A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands

Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome

The cell surface proteome, or surfaceome, is the hub for cells to interact and communicate with the outside world. Many disease-associated changes are hard-wired within the surfaceome, yet approved drugs target less than 50 cell surface proteins. In the past decade, the proteomics community has made significant strides in developing new technologies tailored for studying the surfaceome in all its complexity. In this review, we first dive into the unique characteristics and functions of the surfaceome, emphasizing the necessity for specialized labeling, enrichment, and proteomic approaches. An overview of surfaceomics methods is provided, detailing techniques to measure changes in protein expression and how this leads to novel target discovery. Next, we highlight advances in proximity labeling proteomics (PLP), showcasing how various enzymatic and photoaffinity proximity labeling techniques can map protein-protein interactions and membrane protein complexes on the cell surface. We then review the role of extracellular post-translational modifications, focusing on cell surface glycosylation, proteolytic remodeling, and the secretome. Finally, we discuss methods for identifying tumor-specific peptide MHC complexes and how they have shaped therapeutic development. This emerging field of neo-protein epitopes is constantly evolving, where targets are identified at the proteome level and encompass defined disease-associated PTMs, complexes, and dysregulated cellular and tissue locations. Given the functional importance of the surfaceome for biology and therapy, we view surfaceomics as a critical piece of this quest for neo-epitope target discovery.

Developing T Cell Epitope-Based Vaccines Against Infection: Challenging but Worthwhile

T cell epitope-based vaccines are designed to elicit long-lived pathogen-specific memory T cells that can quickly activate protective effector functions in response to subsequent infections. These vaccines have the potential to provide sustained protection against mutated variants, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which are increasingly capable of evading neutralizing antibodies. Recent advancements in epitope discovery, T cell receptor analysis, and bioinformatics have enabled the precise selection of epitopes and the sophisticated design of epitope-based vaccines. This review outlines the development process for T cell epitope-based vaccines. We summarize the current progress in T cell epitope discovery technologies, highlighting the advantages and disadvantages of each method. We also examine advancements in the design and optimization of epitope-based vaccines, particularly through bioinformatics tools. Additionally, we discuss the challenges of validating the accurate processing and presentation of individual epitopes and establishing suitable rodent models to evaluate vaccine immunogenicity and protective efficacy.

Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets

T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.

ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

Components, Formulations, Deliveries, and Combinations of Tumor Vaccines

Tumor vaccines, an important part of immunotherapy, prevent cancer or kill existing tumor cells by activating or restoring the body's own immune system. Currently, various formulations of tumor vaccines have been developed, including cell vaccines, tumor cell membrane vaccines, tumor DNA vaccines, tumor mRNA vaccines, tumor polypeptide vaccines, virus-vectored tumor vaccines, and tumor-in-situ vaccines. There are also multiple delivery systems for tumor vaccines, such as liposomes, cell membrane vesicles, viruses, exosomes, and emulsions. In addition, to decrease the risk of tumor immune escape and immune tolerance that may exist with a single tumor vaccine, combination therapy of tumor vaccines with radiotherapy, chemotherapy, immune checkpoint inhibitors, cytokines, CAR-T therapy, or photoimmunotherapy is an effective strategy. Given the critical role of tumor vaccines in immunotherapy, here, we look back to the history of tumor vaccines, and we discuss the antigens, adjuvants, formulations, delivery systems, mechanisms, combination therapy, and future directions of tumor vaccines.

pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads

Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I-associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I-associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.

Protein degradation by human 20S proteasomes elucidates the interplay between peptide hydrolysis and splicing

If and how proteasomes catalyze not only peptide hydrolysis but also peptide splicing is an open question that has divided the scientific community. The debate has so far been based on immunopeptidomics, in vitro digestions of synthetic polypeptides as well as ex vivo and in vivo experiments, which could only indirectly describe proteasome-catalyzed peptide splicing of full-length proteins. Here we develop a workflow-and cognate software - to analyze proteasome-generated non-spliced and spliced peptides produced from entire proteins and apply it to in vitro digestions of 15 proteins, including well-known intrinsically disordered proteins such as human tau and α-Synuclein. The results confirm that 20S proteasomes produce a sizeable variety of cis-spliced peptides, whereas trans-spliced peptides are a minority. Both peptide hydrolysis and splicing produce peptides with well-defined characteristics, which hint toward an intricate regulation of both catalytic activities. At protein level, both non-spliced and spliced peptides are not randomly localized within protein sequences, but rather concentrated in hotspots of peptide products, in part driven by protein sequence motifs and proteasomal preferences. At sequence level, the different peptide sequence preference of peptide hydrolysis and peptide splicing suggests a competition between the two catalytic activities of 20S proteasomes during protein degradation.

