The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The Immunopeptidomics Ontology (ImPO)

The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization-there is neither an established terminology nor formally defined semantics-a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future. Ontology URL: https://zenodo.org/record/10237571 Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl.

Facts and Hopes in Immunotherapy Strategies Targeting Antigens Derived from KRAS Mutations

In this commentary, we advance the notion that mutant KRAS (mKRAS) is an ideal tumor neoantigen that is amenable for targeting by the adaptive immune system. Recent progress highlights key advances on various fronts that validate mKRAS as a molecular target and support further pursuit as an immunological target. Because mKRAS is an intracellular membrane localized protein and not normally expressed on the cell surface, we surmise that proteasome degradation will generate short peptides that bind to HLA class I (HLA-I) molecules in the endoplasmic reticulum for transport through the Golgi for display on the cell surface. T-cell receptors (TCR)αβ and antibodies have been isolated that specifically recognize mKRAS encoded epitope(s) or haptenated-mKRAS peptides in the context of HLA-I on tumor cells. Case reports using adoptive T-cell therapy provide proof of principle that KRAS G12D can be successfully targeted by the immune system in patients with cancer. Among the challenges facing investigators is the requirement of precision medicine to identify and match patients to available mKRAS peptide/HLA therapeutics and to increase the population coverage by targeting additional mKRAS epitopes. Ultimately, we envision mKRAS-directed immunotherapy as an effective treatment option for selected patients that will complement and perhaps synergize with small-molecule mKRAS inhibitors and targeted mKRAS degraders.

Transposable elements regulate thymus development and function

Transposable elements (TEs) are repetitive sequences representing ~45% of the human and mouse genomes and are highly expressed by medullary thymic epithelial cells (mTECs). In this study, we investigated the role of TEs on T-cell development in the thymus. We performed multiomic analyses of TEs in human and mouse thymic cells to elucidate their role in T-cell development. We report that TE expression in the human thymus is high and shows extensive age- and cell lineage-related variations. TE expression correlates with multiple transcription factors in all cell types of the human thymus. Two cell types express particularly broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDCs). In mTECs, transcriptomic data suggest that TEs interact with transcription factors essential for mTEC development and function (e.g., PAX1 and REL), and immunopeptidomic data showed that TEs generate MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 regulate small yet non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large numbers of TEs that likely form dsRNA, which can activate innate immune receptors, potentially explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study highlights the diversity of interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. Therefore, we propose that orchestrating TE expression in thymic cells is critical to prevent autoimmunity in vertebrates.

The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics

The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Scaling up robust immunopeptidomics technologies for a global T cell surveillance digital network

The human immunopeptidome plays a central role in disease susceptibility and resistance. In our opinion, the development of immunopeptidomics and other peptide sequencing technologies should be prioritized during the next decade, particularly within the framework of the Human Immunopeptidome Project initiative. In this context, we present bold ideas, fresh arguments, and call upon industrial partners and funding organizations to support and champion this important initiative that we believe has the potential to save countless lives in the future.

Robust and scalable single-molecule protein sequencing with fluorosequencing

The need to accurately survey proteins and their modifications with ever higher sensitivities, particularly in clinical settings with limited samples, is spurring development of new single molecule proteomics technologies. Fluorosequencing is one such highly parallelized single molecule peptide sequencing platform, based on determining the sequence positions of select amino acid types within peptides to enable their identification and quantification from a reference database. Here, we describe substantial improvements to fluorosequencing, including identifying fluorophores compatible with the sequencing chemistry, mitigating dye-dye interactions through the use of extended polyproline linkers, and developing an end-to-end workflow for sample preparation and sequencing. We demonstrate by fluorosequencing peptides in mixtures and identifying a target neoantigen from a database of decoy MHC peptides, highlighting the potential of the technology for high sensitivity clinical applications.

Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

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.

The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer

T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.

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.

