Mild Acid Elution and MHC Immunoaffinity Chromatography Reveal Similar Albeit Not Identical Profiles of the HLA Class I Immunopeptidome

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Gemcitabine Modulates HLA-I Regulation to Improve Tumor Antigen Presentation by Pancreatic Cancer Cells

Pancreatic cancer is a lethal disease, harboring a five-year overall survival rate of only 13%. Current treatment approaches thus require modulation, with attention shifting towards liberating the stalled efficacy of immunotherapies. Select chemotherapy drugs which possess inherent immune-modifying behaviors could revitalize immune activity against pancreatic tumors and potentiate immunotherapeutic success. In this study, we characterized the influence of gemcitabine, a chemotherapy drug approved for the treatment of pancreatic cancer, on tumor antigen presentation by human leukocyte antigen class I (HLA-I). Gemcitabine increased pancreatic cancer cells' HLA-I mRNA transcripts, total protein, surface expression, and surface stability. Temperature-dependent assay results indicated that the increased HLA-I stability may be due to reduced binding of low affinity peptides. Mass spectrometry analysis confirmed changes in the HLA-I-presented peptide pool post-treatment, and computational predictions suggested improved affinity and immunogenicity of peptides displayed solely by gemcitabine-treated cells. Most of the gemcitabine-exclusive peptides were derived from unique source proteins, with a notable overrepresentation of translation-related proteins. Gemcitabine also increased expression of select immunoproteasome subunits, providing a plausible mechanism for its modulation of the HLA-I-bound peptidome. Our work supports continued investigation of immunotherapies, including peptide-based vaccines, to be used with gemcitabine as new combination treatment modalities for pancreatic cancer.

COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

BACKGROUND

Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality.

METHOD

In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides.

RESULT

The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens.

CONCLUSION

Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.

Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells

Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML.

SIGNIFICANCE

The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.

Peptide-based vaccine for cancer therapies

Different strategies based on peptides are available for cancer treatment, in particular to counter-act the progression of tumor growth and disease relapse. In the last decade, in the context of therapeutic strategies against cancer, peptide-based vaccines have been evaluated in different tumor models. The peptides selected for cancer vaccine development can be classified in two main type: tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs), which are captured, internalized, processed and presented by antigen-presenting cells (APCs) to cell-mediated immunity. Peptides loaded onto MHC class I are recognized by a specific TCR of CD8+ T cells, which are activated to exert their cytotoxic activity against tumor cells presenting the same peptide-MHC-I complex. This process is defined as active immunotherapy as the host's immune system is either de novo activated or restimulated to mount an effective, tumor-specific immune reaction that may ultimately lead to tu-mor regression. However, while the preclinical data have frequently shown encouraging results, therapeutic cancer vaccines clinical trials, including those based on peptides have not provided satisfactory data to date. The limited efficacy of peptide-based cancer vaccines is the consequence of several factors, including the identification of specific target tumor antigens, the limited immunogenicity of peptides and the highly immunosuppressive tumor microenvironment (TME). An effective cancer vaccine can be developed only by addressing all such different aspects. The present review describes the state of the art for each of such factors.

Immunoprecipitation methods impact the peptide repertoire in immunopeptidomics

Introduction

Mass spectrometry-based immunopeptidomics is the only unbiased method to identify naturally presented HLA ligands, which is an indispensable prerequisite for characterizing novel tumor antigens for immunotherapeutic approaches. In recent years, improvements based on devices and methodology have been made to optimize sensitivity and throughput in immunopeptidomics. However, developments in ligand isolation, mass spectrometric analysis, and subsequent data processing can have a marked impact on the quality and quantity of immunopeptidomics data.

Methods

In this work, we compared the immunopeptidome composition in terms of peptide yields, spectra quality, hydrophobicity, retention time, and immunogenicity of two established immunoprecipitation methods (column-based and 96-well-based) using cell lines as well as primary solid and hematological tumor samples.

Results

Although, we identified comparable overall peptide yields, large proportions of method-exclusive peptides were detected with significantly higher hydrophobicity for the column-based method with potential implications for the identification of immunogenic tumor antigens. We showed that column preparation does not lose hydrophilic peptides in the hydrophilic washing step. In contrast, an additional 50% acetonitrile elution could partially regain lost hydrophobic peptides during 96-well preparation, suggesting a reduction of the bias towards the column-based method but not completely equalizing it.