Non-mutational neoantigens in disease

The ability of mammals to mount adaptive immune responses culminating with the establishment of immunological memory is predicated on the ability of the mature T cell repertoire to recognize antigenic peptides presented by syngeneic MHC class I and II molecules. Although it is widely believed that mature T cells are highly skewed towards the recognition of antigenic peptides originating from genetically diverse (for example, foreign or mutated) protein-coding regions, preclinical and clinical data rather demonstrate that novel antigenic determinants efficiently recognized by mature T cells can emerge from a variety of non-mutational mechanisms. In this Review, we describe various mechanisms that underlie the formation of bona fide non-mutational neoantigens, such as epitope mimicry, upregulation of cryptic epitopes, usage of non-canonical initiation codons, alternative RNA splicing, and defective ribosomal RNA processing, as well as both enzymatic and non-enzymatic post-translational protein modifications. Moreover, we discuss the implications of the immune recognition of non-mutational neoantigens for human disease.

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics

Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

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.

Antigen discovery for the development of cancer immunotherapy

Central to successful cancer immunotherapy is effective T cell antitumor immunity. Multiple targeted immunotherapies engineered to invigorate T cell-driven antitumor immunity rely on identifying the repertoire of T cell antigens expressed on the tumor cell surface. Mass spectrometry-based survey of such antigens ("immunopeptidomics") combined with other omics platforms and computational algorithms has been instrumental in identifying and quantifying tumor-derived T cell antigens. In this review, we discuss the types of tumor antigens that have emerged for targeted cancer immunotherapy and the immunopeptidomics methods that are central in MHC peptide identification and quantification. We provide an overview of the strength and limitations of mass spectrometry-driven approaches and how they have been integrated with other technologies to discover targetable T cell antigens for cancer immunotherapy. We highlight some of the emerging cancer immunotherapies that successfully capitalized on immunopeptidomics, their challenges, and mass spectrometry-based strategies that can support their development.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

Large-Scale Immuno-Peptidome Analysis Reveals Recurrent Post-Translational Splicing of Cancer and Immune-Associated Genes

Post-translational spliced peptides (PTSPs) are a unique class of peptides that have been found to be presented by HLA-class-I molecules in cancer (1). Thus far, no consensus has been reached on the proportion of PTSPs in the immunopeptidome, with estimates ranging from 2% to as high as 45% and stirring significant debate (2-8). Furthermore, the role of the HLA-class-II pathway in PTSP presentation has been studied only in diabetes (9). Here, we exploit our large-scale cancer peptidomics database and our newly devised pipeline for filtering spliced peptide predictions to identify recurring spliced peptides, both for HLA-class-I and -II complexes. Our results indicate that HLA-class-I spliced peptides account for a low percentage of the immunopeptidome (less than 3.1%), yet are larger in number relative to other types of identified aberrant peptides. Therefore, spliced peptides significantly contribute to the repertoire of presented peptides in cancer cells. In addition, we identified HLA-class-II-bound spliced peptides, but to a lower extent (less than 0.5%). The identified spliced peptides include cancer- and immune-associated genes, such as the MITF oncogene, DAPK1 tumor suppressor and HLA-E, which were validated using synthetic peptides. The potential immunogenicity of the DAPK1- and HLA-E-derived PTSPs was also confirmed. In addition, a reanalysis of our published mouse single-cell clone immunopeptidome dataset showed that most of the spliced peptides were found repeatedly in a large number of the single-cell clones. Establishing a novel search-scheme for the discovery and evaluation of recurring PTSPs among cancer patients may assist in identifying potential novel targets for immunotherapy.

Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data

Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.

