TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

Hoenisch Gravel, Naomi; Nelde, Annika; Bauer, Jens; Mühlenbruch, Lena; Schroeder, Sarah M.; Neidert, Marian C.; Scheid, Jonas; Lemke, Steffen; Dubbelaar, Marissa L.; Wacker, Marcel; Dengler, Anna; Klein, Reinhild; Mauz, Paul-Stefan; Löwenheim, Hubert; Hauri-Hohl, Mathias; Martin, Roland; Hennenlotter, Jörg; Stenzl, Arnulf; Heitmann, Jonas S.; Salih, Helmut R.; Rammensee, Hans-Georg; Walz, Juliane S.

doi:10.1038/s41467-023-42692-7

Download PDF

Article
Open access
Published: 17 November 2023

TOF_IMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

Nature Communications volume 14, Article number: 7472 (2023) Cite this article

4036 Accesses
3 Citations
10 Altmetric
Metrics details

Subjects

Abstract

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOF_IMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOF_IMS-based immunopeptidomics, a novel extensive benign_TOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

Differential ion mobility mass spectrometry in immunopeptidomics identifies neoantigens carrying colorectal cancer driver mutations

Article Open access 18 August 2022

Identification of tumor antigens with immunopeptidomics

Article 11 October 2021

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Article Open access 13 March 2024

Introduction

T cell recognition of human leukocyte antigen (HLA)-presented peptides plays a key role in the immune surveillance of malignant diseases^1,2. Various T cell-based immunotherapeutic approaches aim to utilize respective tumor antigens to therapeutically induce anti-tumor T cell responses^3,4,5,6. Thus, identifying suitable antigen targets that show natural, high frequent, and tumor-exclusive presentation on the tumor cell surface and are recognized by the immune system is central for the success of these therapeutic approaches⁷. With regard to tumor-exclusive presentation of HLA-presented peptides not derived from tumor-specific mutations, knowledge on respective peptides presentated on benign tissues is of key importance^8,9. However the availability of reference databases of such benign tissue derived HLA ligands remains limited. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method to identify and characterize such naturally presented HLA class I- and HLA class II-restricted peptides on the cell surface^10,11.

Despite immense technical improvements and optimized immunopeptidomics workflows in the last decades^12,13, sensitivity of shotgun MS discovery approaches remains limited and so far cannot capture the entirety of the immunopeptidome, which represents a highly dynamic, rich, and complex assembly of peptides. Moreover, the MS-based identification of HLA-presented peptides is further hindered by the low abundance, in particular described for mutation-derived neoepitopes, the distinct length, and the specific physicochemical properties of HLA-presented peptides as compared to standard proteomics using tryptic digests^11,12,14.

On-line coupled ion mobility separation (IMS) coupled MS-technology (e.g., trapped ion mobility separation (TIMS) and high field asymmetric waveform ion mobility spectrometry (FAIMS)), providing an additional and orthogonal separation dimension the so-called collisional cross section (CCS), was suggested as next-generation tool increasing sensitivity and high-speed analysis of large cohort samples^15,16,17. In addition to the standard parameters retention time (RT), mass to charge ratio, and fragment spectra, multiple ion mobility resolved MS scans ensure high-resolution MS analysis. This technology has been successfully implemented in other MS-based omics areas, comprising proteomics, metabolomics or lipidomics, showing increased identifications based on faster tandem (MS/MS) scan rates and acquisition without a loss of sensitivity^18,19,20. In contrast, IMS-coupled MS method implementation and large-scale application in immunopeptidomics is so far limited^{12,21,22,23,24,25} and using MS such as Orbitrap without IMS technology still reflects the current state-of-the-field^8,26,27,28.

Here we report on the implementation of TOF_IMS MS for immunopeptidomics and its application for next-generation tumor antigen discovery. TOF_IMS-based immunopeptidomics enabled (i) the large-scale expansion of benign reference databases providing novel insights in the immunopeptidome landscape and the refinement of non-mutated tumor-associated antigen (TAA) definition and (ii) the de novo discovery of not yet described tumor antigens, comprising frequently presented self antigens as well as low abundant mutation-derived neoepitopes, as potential targets for cancer immunotherapy.

Results

TOF_IMS MS enables large-scale identification of naturally HLA-presented peptides

To establish a method for immunopeptidomics using TOF_IMS MS, the monoallelic EBV-transformed human B cell line JY (HLA-A*02, HLA-B*07, HLA-C*07) was analyzed in two dilutions (high peptide concentration (JY 1) and low peptide concentration (JY 2)). First, the liquid chromatography (LC) workflow was optimized and validated, evaluating two different gradient types and four different gradient lengths (Fig. 1a, Supplementary Fig. 1a, Supplementary Data 1). For all gradient types and lengths identified HLA class I-presented peptides showed similar hydropathy profiles (Supplementary Fig. 1b) and length distribution, as expected for HLA class I-presented peptides (Supplementary Fig. 1c). Highest number of HLA class I-presented peptide identifications (1895 for JY 1 and 1252 for JY 2) were detected with the gradient type A with a length of 60 min, which was used for the further implementation of the subsequent MS method. For MS optimization, the impact of various parameters on peptide yields was evaluated (Fig. 1b, Supplementary Data 1), reaching HLA class I peptide yields of 5691 for JY 2 and resulting in a final method for TOF_IMS-based immunopeptidomics (Table 1).

**Fig. 1: Method development for immunopeptidomics using liquid chromatography-coupled TOF_IMS MS.**

Table. 1 TOF_IMS method optimized for immunopeptidomics

Full size table

Analyzing the CCS of identified peptide spectrum matches (PSMs) showed that TOF_IMS’s technology separated ions with similar RT orthogonally according to their CCS values, enabling identification of co-eluting peptides and thus a higher sensitivity (Fig. 1c, d). The identified peptides showed an expected mass distribution from 700 to 1700 Da with the majority around 1000 Da for HLA class I-presented peptides (Fig. 1e). HLA class II peptides showed an expected mass distribution ranging from 700 to 2600 Da with the majority of peptides slightly heavier than HLA class I-presented peptides with 1400–1800 Da (Fig. 1f). Identified HLA class I-presented peptides showed the typical length distribution with 9-mers making up 70% of peptides (Fig. 1g). HLA class II-presented peptides showed a typical length distribution with the majority at about 15 amino acids (Fig. 1h).