Extracellular vesicles as next generation immunotherapeutics

Extracellular vesicles (EVs) function as a mode of intercellular communication and molecular transfer to elicit diverse biological/functional response. Accumulating evidence has highlighted that EVs from immune, tumour, stromal cells and even bacteria and parasites mediate the communication of various immune cell types to dynamically regulate host immune response. EVs have an innate capacity to evade recognition, transport and transfer functional components to target cells, with subsequent removal by the immune system, where the immunological activities of EVs impact immunoregulation including modulation of antigen presentation and cross-dressing, immune activation, immune suppression, and immune surveillance, impacting the tumour immune microenvironment. In this review, we outline the recent progress of EVs in immunorecognition and therapeutic intervention in cancer, including vaccine and targeted drug delivery and summarise their utility towards clinical translation. We highlight the strategies where EVs (natural and engineered) are being employed as a therapeutic approach for immunogenicity, tumoricidal function, and vaccine development, termed immuno-EVs. With seminal studies providing significant progress in the sequential development of engineered EVs as therapeutic anti-tumour platforms, we now require direct assessment to tune and improve the efficacy of resulting immune responses - essential in their translation into the clinic. We believe such a review could strengthen our understanding of the progress in EV immunobiology and facilitate advances in engineering EVs for the development of novel EV-based immunotherapeutics as a platform for cancer treatment.

Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past 2 decades. However, improvement in the accuracy of prediction algorithms is needed for clinical applications like the development of personalized cancer vaccines, the discovery of biomarkers for response to immunotherapies, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic Human Leukocyte Antigen (HLA) Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA allele to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.17-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

Recent advances in top-down proteome sample processing ahead of MS analysis

Top-down proteomics is emerging as a preferred approach to investigate biological systems, with objectives ranging from the detailed assessment of a single protein therapeutic, to the complete characterization of every possible protein including their modifications, which define the human proteoform. Given the controlling influence of protein modifications on their biological function, understanding how gene products manifest or respond to disease is most precisely achieved by characterization at the intact protein level. Top-down mass spectrometry (MS) analysis of proteins entails unique challenges associated with processing whole proteins while maintaining their integrity throughout the processes of extraction, enrichment, purification, and fractionation. Recent advances in each of these critical front-end preparation processes, including minimalistic workflows, have greatly expanded the capacity of MS for top-down proteome analysis. Acknowledging the many contributions in MS technology and sample processing, the present review aims to highlight the diverse strategies that have forged a pathway for top-down proteomics. We comprehensively discuss the evolution of front-end workflows that today facilitate optimal characterization of proteoform-driven biology, including a brief description of the clinical applications that have motivated these impactful contributions.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

'Cannibalism' of exogenous DNA sequences: The ancestral form of adaptive immunity which entails recognition of danger

Adaptive immunity is a sophisticated form of immune response capable of retaining the molecular memory of a very great diversity of target antigens (epitopes) as non-self. It is capable of reactivating itself upon a second encounter with an immunoglobulin or T-cell receptor antigen-binding site with a known epitope that had previously primed the host immune system. It has long been considered that adaptive immunity is a highly evolved form of non-self recognition that appeared quite late in speciation and complemented a more generalist response called innate immunity. Innate immunity offers a relatively non-specific defense (although mediated by sensors that could specifically recognize virus or bacteria compounds) and which does not retain a memory of the danger. But this notion of recent acquisition of adaptive immunity is challenged by the fact that another form of specific recognition mechanisms already existed in prokaryotes that may be able to specifically auto-protect against external danger. This recognition mechanism can be considered a primitive form of specific (adaptive) non-self recognition. It is based on the fact that many archaea and bacteria use a genome editing system that confers the ability to appropriate viral DNA sequences allowing prokaryotes to prevent host damage through a mechanism very similar to adaptive immunity. This is indistinctly called, 'endogenization of foreign DNA' or 'viral DNA predation' or, more pictorially 'DNA cannibalism'. For several years evidence has been accumulating, highlighting the crucial role of endogenization of foreign DNA in the fundamental processes related to adaptive immunity and leading to a change in the dogma that adaptive immunity appeared late in speciation.

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.

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.

