Regular Article

LYMPHOID NEOPLASIA

The antigenic landscape of multiple myeloma: mass spectrometry(re)defines targets for T-cell–based immunotherapySimon Walz,1,2 Juliane S. Stickel,1 Daniel Johannes Kowalewski,2 Heiko Schuster,2 Katja Weisel,1 Linus Backert,2,3

Stefan Kahn,2 Annika Nelde,2 Tatjana Stroh,2 Martin Handel,4 Oliver Kohlbacher,3 Lothar Kanz,1 Helmut Rainer Salih,1,5

Hans-Georg Rammensee,2,6 and Stefan Stevanovic2,6

1Department of Hematology and Oncology, 2Institute for Cell Biology, Department of Immunology, and 3Applied Bioinformatics, Center for Bioinformatics

and Department of Computer Science, University of Tubingen, Tubingen, Germany; 4Department of Orthopedics, Hospital Group South-West, Calw,

Germany; and 5Clinical Cooperation Unit Translational Immunology and 6Cancer Immunotherapy Unit, German Cancer Consortium (DKTK), German

Cancer Research Center (DKFZ) Partner Site Tubingen, Tubingen, Germany

Key Points

• Direct analysis of the HLA-presented peptidomeidentifies a distinct antigenicsignature in MM.

• T-cell responses for theseantigens are detectableexclusively in MM patientsand can be induced in vitroin response-naive patients.

Direct analysis of HLA-presented antigens bymass spectrometry provides a comprehen-

sive view on the antigenic landscape of different tissues/malignancies and enables the

identification of novel, pathophysiologically relevant T-cell epitopes. Here, we present a sys-

tematic and comparative study of the HLA class I and II presented, nonmutant antigenome

ofmultiplemyeloma (MM).QuantificationofHLAsurfaceexpression revealedelevatedHLA

molecule counts onmalignant plasma cells comparedwith normal B cells, excluding relevant

HLA downregulation in MM. Analyzing the presentation of established myeloma-associated

T-cell antigenson theHLAligandomelevel,we foundasubstantialproportionofantigens tobe

only infrequently presented on primary myelomas or to display suboptimal degrees of mye-

loma specificity. However, unsupervised analysis of our extensive HLA ligand data set delin-

eated a panel of 58 highly specificmyeloma-associated antigens (includingmultiplemyeloma

SET domain containing protein) which are characterized by frequent and exclusive presen-

tation on myeloma samples. Functional characterization of these target antigens revealed

peptide-specific, preexisting CD81 T-cell responses exclusively in myeloma patients, which is indicative of pathophysiological relevance.

Furthermore, in vitro priming experiments revealed that peptide-specific T-cell responses can be induced in response-naive myeloma

patients. Together, our results serve to guide antigen selection for T-cell–based immunotherapy of MM. (Blood. 2015;126(10):1203-1213)

Introduction

Antigen-specific immunotherapy holds the potential to induceclinically effective anticancer T-cell responses1,2 and might beharnessed to guide and increase the specificity of cancer immuno-therapy in future combination trials.3 To this end, the exactknowledge of tumor-associated/specific T-cell epitopes is crucial.After decades of research into overexpressed tumor antigens, morerecently the focus has shifted to the patient-individualized identificationof mutation-derived neoantigens.4,5 The encouraging findings of thesenew studies6-8 have led to neoepitopes being viewed as the dominanttargets of anticancer immune responses.9-11

However, analyzing the antigenome of hematologic malignancies,we have recently demonstrated that nonmutated antigens are relevanttargets of spontaneous antileukemia T-cell responses.12,13 The strategyimplemented in these studies differentiallymaps the naturally presentedHLA ligandomes of hematologic cells in health and disease by massspectrometry and was found to efficiently identify relevant tumor-associated antigens (TAAs).

Here, we translated this approach to multiple myeloma (MM), alow-grade B-cell lymphoma, characterized by the proliferation of

malignant plasma cells in the bone marrow.14 Despite recent advancesin treatment, including high-dose chemotherapy followed by autolo-gous stem cell transplantation, novel immunomodulatory drugs, andproteasome inhibitors,MMremains largely incurable.15,16 This ismostlydue to the persistence of minimal residual disease (MRD), which leadsto high relapse rates.17,18 So far, the only established immunotherapeuticapproach for MM is allogenic stem cell transplantation, which is asso-ciated with a high morbidity and mortality and remains an option foronly a fraction of patients.19-21 Antigen-specific T-cell–basedimmunotherapy,22,23 especially in the constellation of MRD charac-terized by favorable effector-to-target ratios, might present an ef-fective, low side-effect option.24

An array ofmyeloma-associated T-cell antigens has been describedin previous studies.25-35 Most of these antigens were identified basedon gene expression analysis and reverse immunology. Some of theseantigens (WT1,36,37 RHAMM,38,39 hTERT,40 and Survivin40,41) havealready found their way into clinical trials, showing promising resultsin terms of induction of specific T-cell responses as well as clinicalresponses in single patients. However, broad clinical effectiveness has

Submitted April 17, 2015; accepted June 22, 2015. Prepublished online as

Blood First Edition paper, July 2, 2015; DOI 10.1182/blood-2015-04-640532.

S.W. and J.S.S. contributed equally to this work.

The online version of this article contains a data supplement.

There is an Inside Blood Commentary on this article in this issue.

