The SysteMHC Atlas project.

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts

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

Applied immunoinformatics: HLA peptidome analysis for cancer immunotherapy

Despite different therapeutic approaches, cancer is one of the leading causes of death worldwide. Therefore, new therapies, like immunotherapy, are being developed to cure cancer. All immunotherapies have in common that they need targets to recognize malignant cells. Both the malignant and the benign immunopeptidome have to be examined, to define these new targets. We herein present a large immunopeptidome dataset of benign tissues containing multiple tissue types from different individuals. Moreover, we introduce the HLA Ligand Atlas, a web-interface we developed to accompany the data. It provides user-friendly access to the data, a fast, interactive search option which can be used to search for tissue specific HLA-peptides, and provides common statistics to the user. Using the large dataset of benign samples, we were able to define general properties of the immunopeptidome. First, we showed that a short time storage of the samples at 8 °C does not alter the immunopeptidome in terms of the number of found peptides and their quality. Next, we performed quality control, in which we found an altered immunopeptidome in the samples of stomach tissue, which might be caused by pepsin in the samples. In addition, we analyzed both the inter- and the intra-individual variability of the immunopeptidome on protein and peptide level. This analysis revealed that sample variability was better explained by HLA type than by tissue-specific peptide presentation. Finally, the large dataset of benign samples allows us to describe properties like the length distribution of different HLA alleles and the nestedness of the peptides in the two HLA classes. In the last part of this thesis, we show how targets can be defined using immunopeptidome data. In this case, we investigated four different hematological malignancies. We describe entity-dividing lines by using a unsupervised hierarchical clustering of allotype-specific peptides, which showed that entity-specific analysis is recommended. Nevertheless, we found "panleukemia"- antigens shared across all four hematological malignancies, which were cancer exclusive.Trotz verschiedenster therapeutischen Behandlungsmethoden ist Krebs noch immer eine der häufigsten Todesursachen weltweit. Deshalb werden weiterhin neue Therapieansätze, wie zum Beispiel Immunotherapie, entwickelt, um Krebs zu heilen. Zur Entwicklung von Immunotherapien gegen Tumorzellen werden Angriffsziele benötigt, anhand derer Krebszellen erkannt werden können. Zur Bestimmung dieser ist es notwendig sowohl das Immunopeptidom von Krebszellen als auch das von gesundem Gewebe zu kennen. Wir präsentieren einen großen Immunopeptidomdatensatz von gesundem Gewebe, der sowohl verschiedene Organtypen eines Individuums, als auch verschiedene Individuen beinhaltet. Wir haben ein Webinterface - den HLA Ligand Atlas - entwickelt, um einen benutzerfreundlichen Zugriff auf die Daten zu ermöglichen. Dieses Webinterface erlaubt eine schnelle interaktive Suche im Datensatz, wie die Suche nach organspezifischen HLA Peptiden, und stellt zusätzliche Statisken bereit. Des Weiteren erlaubt es die Darstellung der Massenspektrometriespektren in einem interaktivem Spektrumviewer. Mit Hilfe des großen Datensatz an Normalgewebe konnten wir allgemeine Eigenschaften des Immunpeptidom bestimmen. Zuerst zeigen wir, dass das Immunopeptidom sich sowohl quantitativ als auch qualitativ nicht ändert, wenn die Probe kurzzeitig bei 8 °C gelagert wird. Als nächsten führten wir eine Qualitätskontrolle durch, die ein verändertes Immunopeptidom bei den Proben des Magengewebes aufzeigte, welches möglicherweise durch Pepsin in den Proben verursacht wurde. Zusätzlich untersuchten wir die inter- und intra-individuelle Variabilität des Immunopeptidom auf Protein- und Peptideebene. Die Analyse zeigte hier, dass der HLA-Typ einen größeren Einfluss auf die Variablität hat als die organspezifische Präsentation. Der große Datensatz von Normalgewebe erlaubte uns auch die Beschreibung weiterer Eigenschaften, wie die Peptidlängenverteilung für verschieden HLA Allele und die Beschreibung von Längenvarianten in den zwei HLA Klassen. Im letzten Teil dieser Doktorarbeit zeigen wir wie neue Angriffsziele mit Hilfe von Immunopeptidomdaten gefunden werden können. In unserem Fall untersuchten wir vier verschieden hämatologische Krebsarten. Durch eine unüberwachte hierarchische Clusteranalyse auf allotypspezifischen Peptide wurden hier klare, entitätsspezifische Cluster identifiziert. Dieser Befund spricht für die Notwendigkeit einer entitätsspezifischen Anaylse solcher Datenätze. Nichtsdestotrotz konnten wir auf allen vier hämatologischen Krebsarten „Pan-leukemia“ Antigene finden, die krebsexklusiv sind

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

Mass spectrometry guided immunoinformatics

A key task of the human immune system is the recognition and surveillance of peptides presented by the HLA complex on the surface of body cells. In this way, abnormalities can be discovered rapidly to elicit targeted immune responses. The identification of the HLA-presented immunopeptidome is thus of tremendous interest for research questions ranging from basic immunological processes to the design of immunotherapies such as vaccinations against infectious diseases and cancer. With the advancement of technical developments in biological high-throughput methods such as mass spectrometry it has become possible to identify thousands of sequences of HLA-presented peptides from a single sample of cells or human tissues. This has enabled researchers to directly investigate the peptide sequences presented in the human body and gain information on their properties. However, the acquisition and evaluation of large amounts of mass spectrometry measurements and HLA peptide sequence characteristics is a highly complex task that requires the development of sophisticated experimental and computational methods. This research work has focused on the evaluation and improvement of existing methodology to identify HLA-bound peptides and further to investigate various aspects of the immunopeptidome presented by human non-malignant and cancer tissues. An essential part of this effort was the development of novel automated, digital processing pipelines for HLA immunopeptidomics data. Specifically, two pipelines - “MHCquant“ that achieved superior sensitivity in contrast to existing software solutions and “DIAproteomics“ that allowed to explore the application of the novel method of data-independent acquisition to immunopeptidomics were developed. Application of the “MHCquant“ pipeline to the currently largest existing immunopeptidomics data set of human non-malignant tissues, allowed to construct the novel data resource “The HLA Ligand Atlas“. This benign reference data set is of great significance for the comparison with diseased state tissues and was thoroughly evaluated for differences across the human population, tissue specificity and the presence of cryptic peptides from non-canonical genomic origins. Finally, the HLA immunopeptidome of multiple clinical hepatocellular carcinoma samples was analysed in combination with next generation genomic sequencing measurements in an in-depth multi-omics approach in order to discover tumor-associated mutated antigens as suitable targets for cancer immunotherapy. While the effort did not result in the determination of particular mutated antigens, it was possible to pinpoint tumor somatic mutations that are likely presented as epitopes. Ultimately, the missing findings are discussed as a consequence of technological limitations and the low mutational burden of hepatocellular carcinoma. The developed computational workflows as well as the investigated data sets were made publicly available to serve the scientific community of future generations as a standard to reanalyze and compare novel results with and advance the holistic understanding of immunological processes in the human body