To further delineate the eligibility of TOF_IMS for HLA-presented peptide analysis, in particular to provide novel insights to and expansion of the immunopeptidome landscape of large-scale benign and malignant datasets, an alignment of primary benign and malignant samples (n = 10, Supplementary Data 2) analyzed using TOF_IMS and the current state-of-the-field (Orbitrap) technology, applied in current immunopeptidome references^26,27,28, was performed. Peptide length and mass distribution, quality score (−10lgP), frequency of HLA allotype allocation, and technical overlap were comparable for the two technologies (Supplementary Fig. 2a–h). Low-frequent peptide artefacts resulting from proteolytic fragmentation originating from endogenous peptidases²⁹ were identified with a median of 2.0% (range 0.1–6.5%) by TOF_IMS and 0.5% (range 0.0–7.2%) by Orbitrap (Supplementary Fig. 2i). Of note, 91% of peptides classified as proteolytic were not annotated as HLA ligands. Focussing on the normalized reported area per peptide, 41% of TOF_IMS and 20% of Orbitrap were exclusively identified in the lower rank (Supplementary Fig. 2j).

A median of 89% and 96% of all identified HLA class I ligands and HLA class II-presented peptides could be identified by TOF_IMS, respectively. Up to 57% of HLA class I ligands and 76% of HLA class II-presented peptides exclusive for TOF_IMS datasets, which were significantly more hydrophobic and identified during the whole LC separation run (Supplementary Fig. 2k–o).

TOF_IMS-based immunopeptidomics application for benign tissue-derived dataset

The relevance of benign immunopeptidome databases as reference has widely been recognized in the search for immunotherapy-relevant tumor-associated antigen (TAA) discovery. Based on the increase in HLA-presented peptide discovery using TOF_IMS MS, HLA class I and HLA class II immunopeptidome analyses of benign primary samples (n = 92 for HLA class I, n = 94 for HLA class II), comprising solid tissues of 28 different organ origins and peripheral blood mononuclear cell (PBMC) samples, were performed (benign_TOFIMS dataset, Fig. 2a, Supplementary Data 2). The HLA allotypes included within the benign dataset represent 99% of the world population with at least one allotype (Fig. 2b, c). A median of 3720 (range 41–15,139) HLA class I ligands and 5062 (range 125–14,330) HLA class II-presented peptides were identified across the samples (n = 92 for HLA class I, n = 94 for HLA class II, Fig. 2d, Supplementary Data 3). In total, 137,463 unique HLA class I ligands and 175,469 HLA class II-presented peptides were identified. As described before⁸, the samples showed tissue-dependent peptide yields (Fig. 2e). Up to 63% (median 54%, range 32–63%) and 64% (median 54%, range 35–64%) of HLA class I and HLA class II-presented peptides, respectively, were identified in all technical replicates of a sample (Fig. 2f). The identified peptides showed the common length distribution for HLA class I- and HLA class II-presented peptides (Fig. 2g). Donor-specific samples derived from different tissues showed a heterogenous peptide overlap between the tissue samples (Fig. 2h, i). For HLA class I, up to 50% (mean 46%, range 42–50%) of all HLA class I peptides were identified in only one sample from the same donor, 0.5% (mean 0.2%, range < 0.1–0.5%) were found in all tissues of one donor. For HLA class II-presented peptides up to 73% (mean 69%, range 66–73%) and 0.1% (mean 0.1%, range 0.1–0.1%) of HLA class II-presented peptides were found in one and all tissues of one donor, respectively.

**Fig. 2: Characterization of Benign_TOFIMS dataset.**

Benign_TOFIMS dataset expands benign references of HLA-presented peptides

Comparing the generated benign_TOFIMS immunopeptidome dataset with published benign immunopeptidome repositories^8,30,31, 46% of HLA class I ligands and 54% of HLA class II-presented peptides have not yet been described in either the Immune Epitope Database IEDB³¹ (n = n.a.) or other benign datasets acquired using state-of-the-field Orbitrap technology^8,30 (n = 297, Fig. 3a, b). Despite containing only one-third of samples (n = 92 for HLA class I, n = 94 for HLA class II), 1.5-fold the amount of peptides were identified in the TOF_IMS benign dataset, showing a mean immunopeptidome size of > 4700 compared to > 1500 peptides per sample for the other published benign Orbitrap datasets (n = 297, Fig. 3c). Benign_TOFIMS-exclusive peptides were significantly more hydrophobic, with a median GRAVY score of 0.13 for HLA class I ligands (range −3.50 to 4.03) and −0.26 for HLA class II-presented peptides (range −3.54 to 4.33) compared to previously described benign-associated peptides (median 0.133 and −0.45, range −3.77 to 4.40 and −4.04 to 3.87, respectively, Fig. 3d). This results in a median predicted immunogenicity of 0.05 (range −0.76 to 0.78) for the exclusive benign_TOFIMS HLA class I ligands compared to 0.02 (range −0.86 to 0.66) for previously described benign peptides (Fig. 3e). The benign_TOFIMS-exclusive peptides do not differ from the published benign-associated peptides^8,30,31 in terms of amino acid composition for both HLA class I ligands (Fig. 3f) and HLA class II-presented peptides (Fig. 3g). For biological relevance the source proteins were annotated to KEGG pathways and their subcellular location according to the human protein atlas. In both cases, benign_TOFIMS exclusive peptides did not differ from published benign associated peptides (Fig. 3h–k)

**Fig. 3: Benign_TOFIMS immunopeptidome dataset expands benign reference datasets of HLA-presented peptides.**

Benign_TOFIMS immunopeptidome dataset refines the identification of tumor antigens for peptide-based immunotherapy

Comparative immunopeptidome profiling is central for the selection of tumor antigens to be applied in immunotherapeutic approaches. Tumor-exclusive presentation without representation of the respective antigen on benign tissue enables tumor-directed immune targeting without the risk of on-target-off-tumor adverse events. The novel benign_TOFIMS immunopeptidome dataset rejected between 28% and 60% (median 40%) of previously published TAAs for various malignant diseases (ovarian carcinoma (OvCa)³², chronic lymphocytic leukemia (CLL)³³ and chronic myeloid leukemia (CML)³⁰) as they are not tumor-exclusive anymore (Fig. 4a, b). Whereas the OvCa rejected peptides were identified in samples of multiple benign tissue origin, > 45% of rejected CLL and CML peptides were identified in samples of hematological origin (Supplementary Fig. 3a) within the benign_TOFIMS.

