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. 2017 Mar 30;543(7647):723-727.
doi: 10.1038/nature21433. Epub 2017 Mar 22.

Antigen Presentation Profiling Reveals Recognition of Lymphoma Immunoglobulin Neoantigens

Free PMC article

Antigen Presentation Profiling Reveals Recognition of Lymphoma Immunoglobulin Neoantigens

Michael S Khodadoust et al. Nature. .
Free PMC article


Cancer somatic mutations can generate neoantigens that distinguish malignant from normal cells. 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.

Conflict of interest statement

Conflict of interest: 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.


Extended Data Figure 1
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-mer MHC-I ligands for four representative patients. (d) The overlap of MHC-I ligands for two patients with a completely distinct MHC-I profile (i.e. 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 analyzed for their predicted affinity to either the patient’ own HLA alleles (“self”, brown) or the HLA alleles of patient MCL052 (“nonself”, blue) using netMHC. (f) MHC-I ligands from patient MCL052 were analyzed 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 x-axis. (g) The number of unique peptides presented by MHC-I across all patients was determined for each gene. The genes which produced the most unique peptides (top 5th percentile) were analyzed by gene set enrichment using the PANTHER pathways database, revealing enrichment of B-cell activation genes.
Extended Data Figure 2
Extended Data Figure 2. Correlation of protein abundance with MHC-I and MHC-II presentation
Two mantle cell lymphoma 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 distribution of protein abundance for each set of proteins (MHC-I presented, MHC-II presented, and mutated proteins) were compared to 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
Extended Data Figure 3. Comparison of antigen presentation between patients
(a, b) Heatmap of Sorenson 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 Sorenson 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 Sorenson 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 colored accordingly. For HLA-DR, patients are colored by presence of four specific HLA-DR alleles. Edge weight and color are determined by strength of Sorenson similarity (minimum Sorenson similarity 0.15 per edge). Node size is determined by number of MHC ligands for each patient. Nodes are colored by membership to MHC-I supertype family (e) or HLA-DR allele (f).
Extended Data Figure 4
Extended Data Figure 4. Presentation of heterozygous single nucleotide polymorphisms
(Top) Germline heterozygous nonsynonymous SNPs were determined for all seventeen patients. MHC-I and MHC-II associated peptides that span each SNP were identified from our LC-MS/MS dataset. The number of SNP loci which produced an MHC-bound peptide bearing the respective SNP is shown in red. The number of SNP loci which produced an MHC-bound peptide bearing the corresponding hg19 reference allele is shown in blue. Overlap (purple) indicates a SNP loci for which both SNP and hg19 reference allele 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). Comparison of recovery for germline SNPs and somatic variants was performed by Fisher’s exact test.
Extended Data Figure 5
Extended Data Figure 5. Peptide-MHC tetramer T-cell responses against predicted HLA-A2 neoantigens
HLA-A2 restricted neoantigens were predicted for 8 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, patienT-cells were mixed and co-stained with fluorescently-barcoded cells from 2 or 3 healthy donors. A representative reaction with multiplexing of 3 healthy donors using cytomegalovirus peptide-MHC tetramer is shown. (b) Frequency of neoantigen specific CD8 T-cells from peripheral blood T-cells is shown (closed 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 3 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
Extended Data Figure 6. MHC-I presentation of lymphoma immunoglobulin neoantigens in the Raji lymphoma cell line
(a) Top, MHC-I binding affinity predictions were performed for all potential 9-mer peptides from the Raji immunoglobulin 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 immunoglobulin heavy and light chain sequences. Positions differing from the germline variable region were altered by either somatic hypermutation or VDJ recombination are shown in red. Three peptides corresponding to Raji immunoglobulin 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
Extended Data Figure 7. Experimental determination affinity of HLA-DRB1*04:01 with associated immunoglobulin neoantigens
Six neoantigen peptides identified from 3 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 unto the DR4 molecules. HLA-DR4 molecules were then bound to streptavidin coated microsphere beads and co-stained anti-HLA-DR antibody and anti-DNP antibody. Beads were then washed and analyzed 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 due to somatic hypermutation events.
Extended Data Figure 8
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 labeled and stimulated for 24 hours with unlabeled autologous PBMCs loaded with either a pool of 3 immunoglobulin neoantigen peptides, a pool of 3 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, labeled, and re-stimulated as in panel b. Expression of CD25, Ki67, IL4, and granzyme B is shown for CD4-gated T-cells re-stimulated with neoantigen peptide-loaded PBMC.
