Abstract

The immune system influences the fate of developing cancers by not only functioning as a tumour promoter that facilitates cellular transformation, promotes tumour growth and sculpts tumour cell immunogenicity1,2,3,4,5,6, but also as an extrinsic tumour suppressor that either destroys developing tumours or restrains their expansion1,2,7. Yet, clinically apparent cancers still arise in immunocompetent individuals in part as a consequence of cancer-induced immunosuppression. In many individuals, immunosuppression is mediated by cytotoxic T-lymphocyte associated antigen-4 (CTLA-4) and programmed death-1 (PD-1), two immunomodulatory receptors expressed on T cells8,9. Monoclonal-antibody-based therapies targeting CTLA-4 and/or PD-1 (checkpoint blockade) have yielded significant clinical benefits—including durable responses—to patients with different malignancies10,11,12,13. However, little is known about the identity of the tumour antigens that function as the targets of T cells activated by checkpoint blockade immunotherapy and whether these antigens can be used to generate vaccines that are highly tumour-specific. Here we use genomics and bioinformatics approaches to identify tumour-specific mutant proteins as a major class of T-cell rejection antigens following anti-PD-1 and/or anti-CTLA-4 therapy of mice bearing progressively growing sarcomas, and we show that therapeutic synthetic long-peptide vaccines incorporating these mutant epitopes induce tumour rejection comparably to checkpoint blockade immunotherapy. Although mutant tumour-antigen-specific T cells are present in progressively growing tumours, they are reactivated following treatment with anti-PD-1 and/or anti-CTLA-4 and display some overlapping but mostly treatment-specific transcriptional profiles, rendering them capable of mediating tumour rejection. These results reveal that tumour-specific mutant antigens are not only important targets of checkpoint blockade therapy, but they can also be used to develop personalized cancer-specific vaccines and to probe the mechanistic underpinnings of different checkpoint blockade treatments.

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Accessions

Primary accessions

Gene Expression Omnibus

Data deposits

RNA-sequencing data are available at Gene Expression Omnibus (GEO) repository at http://www.ncbi.nlm.nih.gov/geo/ (accession number GSE62771).

References

  1. 1.

    et al. IFNγ and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature 410, 1107–1111 (2001)

  2. 2.

    , , , & Cancer immunoediting: from immunosurveillance to tumor escape. Nature Immunol. 3, 991–998 (2002)

  3. 3.

    , , & Cancer-related inflammation. Nature 454, 436–444 (2008)

  4. 4.

    , & Immunity, inflammation, and cancer. Cell 140, 883–899 (2010)

  5. 5.

    Cancer and inflammation: an old intuition with rapidly evolving new concepts. Annu. Rev. Immunol. 30, 677–706 (2012)

  6. 6.

    , & Neutralizing tumor-promoting chronic inflammation: a magic bullet? Science 339, 286–291 (2013)

  7. 7.

    et al. Adaptive immunity maintains occult cancer in an equilibrium state. Nature 450, 903–907 (2007)

  8. 8.

    , , & Shifting the equilibrium in cancer immunoediting: from tumor tolerance to eradication. Immunol. Rev. 241, 104–118 (2011)

  9. 9.

    The blockade of immune checkpoints in cancer immunotherapy. Nature Rev. Cancer 12, 252–264 (2012)

  10. 10.

    et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013)

  11. 11.

    et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134–144 (2013)

  12. 12.

    et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012)

  13. 13.

    et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010)

  14. 14.

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

  15. 15.

    et al. HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J. Immunol. 191, 5831–5839 (2013)

  16. 16.

    et al. Tight regulation of memory CD8+ T cells limits their effectiveness during sustained high viral load. Immunity 35, 285–298 (2011)

  17. 17.

    T cell exhaustion. Nature Immunol. 12, 492–499 (2011)

  18. 18.

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

  19. 19.

    et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nature Med. 19, 747–752 (2013)

  20. 20.

    et al. HLA-binding properties of tumor neoepitopes in humans. Cancer Immunol. Res. 2, 522–529 (2014)

  21. 21.

    et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–e442 (2013)

  22. 22.

    , , & PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors. Proc. Natl Acad. Sci. USA 107, 4275–4280 (2010)

  23. 23.

    et al. CTLA-4-mediated inhibition of early events of T cell proliferation. J. Immunol. 162, 5813–5820 (1999)

  24. 24.

    , , & PD-1 and its ligands in tolerance and immunity. Annu. Rev. Immunol. 26, 677–704 (2008)

  25. 25.