Discussion

Together, this work showed how different immunoprecipitation methods and their adaptions can impact the peptide repertoire of immunopeptidomic analysis and therefore the identification of potential tumor-associated antigens.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

NLRC5-CIITA Fusion Protein as an Effective Inducer of MHC-I Expression and Antitumor Immunity

Aggressive tumors evade cytotoxic T lymphocytes by suppressing MHC class-I (MHC-I) expression that also compromises tumor responsiveness to immunotherapy. MHC-I defects strongly correlate to defective expression of NLRC5, the transcriptional activator of MHC-I and antigen processing genes. In poorly immunogenic B16 melanoma cells, restoring NLRC5 expression induces MHC-I and elicits antitumor immunity, raising the possibility of using NLRC5 for tumor immunotherapy. As the clinical application of NLRC5 is constrained by its large size, we examined whether a smaller NLRC5-CIITA fusion protein, dubbed NLRC5-superactivator (NLRC5-SA) as it retains the ability to induce MHC-I, could be used for tumor growth control. We show that stable NLRC5-SA expression in mouse and human cancer cells upregulates MHC-I expression. B16 melanoma and EL4 lymphoma tumors expressing NLRC5-SA are controlled as efficiently as those expressing full-length NLRC5 (NLRC5-FL). Comparison of MHC-I-associated peptides (MAPs) eluted from EL4 cells expressing NLRC5-FL or NLRC5-SA and analyzed by mass spectrometry revealed that both NLRC5 constructs expanded the MAP repertoire, which showed considerable overlap but also included a substantial proportion of distinct peptides. Thus, we propose that NLRC5-SA, with its ability to increase tumor immunogenicity and promote tumor growth control, could overcome the limitations of NLRC5-FL for translational immunotherapy applications.

Novel canonical and non-canonical viral antigens extend current targets for immunotherapy of HPV-driven cervical cancer

Current immunotherapeutic approaches for human papillomavirus (HPV)-driven cervical cancer target the viral oncogenes E6 and E7. We report viral canonical and alternative reading frame (ARF)-derived sequences presented on cervical tumor cells, including antigens encoded by the conserved viral gene E1. We confirm immunogenicity of the identified viral peptides in HPV-positive women, and women with cervical intraepithelial neoplasia. We observe consistent transcription of the E1, E6, and E7 genes in 10 primary cervical tumor resections from the four most common high-risk HPV subtypes (HPV16, 18, 31, and 45), suggesting the suitability of E1 as therapeutic target. We finally confirm HLA presentation of canonical peptides derived from E6 and E7, and ARF-derived viral peptides from a reverse-strand transcript spanning the HPV E1 and E2 genes in primary human cervical tumor tissue. Our results extend currently known viral immunotherapeutic targets in cervical cancer and highlight E1 as an important cervical cancer antigen.

Using mass spectrometry to identify neoantigens in autoimmune diseases: The type 1 diabetes example

In autoimmune diseases, recognition of self-antigens presented by major histocompatibility complex (MHC) molecules elicits unexpected attack of tissue by autoantibodies and/or autoreactive T cells. Post-translational modification (PTM) may alter the MHC-binding motif or TCR contact residues in a peptide antigen, transforming the tolerance to self to autoreactivity. Mass spectrometry-based immunopeptidomics provides a valuable mechanism for identifying MHC ligands that contain PTMs and can thus provide valuable insights into pathogenesis and therapeutics of autoimmune diseases. A plethora of PTMs have been implicated in this process, and this review highlights their formation and identification.

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).

Extending the Mass Spectrometry-Detectable Landscape of MHC Peptides by Use of Restricted Access Material

Mass spectrometry-based immunopeptidomics enables the comprehensive identification of major histocompatibility complex (MHC) peptides from a cell culture as well as from tissue or tumor samples and is applied for the identification of tumor-specific and viral T-cell epitopes. Although mass spectrometry is generally considered an "unbiased" method for MHC peptide identification, the physicochemical properties of MHC peptides can greatly influence their detectability. Here, we demonstrate that highly hydrophobic peptides are lost during sample preparation when C18 solid-phase extraction (SPE) is used for separating MHC peptides from proteins. To overcome this limitation, we established an optimized protocol involving restricted access material (RAM). Compared to C18-SPE, RAM-SPE improved the overall MHC peptide recovery and extended the landscape of mass spectrometry-detectable MHC peptides toward more hydrophobic peptides.

