Deciphering the Landscape of HLA class-I and class-II Phosphopeptidomes leads to Robust Predictions of Phosphorylated HLA ligands

Activation of CD8+ and CD4+ T cells through recognition of antigens presented by class I and class II human leukocyte antigen (HLA-I/HLA-II) molecules is crucial for immune responses against infected or malignant cells. In cancer, neoantigens can arise from cancer-specific genomic or proteomic alterations, including mutations and aberrant post-translational modification, such as phosphorylation. Identifying HLA ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. While much work has been done on unmodified HLA-I and HLA-II ligands, only little is known about the presentation of phosphorylated peptides, in particular by HLA-II molecules. Moreover, none of the existing in silico models for predictions of HLA – ligand interactions are specifically trained on phosphorylated ligands. This thesis presents in-depth analyses of phosphorylated HLA-I and HLA-II ligands and introduces predictors for HLA – phosphorylated ligand interactions. The first part of this thesis comprises the curation of phosphorylated HLA-I ligands from several Mass Spectrometry – based peptidomics studies, identifying more than 2,000 unique phosphorylated peptides covering 72 HLA-I alleles. Furthermore, it was see that phosphorylated HLA-I ligands are shaped by a combination of HLA-I binding motifs, intrinsic HLA-I binding properties of phosphorylated ligands and kinase motifs. Combining phosphorylated HLA-I ligands with unmodified data for training a prediction model resulted in improved predictions of phosphorylated HLA-I ligands. The second part addresses phosphorylated HLA-II ligands presented by professional antigen presenting cells for CD4+ T cell activation. MS – based HLA-II peptidomics data resulted in the identification of binding motifs for more than 30 HLA-II alleles, comprising 2,473 unique phosphorylated ligands. These were used to retrain a predictor for HLA-II - ligand interactions and showed improved accuracy for phosphorylated ligands. The analysis of the phosphorylated HLA-II peptidomes revealed a more diverse repertoire of kinases responsible for the phosphorylation of peptides presented on HLA-II compared to HLA-I. In summary, the current work presents in-depth studies on phosphorylated HLA ligands as well as bioinformatics tools for the predictions of phosphorylated peptide interactions with HLA-I and HLA-II molecules. -- L'activation des cellules T CD8+ et CD4+ suite à la reconnaissance d’antigènes présentés par les antigènes des leucocytes humains de classe I et II (HLA-I/HLA-II) est cruciale pour les réponses immunitaires contre les cellules infectées ou cancéreuses. Dans le cancer, les néoantigènes peuvent provenir d'altérations génomiques ou protéomiques spécifiques au cancer, par exemple des mutations ou des modifications post-traductionnelles aberrantes, telles que la phosphorylation. L'identification des ligands HLA reste une tâche difficile qui nécessite soit un travail expérimental lourd pour l'identification in vivo, soit des outils bio-informatiques optimisés pour des prédictions précises. Si beaucoup de travail a été réalisé sur les ligands HLA-I et HLA-II non modifiés, on ne sait que peu de choses sur la présentation des peptides phosphorylés, en particulier par les molécules HLA-II. De plus, aucun des modèles in silico existants pour la prédiction des interactions HLA - ligands n'est spécifiquement entraîné sur les ligands phosphorylés. Cette thèse présente des analyses détaillées sur les ligands HLA-I et HLA-II phosphorylés et introduit des prédicteurs pour les interactions HLA - ligands phosphorylés. La première partie de cette thèse comprend la curation des ligands HLA-I phosphorylés provenant de plusieurs études peptidiques de spectrométrie de masse, identifiant plus de 2’000 peptides phosphorylés uniques couvrant 72 allèles HLA-I. De plus, il a été constaté que les ligands HLA-I phosphorylés sont obtenus par une combinaison de motifs de liaison aux HLA-I, de propriétés intrinsèques de liaison entre les HLA-I et les ligands phosphorylés et de motifs de kinases. La combinaison de ces ligands HLA-I phosphorylés avec des données de ligands non modifiés pour l’entraînement du prédicteur a permis d'améliorer les prédictions des ligands HLA-I phosphorylés. La deuxième partie de cette thèse porte sur les ligands HLA-II phosphorylés qui sont présentés par des cellules présentatrices d'antigènes professionnelles pour l'activation des lymphocytes T CD4+. Les données peptidiques de HLA-II basées sur la spectrométrie de masse ont permis d'identifier des motifs de liaison pour plus de 30 allèles HLA-II, comprenant 2’473 ligands phosphorylés uniques. Ces motifs ont été utilisés pour re-entraîner un prédicteur des interactions entre les ligands et HLA-II qui a montré une meilleure précision pour les ligands phosphorylés. En outre, l'analyse du peptidome HLA-II phosphorylé a révélé un répertoire plus diversifié de kinases responsables de la phosphorylation des peptides présentés par les HLA-II par rapport aux HLA-I. En résumé, cette thèse présente des études détaillées sur les ligands HLA phosphorylés ainsi que des outils bio-informatiques pour la prédiction des interactions des peptides phosphorylés avec les molécules HLA-I et HLA-II