    , , , & A rheostat for immune responses: the unique properties of PD-1 and their advantages for clinical application. Nature Immunol. 14, 1212–1218 (2013)

  26. 26.

    , , & Dual blockade of PD-1 and CTLA-4 combined with tumor vaccine effectively restores T-cell rejection function in tumors. Cancer Res. 73, 3591–3603 (2013)

  27. 27.

    , & Bystander elimination of antigen loss variants in established tumors. Nature Med. 10, 294–298 (2004)

  28. 28.

    , , , & Antigen bias in T cell cross-priming. Science 304, 1314–1317 (2004)

  29. 29.

    et al. Antigen spreading contributes to MAGE vaccination-induced regression of melanoma metastases. Cancer Res. 71, 1253–1262 (2011)

  30. 30.

    et al. Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 344, 641–645 (2014)

  31. 31.

    et al. Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008)

  32. 32.

    & Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 6, 132 (2005)

  33. 33.

    et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 36, W509–W512 (2008)

  34. 34.

    et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2009)

  35. 35.

    , , & The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57, 33–41 (2005)

  36. 36.

    , & RMA/S cells present endogenously synthesized cytosolic proteins to class I-restricted cytotoxic T lymphocytes. J. Exp. Med. 175, 163–168 (1992)

  37. 37.

    et al. Design and use of conditional MHC class I ligands. Nature Med. 12, 246–251 (2006)

  38. 38.

    et al. Parallel detection of antigen-specific T cell responses by combinatorial encoding of MHC multimers. Nature Protocols 7, 891–902 (2012)

  39. 39.

    et al. TIL therapy broadens the tumor-reactive CD8+ T cell compartment in melanoma patients. OncoImmunology 1, 409–418 (2012)

  40. 40.

    & Biochemical large-scale identification of MHC class I ligands. Methods Mol. Biol. 960, 145–157 (2013)

  41. 41.

    et al. Two preferentially expressed proteins protect vascular endothelial cells from an attack by peptide-specific CTL. J. Immunol. 188, 5283–5292 (2012)

  42. 42.

    & Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnol. 28, 710–721 (2010)

  43. 43.

    et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010)

  44. 44.

    , , & Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008)

  45. 45.

    et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012)

  46. 46.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

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Acknowledgements

We are grateful to K. Murphy for the Batf3−/− mice, T. Hansen for providing MHC class I antibodies and the H-2Kb construct, D. Fremont for the human β2m construct, and the National Institutes of Health (NIH) Tetramer Core Facility for producing MHC class I tetramers. We also thank R. Ahmed and M. Hashimoto for the multiplex staining strategy used to define functional and dysfunctional T cells. We thank A. Bensimon, O. Schubert and P. Kouvonen for instrument maintenance and for technical support with the mass spectrometry measurements and R. Vanganipuram, M. Selby and J. Valle for generating and supplying anti-PD-1 and anti-CTLA-4 in endotoxin-free sterile form. We also thank K. Sheehan, P. Allen, G. Dunn and R. Chan for constructive criticisms and comments, all members of the Schreiber laboratory for discussions, and the many members of The Genome Institute at Washington University School of Medicine. We would also like to thank W. Song for his assistance with the bioinformatics approaches, P. Kvistborg for assistance with tetramer combinatorial coding, and Christopher Nelson for advice with peptide-MHC monomer purification. This work was supported by grants to R.D.S. from the National Cancer Institute (RO1 CA043059, U01 CA141541), the Cancer Research Institute and the WWWW Foundation; to R.D.S. and W.E.G. from The Siteman Cancer Center/Barnes-Jewish Hospital (Cancer Frontier Fund); to W.E.G. from Susan G. Komen for the Cure (Promise grant); to E.R.M. from the National Human Genome Research Institute; to G.J.F. from the National Institute of Health (P50 CA101942, P01 AI054456, P50 CA101942); to A.H.S. from the National Institute of Health (P50 CA101942); and to T.N.S. from the Dutch Cancer Society (Queen Wilhelmina Research Award). E.C. is supported by a Marie Curie Intra-European Fellowship within the Seventh Framework Programme of the European Community for Research. M.M.G. was supported by a postdoctoral training grant (T32 CA00954729) from the National Cancer Institute and is currently supported by a postdoctoral training grant (Irvington Postdoctoral Fellowship) from the Cancer Research Institute. Aspects of studies at Washington University were performed with assistance by the Immunomonitoring Laboratory of the Center for Human Immunology and Immunotherapy Programs and the Siteman Comprehensive Cancer Center.

