. Lee, J., Ahn, E., Kissick, H. T. & Ahmed, R. Reinvigorating exhausted T Cells by blockade of the PD-1 pathway. For. Immunopathol. Dis. Therap. 6, 7–17 (2015).

  41. 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).

    Article  CAS  Google Scholar 

  42. Chatterjee, S. et al. CD38-NAD+ axis regulates immunotherapeutic anti-tumor T cell response. Cell Metab. 27, 85–100.e8 (2018).

    Article  CAS  Google Scholar 

  43. Mason, E. F. & Rathmell, J. C. Cell metabolism: an essential link between cell growth and apoptosis. Biochim. Biophys. Acta 1813, 645–654 (2011).

    Article  CAS  Google Scholar 

  44. Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).

    Article  CAS  Google Scholar 

  45. 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).

    Article  CAS  Google Scholar 

  46. Patel, S. P. & Kurzrock, R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol. Cancer Ther. 14, 847–856 (2015).

    Article  CAS  Google Scholar 

  47. Li, Y. et al. Prognostic impact of programed cell death-1 (PD-1) and PD-ligand 1 (PD-L1) expression in cancer cells and tumor infiltrating lymphocytes in colorectal cancer. Mol. Cancer 15, 55 (2016).

    Article  Google Scholar 

  48. Sabatier, R. et al. Prognostic and predictive value of PDL1 expression in breast cancer. Oncotarget 6, 5449–5464 (2015).

    Article  Google Scholar 

  49. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    Article  CAS  Google Scholar 

  50. Buchwald, Z. S. et al. Radiation, immune checkpoint blockade and the abscopal effect: a critical review on timing, dose and fractionation. Front. Oncol. 8, 612 (2018).

    Article  Google Scholar 

  51. Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    Article  CAS  Google Scholar 

  52. Villani, A. C. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    Article  Google Scholar 

  53. Ji, Y. et al. Identification of the genomic insertion site of Pmel-1 TCR α and β transgenes by next-generation sequencing. PLoS ONE 9, e96650 (2014).

    Article  Google Scholar 

  54. Lin, K. Y. et al. Treatment of established tumors with a novel vaccine that enhances major histocompatibility class II presentation of tumor antigen. Cancer Res. 56, 21–26 (1996).

    CAS  PubMed  Google Scholar 

  55. Abu Eid, R. et al. Akt1 and -2 inhibition diminishes terminal differentiation and enhances central memory CD8+ T-cell proliferation and survival. Oncoimmunology 4, e1005448 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the Georgia Cancer Center, Augusta University internal support grant to S.N.K. We acknowledge the Lombardi Comprehensive Cancer Center support grant to the Biostatistics and Bioinformatics shared service (P30 CA 051008). We acknowledge the Ludwig Center for Cancer Immunotherapy for financial support for the Immune Monitoring Core Facility at the MSKCC. This research was funded in part through the National Institutes of Health (NIH)/National Cancer Institute (NCI) Cancer Center Support grant no. P30 CA008748, grant no. NIH/NCI R01 CA056821, the Swim Across America, Ludwig Institute for Cancer Research, Parker Institute for Cancer Immunotherapy and Virginia B. Squiers Foundation to J.W. and T.M. The research related to the human tumor samples was supported by the Cancer Research Institute (N.H.), Adelson Medical Research Foundation (N.H.) and NIH/NCI grant no. R01CA208756 (N.H.). We thank R. Ibrahim, Parker Institute for Cancer Immunotherapy, for reviewing the manuscript.

Author information

Author notes

  1. Vivek Verma, Rahul Nandre, Pankaj Gaur, Jose Lopez, Mikayel Mkrtichyan, Seema Gupta & Samir N. Khleif

    Present address: The Loop Immuno-Oncology Laboratory, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA

  2. Rajeev K Shrimali

    Present address: Therapeutic Discovery, MD Anderson Cancer Center, Houston, TX, USA

  3. Shamim Ahmad

    Present address: Five Prime Therapeutics Inc., South San Francisco, CA, USA

  4. John E. Janik

    Present address: Incyte Inc., Wilmington, DE, USA

  5. Mikayel Mkrtichyan

    Present address: A2 Biotherapeutics, Agoura Hills, CA, USA

Authors and Affiliations

  1. Georgia Cancer Center, Augusta University, Augusta, GA, USA

    Vivek Verma, Rajeev K Shrimali, Shamim Ahmad, Winjie Dai, Hua Wang, Sumin Lu, Rahul Nandre, Pankaj Gaur, John E. Janik, Mikayel Mkrtichyan, Seema Gupta & Samir N. Khleif

