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.
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