**Fig. 4: Refined tumor-associated antigen identification using benign_TOFIMS MS.**

To detect previously undescribed TAAs using TOF_IMS MS-based immunopeptidomics, we performed comparative immunopeptidome analyses of primary malignant samples (n = 2, RCC, HNSCC; Supplementary Data 2) with the TOF_IMS and published benign immunopeptidome datasets^8,30. Of the 13,517 total identified HLA class I ligands from RCC 89% (12,054/ 13,517) were identified using TOF_IMS MS and 43% (5863/ 13,517) using Orbitrap MS. 30% (3999/13,517) of identified HLA class I ligands were found to be tumor-exclusive, of which 77% (3069/3999) were exclusively identified using TOF_IMS (Fig. 4c, d). Similar observations were made for the HNSCC sample (2281 tumor-exclusive HLA class I ligands, of which 66% were TOF_IMS-exclusive; Supplementary Fig. 3b). For HLA class II-presented peptides, 8166 unique peptides were identified on the RCC tumor sample, of which 96% (7873/8166) and 42% (3389/8166) were identified by TOF_IMS. 33% (2676/8166) of identified peptides were tumor-exclusive, of which 72% (1943/ 2676) were identified via TOF_IMS (Fig. 4c). Similar observations were made for the HNSCC sample (6133 tumor-exclusive peptides, of which 60% were TOF_IMS-exclusive; Supplementary Fig. 3c).

As an exemplary use of the benign_TOFIMS dataset, HLA class I and class II immunopeptidome profiling using TOF_IMS MS was performed from primary CLL samples (n = 22, Supplemtary Data 2). The HLA alloytpes include in the CLL cohort were comparable to the alloytpes included in published datasets as well as the benign_TOFIMS dataset (Supplementary Fig 4a–c). A median of 11,706 HLA class I ligands (range 5976 to 18,115) and 7833 HLA class II-presented peptides (range 2208 to 10,484) were identified per sample (Supplementary Fig 3d and Supplementary Data 3). In total 121,871 unique HLA class I ligands and 86,785 HLA class II-presented peptides were identified from the total sample cohort. Comparative immunopeptidome profiling was performed with published benign datasets^8,34 and resulted in 68% of HLA class I ligands (83,074 peptides, Fig. 4e) and 72% of identified HLA class II-presented peptides (62,224 peptides, Fig. 4f) to remain CLL-exclusive. A further alignment of these CLL-exclusive HLA class I and class II peptides using the novel benign_TOFIMS dataset revealed (Fig. 4 e, f). 46% of CLL HLA class I ligands (55,812 peptides) and 68% of CLL HLA class II peptides (49,147 peptides) CLL-exclusive, thus annotating an additional 27,262 HLA class I ligands and 13,077 HLA class II-presented peptides as benign compared to previously published data (Supplementary Fig 4e, f). 727 HLA class I and 1556 HLA class II CLL-exclusive peptides showed a broard presentation within the CLL cohort with a frequency above 20% (up to 59% for HLA class I and 77% for HLA class II). Allotype specific peptide alignment of the most abundant HLA allotypes within the cohort (HLA-A*02, HLA-B*35 and HLA-C*07, Supplemtary Fig. 4g–i) even revealed CLL-exclusive peptides with presentation in up to 100% of HLA-matching samples (HLA-A*02 up to 100%, HLA-B*35 up to 100% and HLA-C*07 up to 50%) representing highly promising, broadly applicable antigen targets for immunotherapeutic approaches. Of note, 98% of these highly promising, broadly applicable antigen targets for immunotherapeutic approaches have never been described in previous large cohort CLL immunopeptidome studies^33,35.

In addition to the identification of novel off-the-shelf tumor-exclusive antigens, screening for naturally presented neoepitopes derived from tumor-specific mutations was performed for TOF_IMS and Orbitrap HNSCC immunopeptidomes, using a sample-specific mutation database generated by next-generation whole exome sequencing of tumor and respective adjacent benign tissue. Of note, two naturally presented neoepitopes (LPADVTEDEF SFPQ_HUMAN_304-313 I308V and VYPLAFVLI MD13L_HUMAN_279-287 S282L) were identified in the TOF_IMS dataset and validated using isotopically labelled synthetic peptides (Fig. 4g, h), whereas none of these neoepitopes were identified in the conventionally-acquired data. Together, TOF_IMS MS provides a next generation immunopeptidomics method that facilitates the further prioritization of established TAAs and enables the identification of a vast array of previously undescribed non-mutated TAAs as well as the detection of naturally presented low abundant neoepitopes for cancer immunotherapy.

Discussion

MS-based immunopeptidomics provides direct evidence of cellular processing and HLA-restricted presentation of peptide antigens, which is an indispensable prerequisite for their therapeutical use, in particular regarding the distorted correlation between gene expression and HLA-restricted antigen presentation^30,36,37,38. In this study we implemented a next-generation MS-based immunopeptidome workflow using TOF_IMS MS to expand benign immunopeptidomics reference databases and improve TAA discovery.

In line with improvements reported for other omics technologies, as well as with FAIMS technology for immunopeptidomics, TOF_IMS methodology enabled high sensitivity and fast track peptide identification. IMS provides a new dimension of separation with the CCS value as an additional peptide property, which has been suggested to improve statistical confidence in peptide identification^39,40. TOF_IMS efficacy relies on releasing ions according to their ion mobility synchronized to the mass analysis via TOF, resulting in a high speed and sensitive detection of HLA-presented peptides. The here established TOF_IMS workflow and detailed method development comprising an optimized IMS and mass range window allow for increased peptide identifications compared to previous reports^21,41. The frequency of proteolytic peptide artefacts²⁹, originating during sample preparation, was slightly increased in the TOF_IMS immunopeptidome datasets, underscoring its ability to detect low abundant peptides. This makes TOF_IMS-based immunopeptidomics attractive for sample-limited applications, such as peptide identification from biopsies, micro-dissected tissues, and sorted cell populations^12,13.