  2. Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA, USA

    Moshe Sade-Feldman, Stacey L. Bjorgaard, Keith T. Flaherty, Ryan J. Sullivan, Gad Getz & Nir Hacohen

  3. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Moshe Sade-Feldman, Keren Yizhak, Stacey L. Bjorgaard, Gad Getz & Nir Hacohen

  4. Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jennifer A. Wargo

  5. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Genevieve M. Boland

  6. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Gad Getz

  7. MedImmune LLC, Gaithersburg, MD, USA

    Scott A. Hammond

  8. Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University, Washington, DC, USA

    Ming Tan

  9. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jingjing Qi, Phillip Wong, Taha Merghoub & Jedd Wolchok

  10. Weill Cornell Medical and Graduate Schools, New York, NY, USA

    Taha Merghoub & Jedd Wolchok

Authors
  1. Vivek Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev K Shrimali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shamim Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Winjie Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sumin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rahul Nandre
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pankaj Gaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jose Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Moshe Sade-Feldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Keren Yizhak
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Stacey L. Bjorgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Keith T. Flaherty
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jennifer A. Wargo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Genevieve M. Boland
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ryan J. Sullivan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Gad Getz
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Scott A. Hammond
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ming Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jingjing Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Phillip Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Taha Merghoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Jedd Wolchok
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Nir Hacohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. John E. Janik
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Mikayel Mkrtichyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Seema Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Samir N. Khleif
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.V., S.G. and S.N.K. were the main investigators and take primary responsibility for the paper. V.V., S.G. and S.N.K. were involved in the conception and design of the study, development of the methodology, analysis and interpretation of the data, administrative, technical or material support and writing and reviewing the manuscript. V.V. performed the experiments with assistance from R.K.S., S.A., W.D., H.W., S.L., R.N., P.G. and J.L. J.E.J. and M.M. helped review the manuscript. The acquisition of human tumor samples and their analysis were performed by M.S.-F., K.Y., S.L.B., K.T.F., J.A.W., G.M.B., R.J.S., G.G. and N.H. The acquisition of human PBMC samples and their analysis were performed by J.Q., P.W., T.M. and J.W. M.T. performed the statistical analysis of the human data. S.A.H. provided the anti-PD-1 antibody.

Corresponding author

Correspondence to Samir N. Khleif.

