timsTOF mass spectrometry-based immunopeptidomics refines tumor antigen identification

doi:10.21203/rs.3.rs-2402111/v1

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timsTOF mass spectrometry-based immunopeptidomics refines tumor antigen identification

https://doi.org/10.21203/rs.3.rs-2402111/v1

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published 17 Nov, 2023

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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 de novo implementation of ion mobility separation-based timsTOF MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. A direct comparison of timsTOF-based with state-of-the-art immunopeptidomics using orbitrap technology showed significantly increased HLA peptide identifications from benign and malignant primary samples of solid tissue and hematological origin. First application of timsTOF-based immunopeptidomics for tumor antigen discovery enabled (i) the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides from 94 primary benign tissue samples, (ii) the refinement of previously described tumor antigens, and (iii) the identification of a vast array of novel tumor antigens, comprising low abundant neoepitopes, that might serve as targets for future cancer immunotherapy development.

Biological sciences/Immunology/Antigen processing and presentation/MHC

Health sciences/Oncology/Cancer/Cancer therapy/Cancer immunotherapy

Biological sciences/Biological techniques/Proteomic analysis

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.^3–6 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 approaches.⁷ 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.^{8, 9}

Despite immense technical improvements and optimized immunopeptidomics workflows in the last decades,^{10, 11} 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.^{9, 10}

timsTOF is a novel 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.^12–14 TIMS provides an orthogonal separation dimension, the so-called collisional cross section (CCS), 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 TOF analysis, thus enabling increased sensitivity and high-speed analysis of large cohort samples.^{12, 13} Other MS-based omics areas, comprising proteomics, metabolomics or lipidomics, have already successfully implemented timsTOF application showing increased identifications based on faster tandem (MS/MS) scan rates and acquisition without a loss of sensitivity.^{15, 16}

Here we report on the implementation of timsTOF MS for immunopeptidomics with a direct comparison of performance and sensitivity to state-of-the-art orbitrap technology and its first application for next-generation tumor antigen discovery. timsTOF-based immunopeptidomics identified significantly increased peptide yields from primary malignant and benign samples, enabling the expansion of benign references of HLA-presented peptides as well as the discovery of a vast array of novel tumor antigens, comprising also low abundant neoepitopes, as potential targets for cancer immunotherapy.

Trapped ion mobility coupled MS enables identification of naturally HLA-presented peptides

To establish a method for immunopeptidomics using timsTOF 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 (1,895 for JY 1, 1,252 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 5,691 for JY 2 and resulting in a final new method for timsTOF-based immunopeptidomics (Table 1).

Table 1

timsTOF method optimized for immunopeptidomics.
MS parameter		setting
source	source	captive spray
source	capillary	1,500 V
TIMS settings	1/k₀ start to end (locked)	0.6–1.6 Vs/cm²
	ramp time	200 ms
	accumulation time	200 ms
	duty cycle (locked)	100%
	ramp rate	4.85 Hz
tune	collision energy	10 eV
	collision RF	1,500 Vpp
	transfer time	60 µs
	pre pulse storage	6 µs
	mass spectra peak detection absolute threshold absolute threshold (per 100 ms accu time)	10 5
	PASEF data denoising mode	no reduction
	mobility peak detection absolute threshold	5,000
MS/MS	number of PASEF ramps	6
	total cycle time	1.44 s
	charge	0–5
	scheduling precursor repetition	linear
	target intensity	20,000
	intensity threshold	1,000
	active exclusion release after	activated 0.4 min
	collision energy settings 1/k₀	0.6–1.6 Vs/cm² 20–59 eV
MS	m/z	100–2,000
	polarity	positive
	scan mode	PASEF
Abbreviations:
eV, electronvolt; Vpp; voltage peak-to-peak; min, minutes; MS, mass spectrometer; PASEF, parallel accumulation-serial fragmentation; accu, accumulation; m/z, mass to charge ratio.

Analyzing the CCS of identified peptide spectrum matches (PSMs) showed that timsTOF’s PASEF technology separated ions with similar retention time (RT) orthogonally according to their CCS values, enabling identification of co-eluting peptides and thus a higher sensitivity (Fig. 1c). The identified peptides showed an expected mass distribution from 700–1,700 Da with the majority around 1,000 Da (Fig. 1d). The timsTOF immunopeptidomics method enabled the identification of single charged ions > 600 m/z and multiple charged ions > 100 m/z, resulting in the expected m/z distribution (Fig. 1e). Identified HLA class I-presented peptides showed the typical length distribution with 9-mers making up 70% of peptides (Fig. 1f). The novel developed method was also utilized for the identification of HLA class II-restricted peptides, which similarly showed the orthogonal separation of peptides by their CCS (Extended Data Fig. 1a), an expected mass distribution ranging from 700–2,600 Da with the majority of peptides slightly heavier than HLA class I-presented peptides with 1,400–1,800 Da (Extended Data Fig. 1b) and a similar m/z distribution (Extended Data Fig. 1c). The identified HLA class II-presented peptides showed a typical length distribution with the majority at about 15 amino acids (Extended Data Fig. 1d).