Extended Data Figure 9
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 tumor vaccination. T-cells were stimulated with autologous PBMCs loaded with either a pool of pathogenic peptides including antigens derived from CMV, EBV, influenza A, and tetanus. After 30 hours, cells were sorted for CD137 expression. (Right) Sorted pathogen antigen-specific cells were expanded using anti-CD3, anti-CD28, IL2, and allogeneic feeders for 3 weeks. The expanded T-cells were co-cultured with fluorescently-labeled autologous lymphoma cells for 24 hours (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 labeled population.
Extended Data Figure 10
Extended Data Figure 10. Predicted MHC-I presentation of lymphoma immunoglobulin molecules
(a) Left, Peptides recovered from MHC-I purification were mapped to the immunoglobulin heavy and light chain. The color heatmap corresponds to the number of peptides recovered at each position by LC-MS/MS. Right, For each patient’s unique lymphoma immunoglobulin sequence and HLA profile, the predicted peptide-HLA affinity was calculated for all possible 8–11mers created from their immunoglobulin using netMHCpan. A heatmap illustrating the number of patients (from a total of 17) with at least 1 peptide predicted to bind self-HLA (IC50 ≤ 500nM) at each position across the immunoglobulin heavy chain is shown. (b) For each position along the immunoglobulin molecule, the number of peptides that were experimentally recovered per position (left y-axis) was determined. Similarly, for each position, the number of patients (of 17 total) with at least one peptide with predicted peptide-MHC affinity IC50 <500nM was determined (right y-axis). Positions from the variable region through the N-terminal 50 amino acids of CH1 (red) were compared to the rest of the constant region (green). p value was calculated using Mann-Whitney test. Error bars show range, black bar shows median.
Fig. 1
Fig. 1. Integrative genomic and proteomic approach for tumor antigen discovery
(a) Whole exome and targeted immunoglobulin sequencing of lymphoma tumor specimens and germline DNA was performed for 17 patients. Sequencing data were integrated with a human proteome database to create patient-specific catalogues incorporating somatically mutated proteins, lymphoma-specific immunoglobulins, and germline variants. MHC-ligands were directly immunoprecipitated using both anti-HLA-A,B,C and anti-HLA-DR antibodies. Peptides were then acid-eluted, profiled by LC-MS/MS and identified with reference to patient-specific catalogues. The number of unique peptides per case (b) and the length distribution of identified MHC ligands (c) are depicted.
Fig. 2
Fig. 2. Characterization of lymphoma-specific MHC-I and MHC-II epitopes and somatic mutations
(a) B-cell receptor pathway components presented by >50% of patients in the context of MHC-I (pink), MHC-II (blue), or both (red). (b) Nonsynonymous somatic mutations, predicted neoantigens, and the degree of somatic hypermutation in IGVH or IGJH are depicted, along with genes recurrently mutated in the cohort by exome sequencing. (c) Antigen presentation of mutated genes across the cohort. Nested ovals depict increasing evidence levels for candidate neoantigens, starting with nonsynonymous mutations (outermost oval) to direct evidence of neoantigen presentation (innermost oval). (d) TP53- or CCND1-derived MHC-I peptides and observed somatic mutations are depicted in relation to the corresponding protein domains.
Fig. 3
Fig. 3. MHC-I and MHC-II presentation of lymphoma immunoglobulins
(a) Heat-map reflects the frequency and distribution of MHC-I (left) and MHC-II (right) presentation of IgM-derived peptides across the cohort. Panels (b) and (c) depict expanded views of antigen presentation from variable regions of Ig heavy and kappa light chains, respectively. Neoantigen peptides created by either somatic hypermutation or VDJ rearrangement are aligned to expanded heat-maps, and grouped by patient (boxes). Red, somatically mutated positions within recovered peptides creating neoantigens. Asterisks, patients/peptides selected for subsequent functional immunological studies.
Fig. 4
Fig. 4. Detection and cytololytic activity of neoantigen-specific CD4 T-cells
(a) Peripheral blood CD4 T-cells from patient MCL041 (top) or a healthy donor (bottom) were stained with neoantigen-specific or CMV-specific HLA-DR tetramers. (b) Neoantigen-specific T-cells were sorted and subjected to single-cell TCRβ sequencing (top) and RNA expression profiling (bottom), with recurrent neoantigen-specific TCRβ clones highlighted. (c) Pre- and post- immunization peripheral blood frequencies of corresponding neoantigen-specific clones from TCRβ repertoire sequencing. (d) CD4 T-cells from a second patient were stimulated with either Ig neoantigen peptides (top) or corresponding unmutated germline counterparts (bottom). (e) Sorted neoantigen-specific cells were expanded and immunophenotyped for granzyme B expression. (f) Cytotoxic activity of neoantigen-specific T-cells against either autologous lymphoma cells (top) or EBV-transformed B-cells (bottom).

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