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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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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

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  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.

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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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