InvitroSPI and a large database of proteasome-generated spliced and non-spliced peptides

Noncanonical epitopes presented by Human Leucocyte Antigen class I (HLA-I) complexes to CD8+ T cells attracted the spotlight in the research of novel immunotherapies against cancer, infection and autoimmunity. Proteasomes, which are the main producers of HLA-I-bound antigenic peptides, can catalyze both peptide hydrolysis and peptide splicing. The prediction of proteasome-generated spliced peptides is an objective that still requires a reliable (and large) database of non-spliced and spliced peptides produced by these proteases. Here, we present an extended database of proteasome-generated spliced and non-spliced peptides, which was obtained by analyzing in vitro digestions of 80 unique synthetic polypeptide substrates, measured by different mass spectrometers. Peptides were identified through invitroSPI method, which was validated through in silico and in vitro strategies. The peptide product database contains 16,631 unique peptide products (5,493 non-spliced, 6,453 cis-spliced and 4,685 trans-spliced peptide products), and a substrate sequence variety that is a valuable source for predictors of proteasome-catalyzed peptide hydrolysis and splicing. Potential artefacts and skewed results due to different identification and analysis strategies are discussed.

Immunogenic self-peptides - the great unknowns in autoimmunity: Identifying T-cell epitopes driving the autoimmune response in autoimmune diseases

HLA-associated autoimmune diseases likely arise from T-cell-mediated autoimmune responses against certain self-peptides from the broad HLA-presented immunopeptidomes. The limited knowledge of the autoimmune target peptides has so far compromised the basic understanding of autoimmune pathogenesis. This is due to the complexity of antigen processing and presentation as well as the polyspecificity of T-cell receptors (TCRs), which pose high methodological challenges on the discovery of immunogenic self-peptides. HLA-class I molecules present peptides to CD8⁺ T cells primarily derived from cytoplasmic proteins. Therefore, HLA-class I-restricted autoimmune responses should be directed against target cells expressing the corresponding parental protein. In HLA-class II-associated diseases, the origin of immunogenic peptides is not pre-specified, because peptides presented by HLA-class II molecules to CD4⁺ T cells may originate from both extracellular and cellular self-proteins. The different origins of HLA-class I and class II presented peptides determine the respective strategy for the discovery of immunogenic self-peptides in approaches based on the TCRs isolated from clonally expanded pathogenic T cells. Both involve identifying the respective restricting HLA allele as well as determining the recognition motif of the TCR under investigation by peptide library screening, which is required to search for homologous immunogenic self-peptides. In HLA-class I-associated autoimmune diseases, identification of the target cells allows for defining the restricting HLA allotype from the 6 different HLA-class I alleles of the individual HLA haplotype. It furthermore limits the search for immunogenic self-peptides to the transcriptome or immunopeptidome of the target cells, although neoepitopes generated by peptide splicing or translational errors may complicate identification. In HLA class II-associated autoimmune diseases, the lack of a defined target cell and differential antigen processing in different antigen-presenting cells complicate identification of the HLA restriction of autoreactive TCRs from CD4⁺ T cells. To avoid that all corresponding HLA-class II allotypes have to be included in the peptide discovery, autoantigens defined by autoantibodies can guide the search for immunogenic self-peptides presented by the respective HLA-class II risk allele. The objective of this article is to highlight important aspects to be considered in the discovery of immunogenic self-peptides in autoimmune diseases.

Prediction of peptide mass spectral libraries with machine learning

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).

Engineering Cell Lines for Specific Human Leukocyte Antigen Presentation

Epitope-specific immunotherapies have enabled the targeted treatment of a variety of diseases, ranging from cancer, infection, and autoimmune disorders. For CD8+ T cell-based therapies, the precise identification of immunogenic peptides presented by human leukocyte antigen (HLA) class I is essential which can be achieved by immunopeptidomics. Here, using lentivirus-mediated transduction and cell sorting approaches, we present a method to engineer a cell line that does not express its native HLA but instead expresses an HLA of interest (in this instance HLA-A*02:01). This technique can be used to elucidate the immunopeptidome of cell lines expressing different HLAs.

Soluble HLA peptidome: A new resource for cancer biomarkers

Using circulating molecular biomarkers to screen for cancer and other debilitating disorders in a high-throughput and low-cost fashion is becoming increasingly attractive in medicine. One major limitation of investigating protein biomarkers in body fluids is that only one-fourth of the entire proteome can be routinely detected in these fluids. In contrast, Human Leukocyte Antigen (HLA) presents peptides from the entire proteome on the cell surface. While peptide-HLA complexes are predominantly membrane-bound, a fraction of HLA molecules is released into body fluids which is referred to as soluble HLAs (sHLAs). As such peptides bound by sHLA molecules represent the entire proteome of their cells/tissues of origin and more importantly, recent advances in mass spectrometry-based technologies have allowed for accurate determination of these peptides. In this perspective, we discuss the current understanding of sHLA-peptide complexes in the context of cancer, and their potential as a novel, relatively untapped repertoire for cancer biomarkers. We also review the currently available tools to detect and quantify these circulating biomarkers, and we discuss the challenges and future perspectives of implementing sHLA biomarkers in a clinical setting.