Ethics declarations

Competing interests

S.N.K., S.G. and V.V. are inventors on patent application related to work on the methods for detecting and reversing immune therapy resistance and the development of PD-1+CD38+ CD8+ T cells as a predictive and therapeutic biomarker for response/resistance to immune checkpoint blockade therapy. S.N.K. reports an honorarium from Syndax, IO Biotech, BioLine, Northwest Biotherapeutics, Advaxis, EMD Serono, GSK, UbiVac, McKinsey, AstraZeneca and Lycera. S.N.K. reports stocks or ownership interest in Advaxis, GeorgiaImmune, IO Biotech and Northwest Therapeutics. S.N.K. is a consultant for Syndax, IO Biotech, BioLine, Kahr, PDS Biotechnology, AstraZeneca, CytomX, NewLink Genetics, AratingaBio, CanImGuide and Lycera. S.N.K. is a board member for Advaxis. S.N.K. has research contracts with Syndax, IO Biotech, BioLine, AstraZeneca, MedImmune and Lycera. T.M. is a consultant for Leap Therapeutics, Immunos Therapeutics and Pfizer and co-founder of Imvaq Therapeutics. T.M. has equity in Imvaq Therapeutics. T.M. reports grants from Bristol-Myers Squibb, Surface Oncology, Kyn Therapeutics, Infinity Pharmaceuticals, Peregrine Pharmaceuticals, Adaptive Biotechnologies, Leap Therapeutics and Aprea Therapeutics. T.M. is an inventor on patent applications related to work on oncolytic viral therapy, alphavirus-based vaccines, neoantigen modeling, CD40, glucocorticoid-induced TNFR-related protein (GITR), OX40, PD-1 and CTLA-4. J.W. is a consultant for Adaptive Biotechnologies, Advaxis, Amgen, Apricity, Array BioPharma, Ascentage Pharma, Astellas Pharma, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Chugai Pharmaceutical, Elucida Oncology, Eli Lilly, F Star, Genentech, Imvaq Therapeutics, Janssen, Kleo Pharmaceuticals, Linneaus, MedImmune, Merck, Neon Therapeutics, Ono Pharmaceutical, Polaris Pharma, Polynoma, PsiOxus Therapeutics, PureTech Health, Recepta Biopharma, Sellas Life Sciences, Serametrix, Surface Oncology and Syndax. J.W. reports an honorarium from Esanex and grants/research support from Bristol-Myers Squibb, MedImmune and Genentech. J.W. has equity in Potenza Therapeutics, Tizona Therapeutics, Adaptive Biotechnologies, Elucida Oncology, Imvaq Therapeutics, BeiGene, Trieza Therapeutics, Serametrix and Linneaus. J.W. is an inventor on patent applications related to work on xenogeneic DNA vaccines, alphavirus replicon particles expressing tyrosinase-related protein-2, myeloid-derived suppressor cell assay, Newcastle disease viruses for cancer therapy, genomic signature to identify responders to ipilimumab in melanoma, engineered vaccinia viruses for cancer immunotherapy, anti-CD40 agonist monoclonal antibody fused to monophosphoryl lipid A for cancer therapy, CAR+ T cells targeting differentiation antigens as means to treat cancer, anti-PD-1 antibody, anti-CTLA-4 antibodies, anti-GITR antibodies and methods of use thereof. P.W. is a consultant for Leap Therapeutics. G.M.B. reports paid lecturing from Novartis, Takeda Oncology; sponsored research agreements with Takeda Oncology; and consulting with NW Biotherapeutics. R.J.S. reports personal fees from Amgen, Merck, Genentech and Novartis; research grants from Amgen and Merck; and clinical trial support from Merck, Tesaro, Sanofi, Genentech and Novartis during the conduct of the study; and personal fees from Compugen, Replimmune, Array and Syndax outside the submitted work. J.A.W. is an inventor on a US patent application (PCT/US17/53.717) submitted by the University of Texas MD Anderson Cancer Center that covers methods to enhance immune checkpoint blockade responses by modulating the microbiome; reports compensation for speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune and Bristol-Myers Squibb; serves as a consultant or advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, Biothera Pharmaceuticals and Microbiome DX; and receives research support from GlaxoSmithKline, Roche/Genentech, Bristol-Myers Squibb and Novartis. K.T.F. owns equity in Shattuck Labs, Checkmate, X4 Pharmaceuticals; consults for Novartis, Genentech, BMS, Merck, Takeda, Verastem, Checkmate, X4 Pharmaceuticals, Sanofi, Amgen, Incyte, Adaptimmune, Shattuck Labs, Arch Oncology and Apricity; and receives research support from Novartis, Genentech, Sanofi and Amgen. N.H. is a founder and science advisory board member of Neon Therapeutics. All other authors declare no competing interests.

Additional information

Peer review information Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Integrated supplementary information

Supplementary Figure 1 Anti-PD-1 prior to antigenic stimulation abrogates the anti-tumor effects of Vax + αPD-1.

Tumor growth profiles of individual mice after various treatments in TC-1 (a) and B16 (b) tumor models. Experiments were repeated twice with an indicated number of mice/group.

Supplementary Figure 2 Prior PD-1 blockade abrogates vaccine-induced tumor-specific immune responses early during the course of treatment.

Tumor tissues were harvested 3 days after priming (D13) or three days after boosting (D20) from B16 melanoma-bearing mice. a-d. Numbers of total (a & c) and antigen-specific CD8+ T cells (b & d) at D20 (a-b) and at D13 (c-d). e-h. Frequencies of Annexin V+ total (e & g) and antigen-specific CD8+ T cells (f & h) in the TME at D20 (e-f) and at D13 (g-h) as indicated. i-l. Frequencies of CD40L+ (i and k) and IFN-γ+ (j & l) CD8+ T cells in the TME at D20 (i-j) and at D13 (k-l) as indicated. Flow cytometry data are the average of two independent experiments. Each dot corresponds to one mouse with the indicated number of mice per group given in parentheses. For comparison purposes, an unpaired, one-tailed Student’s t-test was used. The error bars indicate the s.e.m. NSnon-significant (a) *(lower) p=0.0228, *(middle) p=0.0277, *(upper) p=0.0286, **(left) p=0.0012, **(right) p=0.0014; (b) *(lower) p=0.0333, *(middle) p=0.042, *(upper) p=0.0472, ****p≤0.0001; (d) *(lower) p=0.018, *(upper) p=0.0207, **p=0.0072, ***p=0.0004, ****p≤0.0001; (e) *(lower) p=0.05, *(upper) p=0.03, **p=0.0041; (f) *(lower) p=0.0462, *(upper) p=0.0181, **p=0.0094; (g) *(lower) p=0.0157, *(upper) p=0.0469, ***p=0.0006; (h) *(lower) p=0.0265, *(upper) p=0.0152, **p=0.0062 (i) *(left) p=0.0384, *(middle) p=0.0399, *(right) p=0.0165; (j) *(lower) p=0.0414, *(upper) p=0.0260, **p=0.0013, ***p=0.0007; (k) *p=0.0343, **p=0.006, ****p≤0.0001; (l) *p=0.0156. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Supplementary Figure 3 PD-1 blockade prior to antigenic stimulation generates dysfunctional PD-1+CD38+ CD8+ T cells.

