Dataset Information

The immunopeptidomic landscape of ovarian carcinoma

ABSTRACT: Immunopeptidome analysis of ovarian carcinoma tissues was performed to characterize the HLA class I and class II presented ligandome of this malignancy.

OTHER RELATED OMICS DATASETS IN: PRJNA398141

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Ovarian Serous Carcinoma Cell

DISEASE(S): Malignant Neoplasm Of Ovary

SUBMITTER: Heiko Schuster

LAB HEAD: Hans-Georg Rammensee

PROVIDER: PXD007635 | Pride | 2017-11-02

REPOSITORIES: Pride

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Publications

The immunopeptidomic landscape of ovarian carcinomas.

Schuster Heiko H Peper Janet K JK Bösmüller Hans-Christian HC Röhle Kevin K Backert Linus L Bilich Tatjana T Ney Britta B Löffler Markus W MW Kowalewski Daniel J DJ Trautwein Nico N Rabsteyn Armin A Engler Tobias T Braun Sabine S Haen Sebastian P SP Walz Juliane S JS Schmid-Horch Barbara B Brucker Sara Y SY Wallwiener Diethelm D Kohlbacher Oliver O Fend Falko F Rammensee Hans-Georg HG Stevanović Stefan S Staebler Annette A Wagner Philipp P

Proceedings of the National Academy of Sciences of the United States of America 20171101 46

Immunotherapies, particularly checkpoint inhibitors, have set off a revolution in cancer therapy by releasing the power of the immune system. However, only little is known about the antigens that are essentially presented on cancer cells, capable of exposing them to immune cells. Large-scale HLA ligandome analysis has enabled us to exhaustively characterize the immunopeptidomic landscape of epithelial ovarian cancers (EOCs). Additional comparative profiling with the immunopeptidome of a variety ...[more]

PMID: 29093164

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Project description:Biomaterials play an increasing role in clinical applications and regenerative medicine. Modern biomaterials should imitate the function of damaged tissue without triggering an immune response, initiate self-regeneration of the body and gradually degrade after implantation. The immune system is now well recognized to play a major role in influencing the biocompatibility of implanted medical devices. To develop a better understanding of the effects of biomaterials on the immune response, we have developed a highly sensitive novel test system capable of examining changes in the immune system by biomaterial. Here, we evaluate for the first time the immunopeptidome, a highly sensitive system that reflects cancer transformation, virus or drug influences and passes these changes directly to T cells, as a test system to examine the effects of contact with materials. Since monocytes are one of the first immune cells reacting to biomaterials, we have tested the influence of different materials - stainless steel, aluminum, zinc, high-density polyethylene, polyurethane films containing zinc diethyldithiocarbamate, copper, and zinc sulphate - on the monocytic THP-1 cell line. Material-associated peptides were identified after stimulation with all material types examined. The magnitude of induced changes in the immunopeptidome after the stimulation appears comparable to that of bacterial lipopolysaccharides (LPS). The source proteins of many detected peptides are associated with cytotoxicity, fibrosis, autoimmunity, inflammation and cellular stress. Considering all tested materials, it was found that the LPS-induced cytotoxicity-, inflammation- and cellular stress-associated HLA class I peptides were mainly induced by aluminum and HLA class II peptides mainly by stainless steel. These findings provide some of the first insights into the effects on the immunopeptidome by biomaterials. A more thorough understanding of these effects may enable the design of more biocompatible implant materials using in vitro models in future. This may be achieved through developing a deeper understanding of possible immune responses induced by biomaterials such as fibrosis, inflammation, cytotoxicity, and autoimmune reactions.

Dataset Information

The immunopeptidomic landscape of ovarian carcinoma

Publications

The immunopeptidomic landscape of ovarian carcinomas.

Similar Datasets

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