The publication costs of this article were defrayed in part by page charge

payment. Therefore, and solely to indicate this fact, this article is hereby

marked “advertisement” in accordance with 18 USC section 1734.

© 2015 by The American Society of Hematology

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not yet been achieved. These previous studies were restricted to veryfew HLA allotypes and single antigens/epitopes,42 limiting both thepopulation of patients eligible for this therapeutic approach and thespectrum of inducible tumor-specific T-cell responses. Of note, recentstudies demonstrated lacking degrees of tumor association for severalof these tumor antigens, both on the transcriptome level43 and im-portantly also on the level of HLA-restricted presentation.12,13

By analyzing the antigenic landscape of MM directly on the HLAligand level, we here provide new insights on antigenic distribution/specificity and identify a panel of novel myeloma-associated epitopessuited for antigen-specific immunotherapy.

Materials and methods

Patients, blood, and bone marrow samples

Bone marrow mononuclear cells (BMNCs) and peripheral blood mononuclearcells (PBMCs) from MM patients at the time of diagnosis or at relapse beforetherapy, as well as PBMCs, BMNCs and granulocytes of healthy volunteers(HVs), were isolated by density gradient centrifugation (Biocoll; BiochromGmbH) and erythrocyte lysis (EL buffer; Qiagen). Informed consent was ob-tained in accordance with the Declaration of Helsinki protocol. The study wasperformed according to the guidelines of the local ethics committee (142/2013BO2). Patient characteristics are provided in Table 1.44 HLA typingwas carried out by the Department of Hematology and Oncology, Universityof Tubingen, Tubingen, Germany.

Myeloma cell lines

For HLA ligandome analysis, myeloma cell lines (MCLs; U266, RPMI 8226,JJN3,LP-1,MM.1S)were cultured in the recommended cellmedia (RPMI1640,Gibco; Iscove modified Dulbecco medium [IMDM], Lonza) supplemented with10%/20% fetal calf serum, 100 IU/L penicillin, 100 mg/L streptomycin, and2 mmol/L glutamine at 37°C and 5% CO2. The MCLs RPMI 8226, JJN3,MM.1S and LP-1 were obtained from the Department of Hematology andOncology, University of Tubingen, Tubingen, Germany.

Quantification of HLA surface expression

HLA surface expression on MM patient and HV bone marrow cells includingCD381CD1381 myeloma cells/plasma cells, CD191CD201 B cells, CD31

T cells, and CD341CD382 hematopoietic progenitor cells (HPCs) were ana-lyzed using the QIFIKIT bead–based quantitative flow cytometric assay (Dako)according to the manufacturer’s instructions as described previously.12 In brief,samples were stained with the pan-HLA class I–specific monoclonal antibody(mAb) W6/32, HLA-DR–specific mAb L243 (produced in-house) or immu-noglobulinG isotype control (BioLegend), respectively. Surfacemarker stainingwas carried out with directly labeled CD138, anti-k, anti-l, CD19, CD20(BioLegend) and CD38, CD3, and CD34 (BD) antibodies. 7-aminoactinomycinD (BioLegend) was added as viability marker immediately before flow cytom-etric analysis on a LSR Fortessa (BD Biosciences).

Isolation of HLA ligands from primary samples and MCLs

HLA class I and II molecules were isolated using standard immunoaffinitypurification as described45 using the pan-HLA class I–specific mAbW6/32, thepan-HLA class II–specific mAb Tu39, and the HLA-DR–specific mAb L243(produced in-house).

Table 1. Patient characteristics

UPN Sex Age (y) ISS StageDurie andSalmon

Light chainrestriction Cytogenetic risk

Previoustherapy HLA typing Experiment

1 M 62 3 3B l High Yes A*26, A*30, B*15, B*42 Q; L

2 F 55 2 1A Non Standard No A*01, A*24, B*18, B*08 L

3 F 69 2 3B k Standard No A*02, A*01, B*08, B*37 L

4 M 72 3 3A k Standard No A*02, A*33, B*15, B*18 L

5 M 54 2 3A k Low No A*03, A*26, B*40, B*55 L

6 M 60 3 3B l High No A*02, A*24, B*07, B*27 L

7 F 39 3 3A k Standard No A*02, A*03, B*07, B*35 L

8 F 73 3 3B l Unknown No A*24, A*25, B*39, B*40 L

9 M 52 3 1B k Unknown No A*02, B*07, B*44 L

10 M 47 1 3A k Standard No A*03, A*33, B*07 L

11 F 72 2 3A k High Yes A*02, B*27, B*44 Q

12 F 68 2 3A k Standard No n.d. Q

13 M 74 1 3A k Standard No n.d. Q

14 F 50 1 3A l Standard No A*01, A*68, B*08, B*35 Q

15 M 62 1 3A k Standard Yes n.d. Q

16 M 74 3 2B k Standard Yes n.d. Q

17 M 68 1 3A k Unknown Yes A*03, A*33, B*14, B*18 Q

18 M 73 1 1A k Standard Yes A*02, A*32, B*40, B*44 Q

19 F 70 1 1A k Standard No n.d. Q

20 F 53 3 3A k Standard No n.d. Q

21 M 59 2 3A l Standard Yes n.d. Q

22 M 57 1 1A k Standard Yes n.d. Q

23 F 65 3 1A l Standard Yes n.d. Q

24 M 76 2 3A l Standard Yes n.d. Q

25 F 74 2 2A k Standard Yes A*01, A*02, B*08, B*44 Q

26 F 67 1 3A l Standard Yes n.d. Q

27 M 50 1 1A l Unkwnon Yes A*01, A*03, B*35, B*40 Q

28 M 70 3 3A l Unknown No n.d. Q

29 M 75 1 3A k Standard Yes n.d. Q

Cytogenetic risk according to the IMWG risk stratification.44

F, female; IMWG, International Myeloma Working Group; ISS, International Staging System; L, HLA ligand isolation; M, male; n.d., not determined; Q, HLA quantification;

UPN, uniform patient number; y, years.