Author information

Affiliations

  1. Department of Pathology and Immunology, Washington University School of Medicine, 660 South Euclid Avenue, St Louis, Missouri 63110, USA

    • Matthew M. Gubin
    • , Jeffrey P. Ward
    • , Takuro Noguchi
    • , Yulia Ivanova
    • , Cora D. Arthur
    • , Matthew D. Vesely
    • , Samuel S. K. Lam
    • , Erika L. Pearce
    • , Maxim N. Artyomov
    •  & Robert D. Schreiber
  2. Department of Surgery, Washington University School of Medicine, 660 South Euclid Avenue, St Louis, Missouri 63110, USA

    • Xiuli Zhang
    •  & William E. Gillanders
  3. Department of Immunology, Institute of Cell Biology, and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) Partner Site Tübingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany

    • Heiko Schuster
    •  & Hans-Georg Rammensee
  4. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    • Etienne Caron
    •  & Ruedi Aebersold
  5. Department of Medicine, Division of Oncology, Washington University School of Medicine, 660 South Euclid Avenue, St Louis, Missouri 63110, USA

    • Jeffrey P. Ward
  6. The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St Louis, Missouri 63108, USA

    • Jasreet Hundal
    •  & Elaine R. Mardis
  7. ISA Therapeutics B.V., 2333 CH Leiden, The Netherlands

    • Willem-Jan Krebber
    • , Gwenn E. Mulder
    •  & Cornelis J. M. Melief
  8. Division of Immunology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands

    • Mireille Toebes
    •  & Ton N. Schumacher
  9. Bristol-Myers Squibb, 700 Bay Road, Redwood City, California 94063, USA

    • Alan J. Korman
  10. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA

    • James P. Allison
  11. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Gordon J. Freeman
  12. Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Arlene H. Sharpe
  13. Faculty of Science, University of Zurich, Zurich, 8093 Zurich, Switzerland

    • Ruedi Aebersold
  14. Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, 2333ZA Leiden, The Netherlands

    • Cornelis J. M. Melief
  15. Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St Louis, Missouri 63110, USA

    • Elaine R. Mardis

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Contributions

M.M.G. and R.D.S. were involved in all aspects of this study including planning and performing experiments, analysing and interpreting data, and writing the manuscript. X.Z. performed peptide binding experiments, helped design and perform the vaccine experiments. H.S., E.C., R.A. and H.-G.R. planned and performed the mass spectrometry analyses, interpreted the data and were involved in writing the manuscript. T.N., J.P.W., C.D.A., M.D.V., S.S.K.L. and E.L.P., participated in assessing the phenotypes of the tumour-specific T-cell lines, interpreting the data and in writing the manuscript. M.T. helped generate MHC class I multimers. A.J.K., J.P.A., G.J.F. and A.H.S. provided mAbs, helped plan the checkpoint blockade therapy experiments, and contributed to writing the manuscript. T.N.S. helped generate MHC class I multimers, analysed data and was involved in writing the manuscript. W.-J.K., G.E.M. and C.J.M.M. produced and purified the synthetic long peptides, participated in the planning of the vaccine experiments, analysed data and were involved in writing the manuscript. J.H. and E.R.M. were responsible for genomic analyses and epitope prediction and participated in writing the manuscript. W.E.G. contributed to the design and analysis of peptide binding and vaccine experiments and in writing the manuscript. Y.I. and M.N.A were responsible for optimizing the epitope prediction method, performing the RNA-sequencing analyses, analysing data and writing the manuscript. R.D.S. oversaw all the work performed.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Robert D. Schreiber.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Data 1

    This file contains MS traces for each of the mutant H-2Kb d42m1-T3 epitopes tested by targeted MS.

  2. 2.

    Supplementary Information

    This file contains Supplementary Tables 1-3. Supplementary Table 1 contains a complete list of d42m1-T3 H-2Kb-bound peptides identified by discovery MS. Supplementary Table 2 shows all differentially expressed genes in mLama4-tetramer-positive CD8+ TILs from mice treated with checkpoint blockade therapy compared to mLama4-tetramer-positive CD8+ TILs from control mice. Supplementary Table 3 shows differentially regulated pathways (GSEA pathway analysis) in mLama4-tetramer-positive CD8+ TILs from mice treated with checkpoint blockade therapy compared to mLama4-tetramer-positive CD8+ TILs from control mice.

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DOI

https://doi.org/10.1038/nature13988

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