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition

Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes.Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Karosiene, Edita. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Jurtz, Vanessa. Technical University of Denmark; DinamarcaFil: Edwards, Lindy. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California at San Diego; Estados Unido

'Hotspots' of Antigen Presentation Revealed by Human Leukocyte Antigen Ligandomics for Neoantigen Prioritization.

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived from processing and presentation of mutated proteins are one of the leading targets for T-cell recognition of cancer cells. Currently, most studies attempt to identify neoantigens based on predicted affinity to HLA molecules, but the performance of such prediction algorithms is rather poor for rare HLA class I alleles and for HLA class II. Direct identification of neoantigens by mass spectrometry (MS) is becoming feasible; however, it is not yet applicable to most patients and lacks sensitivity. In an attempt to capitalize on existing immunopeptidomics data and extract information that could complement HLA-binding prediction, we first compiled a large HLA class I and class II immunopeptidomics database across dozens of cell types and HLA allotypes and detected hotspots that are subsequences of proteins frequently presented. About 3% of the peptidome was detected in both class I and class II. Based on the gene ontology of their source proteins and the peptide's length, we propose that their processing may partake by the cellular class II presentation machinery. Our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features such as binding affinity and stability or RNA abundance. We further introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens

Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition

Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests

Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding

An Integrated Immunopeptidomics and Proteogenomics Framework to Discover Non-Canonical Targets for Cancer Immunotherapy