The role of antigen expression in shaping the repertoire of HLA presented ligands

Human leukocyte antigen (HLA) presentation of peptides is a prerequisite of T cell immune activation. The understanding of the rules defining this event has large implications for our knowledge of basic immunology and for the rational design of immuno-therapeutics and vaccines. Historically, most of the available prediction methods have been solely focused on the information related to antigen processing and presentation. Recent work has, however, demonstrated that method performance can be boosted by integrating information related to antigen abundance. Here we expand on these later findings and develop an extended version of NetMHCpan, called NetMHCpanExp, integrating information on antigen abundance from RNA-Seq experiments. In line with earlier works, the model demonstrates improved performance for both HLA ligand and cancer neoantigen epitope prediction. Optimal results are obtained by use of sample-specific abundance information but also reference datasets can be applied with a limited performance drop. The developed tool is available at https://services.healthtech.dtu.dk/service.php?NetMHCpanExp-1.0.

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.

Structural aspects of chemical modifications in the MHC-restricted immunopeptidome; Implications for immune recognition

Significant advances in mass-spectroscopy (MS) have made it possible to investigate the cellular immunopeptidome, a large collection of MHC-associated epitopes presented on the surface of healthy, stressed and infected cells. These approaches have hitherto allowed the unambiguous identification of large cohorts of epitope sequences that are restricted to specific MHC class I and II molecules, enhancing our understanding of the quantities, qualities and origins of these peptide populations. Most importantly these analyses provide essential information about the immunopeptidome in responses to pathogens, autoimmunity and cancer, and will hopefully allow for future tailored individual therapies. Protein post-translational modifications (PTM) play a key role in cellular functions, and are essential for both maintaining cellular homeostasis and increasing the diversity of the proteome. A significant proportion of proteins is post-translationally modified, and thus a deeper understanding of the importance of PTM epitopes in immunopeptidomes is essential for a thorough and stringent understanding of these peptide populations. The aim of the present review is to provide a structural insight into the impact of PTM peptides on stability of MHC/peptide complexes, and how these may alter/modulate immune responses.

An unexplored angle: T cell antigen discoveries reveal a marginal contribution of proteasome splicing to the immunogenic MHC class I antigen pool

In the current era of T cell–based immunotherapies, it is crucial to understand which types of MHC-presented T cell antigens are produced by tumor cells. In addition to linear peptide antigens, chimeric peptides are generated through proteasome-catalyzed peptide splicing (PCPS). Whether such spliced peptides are abundantly presented by MHC is highly disputed because of disagreement in computational analyses of mass spectrometry data of MHC-eluted peptides. Moreover, such mass spectrometric analyses cannot elucidate how much spliced peptides contribute to the pool of immunogenic antigens. In this Perspective, we explain the significance of knowing the contribution of spliced peptides for accurate analyses of peptidomes on one hand, and to serve as a potential source of targetable tumor antigens on the other hand. Toward a strategy for mass spectrometry independent estimation of the contribution of PCPS to the immunopeptidome, we first reviewed methodologies to identify MHC-presented spliced peptide antigens expressed by tumors. Data from these identifications allowed us to compile three independent datasets containing 103, 74, and 83 confirmed T cell antigens from cancer patients. Only 3.9%, 1.4%, and between 0% and 7.2% of these truly immunogenic antigens are produced by PCPS, therefore providing a marginal contribution to the pool of immunogenic tumor antigens. We conclude that spliced peptides will not serve as a comprehensive source to expand the number of targetable antigens for immunotherapies.

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored toward tryptic peptides, we have here retrained MS²PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS²Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

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MS²Rescore significantly boosts immunopeptide identification rates

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Data-driven post-processing allows for a ten-fold increase in specificity

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MS²PIP and DeepLC predictors are integrated with Percolator post-processing

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MS²Rescore accepts identification results from MaxQuant, PEAKS, MS-GF+ and X!Tandem

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MS²Rescore shows great promise to extend current neo- and xeno-epitope landscapes

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.

