BackgroundThe 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.MethodsHuman 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.ResultsThe 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.ConclusionGiven 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.

Clonotype analysis is essential for complete characterization of antigen-specific T cells. Moreover, knowledge on clonal identity allows tracking of antigen-specific T cells in whole blood and tissue infiltrates and can provide information on antigenic specificity. Here, we developed a next generation sequencing (NGS)-based platform for the highly quantitative clonotype characterization of T cells and determined requirements for the unbiased characterization of the input material (DNA, RNA, ex vivo derived or cell culture expanded T cells). Thereafter we performed T cell receptor (TCR) repertoire analysis of various specimens in clinical settings including cytomegalovirus (CMV), polyomavirus BK (BKV) reactivation and acute cellular allograft rejection. Our results revealed dynamic nature of virusspecific T cell clonotypes; CMV reactivation was linked to appearance of new highly abundant antigenspecific clonalities. Moreover, analysis of clonotype overlap between BKV-, alloantigen-specific T cell-, kidney allograft-and urine-derived lymphocytes provided hints for the differential diagnosis of allograft dysfunction and enabled appropriate therapy adjustment. We believe that the established approach will provide insights into the regulation of virus-specific/ anti-tumor immunity and has high diagnostic potential in the clinical routine.

lkuchenb@inf.fu-berlin.de or peter.robinson@charite.de.

Personalized multipeptide vaccines are currently being discussed intensively for tumor immunotherapy. In order to identify epitopesshort, immunogenic peptidessuitable for eliciting a tumor-specific immune response, human leukocyte antigen-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed by liquid chromatographycoupled tandem mass spectrometry (LC−MS/MS). Here, we present MHCquant, a fully automated, portable computational pipeline able to process LC−MS/MS data automatically and generate annotated, false discovery rate-controlled lists of (neo-)epitopes with associated relative quantification information. We could show that MHCquant achieves higher sensitivity than established methods. While obtaining the highest number of unique peptides, the rate of predicted MHC binders remains still comparable to other tools. Reprocessing of the data from a previously published study resulted in the identification of several neoepitopes not detected by previously applied methods. MHCquant integrates tailor-made pipeline components with existing open-source software into a coherent processing workflow. Container-based virtualization permits execution of this workflow without complex software installation, execution on cluster/cloud infrastructures, and full reproducibility of the results. Integration with the data analysis workbench KNIME enables easy mining of large-scale immunopeptidomics data sets. MHCquant is available as open-source software along with accompanying documentation on our website at https://www. openms.de/mhcquant/.

ABSTRACTThe human leukocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Here, we describe the HLA Ligand Atlas, an extensive collection of mostly matched HLA-I and -II ligandomes from 225 benign samples (29 tissues, 21 subjects). The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 89,853 HLA-I- and 140,861 HLA-II ligands. We observe that the immunopeptidomes differ considerably between tissues and individuals on both source protein and HLA-ligand level. 1,407 HLA-I ligands stem from non-canonical genomic regions. We highlight the importance of comparatively analyzing both benign and malignant tissues to inform tumor association, based on a case study in three glioblastoma patients. The resource provides insights into applied and basic immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy, and autoimmunity. It is publicly available at www.hla-ligand-atlas.org.

No abstract

Influenza vaccination is a common approach to prevent seasonal and pandemic influenza. Pre-existing antibodies against close viral strains might impair antibody formation against previously unseen strains–a process called original antigenic sin. The role of this pre-existing cellular immunity in this process is, despite some hints from animal models, not clear. Here, we analyzed cellular and humoral immunity in healthy individuals before and after vaccination with seasonal influenza vaccine. Based on influenza-specific hemagglutination inhibiting (HI) titers, vaccinees were grouped into HI-negative and -positive cohorts followed by in-depth cytometric and TCR repertoire analysis. Both serological groups revealed cross-reactive T-cell memory to the vaccine strains at baseline that gave rise to the majority of vaccine-specific T-cells post vaccination. On the contrary, very limited number of vaccine-specific T-cell clones was recruited from the naive pool. Furthermore, baseline quantity of vaccine-specific central memory helper T-cells and clonotype richness of this population directly correlated with the vaccination efficacy. Our findings suggest that the deliberate recruitment of pre-existing cross-reactive cellular memory might help to improve vaccination outcome.

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min.