Beyond the selection of immunogenic antigens from tumor tissue, tumor-exclusive presentation without representation of the respective antigen on benign tissue is of central importance to avoid on-target-off-tumor adverse events and enable tumor-directed immune targeting. In particular, for non-mutated TAA arising through differential gene expression and protein processing in malignant cells, and playing a central role as immunotherapeutic targets in low-mutational burden tumor entities, knowledge of the immunopeptidome of benign tissue is a key prerequisite to define safe T cell-based cancer immunotherapies and has led to the development of benign immunopeptidome repositories^{8,30,31,42,43,44}. However, despite the constant growth of these databases, the landscape of the whole benign immunopeptidome is still not covered and it is unclear when a saturation of peptide identifications will be reached.

Using TOF_IMS-based immunopeptidomics we build a novel benign tissue repository that substantially expanded these references, providing more than 150,000 previously undescribed HLA class I- and HLA class II-presented peptides from benign tissue origin. With this currently available reference databases were expanded by more than 50%, suggesting the benign_TOFIMS database as future state-of-the-art benign reference for future selection and validation of non-mutated TAAs. Of note, HLA-presented peptides exclusively identified within the benign_TOFIMS dataset showed significantly increased hydrophobicity, which was described as a hallmark of immunogenic T cell epitopes^45,46. In line, predicted immunogenicity was higher in the benign_TOFIMS exclusive peptides compared to published data^8,30,31, however both datasets present a general low median predicted immunogenicity which aligns with their benign origin. First application of these benign_TOFIMS immunopeptidome dataset enabled the refinement of TAAs identified in previous studies for multiple tumor entities^30,32,33. Moreover, TOF_IMS-based immunopeptidomics led to the identification of novel, high frequent non-mutated tumor-exclusive peptide antigens from primary CLL samples, highlighting the potential of this approach to expand the number of antigen targets to be applied in T cell-based immunotherapies.

In addition to non-mutated tumor antigens, neoepitopes arising from tumor-specific mutations have been identified in recent years as the main specificity of anti-cancer T cell responses induced by immune checkpoint inhibitors and were in turn suggested as prime candidates for T cell-based immunotherapy approaches^36,47,48. In line, response to immune checkpoint inhibitors correlates with high mutational burden and neoepitope-based immunotherapies have been applied in various tumor patients^4,5,49. However, the limited number and low abundance of somatic mutations that are ultimately translated, processed, and presented as HLA-restricted neoepitopes on the tumor cells^{3,36,50,51,52} has hampered the MS-based identification and thus selection of optimal neoepitopes for cancer immunotherapy. Our TOF_IMS immunopeptidomics workflow has led to the de novo identification of mutation-derived HLA-presented peptides, suggesting that the increased sensitivity of this approach might further improve the detection of naturally presented neoepitopes.

The limitations of this study comprise the lack of sample-specific sequencing, and the inability of the spectrum-annotation software to distinguish between isomers leucine and isoleucine and thus resulting possible inclusion of both sequences.

Together, our study provides a novel TOF_IMS-based immunopeptidome benign reference that enables the highly sensitive identification of HLA-presented peptides that will refine tumor antigen discovery.

Methods

Sample collection

Benign solid tissue samples were collected within 72 h post-mortem during routine autopsies at the University Hospital Zürich. Subjects included in this study were not diagnosed with any malignant disease. The tissue was annotated by board-certified pathologists, snap-frozen in liquid nitrogen and stored at −80 °C. Thymus samples were obtained from the University Children’s Hospital Zürich and were removed during heart surgery for other medical reasons than cancer. Tumor samples and blood donation by-products for peripheral blood mononuclear cell (PBMCs) isolation from healthy individuals were collected at the University Hospital Tübingen. Informed consent was obtained in accordance with the Declaration of Helsinki protocol. The study was approved by and performed according to the guidelines of the local ethics committee (Req-2016-00604, EC-Nr. 2014-0699, PB_2017-00631, 424/2007B02, 373/2011B02, 431/2012BO2, 454/2016B02, 356/2017BO2, 406/2019B02). Donor characteristics are provided in Supplementary Data 2.

Cell lines

EBV-transformed human B cell line JY (ECACC, England, UK 94022533; HLA-A*02, HLA-B*07, HLA-C*07) was cultivated in RPMI1640 with 10% heat-inactivated fetal bovine serum (FBS, Lonza, Basel, Switzerland) and 1% penicillin/streptomycin (Merck, Darmstadt, Germany). Prior to sample preparation, cells were washed three times in phosphate-buffered saline (PBS) before 15 min centrifugation at 190 x g for harvest, and frozen at −80 °C at 1.2 x 10⁷ (JY 1) and 8 x 10⁶ cells (JY 2).

Isolation of HLA ligands

HLA class I and HLA class II molecules were isolated by previously described immunoaffinity chromatography protocols⁵³ using the pan HLA class I-specific W6/32⁵⁴, pan HLA class II-specific Tü-39⁵⁵ and HLA-DR-specific L243⁵⁶ monoclonal antibodies. All antibodies were produced in-house at the Department of Immunology, University of Tübingen. For the ten samples included in the comparative immunopeptidome profiling between TOF_IMS and Orbitrap MS acquired data, 50% of each sample was analyzed per device in technical triplicates (two technical replicates for the HNSCC sample) with 5 µL per injection, respectively. For the immunopeptidome analysis of the TOF_IMS benign dataset (n = 94), 60% of the sample were injected in three technical replicates with 5 µL per injection.