Un élément essentiel de l’immunothérapie appliquée au cancer est l’identification de peptides liant les antigènes des leucocytes humains (HLA) et capables d’induire une puissante réponse T anti-tumorale. La spectrométrie de masse (MS) constitue actuellement la seule méthode non-biaisée permettant une analyse détaillée du panel d’antigènes susceptibles d’être présentés aux lymphocytes T in vivo. L’utilisation de cette méthode en clinique requiert toutefois des améliorations significatives de la méthodologie utilisée lors de l’identification des peptides HLA. Un consortium multidisciplinaire de chercheurs a récemment mis en lumière les problèmes actuellement liés à l’utilisation de la MS en immunopeptidomique, soulignant le besoin de développer de nouvelles méthodes et mettant en évidence le défi que représente la standardisation de l’immuno-purification des molécules HLA. La première partie de cette thèse vise à optimiser les méthodes expérimentales permettant l’extraction des peptides apprêtés aux HLA. L’optimisation de la méthodologie de base a permis des améliorations notables en terme de débit, de reproductibilité, de sensibilité et a permis une purification séquentielle des molécules de HLA de classe I de classe II ainsi que de leurs peptides, à partir de lignées cellulaires ou de tissus. En comparaison avec les méthodes existantes, ce protocole comprend moins d’étapes et permet de limiter la manipulation des échantillons ainsi que le temps de purification. Cette méthode, pour les peptides HLA extraits, a permis d’obtenir des taux de reproductibilité et de sensibilité sans précédents (corrélations de Pearson jusqu'à 0,98 et 0,97 pour les HLA de classe I et de classe II, respectivement). De plus, la faisabilité d’études comparatives robustes a été démontrée à partir d’une lignée cellulaire de cancer de l’ovaire, traitée à l'interféron gamma. En effet, cette nouvelle méthode a mis en évidence des changements quantitatifs et qualitatifs du catalogue de peptides présentés aux HLA. Les résultats obtenus ont mis en avant une augmentation de la présentation de longs ligands chymotryptiques de classe I. Ce phénomène est probablement lié à la modulation de la machinerie de traitement et de présentation des antigènes. Dans cette première partie de thèse, nous avons développé une méthodologie robuste et rationalisée, facilitant la purification des HLA et pouvant être appliquée en recherche fondamentale et translationnelle. Bien que les néoantigènes représentent une cible attractive, des études récentes ont mis en évidence l’existence des antigènes non canoniques. Ces antigènes tumoraux, bien que non mutés, sont aussi spécifiques aux cellules cancéreuses et semblent jouer un rôle important dans l’immunité anti-tumorale. La seconde partie de cette thèse a pour objectif le développement d’une méthodologie d’analyse permettant l’identification ainsi que la validation de ces antigènes particuliers. Les antigènes non canoniques sont d'origine présumée non codante et ne sont, par conséquent, que rarement inclus dans les bases de données des séquences de protéines de référence. De ce fait, ils ne sont généralement pas pris en compte lors des recherches de MS utilisant de telles bases de données. Afin de palier ce problème et de permettre leur identification par MS, le séquençage de l'exome entier, le séquençage de l'ARN sur une population de cellules et sur des cellules uniques, ainsi que le profilage des ribosomes ont été intégrés aux données d’immunopeptidomique. Ainsi, NewAnce, un programme informatique permettant de combiner les données de deux outils de recherche MS en tandem, a été développé afin de calculer le taux d’antigènes non canoniques identifiés comme faux positifs. L’utilisation de NewAnce sur des lignées cellulaires provenant de patients atteints de mélanomes ainsi que sur des biopsies de cancer du poumon a permis l’identification précise de centaines de peptides HLA non classiques, spécifiques aux cellules tumorales et communs à plusieurs patients. Le niveau de confirmation des peptides non canoniques a ensuite été testé à l’aide d’une approche de MS ciblée. Les peptides résultant de ces analyses ont été minutieusement validés pour un des échantillons de mélanome disponibles. De plus, le profilage des ribosomes a révélé que les nouveaux cadres de lecture ouverts, desquels résultent certains de ces peptides non classiques, sont activement traduits. L’évaluation de l’immunogenicité de ces peptides a été évaluée avec des cellules immunitaires autologues et a révélé un épitope immunogène non canonique, provenant d'un cadre de lecture ouvert alternatif du gène ABCB5, un marqueur des cellules souches du mélanome. De manière globale, les résultats obtenus au cours de cette thèse soulignent la possibilité d’inclure ce type d’analyse de proteogénomique dans un protocole d’identification de néoantigènes existant. Cela permettrait d’inclure et prioriser des antigènes tumoraux non classiques et de proposer aux patients en impasse thérapeutique des immunothérapies anti-tumorales personnalisées. -- A central factor to the development of cancer immunotherapy is the identification of clinically relevant human leukocyte antigen (HLA)-bound peptides that elicit potent anti-tumor T cell responses. Mass spectrometry (MS) is the only unbiased technique that captures the in vivo presented HLA repertoire. However, significant improvements in MS-based HLA peptide discovery methodologies are necessary to enable the smooth transition to the clinic. Recently, a consortium of multidisciplinary researchers presented current issues in clinical MS-based immunopeptidomics, highlighting method development and standardization challenges in HLA immunoaffinity purification. The first part of this thesis addresses improvements to the experimental method for HLA peptide extraction. The approach was optimized with several new developments, facilitating high-throughput, reproducible, scalable, and