timsTOF-based Immunopeptidomics Significantly Increases HLA-presented Peptide Identifications Compared To Orbitrap-based MS

To delineate the eligibility of timsTOF for HLA-presented peptide detection, a direct comparison between timsTOF and orbitrap technology was performed across different primary samples (n = 10, Supplementary Data 2), comprising benign and malignant samples of solid tissue and hematological origin. Samples were distributed equally after peptide isolation and measured in technical triplicates on each device. Direct sample-distinct comparison of timsTOF- and orbitrap-acquired data showed that up to 92% (median 89%, range 86% − 92%) and 96% (median 93%, range 77% − 96%) of all identified HLA class I ligands and HLA class II-presented peptides could be identified by timsTOF, respectively, whereas only up to 14% of HLA class I ligands (median 11%, range 8% − 14%) and 23% of HLA class II-presented peptides (median 7%, range 3% − 23%) were exclusive for orbitrap datasets (Fig. 2a and Extended Data Fig. 2a). A median of 36% of HLA class I ligands (range 32% − 40%) and 36% of HLA class II-presented peptides (range 19% − 39%) were shared between timsTOF and orbitrap immunopeptidomes, respectively (Fig. 2a and Extended Data Fig. 2a). timsTOF MS identified significantly higher yields of HLA class I ligands (median 10,412, range 1,654 − 12,054) and HLA class II-presented peptides (median 7,868, range 672 − 14,053) compared to orbitrap MS (HLA class I: median 5,552, range 757–6,161, HLA class II: median 3,404, range 170–7,319), with a median increase in HLA class I ligand and HLA class II-presented peptide yields per sample of 4,842 (range 897–6,191, Fig. 2b) and 4,209 (range 502–7,538, Extended Data Fig. 2b), respectively.

HLA class I ligands and HLA class II-presented peptides identified by timsTOF MS show comparable length and mass distributions to peptides identified by orbitrap MS (Fig. 2c, d, Extended Data Fig. 2c, d). timsTOF-exclusive HLA class I ligands and HLA class II-presented peptides were identified during the whole LC separation run (Fig. 2e, Extended Data Fig. 2e), emphasizing the improved identification of co-eluting peptides by timsTOF compared to conventional MS.¹² In terms of peptide quality criteria, a similar − 10lgP distribution was observed for shared peptides identified with both devices (median 31.03, range 14.12–53.54 for timsTOF, median 32.80, range 14.10–52.65 for orbitrap). For exclusively identified peptides, the median − 10lgP was higher for timsTOF identified HLA class I ligands with a median of 26.33 (range 14.10–58.54) and median of 26.06 (range 14.12–49.27) for orbitrap (Fig. 2f, Supplementary Fig. 2). Exclusively identified HLA class II-presented peptides identified by timsTOF showed a lower − 10lgP, median 37.21 (range 17.60 to 71.35) compared to exclusively identified using orbitrap, median 38.71 (range 18.21–76.26, Extended Data Fig. 2f). Device-exclusive peptides showed significant differences in terms of their hydropathy with timsTOF-identified peptides being significantly more hydrophobic than orbitrap-exclusive identified peptides (Fig. 2g, Extended Data Fig. 2g). A similar frequency of HLA allotype allocation was observed for HLA class I ligands identified on timsTOF and orbitrap devices across all samples (Fig. 2h).

HLA class I-presented peptides were further analyzed for low-frequent peptide artefacts resulting from proteolytic fragmentation originating from endogenous peptidases.¹⁷ A median of 2.0% (range 0.1% − 6.5%) and 0.5% (0.0% − 7.2%) of peptides identified by timsTOF and orbitrap, respectively, were of proteolytic origin (Fig. 2i). However, 91% of the peptides classified as proteolytic were not annotated as HLA ligands.

timsTOF-based Immunopeptidomics Expands Benign References Of HLA-presented Peptides

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 significant increase in HLA-presented peptide discovery using timsTOF 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 27 different organ origins and peripheral blood mononuclear cell (PBMC) samples, were performed (Supplementary Data 2). In total, 137,463 unique HLA class I ligands and 175,469 HLA class II-presented peptides were identified. Comparing this timsTOF benign immunopeptidome dataset with published benign immunopeptidome repositories,^18–20 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 IEDB²⁰ (n = n.a.) or benign orbitrap datasets^{18, 19} (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 timsTOF benign dataset, showing more than 1,500 unique peptides per sample compared to less than 500 unique peptides per sample for published benign orbitrap datasets (n = 297, Fig. 3c).

timsTOF Benign 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 ontarget-off-tumor adverse events. The novel timsTOF benign immunopeptidome dataset rejected between 28% and 60% (median 40%) of previously published TAAs for various malignant diseases (ovarian carcinoma (OvCa),²¹ chronic lymphocytic leukemia (CLL)²² and chronic myeloid leukemia (CML)¹⁹) as they are not tumor-exclusive anymore (Fig. 3d, e).

