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Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens

Nature volume 543, pages 723–727 (2017)Cite this article

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Abstract

Cancer somatic mutations can generate neoantigens that distinguish malignant from normal cells1,2,3,4,5,6,7. However, the personalized identification and validation of neoantigens remains a major challenge. Here we discover neoantigens in human mantle-cell lymphomas by using an integrated genomic and proteomic strategy that interrogates tumour antigen peptides presented by major histocompatibility complex (MHC) class I and class II molecules. We applied this approach to systematically characterize MHC ligands from 17 patients. Remarkably, all discovered neoantigenic peptides were exclusively derived from the lymphoma immunoglobulin heavy- or light-chain variable regions. Although we identified MHC presentation of private polymorphic germline alleles, no mutated peptides were recovered from non-immunoglobulin somatically mutated genes. Somatic mutations within the immunoglobulin variable region were almost exclusively presented by MHC class II. We isolated circulating CD4+ T cells specific for immunoglobulin-derived neoantigens and found these cells could mediate killing of autologous lymphoma cells. These results demonstrate that an integrative approach combining MHC isolation, peptide identification, and exome sequencing is an effective platform to uncover tumour neoantigens. Application of this strategy to human lymphoma implicates immunoglobulin neoantigens as targets for lymphoma immunotherapy.

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Figure 1: Integrative genomic and proteomic approach for tumour antigen discovery.
Figure 2: Characterization of lymphoma-specific MHC-I and MHC-II epitopes and somatic mutations.
Figure 3: MHC-I and MHC-II presentation of lymphoma Ig.
Figure 4: Detection and cytololytic activity of neoantigen-specific CD4 T cells.

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References

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

    Article  CAS  PubMed  Google Scholar 

  2. Linnemann, C. et al. High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nature Med. 21, 81–85 (2015)

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  4. 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  PubMed  Google Scholar 

  5. Rajasagi, M. et al. Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. Blood 124, 453–462 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Khodadoust, M. S. & Alizadeh, A. A. Tumor antigen discovery through translation of the cancer genome. Immunol. Res. 58, 292–299 (2014)

    Article  CAS  PubMed  Google Scholar 

  7. Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520, 692–696 (2015)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  8. Kowalewski, D. J . et al. HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL). Proc. Natl Acad. Sci. USA 112, E166–E175 (2015)

    Article  CAS  PubMed  Google Scholar 

  9. Berlin, C. et al. Mapping the HLA ligandome landscape of acute myeloid leukemia: a targeted approach toward peptide-based immunotherapy. Leukemia 29, 647–659 (2015)

    Article  CAS  PubMed  Google Scholar 

  10. Dutoit, V. et al. Exploiting the glioblastoma peptidome to discover novel tumour-associated antigens for immunotherapy. Brain 135, 1042–1054 (2012)

    Article  PubMed  Google Scholar 

  11. Kalaora, S. et al. Use of HLA peptidomics and whole exome sequencing to identify human immunogenic neo-antigens. Oncotarget 7, 5110–5117 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nature Commun. 7, 13404 (2016)

    Article  CAS  ADS  Google Scholar 

  13. Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014)

    Article  CAS  ADS  PubMed  Google Scholar 

  14. Walz, S. et al. The antigenic landscape of multiple myeloma: mass spectrometry (re)defines targets for T-cell-based immunotherapy. Blood 126, 1203–1213 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Green, M. R. et al. Hierarchy in somatic mutations arising during genomic evolution and progression of follicular lymphoma. Blood 121, 1604–1611 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Green, M. R . et al. Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation. Proc. Natl Acad. Sci. USA 112, E1116–E1125 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Beà, S. Cyclin D1 transcriptional activation in MCL. Blood 123, 1979–1980 (2014)

    Article  PubMed  Google Scholar 

  18. Zhang, J. et al. The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells. Blood 123, 2988–2996 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Beà, S. et al. Landscape of somatic mutations and clonal evolution in mantle cell lymphoma. Proc. Natl Acad. Sci. USA 110, 18250–18255 (2013)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  20. Kridel, R. et al. Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood 119, 1963–1971 (2012)

    Article  CAS  PubMed  Google Scholar 

  21. 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  PubMed  PubMed Central  Google Scholar 

  22. Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nature Biotechnol. 32, 684–692 (2014)