sensitive sequential immunoaffinity purification of HLA class I and class II peptides from cell lines and tissue samples. The method showed increased speed, and reduced sample handling when compared to previous methods. Unprecedented depth and high reproducibility were achieved for the obtained HLA peptides (Pearson correlations up to 0.98 and 0.97 for HLA class I and HLA class II, respectively). Additionally, the feasibility of performing robust comparative studies was demonstrated on an ovarian cancer cell line treated with interferon gamma. Both quantitative and qualitative changes were detected in the cancer HLA repertoire upon treatment. Specifically, a yet unreported and interesting phenomenon was the upregulated presentation of longer and chymotryptic-like HLA class I ligands, likely related to the modulation of the antigen processing and presentation machinery. Taken together, a robust and streamlined framework was built that facilitates peptide purification and its application in basic and translational research. Furthermore, recent studies have shed light that, along with the highly attractive mutated neoantigens, other non-mutated, yet tumor-specific, non-canonical antigens may also play an important role in anti-tumor immunity. Non-canonical antigens are of presumed non-coding origin and not commonly included in protein reference databases, and are therefore typically disregarded in database-dependent MS searches. The second part of this thesis develops an analytical workflow enabling the confident identification and validation of non- canonical tumor antigens. For this purpose, whole exome sequencing, bulk and single-cell RNA sequencing and ribosome profiling were integrated with MS-based immunopeptidomics for personalized non-canonical HLA peptide discovery. A computational module called NewAnce was designed, which combines the results of two tandem MS search tools and implements group-specific false discovery rate calculations to control the error specifically for the non-canonical peptide group. When applied to patient-derived melanoma cell lines and paired lung cancer and normal tissues, NewAnce resulted in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides. Next, the level of non-canonical peptide confirmation was tested in a targeted MS-based approach, and selected non-canonical peptides were extensively validated for one melanoma sample. Furthermore, the novel open reading frames that generate a selection of these non- canonical peptides were found to be actively translated by ribosome profiling. Importantly, these peptides were assessed with autologous immune cells and a non-canonical immunogenic epitope was discovered from an alternative open reading frame of melanoma stem cell marker gene ABCB5. This thesis concludes by highlighting the possibility of incorporating the proteogenomics pipeline into existing neoantigen discovery engines in order to prioritize tumor-specific non-canonical peptides for cancer immunotherapy. -- Maladie très hétérogène et multifactorielle, le cancer représente à ce jour la seconde cause de décès dans le monde. Bien que le système immunitaire soit capable de reconnaître puis d’éliminer les cellules cancéreuses, ces dernières peuvent à leur tour s’adapter et accumuler des mutations leur permettant d’échapper à cette reconnaissance. L’immunothérapie anti-tumorale démontre le rôle clé de l’immunité dans l’éradication des tumeurs. Cependant, ces thérapies prometteuses ne sont efficaces que chez une petite proportion des patients traités. Une étape majeure dans l’établissement d’une réponse immunitaire anti-tumorale est la reconnaissance d’antigènes associés aux tumeurs. Des études récentes ont montré que les antigènes tumoraux issus de régions non-codantes du génome (antigènes non-canoniques) peuvent jouer un rôle clé dans l’induction de réponses immunitaires. Ainsi, l’identification de ces antigènes tumoraux particuliers permettrait de guider le développement d’immunothérapies anti-cancéreuses personnalisées telles que la vaccination ou encore le transfert adoptif de lymphocytes T reconnaissant ces cibles. La spectrométrie de masse (MS) est une technique non biaisée permettant l’identification et l’analyse du répertoire des antigènes présentés in vivo. Cependant, cette technique nécessite d’être optimisée et standardisée afin d’être utilisée en clinique. Ainsi, la première partie de ces travaux de thèse a été dédiée à l’optimisation expérimentale de cette méthode à partir d’échantillons de tissus et de lignées cellulaires. En comparaison avec les protocoles standards, cette technique permet une couverture plus complète, rapide et reproductible du répertoire de peptides apprêtés aux HLA. La seconde partie de cette thèse a été consacrée au développement d’une méthode permettant l’identification d’antigènes tumoraux non-canoniques via le séquençage d’ARN cellulaire, ribosomique et l’utilisation de notre méthode d’immunopeptidomique optimisée. Afin de contrôler l’identification de faux positifs, nous avons élaboré un nouveau module computationnel. Ce module a permis l’identification de plusieurs centaines de peptides-HLA non-canoniques, partagés et spécifiques au mélanome et au cancer du poumon. Le séquençage des ARN ribosomiques a mis en évidence la traduction de nouveaux cadre ouverts de lecture desquels sont traduits de nouveaux peptides non-canoniques. Cette technique nous a permis de mettre en évidence un épitope immunogène issu du gène ABCB5, un marqueur de cellules souches cancéreuses préalablement identifié dans le mélanome. De manière globale, ces travaux de thèse, alliant immunopeptidomique et protéogénomique, ont permis la mise au point d’une méthode expérimentale permettant une meilleure identification d’antigènes tumoraux. Nous espérons que ces résultats amélioreront l’identification et la priorisation de cibles pertinentes pour l’immunothérapie anti-cancéreuse en clinique

