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SignificanceDespite the revolution in cancer therapy initiated by checkpoint inhibitors, durable clinical responses remain sporadic in many types of cancer, including ovarian cancer. Understanding which antigens are essentially presented by tumor cells and further able to be recognized by T cells provides a major step toward novel effective targeted immunotherapies. In this study, we comprehensively analyzed the immunopeptidomic landscape of ovarian carcinoma and compared it to variety of benign sources to identify antigens exclusively presented on tumor cells. With personalized therapies moving into the focus of clinical cancer therapy, we further present insights on how gene-expression analysis and immunohistochemistry can support antigen selection for individualized immunotherapy.

The breakthrough development of clinically effective immune checkpoint inhibitors illustrates the potential of T-cell-based immunotherapy to effectively treat malignancies. A remaining challenge is to increase and guide the specificities of anticancer immune responses, e.g., by therapeutic vaccination or by adoptive T-cell transfer. By analyzing the landscape of naturally presented HLA class I and II ligands of primary chronic lymphocytic leukemia (CLL), we delineated a novel category of tumor-associated T-cell antigens based on their exclusive and frequent representation in the HLA ligandome of leukemic cells. These antigens were validated across different stages and mutational subtypes of CLL and found to be robustly represented in HLA ligandomes of patients undergoing standard chemo-/immunotherapy. We demonstrate specific immune recognition of these antigens exclusively in CLL patients, with the frequencies of representation in CLL ligandomes correlating with the frequencies of immune recognition by patient T cells. Moreover, retrospective survival analysis revealed survival benefits for patients displaying immune responses to these antigens. These results directly imply these nonmutant self-peptides as pathophysiologically relevant tumor antigens and encourages their implementation for cancer immunotherapy.

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.

Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

The classical HLA-C and the nonclassical HLA-E and HLA-G molecules play important roles both in the innate and adaptive immune system. Starting already during embryogenesis and continuing throughout our lives, these three Ags exert major functions in immune tolerance, defense against infections, and anticancer immune responses. Despite these important roles, identification and characterization of the peptides presented by these molecules has been lacking behind the more abundant HLA-A and HLA-B gene products. In this study, we elucidated the peptide specificities of these HLA molecules using a comprehensive analysis of naturally presented peptides. To that end, the 15 most frequently expressed HLA-C alleles as well as HLA-E*01:01 and HLA-G*01:01 were transfected into lymphoblastoid C1R cells expressing low endogenous HLA. Identification of naturally presented peptides was performed by immunoprecipitation of HLA and subsequent analysis of HLA-bound peptides by liquid chromatographic tandem mass spectrometry. Peptide motifs of HLA-C unveil anchors in position 2 or 3 with high variances between allotypes, and a less variable anchor at the C-terminal end. The previously reported small ligand repertoire of HLA-E was confirmed within our analysis, and we could show that HLA-G combines a large ligand repertoire with distinct features anchoring peptides at positions 3 and 9, supported by an auxiliary anchor in position 1 and preferred residues in positions 2 and 7. The wealth of HLA ligands resulted in prediction matrices for octa-, nona-, and decamers. Matrices were validated in terms of their binding prediction and compared with the latest NetMHC prediction algorithm NetMHCpan-3.0, which demonstrated their predictive power.

Key Points• Direct analysis of the HLApresented peptidome identifies a distinct antigenic signature in MM.• T-cell responses for these antigens are detectable exclusively in MM patients and can be induced in vitro in response-naive patients.Direct analysis of HLA-presented antigens by mass spectrometry provides a comprehensive view on the antigenic landscape of different tissues/malignancies and enables the identification of novel, pathophysiologically relevant T-cell epitopes. Here, we present a systematic and comparative study of the HLA class I and II presented, nonmutant antigenome of multiple myeloma (MM). Quantification of HLA surface expression revealed elevated HLA molecule counts on malignant plasma cells compared with normal B cells, excluding relevant HLA downregulation in MM. Analyzing the presentation of established myeloma-associated T-cell antigens on the HLA ligandome level, we found a substantial proportion of antigens to be only infrequently presented on primary myelomas or to display suboptimal degrees of myeloma specificity. However, unsupervised analysis of our extensive HLA ligand data set delineated a panel of 58 highly specific myeloma-associated antigens (including multiple myeloma SET domain containing protein) which are characterized by frequent and exclusive presentation on myeloma samples. Functional characterization of these target antigens revealed peptide-specific, preexisting CD8 1 T-cell responses exclusively in myeloma patients, which is indicative of pathophysiological relevance.Furthermore, in vitro priming experiments revealed that peptide-specific T-cell responses can be induced in response-naive myeloma patients. Together, our results serve to guide antigen selection for T-cell-based immunotherapy of MM. (Blood. 2015;126(10):1203-1213

In light of continuously accumulating data and knowledge on major human pathogens, comprehensive and up-to-date sources of easily accessible information are urgently required. The AureoWiki database (http://aureowiki.med.uni-greifswald.de) provides detailed information on the genes and proteins of clinically and experimentally relevant S. aureus strains, currently covering NCTC 8325, COL, Newman, USA300_FPR3757, and N315. By implementing a pan-genome approach, AureoWiki facilitates the transfer of knowledge gained in studies with different S. aureus strains, thus supporting functional annotation and better understanding of this organism. All data related to a given gene or gene product is compiled on a strain-specific gene page. The gene pages contain sequence-based information complemented by data on, for example, protein function and localization, transcriptional regulation, and gene expression. The information provided is connected via links to other databases and published literature. Importantly, orthologous genes of the individual strains, which are linked by a pan-genome gene identifier and a unified gene name, are presented side by side using strain-specific tabs. The respective pan-genome gene page contains an orthologue table for 32 S. aureus strains, a multiple-strain genome viewer, a protein sequence alignment as well as other comparative information. The data collected in AureoWiki is also accessible through various download options in order to support bioinformatics applications. In addition, based on two large-scale gene expression data sets, AureoWiki provides graphical representations of condition-dependent mRNA levels and protein profiles under various laboratory and infection-related conditions.