    Article  CAS  Google Scholar 

  23. Quezada, S. A. et al. Tumor-reactive CD4+ T cells develop cytotoxic activity and eradicate large established melanoma after transfer into lymphopenic hosts. J. Exp. Med. 207, 637–650 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Trojan, A. et al. Immunoglobulin framework-derived peptides function as cytotoxic T-cell epitopes commonly expressed in B-cell malignancies. Nature Med. 6, 667–672 (2000)

    Article  CAS  PubMed  Google Scholar 

  25. Strothmeyer, A. M. et al. Comparative analysis of predicted HLA binding of immunoglobulin idiotype sequences indicates T cell-mediated immunosurveillance in follicular lymphoma. Blood 116, 1734–1736 (2010)

    Article  CAS  PubMed  Google Scholar 

  26. Zangani, M. M. et al. Lymphomas can develop from B cells chronically helped by idiotype-specific T cells. J. Exp. Med. 204, 1181–1191 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kwak, L. W. et al. Induction of immune responses in patients with B-cell lymphoma against the surface-immunoglobulin idiotype expressed by their tumors. N. Engl. J. Med. 327, 1209–1215 (1992)

    Article  CAS  PubMed  Google Scholar 

  28. Neelapu, S. S. et al. Vaccine-induced tumor-specific immunity despite severe B-cell depletion in mantle cell lymphoma. Nature Med. 11, 986–991 (2005)

    Article  CAS  PubMed  Google Scholar 

  29. Freedman, A. et al. Placebo-controlled phase III trial of patient-specific immunotherapy with mitumprotimut-T and granulocyte-macrophage colony-stimulating factor after rituximab in patients with follicular lymphoma. J. Clin. Oncol. 27, 3036–3043 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Levy, R. et al. Active idiotypic vaccination versus control immunotherapy for follicular lymphoma. J. Clin. Oncol. 32, 1797–1803 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schuster, S. J. et al. Vaccination with patient-specific tumor-derived antigen in first remission improves disease-free survival in follicular lymphoma. J. Clin. Oncol. 29, 2787–2794 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  33. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015)

    Article  CAS  ADS  PubMed  PubMed Central  Google Scholar 

  34. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Koboldt, D. C., Larson, D. E. & Wilson, R. K. Using VarScan 2 for germline variant calling and somatic mutation detection. Curr. Protoc. Bioinformatics 44, 15.14.1–15.14.17 (2013)

    Google Scholar 

  37. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bai, Y., Ni, M., Cooper, B., Wei, Y. & Fury, W. Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads. BMC Genomics 15, 325 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lundegaard, C. 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)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nielsen, M. et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2, e796 (2007)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. van Dongen, J. J. et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 17, 2257–2317 (2003)

    Article  CAS  PubMed  Google Scholar 

  42. Lefranc, M. P. et al. IMGT, the international ImMunoGeneTics database. Nucleic Acids Res. 27, 209–212 (1999)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature Methods 4, 207–214 (2007)

    Article  CAS  PubMed  Google Scholar 

  44. Hunt, D. F. et al. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255, 1261–1263 (1992)

    Article  CAS  ADS  PubMed  Google Scholar 

  45. Hunt, D. F. et al. Peptides presented to the immune system by the murine class II major histocompatibility complex molecule I-Ad. Science 256, 1817–1820 (1992)

    Article  CAS  ADS  PubMed  Google Scholar 

  46. Barnstable, C. J. et al. Production of monoclonal antibodies to group A erythrocytes, HLA and other human cell surface antigens-new tools for genetic analysis. Cell 14, 9–20 (1978)

    Article  CAS  PubMed  Google Scholar 

  47. Lampson, L. A. & Levy, R. Two populations of Ia-like molecules on a human B cell line. J. Immunol. 125, 293–299 (1980)

    CAS  PubMed  Google Scholar 

  48. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003)

    Article  CAS  PubMed  Google Scholar 

  49. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994)

    Article  CAS  PubMed  Google Scholar 

  50. Zhang, J. et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell Proteom. 11, M111.010587 (2012)

    Article  Google Scholar 

  51. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4, 923–925 (2007)

    Article  PubMed  Google Scholar 

  52. Colaert, N., Helsens, K., Martens, L., Vandekerckhove, J. & Gevaert, K. Improved visualization of protein consensus sequences by iceLogo. Nature Methods 6, 786–787 (2009)