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Analysis of HLA ligands by liquid chromatography–tandem

mass spectrometry

HLA ligand extracts were analyzed in 5 technical replicates as describedpreviously.13 In brief, peptide samples were separated by nanoflow high-performance liquid chromatography (RSLCnano; Thermo Fisher Scien-tific) using a 50mm3 25 cmPepMap rapid separation liquid chromatographycolumn (Thermo Fisher Scientific) and a gradient ranging from 2.4% to32.0% acetonitrile over the course of 90 minutes. Eluting peptides wereanalyzed in an online-coupled LTQ Orbitrap XL mass spectrometer(Thermo Fisher Scientific) using a top 5 collision-induced dissociationfragmentation method.

Database search and spectral annotation

Data processing was performed as described previously.13 In brief, the Mascotsearch engine (Mascot 2.2.04; Matrix Science) was implemented to search thehuman proteome as comprised in the Swiss-Prot database (20 279 reviewedprotein sequences, September 2013) without enzymatic restriction. Potentialmutated HLA ligands were searched implementing a database containing thehuman proteome concatenated with proteins containing single amino acidvariants listed in the COSMIC database (http://cancer.sanger.ac.uk/cosmic/).Only recurrent single amino acid variants described in 2 or more samplesof hematologic origin were included. Oxidized methionine was allowed as adynamic modification. The false discovery rate (FDR) was estimated using thePercolator algorithm46 and set to 5%. Peptide lengths were limited to 8 to 12 aaforHLAclass I and12 to 25 aa forHLAclass II. Protein inferencewas disabled,allowing for multiple protein annotations of peptides. HLA annotation wasperformed using SYFPEITHI47 or an extended in-house database. Experimen-tal validation of peptide identifications andHLA annotations was performed bymass spectrometric and functional characterization of synthetic peptides fora subset of peptides.

Peptide and HLA peptide monomer synthesis

The automated peptide synthesizer EPS221 (Abimed) was used to synthesizepeptides using the 9-fluorenylmethyl-oxycarbonyl/tert-butyl (Fmoc/tBu)strategy.48 Synthetic peptideswere used for validation of liquid chromatography–tandem mass spectrometry (MS) identifications as well as for functional exper-iments. Biotinylated recombinant HLA molecules and fluorescent HLA-peptide tetramers were produced as described previously.49

Amplification of peptide-specific T cells

PBMCs fromMMpatients and HVs were cultured as described previously.12,13

In brief, for CD81 T-cell stimulation, PBMCs were pulsed with 1 mg/mL perpeptide and cultured for 12 days adding interleukins 4 and 7 (IL-4 and IL-7) ondays 0 and 1 aswell as IL-2 on days 3, 5, 7, and 9. HLA-A*02 (KLFEKVKEV)-andB*07 (KPSEKIQVL)-restricted control peptides derived frombenign tissues(HV-exclusive HLA ligands) served as negative control. Peptide-stimulatedPBMCs were analyzed by enzyme-linked immunospot (ELISPOT) assays onday 12. For CD41 T-cell stimulation, culture was performed as described forCD81T cells except for 2modifications: pulsingwas carried outwith 10mg/mLHLA class II peptide and no IL-4 or IL-7 was added.

IFNg ELISPOT assay

Interferong (IFNg) ELISPOT assayswere carried out as described previously.50

In brief, 96-well nitrocellulose plates (Millipore) were coated with 1 mg/mLIFNg mAb (Mabtech) and incubated overnight at 4°C. Plates were blockedwith 10% human serum for 2 hours at 37°C. Prestimulated PBMCs (2.53 105

cells perwell)were pulsedwith 1mg/mL (HLAclass I) or 2.5mg/mL (HLAclass II)peptide and incubated for 24 to 26 hours. Readout was performed according tothe manufacturer’s instructions. Phytohemagglutinin was used as positive con-trol. HLA-A*02 (KLFEKVKEV)- and B*07 (KPSEKIQVL)-restricted controlpeptides derived from benign tissues (HV-exclusive HLA ligands) served asnegative control. Spots were counted using an ImmunoSpot S5 analyzer (CTL).T-cell responses were considered to be positive when.10 spots per well werecounted and the mean spot count per well was at least threefold higher than the

mean number of spots in the negative control wells (according to the cancerimmunoguiding program guidelines51).

aAPC priming of peptide-specific T cells

For the generation of artificial antigen-presenting cells (aAPCs), 4 3 106

streptavidin-coated polystyrene particles (BangsLaboratories) permilliliterwereresuspended in PBE (phosphate-buffered saline/bovine serum albumin/EDTA;Gibco/Sigma Aldrich/Lonza) containing 200 pM biotinylated MHC-peptidemonomer and 20 nM antihuman biotinylated CD28 antibody and incubated atroom temperature for 30 minutes. After washing, the aAPCs were stored at 4°Cprior to use.52CD81Tcells fromMMpatients andHVwere enrichedbypositiveselection using magnetic cell sorting (Miltenyi Biotec). Stimulations were in-itiated in 96-well plates with 13 106 T cells plus 23 105 aAPCs in 200 mL ofT-cell medium complemented with 5 ng/mL human IL-12 (PromoKine). IL-2(65 U/mL; R&D Systems) was added on day 5. aAPC stimulation was repeatedon day 10, for a total of 3 cycles.