To detect novel TAAs using timsTOF MS-based immunopeptidomics, we performed comparative immunopeptidome analyses of primary malignant samples (n = 4, RCC, HNSCC, CLL_01 and CLL_02, Supplementary Data 2) with the timsTOF and orbitrap benign immunopeptidome datasets. Of the 13,517 total identified HLA class I ligands from RCC 89% (12,054/13,517) were identified using timsTOF MS and 43% (5,863/13,517) using orbitrap MS. 30% (3,999/13,517) of identified HLA class I ligands were found to be tumor-exclusive, of which 77% (3,069/3,999) were exclusively identified using timsTOF, whereas only 10% (380/3,999) were exclusively identfied using orbitrap. 14% (551/3,999) of tumor-exclusive ligands were identified by both devices (Fig. 3f). Similar observations were made for the HNSCC (2,281 tumor-exclusive HLA class I ligands, of which 66% were timsTOF- and 11% orbitrap-exclusive) and CLL (7,465 tumor-exclusive HLA class I ligands, of which 65% were timsTOF- and 12% orbitrap-exclusive) samples (Supplementary Fig. 3a). For HLA class II-presented peptides, 8,166 unique peptides were identified on the RCC tumor sample, of which 96% (7,873/8,166) and 42% (3,389/8,166) were identified by timsTOF and orbitrap MS, respectively. 33% (2,676/8,166) of identified peptides were tumor-exclusive, of which 72% (1,943/2,676) were identified via timsTOF, 4% (97/2,676) using orbitrap and 24% (636/2,676) using both devices (Fig. 3g). Similar observations were made for the HNSCC (6,133 tumor-exclusive peptides, of which 60% were timsTOF and 17% orbitrap-exclusive) and CLL (5,079 tumor-exclusive peptides, of which 72% were timsTOF and 51% orbitrap-exclusive) samples (Supplementary Fig. 3b).

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 timsTOF 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 timsTOF dataset and validated using isotopically labelled synthetic peptides (Fig. 3h and i), whereas none of these neoepitopes were identified in the orbitrap-acquired data. Together, timsTOF MS provides a next generation immunopeptidomics method that facilitates the further prioritization of established TAAs and enables the identification of a vast array of novel non-mutated TAAs as well as the detection of naturally presented low abundant neoepitopes for cancer immunotherapy.

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.^{19, 23–25} In this study we implemented a next-generation MS-based immunopeptidome workflow using novel timsTOF MS to improve sensitivity and speed of HLA-presented peptide identification for TAA discovery.

In contrast to other ion mobility spectrometry-coupled MS approaches that use asymmetric waveform ion separation,²⁶ the timsTOF detector enables a sequencing speed above 100 Hz¹² compared to 20 Hz for highfield orbitrap devices.²⁷ The advantage of the TIMS module is its particularly efficiency in 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. Moreover, the TIMS 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.^{28, 29}

In a direct comparison with state-of-the-art immunopeptidomics using orbitrap technology^30–32 which lacks ion mobility separation, timsTOF-based immunopeptidomics revealed significantly increased HLA-presented peptide identifications from benign and malignant primary samples of solid tissues and hematological cells analyzed after equal sample separation post preparation. This makes timsTOF-based immunopeptidomics attractive for sample-limited applications, such as peptide identification from biopsies, micro-dissected tissues, and sorted cell populations.^{10, 11} HLA peptides identified via timsTOF showed comparable characteristics in terms of physicochemical properties as well as confidence levels of spectrum identification. The frequency of proteolytic peptide artefacts,¹⁷ originating during sample preparation, was slightly increased in the timsTOF immunopeptidome datasets, underscoring timsTOF’s ability to detect low abundant peptides. Of note, HLA-presented peptides exclusively identified by timsTOF MS showed significantly increased hydrophobicity. As hydrophobicity of HLA-presented peptides and their T cell receptor contact residues was described as a hallmark of immunogenic T cell epitopes,^{33, 34} timsTOF immunopeptidomics might improve the identification of effective antigens for tumor therapy.³⁵

Beyond the selection of immunogenic antigens, 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. Therefore, understanding 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.^{18–20, 36} Using timsTOF-based immunopeptidomics we substantially expanded these references, providing more than 150,000 novel HLA class I- and HLA class II-presented peptides from benign tissue origin. First application of these timsTOF benign immunopeptidome dataset already enabled the refinement of TAAs identified in previous studies for multiple tumor entities.^{19, 21, 22} Moreover, timsTOF-based immunopeptidomics led to the identification of additional non-mutated tumor-exclusive peptides from various tumor entities, 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.^{23, 37, 38} 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, 39} 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, 23, 40–42} has hampered the MS-based identification and thus selection of optimal neoepitopes for cancer immunotherapy. Our timsTOF immunopeptidomics workflow has led to the identification of mutation-derived HLA-presented peptides that were not identified on state-of the art Orbitrap devices, suggesting that the increased sensitivity of this approach might further improve the detection of naturally presented neoepitopes.