    Article  CAS  PubMed  Google Scholar 

  53. Vita, R. et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 43, D405–D412 (2015)

    Article  CAS  PubMed  Google Scholar 

  54. Gilar, M., Olivova, P., Daly, A. E. & Gebler, J. C. Orthogonality of separation in two-dimensional liquid chromatography. Anal. Chem. 77, 6426–6434 (2005)

    Article  CAS  PubMed  Google Scholar 

  55. Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wis´niewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497–3506 (2014)

    Article  Google Scholar 

  57. Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 44 (D1), D336–D342 (2016)

    Article  CAS  PubMed  Google Scholar 

  58. Day, C. L. et al. Ex vivo analysis of human memory CD4 T cells specific for hepatitis C virus using MHC class II tetramers. J. Clin. Invest. 112, 831–842 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kirsch, I. R. et al. TCR sequencing facilitates diagnosis and identifies mature T cells as the cell of origin in CTCL. Sci. Transl. Med. 7, 308ra158 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  60. Lefranc, M. P. et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 37, D1006–D1012 (2009)

    Article  CAS  PubMed  Google Scholar 

  61. Rodenko, B. et al. Generation of peptide-MHC class I complexes through UV-mediated ligand exchange. Nature Protocols 1, 1120–1132 (2006)

    Article  CAS  PubMed  Google Scholar 

  62. Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Newell, E. W., Klein, L. O., Yu, W. & Davis, M. M. Simultaneous detection of many T-cell specificities using combinatorial tetramer staining. Nature Methods 6, 497–499 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to the patients and their families who participated in this study, as well as to M. Diehn, S. Levy, and members of their laboratories for feedback. This work was supported by the following grants: National Institutes of Health (NIH) U01 CA194389 (M.M.D., R.L., J.E.E., A.A.A.), NIH PPG CA49605 (R.L.); American Society of Hematology Scholar Award (A.A.A.), V-Foundation (A.A.A.), Damon Runyon Cancer Research Foundation (A.A.A.); Damon Runyon-Rachleff Innovation Award (J.E.E.), W.M. Keck Foundation Medical Research Grant (J.E.E.); Conquer Cancer Foundation Young Investigator Award (M.S.K.), Fellow Award from the Leukemia & Lymphoma Society (M.S.K.); Knut and Alice Wallenberg Foundation Postdoctoral Fellowship (N.O.); Stanford Translational Research and Applied Medicine Pilot Grant (M.R.G., H.E.K., and A.A.A.); NIH S10 RR02933801. We thank the NIH Tetramer Facility for providing recombinant HLA-A0201 for tetramer experiments.

Author information

Author notes

  1. Michael R. Green

    Present address: † Present address: Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA.,

  2. Holbrook E. Kohrt: Deceased.

  3. Michael S. Khodadoust and Niclas Olsson: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine, Division of Oncology, Stanford University, Stanford, 94305, California, USA

    Michael S. Khodadoust, Ole A. W. Haabeth, Binbin Chen, Chih Long Liu, Henning Stehr, Aaron M. Newman, Debra K. Czerwinski, Holbrook E. Kohrt, Michael R. Green, Ronald Levy & Ash A. Alizadeh

  2. Department of Chemical & Systems Biology, Stanford University, Stanford, 94305, California, USA

    Niclas Olsson, Kavya Swaminathan, Keith Rawson, Samhita Rao, Lichao Zhang & Joshua E. Elias

  3. Department of Microbiology & Immunology, Stanford University, Stanford, 94305, California, USA

    Lisa E. Wagar, Peder Lund, Caleb Marceau, Jan Carette & Mark M. Davis

  4. Department of Genetics, Stanford University, Stanford, 94305, California, USA

    Binbin Chen

  5. Department of Pathology, Stanford University, Stanford, 94305, California, USA

    David Steiner

  6. Center for Cancer Systems Biology, Stanford University, Stanford, 94305, California, USA

    Aaron M. Newman & Ash A. Alizadeh

  7. Adaptive Biotechnologies, South San Francisco, California, 94080, USA

    Victoria E. H. Carlton, Martin Moorhead & Malek Faham

  8. Howard Hughes Medical Institute, Stanford University, Stanford, 94305, California, USA

    Mark M. Davis

  9. Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, 94305, California, USA

    Ash A. Alizadeh

Authors
  1. Michael S. Khodadoust
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Contributions