Tetramer staining

Thefrequencyofpeptide-specificCD81TcellswasdeterminedonaFACSCantoII cytometer (BD Bioscience) by staining with anti-CD8 (Biolegend) and HLA:peptide-tetramer-phycoerythrin as described previously.52 Staining withtetramers containing the cytomegalovirus (CMV) pp65 A*02 peptideNLVPMVATV served as positive control, tetramers containing irrelevant; non-primed A*02-restricted control peptides served as negative controls. Successfulpriming was considered if frequency of peptide-specific CD81 T cells was.0.1%of viable cells and at least threefold higher than the frequency of peptide-specific CD81 T cells in the negative control.

Software and statistical analysis

Flow cytometric data analysis was performed using FlowJo 7.2 (TreeStar).In-house R and Python scripts were used for the generation of virtualligandomes and definition of virtual TAAs in the analysis of TAA FDRsand for the TAA-plateau regression analysis. The standard R heatmap.2script was used for the unsupervised cluster analysis of HLA ligand sourceproteins. GraphPad Prism 6.0 (GraphPad Software) was used for statisticalanalysis. Statistical analysis of HLA surface expression was based onunpaired t tests.

Results

HLA class I surface expression is elevated on myeloma cells

As loss or downregulation of HLA expression on target cells mightseverely hamper the effectiveness of T-cell–based immunotherapy, wequantified HLA class I and II surface molecule counts on primarymyeloma cells compared with autologous hematopoietic cells andplasma cells derived from the bone marrow of HVs. In MM patients(n5 20), HLA class I expression was found to be heterogeneous withmean expression levels onCD381CD1381myeloma cells of 416 000654 500, which was found to be significantly higher as compared withautologous normal CD191CD201 B cells (198 5000 6 20 500,P5 .001), CD31 T cells (167 5006 15 500, P5 .0002), and CD341

CD382 HPCs (204 0006 32 500, P5 .002, Figure 1A). In addition,HLA class I expression on primary MM cells was also found to besignificantly higher than that on CD381CD1381 plasma cells of HVs(n 5 15, 291 500 6 25 500, P , .05; Figure 1C). No significantdifferences in HLA class I expression were observed when comparingnormal B cells, T cells, and HPCs ofMMpatients to the correspondingcell populations ofHVs (supplemental Figure 1, see supplemental Dataavailable on the Blood Web site). HLA-DR expression levels onmyelomacellswere generally found to bemuch lower thanHLAclass Ilevels. Mean HLA-DR surface molecule counts on myeloma cells

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(27 000 6 7000) showed no significant difference compared withautologousHPCs (35 0006 5000) and T cells (18 0006 13 000) orplasma cells of HVs (39 500 6 5000) (Figure 1B,D). HLA-DRexpression ofMMpatient CD191CD201B cells (104 0006 7000)was significantly higher comparedwithmyeloma cells (P, .0001).No correlation of HLA surface expression on myeloma cells withpatient characteristics such as sex, age, disease stage, risk classifica-tion, or prior therapy was observed (supplemental Table 1).

MS identifies naturally presented HLA ligands of primary MM

samples and MCLs

Mapping the HLA class I ligandomes of 10 myeloma patients and5 MCLs (Table 1), we identified a total of 17 583 different peptidesrepresenting 7574 source proteins, attaining .80% of the maximumattainable coverage (Figure 2A). The mean number of unique peptideidentifications (IDs) was 1059 IDs for primary myeloma sam-ples and 2243 IDs for MCLs (supplemental Table 2). Overall,peptides restricted by 20 different HLA-A and -B allotypes wereidentified in this study, covering 99.3% of the German popu-lation (calculated according to Bui et al53). No mutated peptide iden-tifications were obtained when processing the data set against areference proteome containing recurrent mutations in hematologicmalignancies.

As controls, we analyzed the HLA class I ligandomes of 45 HV-derived samples (30 PBMC, 10 BMNC, and 5 granulocyte specimens)identifying a total of 20 171 different peptides representing 7729 sourceproteins (supplemental Table 3). The HLA allotype distribution in theHV cohort covered.80% of HLA-A and -B alleles in theMM samplecohort.54 Analysis of HLA class II ligandomes was performed for7MMpatients and 5MCLs. A total of 6076 unique peptides represent-ing 1743 source proteins were identified. The HLA class II HV cohort

(13 PBMC, 5BMNC, 5 granulocyte specimens) yielded 2899 differentpeptides representing 889 source proteins (supplemental Table 2).