Pathology of T-cell-mediated drug hypersensitivity reactions and impact of tolerance mechanisms on patient susceptibility

PURPOSE OF REVIEW

T-cell-mediated drug hypersensitivity is responsible for significant morbidity and mortality, and represents a substantial clinical concern. The purpose of this article is to focus on T-cell reactions and discuss recent advances in disease pathogenesis by exploring the impact of tolerance mechanisms in determining susceptibility in genetically predisposed patients.

RECENT FINDINGS

Certain drugs preferentially activate pathogenic T cells that have defined pathways of effector function. Thus, a critical question is what extenuating factors influence the direction of immune activation. A large effort has been given towards identifying phenotypic (e.g., infection) or genotypic (e.g., human leukocyte antigen) associations which predispose individuals to drug hypersensitivity. However, many individuals expressing known risk factors safely tolerate drug administration. Thus, mechanistic insight is needed to determine what confers this tolerance. Herein, we discuss recent clinical/mechanistic findings which indicate that the direction in which the immune system is driven relies upon a complex interplay between co-stimulatory/co-regulatory pathways which themselves depend upon environmental inputs from the innate immune system.

SUMMARY

It is becoming increasingly apparent that tolerance mechanisms impact on susceptibility to drug hypersensitivity. As the field moves forward it will be interesting to discover whether active tolerance is the primary response to drug exposure, with genetic factors such as HLA acting as a sliding scale, influencing the degree of regulation required to prevent clinical reactions in patients.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

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.

Understanding the constitutive presentation of MHC class I immunopeptidomes in primary tissues

Understanding the molecular principles that govern the composition of the MHC-I immunopeptidome across different primary tissues is fundamentally important to predict how T cells respond in different contexts in vivo. Here, we performed a global analysis of the MHC-I immunopeptidome from 29 to 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, HLA-B, and HLA-C allotypes do not contribute evenly to the global composition of the MHC-I immunopeptidome across multiple human tissues. Second, we found that tissue-specific and housekeeping MHC-I peptides share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-I immunopeptidome at the organism-wide scale. Fourth, we uncovered new components of the antigen processing and presentation network, including the carboxypeptidases CPE, CNDP1/2, and CPVL. Together, this study opens up new avenues toward a system-wide understanding of antigen presentation in vivo across mammalian species.

•

Tissue-specific and housekeeping MHC class I peptides share distinct properties

•

HLA-A, HLA-B, and HLA-C allotypes contribute very unevenly to the pool of class I peptides

•

MHC-I immunopeptidomes are represented by evolutionarily conserved proteins

•

An extended antigen processing and presentation pathway is uncovered

HLA Allele-Specific Quantitative Profiling of Type 1 Diabetic B Lymphocyte Immunopeptidome

Peptide ligands presented by human leukocyte antigen (HLA) molecules on the cell surface represent the immunopeptidome that could be utilized for identification of antigenic peptides for immunotherapy and prevention of autoimmune diseases. Although T-cells are well-known key players in the destruction of pancreatic beta-cells in type 1 diabetes (T1D), increasing evidence points toward a role for B-cells in disease pathogenesis. However, as antigen presenting cells, little is known about the comprehensive immunopeptidome of B cells and their changes in the context of T1D. We performed HLA allele-specific quantitative immunopeptidomics using B lymphocytes derived from T1D patients and healthy controls. Hundreds of HLA-I and HLA-II immunopeptides were identified as differentially regulated in T1D per HLA allele for B cells sharing identical HLA alleles. The results were further validated using additional T1D and healthy B cells with partially overlapped HLA alleles. Differentially expressed immunopeptides were confirmed with targeted proteomics and for reactivity using known T-cell assays in the immune epitope database. Considering samples with identical HLA alleles are difficult to obtain for T1D and other similar HLA-restricted diseases, our work represents a viable approach to better understand HLA allele-specific antigen presentation and may facilitate identification of immunopeptides for therapeutic applications in autoimmune diseases. Data are available via ProteomeXchange with identifier PXD026184.