New Insights into the Mechanisms of Proteasome-Mediated Peptide Splicing Learned from Comparing Splicing Efficiency by Different Proteasome Subtypes

By tying peptide fragments originally distant in parental proteins, the proteasome can generate spliced peptides that are recognized by CTL. This occurs by transpeptidation involving a peptide-acyl-enzyme intermediate and another peptide fragment present in the catalytic chamber. Four main subtypes of proteasomes exist: the standard proteasome (SP), the immunoproteasome, and intermediate proteasomes β1-β2-β5i (single intermediate proteasome) and β1i-β2-β5i (double intermediate proteasome). In this study, we use a tandem mass tag-quantification approach to study the production of six spliced human antigenic peptides by the four proteasome subtypes. Peptides fibroblast growth factor-5_{172-176/217-220,} tyrosinase_{368-373/336-340}, and gp100_40-42/47-52 are better produced by the SP than the other proteasome subtypes. The peptides SP110_{296-301/286-289}, gp100_{195-202/191or192}, and gp100_47-52/40-42 are better produced by the immunoproteasome and double intermediate proteasome. The current model of proteasome-catalyzed peptide splicing suggests that the production of a spliced peptide depends on the abundance of the peptide splicing partners. Surprisingly, we found that despite the fact that reciprocal peptides RTK_QLYPEW (gp100_40-42/47-52) and QLYPEW_RTK (gp100_47-52/40-42) are composed of identical splicing partners, their production varies differently according to the proteasome subtype. These differences were maintained after in vitro digestions involving identical amounts of the splicing fragments. Our results indicate that the amount of splicing partner is not the only factor driving peptide splicing and suggest that peptide splicing efficiency also relies on other factors, such as the affinity of the C-terminal splice reactant for the primed binding site of the catalytic subunit.

Characterization of Proteasome-Generated Spliced Peptides Detected by Mass Spectrometry

CD8⁺ T cells recognize peptides displayed by HLA class I molecules and monitor intracellular peptide pools. It is known that the proteasome splices two short peptide fragments. Recent studies using mass spectrometry (MS) and bioinformatics analysis have suggested that proteasome-generated spliced peptides (PSPs) may account for a substantial proportion of HLA class I ligands. However, the authenticity of the PSPs identified using bioinformatics approaches remain ambiguous. In this study, we employed MS-based de novo sequencing to directly capture cryptic HLA ligands that were not templated in the genome. We identified two PSPs originating from the same protein in a human colorectal cancer line with microsatellite instability. Healthy donor-derived CD8⁺ T cells readily responded to the two PSPs, showing their natural HLA presentation and antigenicity. Experiments using minigene constructs demonstrated proteasome-dependent processing of two PSPs generated by standard and reverse cis splicing, respectively. Our results suggest a broader diversity of HLA class I Ag repertoires generated by proteasomal splicing, supporting the advantage of MS-based approaches for the comprehensive identification of PSPs.

Neo-Splicetopes in Tumor Therapy: A Lost Case?

Proteasome generates spliced peptides by ligating two distant cleavage products in a reverse proteolysis reaction. The observation that CD8+ T cells recognizing a spliced peptide induced T cell rejection in a melanoma patient following adoptive T cell transfer (ATT), raised some hopes with regard to the general therapeutic and immune relevance of spliced peptides. Concomitantly, the identification of spliced peptides was also the start of a controversy with respect to their frequency, abundancy and their therapeutic applicability. Here I review some of the recent evidence favoring or disfavoring an immune relevance of splicetopes and discuss from a theoretical point of view the potential usefulness of tumor specific splicetopes and why against all odds it still may seem worth trying to identify such tumor and patient-specific neosplicetopes for application in ATT.

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.

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.

MHC Class I Immunopeptidome: Past, Present, and Future

In the 35 years since the revelation that short peptides bound to major histocompatibility complex class I and II molecules are the secret of the major histocompatibility complex–restricted nature of T-cell recognition, there has been enormous progress in characterizing the immunopeptidome, the repertoire of peptide presented for immunosurveillance. Here, the major milestones in the journey are marked, the contribution of proteasome-mediated splicing to the immunopeptidome is discussed, and exciting recent findings relating the immunopeptidome to the translatome revealed by ribosome profiling (RiboSeq) is detailed. Finally, what is needed for continued progress is opined about, which includes the infusion of talented young scientists into the antigen-processing field, currently undergoing a renaissance; thanks in part to the astounding success of T-cell–based cancer immunotherapy.