HLA class I ligandome profiling identifies a novel panel of

myeloma-associated antigens

To identify myeloma-associated antigens, we comparatively analyzedthe HLA ligandomes of the MM sample and HV cohorts at the sourceprotein level. Overlap analysis of HLA ligand source proteins identi-fied 2412 proteins (corresponding to 31.8% of the mapped MM HLAsource proteome) to be exclusively represented in theHLA ligandomesof MM samples. Of these MM-exclusive source proteins, 68.3% weresolely identified on MCL samples, whereas 13.2% of proteins werefound to be presented both on MCLs and primary MM samples. Afraction of 18.5% of myeloma-exclusive source proteins was found tobe restricted to primary MM samples (Figure 2B). To identify broadlypresented TAAs, myeloma-exclusive source proteins were rankedaccording to their frequencies of representation in the MM samplecohort (Figure 2C). To statistically assess and optimize the stringencyof antigen identification, we simulated randomized virtual ligandomesin silico and calculated the resultant number of false-positive TAAsat different frequencies of representation (Figure 2D). We set thefrequency threshold for HLA class I TAA definition to .25% ofmyeloma-exclusive antigen presentation, yielding 58 TAAs with anestimatedFDRof 4.1% (supplementalTable 4). Thisnovel panel of fre-quently presented myeloma-associated antigens was represented by197 unique HLA class I ligands and constitutes 0.8% of the mappedmyelomaHLA ligand source proteome. Kyoto Encyclopedia of Genesand Genomes (KEGG) pathway analysis55 and functional annotationclustering of these antigens with respect to their biological func-tion (GO Term BP FAT56) did not identify any statistically signifi-cant overrepresented pathways or functional clusters. Notably, the

Figure 1. HLA class I and II surface expression on

myeloma patient and HV bone marrow cells. Quan-

tification of HLA surface expression was performed

using a bead-based flow cytometric assay. (A) HLA

class I and (B) HLA-DR expression on CD381CD1381

primary myeloma cells compared with autologous

CD341CD382 HPCs, CD191CD201 B cells, and CD31

T cells. (C) HLA class I and (D) HLA-DR expression

on primary MM cells compared with bone marrow–derived

plasma cells of HVs. n.s., not significant; *P , .05;

**P , .01; ***P , .001.

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proto-oncogene multiple myeloma SET domain containing pro-tein (MMSET) was identified as a TAA showing representation in33% of MM patient ligandomes and was found to be representedby 3 different HLA ligands (ASNPSNPRPSK (HLA-A*30:01),

KAMEAASSL (A*02:01), SLLEQGLVEA (A*02:01)). Moreover,MMSET was detected on both MM patients with the oncogenictranslocation t(4;14), but only on 1 of 6 patients (17%) without thisaberration.

Figure 2. Comparative HLA ligandome profiling and identification of myeloma-associated antigens. (A) Saturation analysis of HLA class I ligand source protein

identifications in MM patients. Number of unique HLA ligand source protein identifications as a function of cumulative HLA ligand source protein identifications in 10 MM patients.

Exponential regression allowed for the robust calculation (R2 5 0.99) of the maximum attainable number of different source protein identifications (dashed line). The dotted line

depicts the source proteome coverage achieved in our MM patient cohort. (B) Overlap analysis of HLA class I ligand source proteins of primary MM samples (n5 10), MCLs (n5 5),

and HV samples (total n 5 45: PBMC [n 5 30], BMNC [n 5 10], granulocytes [n 5 5]). (C) Comparative profiling of HLA ligand source proteins based on the frequency of HLA-

restricted presentation in MM and HV ligandomes. Frequencies of MMs/HVs positive for HLA-restricted presentation of the respective source protein (x-axis) are indicated on the

y-axis. The box on the left highlights the subset of myeloma-associated antigens showing MM-exclusive presentation in .25% of myeloma samples. (D) Statistical assessment of

false-positive myeloma-antigen IDs at different threshold values. The numbers of original TAAs identified based on the analysis of the MM and HV cohorts were compared with

random virtual TAAs. Virtual MM and HV samples were generated in silico based on random weighted sampling from the entirety of protein identifications in both original cohorts.

These randomized virtual ligandomes of defined size (n5 957 proteins, which is the mean number of protein identifications in all analyzed samples) were used to define TAAs based

on simulated cohorts of 15 MM vs 45 HV samples. The process of protein randomization, cohort assembly, and TAA identification was repeated 1000 times and the mean value of

resultant virtual TAAs was calculated and plotted for the different threshold values. The corresponding FDRs for any chosen TAA threshold are listed below the x-axis. sum, summary.

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Representation of established myeloma-associated antigens in

the HLA class I ligandome

Based on our extensive HLA ligandome data set, we investigated thepresentation of established myeloma-associated antigens within thedifferent sample cohorts. We identified 73 different HLA ligands re-presenting 22 of 25 (88%) previously described myeloma antigens.42

We found 9 of the 22 detectable antigens (41%) to be exclusivelypresented onMMsamples, 10 of 22 antigens (45.5%) to be representedboth on MM and HV samples, and 3 of 22 (13.6%) exclusively pre-sented on HV-derived samples (Figure 3A). Of note, 7 of 9 MM-exclusive antigens (77.8%)were only detectable onMCLs. Only 2 of 9(22.2%) of these MM-exclusive antigens HLA ligands were detectedonprimaryMMpatient samples (Figure3B). For reference, only7of58(12.1%) of the newly defined myeloma antigens showed presentationexclusively on MCLs, whereas the majority (51 of 58 [87.9%]) ofantigens were also presented on primary MM patient samples as well,which underlines their potential as clinical target antigens (Figure 3C).