M.S.K., N.O., M.M.D., R.L., J.E.E., and A.A.A. developed the concept, designed the experiments, and analysed the data. R.L. provided patient material from the associated clinical trial. M.S.K., M.R.G., and A.A.A. performed molecular biology, genomic analyses, and neoantigen classification with assistance from C.L.L., H.S., H.E.K., D.S., and B.C. N.O., K.R., M.S.K., and J.E.E. performed proteomic studies and analyses with assistance from B.C., S.R., L.Z., K.S., P.L., C.M., and J.C. M.S.K., L.E.W., O.A.W.H., and D.K.C. performed immunological experiments and analyses. V.E.H.C., M.M., and M.F. performed TCR repertoire sequencing. M.S.K., N.O., B.C., K.R., A.M.N., C.L.L., D.S., J.E.E., and A.A.A. performed integrative data analyses. M.S.K., N.O., R.L., J.E.E., and A.A.A. wrote the manuscript with contributions from all authors, who commented on it at all stages.

Corresponding authors

Correspondence to Joshua E. Elias or Ash A. Alizadeh.

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Competing interests

V.E.H.C. and M.M. are employees of Adaptive Biotechnologies. M.F. is a former employee of Adaptive Biotechnologies. Other authors declare no conflict of interest.

Additional information

Reviewer Information Nature thanks C. Melief and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 MHC ligand characteristics.

a, Length distribution of recovered MHC-I peptides plotted alongside the completed Immuno Epitope Database (IEDB) of human HLA ligands. b, Length distribution of recovered MHC-I peptides plotted with IEDB human HLA ligand dataset. c, Weblogo from all 9-amino-acid MHC-I ligands for four representative patients. d, Overlap of MHC-I ligands for two patients with a completely distinct MHC-I profile (that is, no shared HLA-A, HLA-B, or HLA-C alleles). The overlap of peptides (left) and genes (right) presented by MHC-I for each patient is shown. e, MHC-I ligands from patient MCL001 were analysed for their predicted affinity to either the patient’s own HLA alleles (‘self’, brown) or the HLA alleles of patient MCL052 (‘nonself’, blue) using netMHC. f, MHC-I ligands from patient MCL052 were analysed for predicted affinity to either the patient’s own HLA alleles (‘self’, brown) or the HLA alleles of patient MCL001 (‘nonself’, blue). White breaks indicate a change in scale of the x axis. g, The number of unique peptides presented by MHC-I across all patients was determined for each gene. The genes that produced the most unique peptides (top fifth percentile) were analysed by gene set enrichment using the PANTHER pathways database, revealing enrichment of B-cell activation genes.

Extended Data Figure 2 Correlation of protein abundance with MHC-I and MHC-II presentation.

Two MCL cell lines, JEKO (left) and L128 (right), were digested with trypsin and peptides identified by LC–MS/MS. a, Violin plots: distribution of protein abundance is plotted as kernel density violin plots with mean value indicated by a bar for all proteins (grey), proteins presented by MHC-I (blue), proteins presented by MHC-II (red), and mutated proteins where a mutated peptide was identified from whole-proteome analysis (green). The distributions of protein abundance for each set of proteins (MHC-I presented, MHC-II presented, and mutated proteins) were compared with the distribution for all proteins by Kolmogorov–Smirnov test. For each line, *P < 10−3, **P < 10−5, ***P < 10−9. b, The abundance of each protein was estimated using the histone ruler approach (black). Each protein with at least one peptide presented by MHC-I (blue) or MHC-II (red) is marked with an adjacent tick to the right and left (respectively) of the estimated protein abundance line. Proteins for which a mutated peptide was recovered by whole-proteomic analysis (not from MHC immunoprecipitation) are indicated by ticks on the left of the figure (green). Noteworthy genes involved in MCL pathogenesis are highlighted. Proteins for which a mutated peptide was recovered by proteomic analysis are indicated by ticks on the left of the figure.