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

Understanding the properties of human leukocyte antigen (HLA) peptides (immunopeptides) is essential for precision cancer medicine, while the direct identification of immunopeptides from small biopsies of clinical tissues by mass spectrometry (MS) is still confronted with technical challenges. Here, to overcome these hindrances, high-field asymmetric waveform ion mobility spectrometry (FAIMS) is introduced to conduct differential ion mobility (DIM)-MS by seamless gas-phase fractionation optimal for scarce samples. By established DIM-MS for immunopeptidomics analysis, on average, 42.9 mg of normal and tumor colorectal tissues from identical patients (n = 17) were analyzed, and on average 4921 immunopeptides were identified. Among these 44,815 unique immunopeptides, two neoantigens, KRAS-G12V and CPPED1-R228Q, were identified. These neoantigens were confirmed by synthetic peptides through targeted MS in parallel reaction monitoring (PRM) mode. Comparison of the tissue-based personal immunopeptidome revealed tumor-specific processing of immunopeptides. Since the direct identification of neoantigens from tumor tissues suggested that more potential neoantigens have yet to be identified, we screened cell lines with known oncogenic KRAS mutations and identified 2 more neoantigens that carry KRAS-G12V. These results indicated that the established FAIMS-assisted DIM-MS is effective in the identification of immunopeptides and potential recurrent neoantigens directly from scarce samples such as clinical tissues.

What a Difference an Amino Acid Makes: An All-Atom Simulation Study of Nonameric Peptides in Inhibitory HLA-E/NKG2A/CD94 Immune Complexes

MHC class I antigen E (HLA-E), a ligand for the inhibitory NKG2A/CD94 receptor of the immune system, is responsible for evading the immune surveillance in several settings, including senescent cell accumulation and tumor persistence. The formation of this ligand-receptor interaction promotes the inhibition of the cytolytic action of immune system natural killer (NK) cells and CD8⁺ T-cells expressing this receptor. The final outcome of the HLA-E/NKG2A/CD94 interaction on target cells is also highly dependent on the identity of the nonameric peptide incorporated into the HLA-E ligand. To better understand the role played by a nonameric peptide in these immune complexes, we performed a series of multi-microsecond all-atom molecular dynamics simulations. We generated natural and alternative variants of the nonameric peptide bound to the HLA-E ligand alone or in the HLA-E/NKG2A/CD94 complexes. A systematic study of molecular recognition between HLA-E and peptides led to the development of new variants that differ at the strategic 6th position (P6) of the peptide and have favorable in silico properties comparable to those of natural binding peptides. Further examination of a selected subset of peptides in full complexes revealed a new variant that, according to our previously derived atomistic model, can interfere with the signal transduction via HLA-E/NKG2A/CD94 and thus prevent the target cell from evading immune clearance by NK and CD8⁺ T-cells. These simulations provide an atomistic picture of how a small change in amino acid sequence can lead to a profound effect on binding and molecular recognition. Furthermore, our study also provides new data on the peptide interaction motifs as well as the energetic and conformational properties of the binding interface, laying the structure-based foundation for future development of potential therapeutic peptides, peptidomimetics, or even small molecules that would bind to the HLA-E ligand and abrogate NKG2A/CD94 recognition. Such external intervention would be useful in the emerging field of targeting senescent cells in a variety of age-related diseases, as well as in novel cancer immunotherapies.

New insights and approaches for analyses of immunopeptidomes

Human leucocyte antigen (HLA) molecules play a key role in health and disease by presenting antigen to T-lymphocytes for immunosurveillance. Immunopeptidomics involves the study of the collection of peptides presented within the antigen-binding groove of HLA molecules. Identifying their nature and diversity is crucial to understanding immunosurveillance especially during infection or for the recognition and potential eradication of tumours. This review discusses recent advances in the isolation, identification, and quantitation of these peptide antigens. New informatics approaches and databases have shed light on the extent of peptide antigens derived from unconventional sources including peptides derived from transcripts associated with frame shifts, long noncoding RNA, incorrectly annotated untranslated regions, post-translational modifications, and proteasomal splicing. Several challenges remain in successful analysis of immunopeptides, yet recent developments point to unexplored biology waiting to be unravelled.