Moreover, unsupervised clustering of source protein presentation inthe HLA ligandomes revealed the cluster ofMCLs to be highly distinctfrom primary MM samples (supplemental Figure 2).

Analysis of HLA class II ligandomes identifies potentially

synergistic vaccine candidates

As the direct involvement of CD41 T cells in tumor control is es-tablished,57we further aimed to identifyHLAclass II antigens.Overlapanalysis of HLA class II ligand source proteins identified 1135 my-eloma exclusive antigens (Figure 4A). Comparative profiling of HLAclass II ligandomes identified a single antigen (TFRC) representedby 67 HLA class II ligands showing MM-exclusive presentation atFDR ,5% (Figure 4B-C). Functional characterization of the mostabundant TFRC peptide (NSVIIVDKNGRLV) by IFNg ELISPOTrevealed memory T-cell responses in 2 of 5 MM patients (sup-plemental Figure 3A).

AsCD41Tcells playpivotal roles in the induction andmaintenanceof antigen-specific CD81 T-cell responses,58-60 we implementeda second approach to identify potentially synergistic HLA class II–restricted peptides derived from HLA class I TAAs. Overlap

analysis of the 58 HLA class I antigens with the 1135 HLA class IIpresentedMM-exclusive proteins identified a panel of 6 class-spanningantigens represented by 31 peptides (Figure 4D-E; supplementalTable 5). Functional characterization of synergistic HLA class II li-gands revealed peptide-specific T-cell responses in myeloma patientsfor 3 of 5 tested peptides (Figure 4E, supplemental Figure 3B).

The overall comparison of the HLA class I and II ligandomes ofMM samples revealed 80% (1622) of HLA class II–presented proteinsto be also presented on HLA class I (Figure 4F). Functional annotationclustering (GOTermCCclustering usingDAVID56)was performed onthe top 500 most frequently presented proteins in each HLA class toidentify the cellular compartments from which these proteins derive.Antigens presented on class I displayed highly enriched clusters fornuclear proteins as well as for ribosomal, cytoskeletal, and vesicle-derived proteins. Notably, this pattern was recapitulated in the clus-tering of proteins presented on both HLA classes, albeit with a higherranking and an almost threefold higher enrichment for vesicle-derivedproteins.HLA class II–presented antigens showed intermediate enrich-ment for plasma membrane, vesicle-derived, and lysosomal proteins(supplemental Table 6).

HLA class I TAAs are targeted by spontaneous T-cell responses

in myeloma patients

Functional characterization of the novel myeloma antigens wasperformed in panels of 11HLA-A*02– and 2HLA-B*07–restrictedpeptides, including 2 HLA-A*02 ligands derived from MMSET(Figure 5A). Myeloma-associated peptides were evaluated in 12-dayrecall IFNgELISPOT assays using PBMCobtained fromMMpatientsand HVs. We observed IFNg secretion for 5 of 11 A*02 ligands and1 of 2 B*07 ligands in myeloma patients, as shown exemplarily inFigure 5C. Both peptides (P1 and P2) derived from MMSET showedspecific T-cell recognition in 2 of 16 (13%) and 1 of 8 (13%) MMpatients, respectively. Importantly, no myeloma peptide-specificIFNg secretion was observed in 10 HLA-matched healthy controls(Figure 5B). Notably, T-cell responses were only observed formyeloma-associated peptides identified on primary myeloma samples(10 of 13), and never for the 3 of 13 peptides identified onMCLs only.

Figure 3. Representation of established myeloma-

associated antigens in the HLA ligandomes of MM

and HV. (A) Representation of previously described

MM-associated antigens in HLA class I ligandomes.

Bars indicate relative representation (%) of respective

antigens by HLA class I ligands on primary MM sam-

ples, MCLs, and HV samples. Dashed lines divide the

antigens into 4 groups according to their degree of MM

association (MM and MCL-exclusive, MCL-exclusive,

mixed presentation, HV-exclusive). (B-C) Distribution

of myeloma-exclusive antigen presentation for (B) pre-

viously described antigens and (C) ligandome-defined

TAAs on MCLs (white) and MM plus MCLs (shaded).

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The frequencies of peptide-specific T-cell responses detected in MMpatients by ELISPOT were generally in the same range as thefrequencies of presentation of the respective peptide in allotype-

matched ligandomes ofMMpatients (Figure 5A). Due to limitations inthe numbers of cells available for analysis, further controls with targetcells expressing the corresponding antigens could not be performed.

Figure 4. Identification of synergistic HLA class II–restricted myeloma-associated antigens. (A) Overlap analysis of HLA class II ligand source proteins of primary MM

samples (n5 7), MCLs (n5 5), and HV samples (total, n5 23: PBMC [n5 13], BMNC [n5 5], granulocytes [n5 5]). (B) Statistical analysis of false-positive myeloma-antigen

identifications at different threshold values, as described in Figure 2. Randomized virtual ligandome sizes were set to 226 proteins and TAAs were defined based on simulated

cohorts of 12 MM vs 23 HV samples. (C) Comparative profiling of HLA class II ligand source proteins based on the frequency of HLA-restricted presentation in MM and HV

ligandomes. Frequencies of MMs/HVs positive for HLA-restricted presentation of the respective source protein (x-axis) are indicated on the y-axis. (D) Overlap analysis of

HLA class I TAAs (n 5 58) and HLA class II MM-exclusive antigens (n 5 1135). (E) HLA class I TAAs, which also yield potentially synergistic HLA class II ligands. (F) Overlap

analysis comprising the entire HLA class I and II ligand source proteomes of MM samples. sum, summary; rep., representation.