Together, our study provides a novel timsTOF-based immunopeptidomics application that enables the highly sensitive identification of HLA-presented peptides that will refine tumor antigen discovery.

Sample collection

Benign solid tissue samples were collected within 72h 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 1,000 rpm 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 protocols⁴³ using the pan HLA class I-specific W6/32,⁴⁴ pan HLA class II-specific Tü-39⁴⁵ and HLA-DR-specific L243⁴⁶ monoclonal antibodies. All antibodies were produced in-house at the Department of Immunology, University of Tübingen. For comparative immunopeptidome profiling between timsTOF and orbitrap acquired data, 50% of samples were analyzed per device in technical triplicates (2 technical replicates for the HNSCC sample) with 5 µL per injection, respectively. For the immunopeptidome analysis of the timsTOF benign dataset, 60% of the sample were injected in three technical replicates with 5 µL.

timsTOF Mass Spectrometric Data Acquisition

For timsTOF method development various parameters were tested as indicated in Supplementary Data 1 using the Bruker Daltonic’s timsTOF Pro device. Samples included in the timsTOF-orbitrap comparison as well as the timsTOF 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 0–95% Solvent B (AcN with 0.01% FA) over the course of 60 min with consequtive ramps from 0–32% (30 min) and 32–40% (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.³⁰ 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 2.4–32% 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 1,000 m/z with charge states 2 + to 5 + selected for fragmentation.

Whole Exome Sequencing

Whole exome sequencing was performed from snap-frozen tumor tissue using 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 organ 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 data) or 20 ppm (timsTOF 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 1.0⁴⁷ and netMHCpan4.1⁴⁸ 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 peptide.⁴⁹

Identifying Proteolytic Fragments

To identify proteolytic peptide artifacts, a statistical method¹⁷ that calculates the proposed protein coverage ratio, peptide coverage ratio, and HLA ligand propensity scores for each peptide, was implemented. An expectation-maximiation algorithm was used to deconvolute the Gaussian mixture into two distributions, defining a threshold for each of the scores with a FDR of 0.05. A peptide was classified as proteolytic if it superseded two of the three thresholds, similar to the original publication.¹⁷ The three parameters were computed based on the ten samples in the timsTOF orbitrap comparison. The implementation can be found at (https://github.com/AG-Walz/proteolytic_degradation_timstof_orbitrap).

Comparison Of timsTOF Pro Benign 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)¹⁸ and hematological cells¹⁹ as well as the IEDB database,²⁰ 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 OvCa,²¹ CLL²² and CML¹⁹ were retrieved from previous publications.

Statistical analysis

Overlap and UpSet plot analysis were performed using BioVenn⁵⁰ and UpSetR Shiny.⁵¹ Grand average of hydropathy (GRAVY score) was calculated using the GRAVY calculator (https://www.gravy-calculator.de). All figures and statistical analysis were generated using GraphPad Prism 9.2.0 (GraphPad Software). 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 (http://proteomecentral.proteomechange.org) via the PRIDE partner repository⁵² under dataset identifier PXD038782 (reviewer account details: username [email protected], password EQGPC3EA).

Acknowledgements and Funding

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 05R/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.)).

Author 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., J.S., S.L. 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.

Competing interests

The authors declare no competing interests.

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timsTOF mass spectrometry-based immunopeptidomics refines tumor antigen identification

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Abstract

Figures

Introduction

Results

Trapped ion mobility coupled MS enables identification of naturally HLA-presented peptides

timsTOF-based Immunopeptidomics Significantly Increases HLA-presented Peptide Identifications Compared To Orbitrap-based MS

timsTOF-based Immunopeptidomics Expands Benign References Of HLA-presented Peptides

timsTOF Benign Immunopeptidome Dataset Refines The Identification Of Tumor Antigens For Peptide-based Immunotherapy

Discussion

Methods

Sample collection

Cell Lines

Isolation Of HLA Ligands

timsTOF Mass Spectrometric Data Acquisition

Orbitrap Mass Spectrometric Data Acquisition

Whole Exome Sequencing

Database Search

Synthesis Of Isotope-labeled Peptides

Spectrum validation

Identifying Proteolytic Fragments

Comparison Of timsTOF Pro Benign Immunopeptidome Data With Published Datasets

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

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