Extended Data Figure 3 Comparison of antigen presentation between patients.

a, b, Heatmap of Sørensen similarity coefficient between patients for the set of genes presented by MHC-I (a) and MHC-II (b). Patients were clustered by hierarchical clustering. Gene presentation by MHC was true if one or more peptides encoded by the gene were presented by MHC. c, d, Heatmap of Sørensen similarity index between patients for peptide ligands presented from MHC-I (c) or MHC-II (d). Patients were clustered by hierarchical clustering. e, f, Two-dimensional visualization of the similarity in MHC ligands between patients. Relationship between samples is shown through a Fruchterman–Reingold layout of a force-directed graph of Sørensen similarity (edges) between patients (nodes) for MHC-I (e) or MHC-II (f). For MHC-I, patients with at least one HLA allele belonging to the HLA-A02 (pink), HLA-B44 (blue), or HLA-A03 (green) supertype family are coloured accordingly. For HLA-DR, patients are coloured by presence of four specific HLA-DR alleles. Edge weight and colour are determined by strength of Sørensen similarity (minimum Sørensen similarity 0.15 per edge). Node size is determined by number of MHC ligands for each patient. Nodes are coloured by membership to MHC-I supertype family (e) or HLA-DR allele (f).

Extended Data Figure 4 Presentation of heterozygous SNPs.

Top, germline heterozygous non-synonymous SNPs were determined for all 17 patients. MHC-I- and MHC-II-associated peptides spanning each SNP were identified from our LC–MS/MS dataset. The number of SNP loci that produced an MHC-bound peptide bearing the respective SNP is shown in red. The number of SNP loci that produced an MHC-bound peptide bearing the corresponding hg19 reference allele is shown in blue. Overlap (purple) indicates an SNP locus for which both SNP and hg19 reference alleles were identified from the same patient. Bottom, analogous depiction of recovery of peptides containing a somatic variant (null set; open circle) or the corresponding germline peptide of the variant (yellow circle). Comparison of recovery for germline SNPs and somatic variants was performed by Fisher’s exact test.

Extended Data Figure 5 Peptide–MHC tetramer T-cell responses against predicted HLA-A2 neoantigens.

HLA-A2 restricted neoantigens were predicted for eight patients. Peptide–MHC tetramers were synthesized for 108 predicted HLA-A2 neoantigens. Patient peripheral blood cells were pre-treated with dasatinib and then stained with each tetramer. a, To enable a more accurate estimation of background staining, T cells from patients were mixed and co-stained with fluorescently barcoded cells from two or three healthy donors. A representative reaction with multiplexing of three healthy donors using cytomegalovirus peptide–MHC tetramer is shown. b, Frequency of neoantigen-specific CD8 T cells from peripheral blood (filled circles). The frequency of neoantigen-specific T cells from healthy donor PBMCs for each neoantigen is also shown (open squares). Staining of a CMV peptide–MHC tetramer for each patient is shown in red. c, Representative results for three viral peptides derived from HIV, influenza A (flu), and CMV are shown along with patient-specific neoantigen staining. The neoantigen SLC9C2 tetramer is shown as an example of a tetramer with high background staining of both patient and healthy donor CD8 cells.

Extended Data Figure 6 MHC-I presentation of lymphoma Ig neoantigens in the Raji lymphoma cell line.

a, Top, MHC-I binding affinity predictions were performed for all potential 9-amino-acid peptides from the Raji Ig heavy chain. The best binding affinity for an endogenous class I MHC allele is shown in black. The predicted binding to an ectopically expressed HLA-B35:01 allele is shown in red. Bottom, the Raji lymphoma cell line was transfected with the HLA-B35:01 allele. MHC-I antigen presentation profiling was performed for both the parental cell line and the B35-transfected line. Recovered peptides were matched against the Raji Ig heavy- and light-chain sequences. Positions differing from the germline variable region altered by either somatic hypermutation or V(D)J recombination are shown in red. Three peptides corresponding to Raji Ig were identified, shown in boxes. All three were only found in the B35-transfected Raji cells. Asterisks indicate peptides that were identified proteomically. b, The predicted binding IC50 (nM) based on netMHCpan is shown for each recovered peptide for each of the Raji HLA alleles. Red shading indicates a predicted high-affinity interaction with the corresponding HLA allele.

Extended Data Figure 7 Experimental determination affinity of HLA-DRB1*04:01 with associated Ig neoantigens.