What Is the Role of HLA-I on Cancer Derived Extracellular Vesicles? Defining the Challenges in Characterisation and Potential Uses of This Ligandome

The Human Leukocyte Antigen class I (HLA-I) system is an essential part of the immune system that is fundamental to the successful activation of cytotoxic lymphocytes, and an effective subsequent immune attack against both pathogen-infected and cancer cells. The importance of cytotoxic T cell activity and ability to detect foreign cancer-related antigenic peptides has recently been highlighted by the successful application of monoclonal antibody-based checkpoint inhibitors as novel immune therapies. Thus, there is an increased interest in fully characterising the repertoire of peptides that are being presented to cytotoxic CD8+ T cells by cancer cells. However, HLA-I is also known to be present on the surface of extracellular vesicles, which are released by most if not all cancer cells. Whilst the peptide ligandome presented by cell surface HLA class I molecules on cancer cells has been studied extensively, the ligandome of extracellular vesicles remains relatively poorly defined. Here, we will describe the current understanding of the HLA-I peptide ligandome and its role on cancer-derived extracellular vesicles, and evaluate the aspects of the system that have the potential to advance immune-based therapeutic approaches for the effective treatment of cancer.

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets

Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.

At the Cutting Edge against Cancer: A Perspective on Immunoproteasome and Immune Checkpoints Modulation as a Potential Therapeutic Intervention

Immunoproteasome is a noncanonical form of proteasome with enzymological properties optimized for the generation of antigenic peptides presented in complex with class I MHC molecules. This enzymatic property makes the modulation of its activity a promising area of research. Nevertheless, immunotherapy has emerged as a front-line treatment of advanced/metastatic tumors providing outstanding improvement of life expectancy, even though not all patients achieve a long-lasting clinical benefit. To enhance the efficacy of the currently available immunotherapies and enable the development of new strategies, a broader knowledge of the dynamics of antigen repertoire processing by cancer cells is needed. Therefore, a better understanding of the role of immunoproteasome in antigen processing and of the therapeutic implication of its modulation is mandatory. Studies on the potential crosstalk between proteasome modulators and immune checkpoint inhibitors could provide novel perspectives and an unexplored treatment option for a variety of cancers.

Immune Equilibrium Depends on the Interaction Between Recognition and Presentation Landscapes

In this review, we described the structure and organization of antigen-recognizing repertoires of B and T cells from the standpoint of modern immunology. We summarized the latest advances in bioinformatics analysis of sequencing data from T and B cell repertoires and also presented contemporary ideas about the mechanisms of clonal diversity formation at different stages of organism development. At the same time, we focused on the importance of the allelic variants of the HLA genes and spectra of presented antigens for the formation of T-cell receptors (TCR) landscapes. The main idea of this review is that immune equilibrium and proper functioning of immunity are highly dependent on the interaction between the recognition and the presentation landscapes of antigens. Certain changes in these landscapes can occur during life, which can affect the protective function of adaptive immunity. We described some mechanisms associated with these changes, for example, the conversion of effector cells into regulatory cells and vice versa due to the trans-differentiation or bystander effect, changes in the clonal organization of the general TCR repertoire due to homeostatic proliferation or aging, and the background for the altered presentation of some antigens due to SNP mutations of MHC, or the alteration of the presenting antigens due to post-translational modifications. The authors suggest that such alterations can lead to an increase in the risk of the development of oncological and autoimmune diseases and influence the sensitivity of the organism to different infectious agents.

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.

Neoantigen vaccine: An emerging immunotherapy for hepatocellular carcinoma

Tumor-specific neoantigens, which are expressed on tumor cells, can induce an effective antitumor cytotoxic T-cell response and mediate tumor regression. Among tumor immunotherapies, neoantigen vaccines are in early human clinical trials and have demonstrated substantial efficiency. Compared with more neoantigens in melanoma, the paucity and inefficient identification of effective neoantigens in hepatocellular carcinoma (HCC) remain enormous challenges in effectively treating this malignancy. In this review, we highlight the current development of HCC neoantigens in its generation, screening, and identification. We also discuss the possibility that there are more effective neoantigens in hepatitis B virus (HBV)-related HCC than in non-HBV-related HCC. In addition, since HCC is an immunosuppressive tumor, strategies that reverse immunosuppression and enhance the immune response should be considered for the practical exploitation of HCC neoantigens. In summary, this review offers some strategies to solve existing problems in HCC neoantigen research and provide further insights for immunotherapy.

