Letter | Published:

Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing

Nature volume 515, pages 572576 (27 November 2014) | Download Citation

Abstract

Human tumours typically harbour a remarkable number of somatic mutations1. If presented on major histocompatibility complex class I molecules (MHCI), peptides containing these mutations could potentially be immunogenic as they should be recognized as ‘non-self’ neo-antigens by the adaptive immune system. Recent work has confirmed that mutant peptides can serve as T-cell epitopes2,3,4,5,6,7,8,9. However, few mutant epitopes have been described because their discovery required the laborious screening of patient tumour-infiltrating lymphocytes for their ability to recognize antigen libraries constructed following tumour exome sequencing. We sought to simplify the discovery of immunogenic mutant peptides by characterizing their general properties. We developed an approach that combines whole-exome and transcriptome sequencing analysis with mass spectrometry to identify neo-epitopes in two widely used murine tumour models. Of the >1,300 amino acid changes identified, 13% were predicted to bind MHCI, a small fraction of which were confirmed by mass spectrometry. The peptides were then structurally modelled bound to MHCI. Mutations that were solvent-exposed and therefore accessible to T-cell antigen receptors were predicted to be immunogenic. Vaccination of mice confirmed the approach, with each predicted immunogenic peptide yielding therapeutically active T-cell responses. The predictions also enabled the generation of peptide–MHCI dextramers that could be used to monitor the kinetics and distribution of the anti-tumour T-cell response before and after vaccination. These findings indicate that a suitable prediction algorithm may provide an approach for the pharmacodynamic monitoring of T-cell responses as well as for the development of personalized vaccines in cancer patients.

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References

  1. 1.

    et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

  2. 2.

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

  3. 3.

    , , , & Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405–409 (2012)

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

    et al. Surveillance of the tumor mutanome by T cells during progression from primary to recurrent ovarian cancer. Clin. Cancer Res. 20, 1125–1134 (2014)

  9. 9.

    et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 24, 743–750 (2014)

  10. 10.

    & Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013)

  11. 11.

    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)

  12. 12.

    , , , & Oncolytic virus-initiated protective immunity against prostate cancer. Mol. Ther. 19, 797–804 (2011)

  13. 13.

    et al. The MHC class I peptide repertoire is molded by the transcriptome. J. Exp. Med. 205, 595–610 (2008)

  14. 14.

    et al. MHC I-associated peptides preferentially derive from transcripts bearing miRNA response elements. Blood 119, e181–e191 (2012)

  15. 15.

    et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nature Genet. 44, 685–689 (2012)

  16. 16.

    et al. MED12 and HMGA2 mutations: two independent genetic events in uterine leiomyoma and leiomyosarcoma. Mod. Pathol. 27, 1144–1153 (2014)

  17. 17.

    Functions of the proteasome: from protein degradation and immune surveillance to cancer therapy. Biochem. Soc. Trans. 35, 12–17 (2007)

  18. 18.

    et al. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J. Immunol. 153, 5586–5592 (1994)

  19. 19.

    , , , & Rosetta FlexPepDock web server–high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 39, W249–W253 (2011)

  20. 20.

    , & How TCRs bind MHCs, peptides, and coreceptors. Annu. Rev. Immunol. 24, 419–466 (2006)

  21. 21.

    et al. Cooperation of Tim-3 and PD-1 in CD8 T-cell exhaustion during chronic viral infection. Proc. Natl Acad. Sci. USA 107, 14733–14738 (2010)

  22. 22.

    , & Role of PD-1 in regulating T-cell immunity. Curr. Top. Microbiol. Immunol. 350, 17–37 (2011)

  23. 23.

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

  24. 24.

    et al. Upregulation of Tim-3 and PD-1 expression is associated with tumor antigen-specific CD8+ T cell dysfunction in melanoma patients. J. Exp. Med. 207, 2175–2186 (2010)

  25. 25.

    et al. CD8+ T cells specific for tumor antigens can be rendered dysfunctional by the tumor microenvironment through upregulation of the inhibitory receptors BTLA and PD-1. Cancer Res. 72, 887–896 (2012)

  26. 26.

    , , , & T cell anergy, exhaustion, senescence, and stemness in the tumor microenvironment. Curr. Opin. Immunol. 25, 214–221 (2013)

  27. 27.

    , , , & Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351, 290–296 (1991)

  28. 28.

    & Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010)

  29. 29.

    & GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005)

  30. 30.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)

  31. 31.

    et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010)

  32. 32.

    et al. Structural and biological basis of CTL escape in coronavirus-infected mice. J. Immunol. 180, 3926–3937 (2008)

  33. 33.

    , , & The three-dimensional structure of H-2Db at 2.4 Å resolution: implications for antigen-determinant selection. Cell 76, 39–50 (1994)

  34. 34.

    et al. Affinity thresholds for naive CD8+ CTL activation by peptides and engineered influenza A viruses. J. Immunol. 187, 5733–5744 (2011)

  35. 35.

    et al. How H13 histocompatibility peptides differing by a single methyl group and lacking conventional MHC binding anchor motifs determine self-nonself discrimination. J. Immunol. 168, 283–289 (2002)

  36. 36.

    et al. Peptidic termini play a significant role in TCR recognition. J. Immunol. 169, 3137–3145 (2002)

  37. 37.

    , , & Structural evidence of T cell xeno-reactivity in the absence of molecular mimicry. J. Exp. Med. 189, 359–370 (1999)

  38. 38.

    & Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004)

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Acknowledgements

The authors thank A. Bruce and J. Murphy for excellent assistance with artwork.

Author information

Author notes

    • Mahesh Yadav
    •  & Suchit Jhunjhunwala

    These authors contributed equally to this work.

    • Jennie R. Lill
    •  & Lélia Delamarre

    These authors jointly supervised this work.

Affiliations

  1. Genentech, South San Francisco, California 94080, USA

    • Mahesh Yadav
    • , Suchit Jhunjhunwala
    • , Qui T. Phung
    • , Patrick Lupardus
    • , Joshua Tanguay
    • , Stephanie Bumbaca
    • , Christian Franci
    • , Tommy K. Cheung
    • , Zora Modrusan
    • , Ira Mellman
    • , Jennie R. Lill
    •  & Lélia Delamarre
  2. Immatics Biotechnologies GmbH, 72076 Tubingen, Germany

    • Jens Fritsche
    •  & Toni Weinschenk

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Contributions

M.Y. was involved in planning and performing in vivo experiments, analysing and interpreting data, and writing the manuscript. S.J. analysed and interpreted whole-exome sequencing and RNA sequencing data, generated translated FASTA database, searched for potential neo-epitopes. Q.T.P. and T.K.C. performed mass spectrometric data analysis and peptide validation. P.L. performed the structure prediction of the MHCI–peptide complexes. J.T. performed studies with tumour-bearing mice. S.B. performed and analysed FACS studies on tumour lines. C.F. performed and analysed immunizations experiments. Z.M. oversaw RNA sequencing experiments. I.M. assisted with the study design and the preparation of the manuscript. J.F. and T.W. performed MHCI peptide isolation and mass spectrometric analysis. L.D and J.R.L. oversaw all the work performed, planned experiments, interpreted data and wrote the manuscript.

Competing interests

Mahesh Yadav, Suchit Jhunjhunwala, Qui T. Phung, Patrick Lupardus, Joshua Tanguay, Stephanie Bumbaca, Christian Franci, Tommy K. Cheung, Zora Modrusan, Ira Mellman, Jennie R. Lill, and Lélia Delamarre were employees of Genentech at the time of the work. Jens Fritsche and Toni Weinschenk were employees of Immatics Biotechnologies GmbH at the time of the work. They hence declare competing financial interests.

Corresponding authors

Correspondence to Jennie R. Lill or Lélia Delamarre.

Extended data

Supplementary information

Excel files

  1. 1.

    Supplementary Table 1

    This table contains variant peptides (MC-38).

  2. 2.

    Supplementary Table 2

    This table contains variant peptides (TRAMP-C1).

  3. 3.

    Supplementary Table 3

    This table contains peptides identified by LC-MS.

  4. 4.

    Supplementary Table 4

    This table contains RNA-Seq-based expression data for all genes in MC-38 and TRAMP-C1.

  5. 5.

    Supplementary Table 5

    This table contains MHC Class I-presented variant peptides.

About this article

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DOI

https://doi.org/10.1038/nature14001

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