TOF_IMS mass spectrometric data acquisition

For the method development various parameters were tested as indicated in Supplementary Data 1 using the Bruker Daltonic’s timsTOF Pro device, a MS approach combining the technologies trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) coupled to a time-of-flight (TOF) mass spectrometer^15,16,57. Samples included in the TOF_IMS-Orbitrap MS comparison as well as the TOF_IMS benign dataset were analyzed using the method described in Table 1. Peptide separation was performed on Bruker Daltonic’s nanoElute LC system using an acclaim TM PepMap (Thermo Fisher Scientific, Waltham, USA) and a 75 µm x 25 cm Aurora Series emitter column (IonOpticks, Fitzroy, Australia). Peptides were separated along a gradient ranging from 0% to 95% Solvent B (AcN with 0.01% FA) over the course of 60 min with consecutive ramps from 0% to 32% (30 min) and 32% to 40% (15 min), followed by two 5 min ramps to 60% and 95%, respectively. Eluting peptides were subsequently analyzed in the on-line coupled trapped ion mobility spectrometry and time-of-flight mass spectrometer timsTOF Pro (Bruker Daltonics, Billerica, USA) equipped with a CaptiveSpray ion source using a data-dependent acquisition mode (DDA).

Orbitrap mass spectrometric data acquisition

The orbitrap-based MS analysis was performed as described previously in ref. ⁵⁸. Peptides were separated by nanoflow high-performance liquid chromatography using a Thermo Fisher Scientific’s Ultimate 3000 RSLC Nano UHPLC system, loaded with 1% AcN 0.05% TFA on a 75 µm x 2 cm Acclaim PepMap 100 C18 Nanotrap column at a flow rate of 4 mL/min for 10 min following a separation step using a 50 µm x 25 cm PepMap RSLC C18 column with a particle size of 2 µm. Peptides were eluted at a gradient ranging from 2.4% to 32% AcN over 90 min. Eluted peptides were analyzed in the on-line coupled Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, Waltham, USA) equipped with a nano electrospray ion source in DDA acquisition mode employing a top-speed collisional-induced dissociation (CID, HLA class I-presented peptides, normalized collision energy 35%) or higher-energy collisional dissociation (HCD, HLA class II-presented peptides, normalized collision energy 30%) fragmentation method. Mass range for HLA class I-presented peptide analysis was set to 400–650 m/z with charge states 2+ and 3+ selected for fragmentation. For HLA class II-presented peptide analysis mass range was limited to 400–1000 m/z with charge states 2+ to 5+ selected for fragmentation.

Whole exome sequencing

Whole exome sequencing was performed from the same snap-frozen tumor tissue used for the immunopeptidomics analysis via Illumina NovaSeq 6000 by an external provider (CeGaT GmbH) with a target read length of 100 bp. Single-nucleotide polymorphism mutations were excluded by comparing with adjacent benign tissue from the same patient identifying 271 tumor-exclusive mutations.

Database search

Data processing was performed using PEAKS Studio 10.6 (Bioinformatic Solutions Inc.). All samples were searched against a database containing 20,385 reviewed human UniProt entries downloaded on 14.10.2020. For the HNSCC the corresponding mutations were added to the reference database. The enzyme specificity was set to none, precursor peptide mass error tolerances were set to 5 ppm (orbitrap MS data) or 20 ppm (TOF_IMS data) and 0.02 Da for fragment ions. Oxidized methionine was set as variable modification, with three possible modifications allowed per peptide. Peptide lengths were set to 8–16 amino acids for HLA class I and 8–30 amino acids for HLA class II. A 1% false discovery rate (FDR) was calculated using a decoy database search approach. HLA class I identified peptides were further annotated as ligands using SYFPEITHI 1.0⁵⁹ and netMHCpan4.1⁶⁰ using the sample’s respective HLA allotypes.

Synthesis of isotope-labeled peptides

Isotopically labelled peptides were synthesized using the standard 9-fluorenylmethyl-oxycarbonyl/tert-butyl strategy in a Liberty Blue Automated Peptide Synthesizer (CEM, Kamp-Lintfort, Germany). Peptides were cleaved from the resin using a TFA/triisopropylsilane/water (95%/2.5%/2.5% by vol.) mixture for 1 h, after which peptides were precipitated with diethyl ether and washed with diethyl ether thrice before resuspension in water and lyophilization. Identity and purity were determined via C18-HPLC and LTQ Orbitrap XL MS (both Thermo Fisher Scientific).

Spectrum validation

Spectrum validation of the experimentally eluted peptides was performed by computing the similarity of the spectra with corresponding isotopically labelled synthetic peptides measured in a complex matrix. The spectral correlation coefficient was calculated between the b and y ions of the MS/MS spectra of the eluted and synthetic peptide⁶¹.

Identifying proteolytic fragments

To identify proteolytic peptide artifacts, a statistical method²⁹ that calculates the proposed protein coverage ratio, peptide coverage ratio, and HLA ligand propensity scores for each peptide, was implemented. An expectation-maximization algorithm was used to deconvolute the Gaussian mixture into two distributions, defining a threshold for each of the scores with 0.05 FDR. A peptide was classified as proteolytic if it superseded two of the three thresholds, similar to the original publication²⁹. The three parameters were computed based on the ten comparative samples. The statistical method implementation can be found at [https://github.com/AG-Walz/proteolytic_degradation_timstof_orbitrap].

Comparison of benign_TOFIMS immunopeptidome data with published datasets

The timsTOF Pro benign dataset was compared with published benign orbitrap databases comprising immunopeptidomes derived from benign tissues (HLA Ligand Atlas)⁸ and hematological cells³⁰ as well as the IEDB database³¹, respectively. The HLA Ligand Atlas peptides were filtered to medium and strong binders, for the IEDB healthy, linear peptides of human source obtained through MHC ligand assay were included. Published TAAs for OvCa³², CLL³³ and CML³⁰ were retrieved from previous publications.