Six neoantigen peptides identified from three patients were synthesized with an N-terminal DNP modification and tested for binding to recombinant HLA-DR4 molecules. Recombinant, biotinylated HLA-DR4 molecules were produced with a thrombin-cleavable CLIP peptide. Neoantigen peptides were exchanged onto the DR4 molecules. HLA-DR4 molecules were then bound to streptavidin-coated microsphere beads and co-stained with the anti-HLA-DR antibody and anti-DNP antibody. Beads were then washed and analysed by flow cytometry for dual staining against HLA-DR and DNP-labelled peptide. A known CMV-derived peptide ligand of HLA-DR4 was used as a positive control. Shown above each plot is the predicted affinity of each peptide for both HLA-DR4 and the associated patient’s alternative HLA-DR allele as predicted by netMHCII. Red letters indicate amino acids that differ from the germline variable gene sequence owing to somatic hypermutation events.

Extended Data Figure 8 Phenotyping of neoantigen-specific T cells.

a, Peripheral blood CD4+ T cells were isolated from patient MCL041 and stained with HLA-DR*04:01 tetramers loaded with patient-specific neoantigen and a CMV peptide. Right, gated PD1 and CD45RA expression is shown for neoantigen-specific CD4 T cells (top) and for non-specific CD4 T cells (bottom). b, Vaccine-primed CD4+ T cells from patient MCL030 were stimulated with either a pool of neoantigen peptides or a pool of pathogen-associated (CMV, EBV, influenza, tetanus) peptides and were sorted for CD137 upregulation. The sorted population was expanded ex vivo for 2.5 weeks, then rested for 5 days in the presence of low-dose IL-15. Cells were then fluorescently labelled and stimulated for 24 h with unlabelled autologous PBMCs loaded with a pool of three Ig neoantigen peptides, or a pool of three corresponding peptides with somatic alterations reverted back to the variable gene sequence, or a pool of pathogen-associated peptides. Activation of the T cells was determined by induction of CD25 and CD69 in response to peptide stimulation. c, Neoantigen-specific CD4 T cells from patient MCL030 were expanded, labelled, and re-stimulated as in b. Expression of CD25, Ki67, IL-4, and granzyme B is shown for CD4-gated T cells re-stimulated with neoantigen peptide-loaded PBMC.

Extended Data Figure 9 Lack of cytotoxic activity by pathogen-specific T cells against autologous lymphoma cells.

Left, CD4 T cells were purified from patient MCL030 after autologous tumour vaccination. T cells were stimulated with autologous PBMCs loaded with a pool of pathogenic peptides including antigens derived from CMV, EBV, influenza A, and tetanus. After 30 h, cells were sorted for CD137 expression. Right, sorted pathogen antigen-specific cells were expanded using anti-CD3, anti-CD28, IL-2, and allogeneic feeders for 3 weeks. The expanded T cells were co-cultured with fluorescently labelled autologous lymphoma cells for 24 h (top right). Background cell death of the lymphoma cells is also shown (bottom right). Cytotoxicity of lymphoma cells was determined by 7-AAD uptake of the labelled population.

Extended Data Figure 10 Predicted MHC-I presentation of lymphoma Ig molecules.

a, Left, peptides recovered from MHC-I purification were mapped to the Ig heavy and light chain. The colour heatmap corresponds to the number of peptides recovered at each position by LC–MS/MS. Right, for each patient’s unique lymphoma Ig sequence and HLA profile, the predicted peptide-HLA affinity was calculated for all possible peptides with a length of 8–11 amino acids created from their Ig using netMHCpan. A heatmap illustrating the number of patients (from a total of 17) with at least one peptide predicted to bind self-HLA (IC50 ≤ 500 nM) at each position across the Ig heavy chain is shown. b, For each position along the Ig molecule, the number of peptides that were experimentally recovered per position (left-hand y axis) was determined. Similarly, for each position, the number of patients (of 17 in total) with at least one peptide with predicted peptide–MHC affinity IC50 <500 nM was determined (right-hand y axis). Positions from the variable region to the N-terminal 50 amino acids of CH1 (red) were compared with the rest of the constant region (green). P value was calculated using a Mann–Whitney test. Error bars show range, horizontal black bar shows median.

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Khodadoust, M., Olsson, N., Wagar, L. et al. Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. Nature 543, 723–727 (2017). https://doi.org/10.1038/nature21433

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