Development of Cancer Immunotherapies

Cancer immunotherapy, or the utilization of components of the immune system to target and eliminate cancer, has become a highly active area of research in the past several decades and a common treatment strategy for several cancer types. The concept of harnessing the immune system for this purpose originated over 100 years ago when a physician by the name of William Coley successfully treated several of his cancer patients with a combination of live and attenuated bacteria, later known as "Coley's Toxins", after observing a subset of prior patients enter remission following their diagnosis with the common bacterial infection, erysipelas. However, it was not until late in the twentieth century that cancer immunotherapies were developed for widespread use, thereby transforming the treatment landscape of numerous cancer types. Pivotal studies elucidating molecular and cellular functions of immune cells, such as the discovery of IL-2 and production of monoclonal antibodies, fostered the development of novel techniques for studying the immune system and ultimately the development and approval of several cancer immunotherapies by the United States Food and Drug Association in the 1980s and 1990s, including the tuberculosis vaccine-Bacillus Calmette-Guérin, IL-2, and the CD20-targeting monoclonal antibody. Approval of the first therapeutic cancer vaccine, Sipuleucel-T, for the treatment of metastatic castration-resistant prostate cancer and the groundbreaking success and approval of immune checkpoint inhibitors and chimeric antigen receptor T cell therapy in the last decade, have driven an explosion of interest in and pursuit of novel cancer immunotherapy strategies. A broad range of modalities ranging from antibodies to adoptive T cell therapies is under investigation for the generalized treatment of a broad spectrum of cancers as well as personalized medicine. This chapter will focus on the recent advances, current strategies, and future outlook of immunotherapy development for the treatment of cancer.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome

INTRODUCTION

The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data.

AREAS COVERED

Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches.

EXPERT OPINION

Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.

Immunopeptidogenomics: Harnessing RNA-Seq to Illuminate the Dark Immunopeptidome

Human leukocyte antigen (HLA) molecules are cell-surface glycoproteins that present peptide antigens on the cell surface for surveillance by T lymphocytes, which contemporaneously seek signs of disease. Mass spectrometric analysis allows us to identify large numbers of these peptides (the immunopeptidome) following affinity purification of solubilized HLA-peptide complexes. However, in recent years, there has been a growing awareness of the "dark side" of the immunopeptidome: unconventional peptide epitopes, including neoepitopes, which elude detection by conventional search methods because their sequences are not present in reference protein databases (DBs). Here, we establish a bioinformatics workflow to aid identification of peptides generated by noncanonical translation of mRNA or by genome variants. The workflow incorporates both standard transcriptomics software and novel computer programs to produce cell line-specific protein DBs based on three-frame translation of the transcriptome. The final protein DB also includes sequences resulting from variants determined by variant calling on the same RNA-Seq data. We then searched our experimental data against both transcriptome-based and standard DBs using PEAKS Studio (Bioinformatics Solutions, Inc). Finally, further novel software helps to compare the various result sets arising for each sample, pinpoint putative genomic origins for unconventional sequences, and highlight potential neoepitopes. We applied the workflow to study the immunopeptidome of the acute myeloid leukemia cell line THP-1, using RNA-Seq and immunopeptidome data. We confidently identified over 14,000 peptides from three replicates of purified HLA peptides derived from THP-1 cells using the conventional UniProt human proteome. Using the transcriptome-based DB generated using our workflow, we recapitulated >85% of these and also identified 1029 unconventional peptides not explained by UniProt, including 16 sequences caused by nonsynonymous variants. Our workflow, which we term "immunopeptidogenomics," can provide DBs, which include pertinent unconventional sequences and allow neoepitope discovery, without becoming too large to search. Immunopeptidogenomics is a step toward unbiased search approaches that are needed to illuminate the dark side of the immunopeptidome.

Allotype-Specific Glycosylation and Cellular Localization of Human Leukocyte Antigen Class I Proteins

Presentation of antigens by human leukocyte antigen (HLA) complexes at the cell surface is a key process in the immune response. The α-chain, containing the peptide-binding groove, is one of the most polymorphic proteins in the proteome. All HLA class I α-chains carry a conserved N-glycosylation site, but little is known about its nature and function. Here, we report an in-depth characterization of N-glycosylation features of HLA class I molecules. We observe that different HLA-A α-chains carry similar glycosylation, distinctly different from the HLA-B, HLA-C, and HLA-F α-chains. Although HLA-A displays the broadest variety of glycan characteristics, HLA-B α-chains carry mostly mature glycans, and HLA-C and HLA-F α-chains carry predominantly high-mannose glycans. We expected these glycosylation features to be directly linked to cellular localization of the HLA complexes. Indeed, analyzing HLA class I complexes from crude plasma and inner membrane-enriched fractions confirmed that most HLA-B complexes can be found at the plasma membrane, while most HLA-C and HLA-F molecules reside in the endoplasmic reticulum and Golgi membrane, and HLA-A molecules are more equally distributed over these cellular compartments. This allotype-specific cellular distribution of HLA molecules should be taken into account when analyzing peptide antigen presentation by immunopeptidomics.