Sensitive and Quantitative Detection of MHC-I Displayed Neoepitopes Using a Semiautomated Workflow and TOMAHAQ Mass Spectrometry

Advances in several key technologies, including MHC peptidomics, have helped fuel our understanding of basic immune regulatory mechanisms and the identification of T cell receptor targets for the development of immunotherapeutics. Isolating and accurately quantifying MHC-bound peptides from cells and tissues enables characterization of dynamic changes in the ligandome due to cellular perturbations. However, the current multistep analytical process is challenging, and improvements in throughput and reproducibility would enable rapid characterization of multiple conditions in parallel. Here, we describe a robust and quantitative method whereby peptides derived from MHC-I complexes from a variety of cell lines, including challenging adherent lines such as MC38, can be enriched in a semiautomated fashion on reusable, dry-storage, customized antibody cartridges. Using this method, a researcher, with very little hands-on time and in a single day, can perform up to 96 simultaneous enrichments at a similar level of quality as a manual workflow. TOMAHAQ (Triggered by Offset, Multiplexed, Accurate-mass, High-resolution, and Absolute Quantification), a targeted mass spectrometry technique that combines sample multiplexing and high sensitivity, was employed to characterize neoepitopes displayed on MHC-I by tumor cells and to quantitatively assess the influence of neoantigen expression and induced degradation on neoepitope presentation. This unique combination of robust semiautomated MHC-I peptide isolation and high-throughput multiplexed targeted quantitation allows for both the routine analysis of >4000 unique MHC-I peptides from 250 million cells using nontargeted methods, as well as quantitative sensitivity down to the low amol/μl level using TOMAHAQ targeted MS.

•

Semiautomated peptide immunoprecipitation on reusable antibody cartridges.

•

Application of TOMAHAQ for MHC-I detection and quantitation.

•

Routine analysis of >4000 unique MHC-I peptides from 250 million cells via automation.

•

Quantitative sensitivity down to the low amol/μl level using TOMAHAQ targeted MS.

Withdrawn: Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation

This article has been withdrawn by the authors. A publication of the manuscript with the correct figures and tables has been approved and the authors state the conclusions of the manuscript remain unaffected. Specifically, errors are in Figure 6A, Supplementary Figure 10B, Supplementary Figure 10C, and Supplementary Table 5. The details of the errors are as follows: the HLA types for one sample were incorrectly assigned because of a tumor/normal mislabeling from the biobank vendor. Due to the differing HLA types between the tumor and normal sample, the sequence analysis established that the HLA alleles for this patient had been deleted (HLA LOH). The authors conclude that this was an artifact caused by the normal sample mislabeling. The corrected version can be accessed (Pyke, R.M., Mellacheruvu, D., Dea, S., Abbott, C.W., Zhang, S.V., Philips, N.A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M,P., Chen, R., Boyle, S.M. (2022) Precision Neoantigen Discovery Using Large-Scale Immunopeptidomics and Composite Modeling of MHC Peptide Presentation. Mol. Cell. Proteomics 22, 100506

Restoring the Immunity in the Tumor Microenvironment: Insights into Immunogenic Cell Death in Onco-Therapies

Immunogenic cell death (ICD) elicited by cancer therapy reshapes the tumor immune microenvironment. A long-term adaptative immune response can be initiated by modulating cell death by therapeutic approaches. Here, the major hallmarks of ICD, endoplasmic reticulum (ER) stress, and damage-associated molecular patterns (DAMPs) are correlated with ICD inducers used in clinical practice to enhance antitumoral activity by suppressing tumor immune evasion. Approaches to monitoring the ICD triggered by antitumoral therapeutics in the tumor microenvironment (TME) and novel perspective in this immune system strategy are also reviewed to give an overview of the relevance of ICD in cancer treatment.