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We therefore cannot exclude that T-cell reactivity is directed againstimpurities contained in the synthetic peptide batch. Indeed, it is wellknown that synthetic peptides contain impurities, for example, peptides

modified with a protecting group, and that these impurities are im-munogenic. However, HLA-A*02– and -B*07–restricted control pep-tides derived from benign tissues (HV-exclusive HLA ligands) used in

Figure 5. Functional characterization of myeloma-associated antigens. (A) Myeloma-associated T-cell epitopes with their corresponding HLA restrictions and frequencies of

immune recognition by myeloma patient-derived T cells in IFNg-ELISPOT assays. (B) Example of myeloma-associated T-cell epitopes evaluated in an IFNg-ELISPOT using HV

PBMCs. An EBV epitope mix containing the frequently recognized peptides BRLF109-117 YVLDHLIVV (A*02) and EBNA3247-255 RPPIFIRRL (B*07 served as positive control).

Benign-tissue–derived peptides KLFEKVKEV (HLA-A*02) and KPSEKIQVL (B*07) served as negative control. (C) Examples of myeloma-associated T-cell epitopes evaluated in

IFNg-ELISPOTs using MM patient PBMCs (n5 3). Results are shown only for immunoreactive peptides. An EBV epitope mix containing 5 frequently recognized peptides (BRLF109-

117 YVLDHLIVV [A*02], EBNA3471-479 RLRAEAQVK [A*03], EBNA3247-255 RPPIFIRRL [B*07], BZLF1190-197 RAKFKQLL [B*08], EBNA6162-171 AEGGVGWRHW [B*44])

was used as positive control. Benign-tissue–derived peptides KLFEKVKEV (HLA-A*02) and KPSEKIQVL (B*07) served as negative control. (D-E) Tetramer staining of CD81 T cells

after 3 cycles of aAPC-based in vitro primings using T cells derived from (D) a healthy individual and (E) a myeloma patient: Leftmost panels, P2-tetramer staining of CD81 T cells

primed with P2-aAPCs (SLLEQGLVEA, A*02); left middle panels, ex vivo P2-tetramer staining of CD81 T cells; right middle panels, control staining with A*02-tetramer containing

a nonrelevant A*02-restricted control peptide (KAMEAASSL, A*02) on CD81 T cells derived from the same population as T cells depicted in the left panels. Rightmost panels, Positive

control: tetramer staining of CD81 T cells primed with CMV-aAPCs (NLVPMVATV, A*02). EBV, Epstein-Barr virus; neg., negative; pos., positive; UPN, uniform patient number.

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all ELISPOTs in the study at hand never resulted in significant IFNgrelease (Figure 5C).

Antigen-specific T cells can be induced in vitro from naive

T cells of MM patients or HVs

To assess whether myeloma antigen-specific T-cell responses can beinduced from naive T cells in vitro, we isolated CD81 T cells from 1healthy individual and 1 MM patient. We performed aAPC primingsusing the MMSET-derived peptide SLLEQGLVEA (P2). UsingHV-derived CD81 T cells, a population of 0.403% P2-tetramerpositive CD81 T cells was detected after in vitro priming. Notetramer-positiveT-cell populations.0.1%were detectable ex vivo.After priming of T cells from anMMpatient without previous T-cellreactivity for P2 (as detected by 12-day-recall IFNg-ELISPOT andex vivo tetramer staining), we detected the induction of a small pop-ulation of 0.236% P2-tetramer positive CD81 T cells (Figure 5E).Importantly, control stainings performed with an A*02-tetramercontaining a nonrelevant A*02 control peptide were performed inparallel on cells derived from the same wells as used for the relevantstaining and did not yield any specific tetramer-positive T-cell popu-lations (Figure 5D).

Discussion

With the advent of immune checkpoint modulation, T-cell–based im-munotherapy has opened up new avenues for the treatment of a range ofsolid tumors61-68 and is undergoing clinical evaluation in hematologicmalignancies.69,70 These novel therapeutic options may further be im-proved in combination with antigen-specific immunotherapy, whichmay help induce and guide specific anticancer immune responses. Tothis end, the exact knowledge of tumor-associated epitopes which canact as rejection antigens is indispensable.4,8 In myeloma, a multitude ofstudies have investigated MM-associated antigens, yielding a handfulof promising targets.25-35However, these studieswere limited in scope ofantigens andHLA allotypes.42 In our previous studies in acute myeloidleukemia and chronic lymphocytic leukemia, we demonstrated that thecomprehensive and comparative analysis of the HLA-presented anti-genome can identify extensive panels of broadly presented T-cell epi-topes covering a multitude of HLA allotypes.12,13 Here, we translatedthis systematic approach to MM.