a. Gating strategy. b. FACS contour plots showing frequency of PD-1+CD38+ cells in total CD8+ T cells in the tumors (at D13) following various treatments.

Supplementary Figure 4 PD-1+CD38hi CD8+ T cells induced as a result of anti-PD-1 pre-treatment are dysfunctional.

a-b. Frequency of CD40L+ (a) and IFN-γ+ (b) T cells in PD-1+CD38hi CD8+ T cell population. c-d. Frequencies of Annexin V+ PD-1+CD38hi cells in total (c) and antigen-specific (d) CD8+ T cells at D13 post-B16 tumor implantation. Data are the average of two independent experiments. Each dot corresponds to one mouse with the indicated number of mice per group given in parentheses. The aerror bars indicate the s.e.m. For comparison purposes, an unpaired, one-tailed Student’s t-test was used. NSnon-significant (a) *(lower) p=0.0463, *(upper) p=0.0496, **p=0.0086, ***p=0.0009; (b) *(lower) p=0.018, *(upper) p=0.0441, **p=0.01, ****p≤0.0001; (c) vs. UT: *(lower) p=0.0327 and *(upper) p=0.0275, vs. Vax: *(lower) p=0.0143 and *(upper) p=0.0273, ****p≤0.0001; (d) *(lower) p=0.026, *(upper) p=0.0382, **p=0.01, ****p≤0.0001. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Supplementary Figure 5 Depletion of PD-1+CD38hi CD8+ T cells results in strong anti-tumor response.

a. Tumor growth of variously treated B16-bearing Rag1–/– mice following transfer of either total or PD-1+CD38+ depleted, in vitro activated CD8+ T cells (with an indicated number of mice per group given in parentheses). b. The full scan of the blot showing the expression of CD38 and β-actin in flow-sorted PD-1+CD38+ T cells transfected either with scrambled RNA (scRNA) or CD38 siRNA.

Supplementary Figure 6 ROC analysis to measure the predictive power of CD38+ fraction of PD1+CD8+ T-cells pre- and post- anti-PD-1 therapy in the human tumor and PBMC samples.

a-c. The ROC curves were generated using R version 3.5.1 software in post-therapy tumors with 4% cut-off (a), pre-therapy tumors with 10% cut-off (b), and PBMCs with 5% cut-off (c). AUC and 95% confidence interval (CI) were determined using Delong method with R version 3.5.1 statistical software. The diagnostic tables used for generating each ROC curve as well as the formulae used to calculate sensitivity, specificity, PPV and NPV are provided. Comparison of responding vs. non-responding tumor lesions that had more than 4%# or 10%^ PD-1+CD38+ cells in the CD8+ population in the TME. *For human PBMC data, the CD38+ fraction of PD-1+CD8+ T cells that showed more than 5% decline at 9 weeks when compared with 3 weeks were compared between responders and non-responders post-therapy. AUC: Area Under the Receiver Operating Characteristics (ROC) Curve; AUC 95% CI: There is 95% of confidence that the interval contains the true AUC. For example, there is 95% confidence that (0.679,1) contains the true value of AUC for 5% cut-off; PPP: Positive predictive value; NPV: Negative predictive value.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, V., Shrimali, R.K., Ahmad, S. et al. PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+CD38hi cells and anti-PD-1 resistance. Nat Immunol 20, 1231–1243 (2019). https://doi.org/10.1038/s41590-019-0441-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-019-0441-y

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