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Concise history of the discoveries leading to the molecular explanation for the phenomenon of the MHC class I–restricted nature of T-cell recognition.

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Historical review of how MS became a critical technique for defining MHC class I–associated peptides and understanding how peptides are generated from proteins biosynthesized by the antigen-presenting cell.

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Critical review of recent findings linking the translatome to the MHC class I immunopeptidome and the controversy regarding contribution of proteasome-mediated peptide splicing to the immunopeptidome.

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Speculative discussion of the future contributions of MS to understanding the generation of the MHC class I immunopeptidome.

What’s next in cancer immunotherapy? - The promise and challenges of neoantigen vaccination

The process of tumorigenesis leaves a series of indelible genetic changes in tumor cells, that when expressed, have the potential to be tumor-specific immune targets. Neoantigen vaccines that capitalize on this potential immunogenicity have shown efficacy in preclinical models and have now entered clinical trials. Here we discuss the status of personalized neoantigen vaccines and the current major challenges to this nascent field. In particular, we focus on the types of antigens that can be targeted by vaccination and on the role that preexisting immunosuppression, and in particular T-cell exhaustion, will play in the development of effective cancer vaccines.

Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

Tumor-associated epitopes presented on MHC-I that can activate the immune system against cancer cells are typically identified from annotated protein-coding regions of the genome, but whether peptides originating from novel or unannotated open reading frames (nuORFs) can contribute to antitumor immune responses remains unclear. Here we show that peptides originating from nuORFs detected by ribosome profiling of malignant and healthy samples can be displayed on MHC-I of cancer cells, acting as additional sources of cancer antigens. We constructed a high-confidence database of translated nuORFs across tissues (nuORFdb) and used it to detect 3,555 translated nuORFs from MHC-I immunopeptidome mass spectrometry analysis, including peptides that result from somatic mutations in nuORFs of cancer samples as well as tumor-specific nuORFs translated in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs are an unexplored pool of MHC-I-presented, tumor-specific peptides with potential as immunotherapy targets.

Navigating Critical Challenges Associated with Immunopeptidomics-Based Detection of Proteasomal Spliced Peptide Candidates

Within the tumor immunology community, the topic of proteasomal spliced peptides (PSP) has generated a great deal of controversy. In the earliest reports, careful biological validation led to the conclusion that proteasome-catalyzed peptide splicing was a rare event. To date, six PSPs have been validated biologically. However, the advent of algorithms to identify candidate PSPs in mass spectrometry data challenged this notion, with several studies concluding that the frequency of spliced peptides binding to MHC class I was quite high. Since this time, much debate has centered around the methodologies used in these studies. Several reanalyses of data from these studies have led to questions about the validity of the conclusions. Furthermore, the biological and technical validation that should be necessary for verifying PSP assignments was often lacking. It has been suggested therefore that the research community should unite around a common set of standards for validating candidate PSPs. In this review, we propose and highlight the necessary steps for validation of proteasomal splicing at both the mass spectrometry and biological levels. We hope that these guidelines will serve as a foundation for critical assessment of results from proteasomal splicing studies.

Functional Differences between Proteasome Subtypes

Four proteasome subtypes are commonly present in mammalian tissues: standard proteasomes, which contain the standard catalytic subunits β1, β2 and β5; immunoproteasomes containing the immuno-subunits β1i, β2i and β5i; and two intermediate proteasomes, containing a mix of standard and immuno-subunits. Recent studies revealed the expression of two tissue-specific proteasome subtypes in cortical thymic epithelial cells and in testes: thymoproteasomes and spermatoproteasomes. In this review, we describe the mechanisms that enable the ATP- and ubiquitin-dependent as well as the ATP- and ubiquitin-independent degradation of proteins by the proteasome. We focus on understanding the role of the different proteasome subtypes in maintaining protein homeostasis in normal physiological conditions through the ATP- and ubiquitin-dependent degradation of proteins. Additionally, we discuss the role of each proteasome subtype in the ATP- and ubiquitin-independent degradation of disordered proteins. We also discuss the role of the proteasome in the generation of peptides presented by MHC class I molecules and the implication of having different proteasome subtypes for the peptide repertoire presented at the cell surface. Finally, we discuss the role of the immunoproteasome in immune cells and its modulation as a potential therapy for autoimmune diseases.