Quantification of HLA surface expression on different cell pop-ulations in the bone marrow of myeloma patients and healthy vol-unteers demonstrated that HLA loss or downregulation on malignantplasma cells is of no concern, even in patients who receivedprior therapy. Comparative analysis of the HLA ligandomes ofthese cell populations revealed distinct antigenic signatures andidentified a panel of myeloma-associated antigens. These antigensdid not cluster into any of the major tumor-associated pathwaysand did not show enrichment for highly prioritized targets char-acterized by tumor-associated overexpression. This indicates amore complex interplay of underlying mechanisms such as proteinturnover71 and antigen processing as the source of altered antigenpresentation on tumor cells and further underscores the isolated char-acter of the HLA ligandome and the distorted correlation with its up-stream sources.72,73

Importantly, a substantial proportion of established myeloma anti-genswas found tobe only infrequently presented on primarymyelomasor to show suboptimal degrees of myeloma specificity. Of note, themajority of these antigens was selectively detected on myeloma

cell lines but not in primary samples, indicating that selection ofpathophysiologically relevant antigens should be based on analysisof primary tumor samples.

A notable exception was the established myeloma-associatedprotein MMSET, which is currently being investigated as a target forthe therapy of poor-prognosis t(4;14) myeloma patients.74-77 AlthoughMMSET-derived peptides were frequently identified on t(4;14) my-eloma samples, we also detected MMSET peptides in the HLA li-gandomes of a t(4;14)-negative patient and 1 t(4;14)-negative MCL(U266). Strikingly, functional characterization by ELISPOT revealedmemory T-cell responses targeting these MMSET-derived epitopesexclusively in myeloma patients and not in HV. This suggestsmyeloma-dependent priming of anti-MMSET T-cell responses invivo in MM patients, which underscores the pathophysiologicalrelevance of this antigen. In concordancewith the HLA ligandomicsdata, we found these T-cell responses not to be restricted to t(4;14)myeloma patients. Results of in vitro primings suggest thatMMSET-specific CD81 T-cell responses can be induced from naive T cells,both in healthy individuals and, importantly, also in myeloma pa-tients, albeit with limited magnitudes. With the current strategiesfocusing on inhibition of MMSET by small molecules or smallinterfering RNAs,78,79 our identification of myeloma-exclusiveMMSET-derived T-cell epitopes provides new options for target-ing MMSET by T-cell–based immunotherapy. Notably, this ther-apeutic strategy may not necessarily have to be restricted to t(4;14)myelomas, as we observed MMSET-presentation and immune rec-ognition irrespective of themutational status. Thismight be explainedby the distorted correlation of gene expression and HLA-restrictedantigen presentation aswell as by the subclonal distribution of t(4;14)in myeloma cells and genomic plasticity occurring over the courseof disease.73,80

Together, our findings illustrate how antigen identification guidedby HLA ligandomics can pinpoint novel MM-associated T-cell epi-topes and allow for direct assessment of antigen distribution patternsin patient cohorts. In parallel to our findings with MMSET, our studyfeatures an extensive panel of novel antigens previously not associatedwith myeloma or cancer in general. Analogously to MMSET, we de-tected preexisting T-cell responses against a substantial proportion ofthese targets in myeloma patients, indicating a high enrichment for re-levant MM-associated antigens. In conclusion, our ligandome-centricstudy may guide the design of future antigen-specific T-cell immuno-therapy in MM.

Acknowledgments

The authors thank Claudia Falkenburger, Patricia Hrstic, NicoleZuschke, Katharina Graf, and Beate Pommerl for excellent technicalsupport.

This work was supported by the German Cancer Consortium(DKTK), the Deutsche Forschungsgemeinschaft (DFG; SFB 685),and the EuropeanUnion (EU; ERCAdG 339842MUTAEDITING).

Authorship

Contribution: S.W., J.S.S., D.J.K., S.S., and H.-G.R. designed thestudy; S.W., J.S.S., D.J.K., H.-G.R., L.K., K.W., H.R.S., and S.S.drafted the manuscript; S.W., J.S.S., and H.S. performed flow cyto-metric analysis and HLA surface expression quantitation; S.W.,D.J.K., J.S.S., and S.K. conducted MHC peptide immunoaffinity

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purification; S.W. and D.J.K. performed MS-based analyses andquantitation; S.W., A.N., T.S., J.S.S., and H.S. conducted in vitro T-cell experiments; J.S.S., L.K., K.W., M.H., and H.R.S. conductedpatient data collection and medical evaluation; S.W., J.S.S., D.J.K.,O.K., and L.B. performed statistical analyses; and S.S., H.-G.R., andJ.S.S. supervised the study.

Conflict-of-interest disclosure: The authors declare no competingfinancial interests.

Correspondence: Juliane S. Stickel, Department of Hematologyand Oncology, University of Tubingen, Otfried-Muller Strasse 10,72076 Tubingen, Germany; e-mail: juliane.stickel@med.uni-tuebingen.de.

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online July 2, 2015 originally publisheddoi:10.1182/blood-2015-04-640532

2015 126: 1203-1213  

Helmut Rainer Salih, Hans-Georg Rammensee and Stefan StevanovicBackert, Stefan Kahn, Annika Nelde, Tatjana Stroh, Martin Handel, Oliver Kohlbacher, Lothar Kanz, Simon Walz, Juliane S. Stickel, Daniel Johannes Kowalewski, Heiko Schuster, Katja Weisel, Linus 

based immunotherapy−(re)defines targets for T-cell The antigenic landscape of multiple myeloma: mass spectrometry

 

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