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Identification of O-glycosylated decapeptides within the MUC1 repeat domain as potential MHC class I (A2) binding epitopes.

ABSTRACT: The MUC1 glycoprotein is considered a tumor antigen due to its over expression and aberrant glycosylation in cancer tissues. The latter results in appearance of new antigenic tumor specific glycopeptides not found on normal glycoforms of the mucin. MUC1 glycopeptides can be presented by APCs on MHC class II molecules to activate glycopeptide specific helper T-cells. No study has yet reported presentation of MUC1 glycopeptides on MHC class I molecules as stimulators of cytotoxic T-cells. In this study we show that human immunoproteasomes and cathepsin-L can generate octa to undecameric glycopeptides from the MUC1 repeat domain in vitro. We identified glycosylated fragments of which the decameric glycopeptide SAP10 [SAPDT(GalNAc)RPAPG] containing a single sugar binds with comparable strength to the MHC class I allele HLA A*0201 as predicted high-score binding epitopes of the tandem repeat. The same sequence glycosylated with the disaccharide Gal-GalNAc does not bind. The glycan on SAP10 is predicted by molecular modeling to either protrude out or point into the MHC groove. SAPDTRPAPG peptide and the respective glycopeptide stimulated cytotoxic T-cells in vitro. Our findings suggest that MUC1 tandem repeat glycopeptides are capable of activating both helper and cytotoxic T-cells and thus represent good candidates for further development as vaccines.

SUBMITTER: Ninkovic T

PROVIDER: S-EPMC2788793 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Identification of O-glycosylated decapeptides within the MUC1 repeat domain as potential MHC class I (A2) binding epitopes.

Ninkovic Tanja T Kinarsky Leo L Engelmann Katja K Pisarev Vladimir V Sherman Simon S Finn Olivera J OJ Hanisch Franz-Georg FG

Molecular immunology 20081112 1

The MUC1 glycoprotein is considered a tumor antigen due to its over expression and aberrant glycosylation in cancer tissues. The latter results in appearance of new antigenic tumor specific glycopeptides not found on normal glycoforms of the mucin. MUC1 glycopeptides can be presented by APCs on MHC class II molecules to activate glycopeptide specific helper T-cells. No study has yet reported presentation of MUC1 glycopeptides on MHC class I molecules as stimulators of cytotoxic T-cells. In this ...[more]

PMID: 19007994

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Project description:Tumour-specific mutations are ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognized as neo-antigens by the mature T-cell repertoire. Their systematic targeting by vaccine approaches, however, has been hampered by the fact that every patient's tumour possesses a unique set of mutations ('the mutanome') that must first be identified. Recently, we proposed a personalized immunotherapy approach to target the full spectrum of a patient's individual tumour-specific mutations. Here we show in three independent murine tumour models that a considerable fraction of non-synonymous cancer mutations is immunogenic and that, unexpectedly, the majority of the immunogenic mutanome is recognized by CD4(+) T cells. Vaccination with such CD4(+) immunogenic mutations confers strong antitumour activity. Encouraged by these findings, we established a process by which mutations identified by exome sequencing could be selected as vaccine targets solely through bioinformatic prioritization on the basis of their expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production as synthetic poly-neo-epitope messenger RNA vaccines. We show that vaccination with such polytope mRNA vaccines induces potent tumour control and complete rejection of established aggressively growing tumours in mice. Moreover, we demonstrate that CD4(+) T cell neo-epitope vaccination reshapes the tumour microenvironment and induces cytotoxic T lymphocyte responses against an independent immunodominant antigen in mice, indicating orchestration of antigen spread. Finally, we demonstrate an abundance of mutations predicted to bind to MHC class II in human cancers as well by employing the same predictive algorithm on corresponding human cancer types. Thus, the tailored immunotherapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time'.

Project description:The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.

Dataset Information

Identification of O-glycosylated decapeptides within the MUC1 repeat domain as potential MHC class I (A2) binding epitopes.

Publications

Identification of O-glycosylated decapeptides within the MUC1 repeat domain as potential MHC class I (A2) binding epitopes.

Similar Datasets

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