Immunoinformatics: Predicting Peptide–MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide?MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Buus, Søren. Universidad de Copenhagen; Dinamarc

In Silico Analysis of the Minor Histocompatibility Antigen Landscape Based on the 1000 Genomes Project

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is routinely used to treat hematopoietic malignancies. The eradication of residual tumor cells during engraftment is mediated by donor cytotoxic T lymphocytes reactive to alloantigens. In a HLA-matched transplantation context, alloantigens are encoded by various polymorphic genes situated outside the HLA locus, also called minor histocompatibility antigens (MiHAs). Recently, MiHAs have been recognized as promising targets for post-transplantation T-cell immunotherapy as they have several appealing advantages over tumor-associated antigens (TAAs) and neoantigens, i.e., they are more abundant than TAAs, which potentially facilitates multiple targeting; and unlike neoantigens, they are encoded by germline polymorphisms, some of which are common and thus, suitable for off-the-shelf therapy. The genetic sources of MiHAs are nonsynonymous polymorphisms that cause differences between the recipient and donor proteomes and subsequently, the immunopeptidomes. Systematic description of the alloantigen landscape in HLA-matched transplantation is still lacking as previous studies focused only on a few immunogenic and common MiHAs. Here, we perform a thorough in silico analysis of the public genomic data to classify genetic polymorphisms that lead to MiHA formation and estimate the number of potentially available MiHA mismatches. Our findings suggest that a donor/recipient pair is expected to have at least several dozen mismatched strong MHC-binding SNP-associated peptides per HLA allele (116 ± 26 and 65 ± 15 for non-related pairs and siblings respectively in European populations as predicted by two independent algorithms). Over 70% of them are encoded by relatively frequent polymorphisms (minor allele frequency > 0.1) and thus, may be targetable by off-the-shelf therapeutics. We showed that the most appealing targets (probability of mismatch over 20%) reside in the asymmetric allele frequency region, which spans from 0.15 to 0.47 and corresponds to an order of several hundred (213 ± 47) possible targets per HLA allele that can be considered for immunogenicity validation. Overall, these findings demonstrate the significant potential of MiHAs as targets for T-cell immunotherapy and emphasize the need for the systematic discovery of novel MiHAs

Advancing immunopeptidomics: validation of the method, improved epitope prediction, peptide-based HLA typing and discrimination of healthy and malignant tissue