Statistical analysis

Overlap and UpSet plot analysis were performed using BioVenn⁶² and UpSetR Shiny⁶³. Grand average of hydropathy (GRAVY score) was calculated using the GRAVY calculator (https://www.gravy-calculator.de). For the ranked reported area analysis, peptides with a reported area were normalized according to sample and device specificity. Frequency-based overlap analysis between CLL identified peptides and the benign_TOFIMS dataset show all identified peptides with a frequency > 5% and < 5% if founs on both the CLL and benign_TOFIMS dataset. The population coverage and immunogenicity (only 9-mers) were predicted using IEDB’s population and immunogenicity prediction tools^46,64 [http://tools.iedb.org/population/ and http://tools.iedb.org/immunogenicity/]. The subcellular location analysis was annotated according to the human protein atlas⁶⁵ [https://proteinatlas.org/about/downloads] version 22.0, Ensembl version 103.38. Kyoto Encyclopedia of Genes and Genomes (KEGG)⁶⁶ annotation was performed according to the 106.0 release. Chord plots were performed using pycirclize version 0.4.0 [https://github.com/moshi4/pyCirclize] with Shimoyama et al. library [https://github.com/moshi54/pyCirclize]. All figures and statistical analysis were generated using GraphPad Prism 9.2.0 (GraphPad Software) or MS Office Excel 2019. Data are displayed as mean ± SD, box plots as median with 25th or 75th percentiles and min/max whiskers. Continuous data were tested for distribution and individual groups were tested by use of two-sided Fisher’s exact test, unpaired t-test, unpaired Mann-Whitney-U-test, Kruskal-Wallis test, or paired Wilcoxon signed rank test, all performed as two-sided tests. If applicable, adjustment for multiple testing was made. P-values of < 0.05 were considered statistically significant.

Data availability

The mass spectrometry data have been deposited in the ProteomeXchange Consortium database [https://www.proteomexchange.org/] via the PRIDE partner repository⁶⁷ under dataset identifier PXD03878. Source data are provided with this paper. The data used for comparative purposes can be found at HLA ligand atlas [https://hla-ligand-atlas.org/welcome], IEDB [https://www.iedb.org/] (selecting epitope linear peptide, epitope source human, assay MHC ligand, MHC restriction, disease healthy) and the data set provided with Bilich et al. (2019 in Blood). Source data are provided with this paper.