Optimized liquid and gas phase fractionation increases HLA-peptidome coverage for primary cell and tissue samples

Mass spectrometry is the most effective method to directly identify peptides presented on HLA molecules. However, current standard approaches often use 500 million or more cells as input to achieve high coverage of the immunopeptidome and therefore these methods are not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples off-line followed by ion mobility coupled to LC-MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared to samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8,107 distinct peptides starting with as few as 100 million cells. The increased sensitivity obtained using our methods can provide data useful to improve HLA binding prediction algorithms as well as to enable detection of clinically relevant epitopes such as neoantigens.

Know thy immune self and non-self: Proteomics informs on the expanse of self and non-self, and how and where they arise

T cells play an important role in the adaptive immune response to a variety of infections and cancers. Initiation of a T cell mediated immune response requires antigen recognition in a process termed MHC (major histocompatibility complex) restri ction. A T cell antigen is a composite structure made up of a peptide fragment bound within the antigen‐binding groove of an MHC‐encoded class I or class II molecule. Insight into the precise composition and biology of self and non‐self immunopeptidomes is essential to harness T cell mediated immunity to prevent, treat, or cure infectious diseases and cancers. T cell antigen discovery is an arduous task! The pioneering work in the early 1990s has made large‐scale T cell antigen discovery possible. Thus, advancements in mass spectrometry coupled with proteomics and genomics technologies make possible T cell antigen discovery with ease, accuracy, and sensitivity. Yet we have only begun to understand the breadth and the depth of self and non‐self immunopeptidomes because the molecular biology of the cell continues to surprise us with new secrets directly related to the source, and the processing and presentation of MHC ligands. Focused on MHC class I molecules, this review, therefore, provides a brief historic account of T cell antigen discovery and, against a backdrop of key advances in molecular cell biologic processes, elaborates on how proteogenomics approaches have revolutionised the field.

Deciphering Human Leukocyte Antigen Susceptibility Maps From Immunopeptidomics Characterization in Oncology and Infections

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen [HLA] A, B, and C) may affect susceptibility to many diseases such as cancer, auto-immune or infectious diseases. Individual genetic variation may help to explain different immune responses to microorganisms across a population. HLA typing can be fast and inexpensive; however, deciphering peptides loaded on MHC-I and II which are presented to T cells, require the design and development of high-sensitivity methodological approaches and subsequently databases. Hence, these novel strategies and databases could help in the generation of vaccines using these potential immunogenic peptides and in identifying high-risk HLA types to be prioritized for vaccination programs. Herein, the recent developments and approaches, in this field, focusing on the identification of immunogenic peptides have been reviewed and the next steps to promote their translation into biomedical and clinical practice are discussed.

Isolation of HLA Bound Peptides by Immunoaffinity Capture and Identification by Mass Spectrometry

This article describes the purification of HLA-bound peptides and their subsequent sequencing by mass spectrometry. These methods can be used for both HLA class I and class II molecules and can be adapted to different species depending on the availability of specific antibodies. Peptides can be successfully isolated from a variety of sample types, including in vitro cultured cells and primary tissues. The method involves the affinity capture of HLA-peptide complexes and separation of peptides from HLA heavy chains, followed by tailored interrogation by mass spectrometry to take into account the non-tryptic nature of endogenously derived HLA-bound peptides. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparation of immunoaffinity column Alternate Protocol 1: Preparation of microscale immunoaffinity column Basic Protocol 2: Generation of cell lysate and HLA immunoaffinity purification Alternate Protocol 2: Microscale immunoaffinity purification Basic Protocol 3: Separation of HLA peptides by reverse-phase high-performance liquid chromatography (RP-HPLC) Alternate Protocol 3: Isolation of HLA peptides using molecular weight cutoff (MWCO) filter Basic Protocol 4: Mass spectrometry and data analysis.