Seit fast 30 Jahren wird das Immunpeptidom durch Elution von Peptiden aus HLA-Molekülen analysiert. Weltweit nutzen mittlerweile mehrere Institute und Unternehmen diese Methode für ein breites Spektrum an Untersuchungen, die von der simplen Identifizierung von HLA-Peptidmotiven für verschiedene Organismen bis hin zum Nachweis kryptischer krankheitsspezifischer Peptide reichen. Die Immunpeptidomik ist populärer denn je, seit sich die Medikamentenentwicklung in den letzten Jahren auf die positive Modulation des Immunsystems fokussiert hat. Die Zulassung der ersten Checkpoint-Antikörper leitete die Ära der Immuntherapie ein und spezifische Immuntherapien mit weniger Nebenwirkungen stehen nun im Blickpunkt. Das Anwendungsspektrum der Immunpeptidomik ist mittlerweile breit gefächert, dennoch enthält das Immunpeptidom immer noch eine große Fülle von Informationen, die darauf warten, entschlüsselt zu werden. Aktuell ist die Immunpeptidomik darin eingeschränkt, dass die große Anzahl von Peptiden, mit unterschiedlichen Affinitäten und Stabilitäten der Peptid-HLA-Komplexe, nicht optimal erfasst werden kann und daher unter anderem nur begrenzte Wiederfindungsraten möglich sind. Zu Beginn dieser Doktorarbeit gab es ungelöste Fragestellungen auf dem Gebiet der Immunpeptidomik, die in dieser Arbeit untersucht werden sollten: Ist es möglich, die Immunpeptidomik zu validieren und diese zuverlässig für klinische Studien und die Medikamentenentwicklung einzusetzen? Gibt es heute eine zuverlässige Methode zur Identifizierung von Peptidmotiven für Peptid-präsentierende MHC-Klasse-I-Allotypen, dem Grundstein für Epitopvorhersagen und Wirkstoffidentifizierungen? Ist es möglich, Peptide zur Klassifizierung von HLA-Allotypen oder zur Unterscheidung zwischen gesundem und bösartigem Gewebe zu verwenden? Können tumorspezifische Peptide mit dieser Omik-Technologie zuverlässig charakterisiert werden? In dieser Doktorarbeit wurde die immunpeptidomische Methode validiert, um die Zuverlässigkeit der LC-MS/MS-Peptid-Identifizierung zu gewährleisten, und es wurden alle erforderlichen Parameter der Europäischen Arzneimittel-Agentur und U. S. Food and Drug Administration untersucht. Darüber hinaus wurde ein aktualisiertes Protokoll für die Identifizierung von MHC-Liganden, die Entschlüsselung von Peptidmotiven und die Generierung von Matrizen für die Epitopvorhersage erstellt, das sowohl für monoallele Zellen als auch für multiallele Gewebe verwendet werden kann. Schließlich wurde eine Methode entwickelt, um allotypische Peptide zu identifizieren, die eine HLA-Typisierung ermöglichen. Diese Peptide können auch als interner Standard für die semi-quantitative Untersuchung der Tumorspezifität von Peptiden verwendet werden. Diese Methode wurde erfolgreich implementiert, um gewebe- und dignitätsspezifische Muster im Immunpeptidom zu identifizieren und die Dignität von immunpeptidomischen Proben zu bestimmen.For almost 30 years now, the immunopeptidome has been analyzed by eluting peptides from HLA molecules. This method has already been established in several institutes and companies worldwide and is now used for a wide range of investigations from the simple identification of HLA peptide motifs for different organisms to the detection of cryptic disease-specific peptides. The field of immunopeptidomics is more popular than ever as drug development has focused on the positive modulation of the immune system in recent years. Since the approval of the first checkpoint antibodies, the era of immunotherapy has been running and specific immunotherapies with fewer side effects are in the focus. There is a wide range of applications, yet, the immunopeptidome still contains a great wealth of information waiting to be deciphered. Currently, immunopeptidomics is limited in the identification of the large number of peptides with different affinities and stabilities of the peptide-HLA complexes. Therefore, amongst many other factors, only limited recovery rates are possible. When this doctoral thesis started, there were several unresolved questions in the field of immunopeptidomics that should be approached in this thesis: Is it possible to validate immunopeptidomics and use it reliably for clinical studies and drug development? Is there nowadays a reliable method to identify the peptide motif for peptide presenting MHC class I allotypes, the cornerstone for epitope predictions or active substance identification? Is it possible to use peptides to classify HLA allotypes or differentiate between healthy and malignant tissue? Can tumor-specific peptides be reliably characterized with this omic technology? In this doctoral thesis the immunopeptidomic method was validated to ensure the reliability of LC-MS/MS peptide identification and all required parameters of the European Medicines Agency (EMA) and Food and Drug Administration (FDA) were investigated. In addition, an updated protocol for the identification of MHC ligands, deconvolution of peptide motifs and generation of matrices for epitope prediction was established, which can be used for monoallelic cells as well as multiallelic tissue. Finally, a method was developed to identify allotypic peptides that allow HLA typing. These peptides can also be used as an internal standard for semi-quantitative investigation of the tumor specificity of peptides. The developed method was also successfully implemented to identify tissue and dignity specific patterns in the immunopeptidome and to determine the dignity of immunopeptidomic samples