References

Ryschich, E. et al. Control of T-cell-mediated immune response by HLA class I in human pancreatic carcinoma. Clin. Cancer Res. 11, 498–504 (2005).
Article CAS PubMed Google Scholar
Gao, Q. et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep. 23, 227–238 e223 (2018).
Article CAS PubMed PubMed Central Google Scholar
Loffler, M. W. et al. Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient. J. Hepatol. 65, 849–855 (2016).
Article PubMed PubMed Central Google Scholar
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Article ADS CAS PubMed Google Scholar
Wick, W. et al. GAPVAC-101: First-in-human trial of a highly personalized peptide vaccination approach for patients with newly diagnosed glioblastoma. J. Clin. Oncol. 36, 2000–2000 (2018).
Article Google Scholar
Nelde, A., Rammensee, H. G. & Walz, J. S. The peptide vaccine of the future. Mol. Cell Proteom. 20, 100022 (2021).
Article CAS Google Scholar
Marcu, A. et al. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J. Immunother. Cancer 9, e002071 (2021).
Caron, E., Aebersold, R., Banaei-Esfahani, A., Chong, C. & Bassani-Sternberg, M. A Case for a Human Immuno-Peptidome Project Consortium. Immunity 47, 203–208 (2017).
Article CAS PubMed Google Scholar
Kote, S., Pirog, A., Bedran, G., Alfaro, J. & Dapic, I. Mass Spectrometry-Based Identification of MHC-Associated Peptides. Cancers (Basel) 12, 535 (2020).
Becker, J. P. & Riemer, A. B. The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies. Front. Immunol. 13, 883989 (2022).
Article CAS PubMed PubMed Central Google Scholar
Klaeger, S. et al. Optimized liquid and gas phase fractionation increases HLA-Peptidome coverage for primary cell and tissue samples. Mol. Cell Proteom. 20, 100133 (2021).
Article CAS Google Scholar
Chong, C. et al. High-throughput and Sensitive Immunopeptidomics Platform Reveals Profound Interferongamma-Mediated Remodeling of the Human Leukocyte Antigen (HLA) Ligandome. Mol. Cell Proteom. 17, 533–548 (2018).
Article CAS Google Scholar
Faridi, P., Purcell, A. W. & Croft, N. P. In Immunopeptidomics We Need a Sniper Instead of a Shotgun. Proteomics 18, e1700464 (2018).
Article PubMed Google Scholar
Meier, F. et al. Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer. Mol. Cell Proteom. 17, 2534–2545 (2018).
Article CAS Google Scholar
Meier, F. et al. Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device. J. Proteome Res. 14, 5378–5387 (2015).
Article CAS PubMed Google Scholar
Swearingen, K. E. & Moritz, R. L. High-field asymmetric waveform ion mobility spectrometry for mass spectrometry-based proteomics. Expert Rev. Proteom. 9, 505–517 (2012).
Article CAS Google Scholar
Vasilopoulou, C. G. et al. Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts. Nat. Commun. 11, 331 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Neumann, E. K. et al. Spatial Metabolomics of the Human Kidney using MALDI Trapped Ion Mobility Imaging Mass Spectrometry. Anal. Chem. 92, 13084–13091 (2020).
Article CAS PubMed Google Scholar
Bekker-Jensen, D. B. et al. A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients. Mol. Cell Proteom. 19, 716–729 (2020).
Article CAS Google Scholar
Feola, S. et al. PeptiCHIP: A Microfluidic Platform for Tumor Antigen Landscape Identification. ACS Nano 15, 15992–16010 (2021).
Article CAS PubMed PubMed Central Google Scholar
Feola, S., Chiaro, J., Martins, B. & Cerullo, V. Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process. Cancers (Basel) 12, 1660 (2020).
Minegishi, Y. et al. Differential ion mobility mass spectrometry in immunopeptidomics identifies neoantigens carrying colorectal cancer driver mutations. Commun. Biol. 5, 831 (2022).
Article CAS PubMed PubMed Central Google Scholar
Pfammatter, S. et al. Extending the Comprehensiveness of Immunopeptidome Analyses Using Isobaric Peptide Labeling. Anal. Chem. 92, 9194–9204 (2020).
Article CAS PubMed Google Scholar
Casasola-LaMacchia, A. et al. HLAII peptide presentation of infliximab increases when complexed with TNF. Front Immunol. 13, 932252 (2022).
Article CAS PubMed PubMed Central Google Scholar
Kraemer, A. I. et al. The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer. Nat. Cancer 4, 608–628 (2023).
Article CAS PubMed PubMed Central Google Scholar
Bruno, P. M. et al. High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform. Nat Biotechnol 41, 980–992 (2023).
Nicholas, B. et al. Identification of neoantigens in oesophageal adenocarcinoma. Immunology 168, 420–431 (2023).
Article CAS PubMed Google Scholar
Fritsche, J. et al. Pitfalls in HLA Ligandomics-How to Catch a Li(e)gand. Mol. Cell Proteom. 20, 100110 (2021).
Article CAS Google Scholar
Bilich, T. et al. The HLA ligandome landscape of chronic myeloid leukemia delineates novel T-cell epitopes for immunotherapy. Blood 133, 550–565 (2019).
Article CAS PubMed Google Scholar
Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47, D339–D343 (2019).
Article CAS PubMed Google Scholar
Schuster, H. et al. The immunopeptidomic landscape of ovarian carcinomas. Proc. Natl Acad. Sci. USA 114, E9942–E9951 (2017).
Article CAS PubMed PubMed Central Google Scholar
Nelde, A. et al. Immunopeptidomics-Guided Warehouse Design for Peptide-Based Immunotherapy in Chronic Lymphocytic Leukemia. Front Immunol. 12, 705974 (2021).
Article CAS PubMed PubMed Central Google Scholar
Bilich, T. et al. Mass spectrometry-based identification of a B-cell maturation antigen-derived T-cell epitope for antigen-specific immunotherapy of multiple myeloma. Blood Cancer J. 10, 24 (2020).
Article PubMed PubMed Central Google Scholar
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
Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Weinzierl, A. O. et al. Distorted relation between mRNA copy number and corresponding major histocompatibility complex ligand density on the cell surface. Mol. Cell Proteom. 6, 102–113 (2007).
Article CAS Google Scholar
Fortier, M. H. et al. The MHC class I peptide repertoire is molded by the transcriptome. J. Exp. Med. 205, 595–610 (2008).
Article CAS PubMed PubMed Central Google Scholar
Zeng, W. F. et al. in bioRxiv (07/16/ 2022).
Meier, F. et al. Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nat. Commun. 12, 1185 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Feola, S. et al. A novel immunopeptidomic-based pipeline for the generation of personalized oncolytic cancer vaccines. Elife 11 (2022).
Shao, W. et al. The SysteMHC Atlas project. Nucleic Acids Res. 46, D1237–D1247 (2018).
Article CAS PubMed Google Scholar
Tegeler, C. M. et al. HLA-DR Presentation of the Tumor Antigen MSLN Associates with Clinical Outcome of Ovarian Cancer Patients. Cancers (Basel) 14, 2260 (2022).
Marconato, M., Maringer, Y., Walz, J. S., Nelde, A. & Heitmann, J. S. Immunopeptidome Diversity in Chronic Lymphocytic Leukemia Identifies Patients with Favorable Disease Outcome. Cancers (Basel) 14, 4659 (2022).
Chowell, D. et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc. Natl Acad. Sci. USA 112, E1754–E1762 (2015).
Article CAS PubMed PubMed Central Google Scholar
Calis, J. J. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol. 9, e1003266 (2013).
Article PubMed PubMed Central Google Scholar
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Hellmann, M. D. et al. Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer. Cancer Cell 35, 329 (2019).
Article CAS PubMed Google Scholar
Goodman, A. M. et al. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol. Cancer Ther. 16, 2598–2608 (2017).
Article CAS PubMed PubMed Central Google Scholar
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
Freudenmann, L. K., Marcu, A. & Stevanovic, S. Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry. Immunology 154, 331–345 (2018).
Article CAS PubMed PubMed Central Google Scholar
Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).
Article ADS CAS PubMed Google Scholar
Nelde, A., Kowalewski, D. J. & Stevanovic, S. Purification and Identification of Naturally Presented MHC Class I and II Ligands. Methods Mol. Biol. 1988, 123–136 (2019).
Article CAS PubMed Google Scholar
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
Pawelec, G., Ziegler, A. & Wernet, P. Dissection of human allostimulatory determinants with cloned T cells: stimulation inhibition by monoclonal antibodies TU22, 34, 35, 36, 37, 39, 43, and 58 against distinct human MHC class II molecules. Hum. Immunol. 12, 165–176 (1985).
Article CAS PubMed Google Scholar
Goldman, J. M., Hibbin, J., Kearney, L., Orchard, K. & Th’ng, K. H. HLA-DR monoclonal antibodies inhibit the proliferation of normal and chronic granulocytic leukaemia myeloid progenitor cells. Br. J. Haematol. 52, 411–420 (1982).
Article CAS PubMed Google Scholar
Meier, F., Park, M. A. & Mann, M. Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics. Mol. Cell Proteom. 20, 100138 (2021).
Article CAS Google Scholar
Bauer, J. et al. The oncogenic fusion protein DNAJB1-PRKACA can be specifically targeted by peptide-based immunotherapy in fibrolamellar hepatocellular carcinoma. Nat. Commun. 13, 6401 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Schuler, M. M., Nastke, M. D. & Stevanovikc, S. SYFPEITHI: database for searching and T-cell epitope prediction. Methods Mol. Biol. 409, 75–93 (2007).
Article CAS PubMed Google Scholar
Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, W449–W454 (2020).
Article CAS PubMed PubMed Central Google Scholar
Toprak, U. H. et al. Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics. Mol. Cell Proteom. 13, 2056–2071 (2014).
Article CAS Google Scholar
Hulsen, T., de Vlieg, J. & Alkema, W. BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genom. 9, 488 (2008).
Article Google Scholar
Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R. & Pfister, H. UpSet: Visualization of Intersecting Sets. IEEE Trans. Vis. Comput. Graph 20, 1983–1992 (2014).
Article PubMed PubMed Central Google Scholar
Bui, H. H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinforma. 7, 153 (2006).
Article Google Scholar
Thul, P. J. et al. A subcellular map of the human proteome. Science 356, eaal3321 (2017).
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28, 27–30 (2000).
Article CAS PubMed PubMed Central Google Scholar
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 47, D442–D450 (2019).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