Specificity of Adaptive Immune Responses in Central Nervous System Health, Aging and Diseases

The field of neuroimmunology endorses the involvement of the adaptive immune system in central nervous system (CNS) health, disease, and aging. While immune cell trafficking into the CNS is highly regulated, small numbers of antigen-experienced lymphocytes can still enter the cerebrospinal fluid (CSF)-filled compartments for regular immune surveillance under homeostatic conditions. Meningeal lymphatics facilitate drainage of brain-derived antigens from the CSF to deep cervical lymph nodes to prime potential adaptive immune responses. During aging and CNS disorders, brain barriers and meningeal lymphatic functions are impaired, and immune cell trafficking and antigen efflux are altered. In this context, alterations in the immune cell repertoire of blood and CSF and T and B cells primed against CNS-derived autoantigens have been observed in various CNS disorders. However, for many diseases, a causal relationship between observed immune responses and neuropathological findings is lacking. Here, we review recent discoveries about the association between the adaptive immune system and CNS disorders such as autoimmune neuroinflammatory and neurodegenerative diseases. We focus on the current challenges in identifying specific T cell epitopes in CNS diseases and discuss the potential implications for future diagnostic and treatment options.

Mechanistic diversity in MHC class I antigen recognition

Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.

RHybridFinder: An R package to process immunopeptidomic data for putative hybrid peptide discovery

Identification of proteasomal spliced peptides (PSPs) by mass spectrometry (MS) is not possible with traditional search engines. Here, we provide a protocol for running RHybridFinder (RHF), an R package for the computational inference of putative PSPs detected by MS. RHF extracts high confidence scored de novo sequenced peptides identified by PEAKS software. Those peptides are then matched to protein databases to infer cis- or trans-spliced major histocompatibility complex (MHC)-associated peptides. RHF is relatively fast and straightforward. PSPs have to be validated experimentally. For complete details on the use and execution of the original protocol, please refer to Faridi et al. (2018).

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.

The self-peptide repertoire plays a critical role in transplant tolerance induction

While direct allorecognition underpins both solid organ allograft rejection and tolerance induction, the specific molecular targets of most directly alloreactive CD8+ T cells have not been defined. In this study, we used a combination of genetically engineered major histocompatibility complex class I (MHC I) constructs, mice with a hepatocyte-specific mutation in the class I antigen-presentation pathway, and immunopeptidomic analysis to provide definitive evidence for the contribution of the peptide cargo of allogeneic MHC I molecules to transplant tolerance induction. We established a systematic approach for the discovery of directly recognized pMHC epitopes and identified 17 strongly immunogenic H-2Kb-associated peptides recognized by CD8+ T cells from B10.BR (H-2k) mice, 13 of which were also recognized by BALB/c (H-2d) mice. As few as 5 different tetramers used together were able to identify a high proportion of alloreactive T cells within a polyclonal population, suggesting that there are immunodominant allogeneic MHC-peptide complexes that can account for a large component of the alloresponse.

Identification of tumor antigens with immunopeptidomics

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

Commentary: Are There Indeed Spliced Peptides in the Immunopeptidome?

Proteasome-generated spliced epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry triggered heated debates, which find a representative opinion in one of the two fronts in the recent perspective article by Arie Admon. Briefly, he suggests that proteasomes cannot efficiently catalyze such a reaction, and, thus, that all spliced peptides identified in HLA class I immunopeptidomes and other specimens are artifacts. This hypothesis is in contrast with in vitro, in cellula, and in vivo results published since the discovery of proteasome-catalyzed peptide splicing in 2004.

Immunopathological basis of immune-related adverse events induced by immune checkpoint blockade therapy

Despite the considerable success of cancer immunotherapy with immune checkpoint inhibitors, their nonspecific release of the immunosuppressive mechanism is often associated with immune-related adverse events (irAEs). irAEs significantly disturb patients' quality of life and can even be life-threatening. Therefore, the appropriate management of irAEs is crucial for the development of further reliable cancer immunotherapies. irAEs have the appearance of ordinary autoimmune diseases in one aspect but often have distinct features. Although the detailed pathogenesis of irAEs remains unclear, increasing numbers of studies have provided numerous clues. Here, we review the current knowledge on irAEs, particularly from an immunopathological basis.