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.

Beryllium-specific CD4+ T cells induced by chemokine neoantigens perpetuate inflammation

Discovering dominant epitopes for T cells, particularly CD4+ T cells, in human immune-mediated diseases remains a significant challenge. Here, we used bronchoalveolar lavage (BAL) cells from HLA-DP2-expressing patients with chronic beryllium disease (CBD), a debilitating granulomatous lung disorder characterized by accumulations of beryllium-specific (Be-specific) CD4+ T cells in the lung. We discovered lung-resident CD4+ T cells that expressed a disease-specific public CDR3β T cell receptor motif and were specific to Be-modified self-peptides derived from C-C motif ligand 4 (CCL4) and CCL3. HLA-DP2-CCL/Be tetramer staining confirmed that these chemokine-derived peptides represented major antigenic targets in CBD. Furthermore, Be induced CCL3 and CCL4 secretion in the lungs of mice and humans. In a murine model of CBD, the addition of LPS to Be oxide exposure enhanced CCL4 and CCL3 secretion in the lung and significantly increased the number and percentage of CD4+ T cells specific for the HLA-DP2-CCL/Be epitope. Thus, we demonstrate a direct link between Be-induced innate production of chemokines and the development of a robust adaptive immune response to those same chemokines presented as Be-modified self-peptides, creating a cycle of innate and adaptive immune activation.

Spliced HLA-bound peptides: a Black Swan event in immunology

Peptides that bind to and are presented on the cell surface by human leucocyte antigen (HLA) molecules play a critical role in adaptive immunity. For a long time it was believed that all the HLA-bound peptides were generated through simple proteolysis of linear sequences of cellular proteins, and therefore are templated in the genome and proteome. However, evidence for untemplated peptide ligands of HLA molecules has accumulated during the last two decades, with a recent global analysis of HLA-bound peptides suggesting that a considerable proportion of HLA-bound peptides are potentially generated through splicing/fusion of discontinuous peptide segments from one or two distinct proteins. In this review, we will evaluate recent discoveries and debates on the contribution of spliced peptides to the HLA class I immunopeptidome, consider biochemical rules for splicing and the potential role of these spliced peptides in immune recognition.

In-Vitro Approaches to Predict and Study T-Cell Mediated Hypersensitivity to Drugs

Mitigating the risk of drug hypersensitivity reactions is an important facet of a given pharmaceutical, with poor performance in this area of safety often leading to warnings, restrictions and withdrawals. In the last 50 years, efforts to diagnose, manage, and circumvent these obscure, iatrogenic diseases have resulted in the development of assays at all stages of a drugs lifespan. Indeed, this begins with intelligent lead compound selection/design to minimize the existence of deleterious chemical reactivity through exclusion of ominous structural moieties. Preclinical studies then investigate how compounds interact with biological systems, with emphasis placed on modeling immunological/toxicological liabilities. During clinical use, competent and accurate diagnoses are sought to effectively manage patients with such ailments, and pharmacovigilance datasets can be used for stratification of patient populations in order to optimise safety profiles. Herein, an overview of some of the in-vitro approaches to predict intrinsic immunogenicity of drugs and diagnose culprit drugs in allergic patients after exposure is detailed, with current perspectives and opportunities provided.

HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy

BACKGROUND

The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level.

METHODS

Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing.

RESULTS

The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference.

CONCLUSION

Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .

Most non-canonical proteins uniquely populate the proteome or immunopeptidome

Combining RNA sequencing, ribosome profiling, and mass spectrometry, we elucidate the contribution of non-canonical translation to the proteome and major histocompatibility complex (MHC) class I immunopeptidome. Remarkably, of 14,498 proteins identified in three human B cell lymphomas, 2,503 are non-canonical proteins. Of these, 28% are novel isoforms and 72% are cryptic proteins encoded by ostensibly non-coding regions (60%) or frameshifted canonical genes (12%). Cryptic proteins are translated as efficiently as canonical proteins, have more predicted disordered residues and lower stability, and critically generate MHC-I peptides 5-fold more efficiently per translation event. Translating 5' "untranslated" regions hinders downstream translation of genes involved in transcription, translation, and antiviral responses. Novel protein isoforms show strong enrichment for signaling pathways deregulated in cancer. Only a small fraction of cryptic proteins detected in the proteome contribute to the MHC-I immunopeptidome, demonstrating the high preferential access of cryptic defective ribosomal products to the class I pathway.

The Role of Antigen Processing and Presentation in Cancer and the Efficacy of Immune Checkpoint Inhibitor Immunotherapy

Recent clinical successes of cancer immunotherapy using immune checkpoint inhibitors (ICIs) are rapidly changing the landscape of cancer treatment. Regardless of initial impressive clinical results though, the therapeutic benefit of ICIs appears to be limited to a subset of patients and tumor types. Recent analyses have revealed that the potency of ICI therapies depends on the efficient presentation of tumor-specific antigens by cancer cells and professional antigen presenting cells. Here, we review current knowledge on the role of antigen presentation in cancer. We focus on intracellular antigen processing and presentation by Major Histocompatibility class I (MHCI) molecules and how it can affect cancer immune evasion. Finally, we discuss the pharmacological tractability of manipulating intracellular antigen processing as a complementary approach to enhance tumor immunogenicity and the effectiveness of ICI immunotherapy.

Critical Review of Existing MHC I Immunopeptidome Isolation Methods

Major histocompatibility complex class I (MHC I) plays a crucial role in the development of adaptive immune response in vertebrates. MHC molecules are cell surface protein complexes loaded with short peptides and recognized by the T-cell receptors (TCR). Peptides associated with MHC are named immunopeptidome. The MHC I immunopeptidome is produced by the proteasome degradation of intracellular proteins. The knowledge of the immunopeptidome repertoire facilitates the creation of personalized antitumor or antiviral vaccines. A huge number of publications on the immunopeptidome diversity of different human and mouse biological samples-plasma, peripheral blood mononuclear cells (PBMCs), and solid tissues, including tumors-appeared in the scientific journals in the last decade. Significant immunopeptidome identification efficiency was achieved by advances in technology: the immunoprecipitation of MHC and mass spectrometry-based approaches. Researchers optimized common strategies to isolate MHC-associated peptides for individual tasks. They published many protocols with differences in the amount and type of biological sample, amount of antibodies, type and amount of insoluble support, methods of post-fractionation and purification, and approaches to LC-MS/MS identification of immunopeptidome. These parameters have a large impact on the final repertoire of isolated immunopeptidome. In this review, we summarize and compare immunopeptidome isolation techniques with an emphasis on the results obtained.

DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics

The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.

Self-Peptidome Variation Shapes Individual Immune Responses

The relationship between human genetic variation and disease has not been fully elucidated. According to the present view on infectious diseases pathogen resistance is linked to human leukocyte antigen (HLA) class I/II variants and their individual capacity to present pathogen-derived peptides. Yet, T cell education in the thymus occurs through negative and positive selection, and both processes are controlled by a combination of HLA class I/II variants and peptides from the self. Therefore, the capacity of given HLA class I/II variants to bind pathogen-derived peptides is only one part of the selective process to generate effective immune responses. We thus propose that peptidome variation contributes to shaping T cell receptor (TCR) repertoires and hence individual immune responses, and that this variation represents inherent modulator epitopes.

TCR repertoires emerge in the thymus in each individual as T cells undergo positive and negative selection.

T cell education is controlled by the combination of HLA class I/II molecules and their peptide pools (peptidome).

HLA class I/II molecules are highly plastic in human populations but the peptidome is also a source of variation. Hence combined diversity of HLA class I/II molecules and of self-peptides shapes individual immune responses.

Self-peptide variants that affect T cell repertoires represent inherent modulator epitopes.

Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.