We thank R. Agrusa, C. Falkenburger, and U. Wulle for excellent technical support. This work was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy (Grant EXC2180-390900677 (H.-G.R., H.R.S. and J.S.W.), the German Cancer Consortium (DKTK) (H.-G.R., H.R.S. and J.S.W.), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Grant WA4608/1-2 (J.S.W.), the Wilhelm Sander Stiftung (Grant 2016.177.3 (J.S.W.)), the José Carreras Leukämie-Stiftung (Grant DJCLS 05 R/2017 (J.S.W.)), the Else Kröner Fresenius Stiftung, Translatorik Programm (Grant 2022_EKTP03 (J.S.W.)), the Deutsche Krebshilfe (German Cancer Aid, 70114948 (J.S.W.)), the Zentren für Personalisierte Medizin (ZPM, J.S.W.), the Applied Clinical Research program (AKF, (J.S.W.)), and the Fortüne Program of the University of Tübingen (Fortüne number 2451-0-0 (S.M.S.)). We acknowledge support from the Open Access Publication Fund of the University of Tübingen.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Author information

Jonas S. Heitmann
Present address: Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany

Authors and Affiliations

Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
Naomi Hoenisch Gravel, Annika Nelde, Jens Bauer, Lena Mühlenbruch, Sarah M. Schroeder, Jonas Scheid, Steffen Lemke, Marissa L. Dubbelaar, Marcel Wacker, Anna Dengler & Juliane S. Walz
Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
Naomi Hoenisch Gravel, Annika Nelde, Jens Bauer, Lena Mühlenbruch, Sarah M. Schroeder, Jonas Scheid, Steffen Lemke, Marissa L. Dubbelaar, Marcel Wacker, Anna Dengler, Hans-Georg Rammensee & Juliane S. Walz
Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
Naomi Hoenisch Gravel, Annika Nelde, Jens Bauer, Lena Mühlenbruch, Jonas Scheid, Steffen Lemke, Marissa L. Dubbelaar, Marcel Wacker, Anna Dengler, Jonas S. Heitmann, Helmut R. Salih, Hans-Georg Rammensee & Juliane S. Walz
German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
Lena Mühlenbruch, Helmut R. Salih & Hans-Georg Rammensee
Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
Sarah M. Schroeder, Paul-Stefan Mauz & Hubert Löwenheim
Neuroscience Center Zürich (ZNZ), University of Zürich and ETH Zürich, Zürich, Switzerland
Marian C. Neidert
Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zürich, Switzerland
Marian C. Neidert
Department of Neurosurgery, Cantonal Hospital St. Gallen, Zürich, Switzerland
Marian C. Neidert
Quantitative Biology Center (QBIC), University of Tübingen, Tübingen, Germany
Jonas Scheid, Steffen Lemke & Marissa L. Dubbelaar
Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
Reinhild Klein
Pediatric Stem Cell Transplantation, University Children’s Hospital Zürich, Zürich, Switzerland
Mathias Hauri-Hohl
Neuroimmunology and MS Research, Neurology Clinic, University and University Hospital Zürich, Zürich, Switzerland
Roland Martin
Department of Urology, University Hospital Tübingen, Tübingen, Germany
Jörg Hennenlotter & Arnulf Stenzl
Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
Helmut R. Salih & Juliane S. Walz

Authors

Naomi Hoenisch Gravel
View author publications
You can also search for this author in PubMed Google Scholar
Annika Nelde
View author publications
You can also search for this author in PubMed Google Scholar
Jens Bauer
View author publications
You can also search for this author in PubMed Google Scholar
Lena Mühlenbruch
View author publications
You can also search for this author in PubMed Google Scholar
Sarah M. Schroeder
View author publications
You can also search for this author in PubMed Google Scholar
Marian C. Neidert
View author publications
You can also search for this author in PubMed Google Scholar
Jonas Scheid
View author publications
You can also search for this author in PubMed Google Scholar
Steffen Lemke
View author publications
You can also search for this author in PubMed Google Scholar
Marissa L. Dubbelaar
View author publications
You can also search for this author in PubMed Google Scholar
Marcel Wacker
View author publications
You can also search for this author in PubMed Google Scholar
Anna Dengler
View author publications
You can also search for this author in PubMed Google Scholar
Reinhild Klein
View author publications
You can also search for this author in PubMed Google Scholar
Paul-Stefan Mauz
View author publications
You can also search for this author in PubMed Google Scholar
Hubert Löwenheim
View author publications
You can also search for this author in PubMed Google Scholar
Mathias Hauri-Hohl
View author publications
You can also search for this author in PubMed Google Scholar
Roland Martin
View author publications
You can also search for this author in PubMed Google Scholar
Jörg Hennenlotter
View author publications
You can also search for this author in PubMed Google Scholar
Arnulf Stenzl
View author publications
You can also search for this author in PubMed Google Scholar
Jonas S. Heitmann
View author publications
You can also search for this author in PubMed Google Scholar
Helmut R. Salih
View author publications
You can also search for this author in PubMed Google Scholar
Hans-Georg Rammensee
View author publications
You can also search for this author in PubMed Google Scholar
Juliane S. Walz
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

N.H.G., A.N., J.B., L.M. and J.S.W. conceptualized this study. N.H.G., L.M., S.M.S, A.D. and M.W. performed immunopeptidome experiments. N.H.G., S.L., J.S. and M.L.D. performed bioinformatic analysis. S.M.S., M.C.N., P.-S.M., H.L., M.H.-H., R.M., J.H., A.S., M.H.-H., R.M., J.S.H. and H.R.S. collected the samples included in this study. R.K. performed HLA typing analysis. A.N., J.B., H.-G.R. and J.S.W. supervised this study. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Juliane S. Walz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Koji Ueda and the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review File

Description of Additional Supplementary Files

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Hoenisch Gravel, N., Nelde, A., Bauer, J. et al. TOF_IMS mass spectrometry-based immunopeptidomics refines tumor antigen identification. Nat Commun 14, 7472 (2023). https://doi.org/10.1038/s41467-023-42692-7

Download citation

Received: 12 January 2023
Accepted: 18 October 2023
Published: 17 November 2023
DOI: https://doi.org/10.1038/s41467-023-42692-7

This article is cited by

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model
- David Gomez-Zepeda
- Danielle Arnold-Schild
- Stefan Tenzer
Nature Communications (2024)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.