title: Identification of noncanonical melanoma-associated T cell epitopes for cancer immunotherapy.
abstract: The identification of tumor-associated T cell epitopes has contributed significantly to the understanding of the interrelationship of tumor and immune system and is instrumental in the development of therapeutic vaccines for the treatment of cancer. Most of the known epitopes have been identified with prediction algorithms that compute the potential capacity of a peptide to bind to HLA class I molecules. However, naturally expressed T cell epitopes need not necessarily be strong HLA binders. To overcome this limitation of the available prediction algorithms we established a strategy for the identification of T cell epitopes that include suboptimal HLA binders. To this end, an artificial neural network was developed that predicts HLA-binding peptides in protein sequences by taking the entire sequence context into consideration rather than computing the sum of the contribution of the individual amino acids. Using this algorithm, we predicted seven HLA A*0201-restricted potential T cell epitopes from known melanoma-associated Ags that do not conform to the canonical anchor motif for this HLA molecule. All seven epitopes were validated as T cell epitopes and three as naturally processed by melanoma tumor cells. T cells for four of the new epitopes were found at elevated frequencies in the peripheral blood of melanoma patients. Modification of the peptides to the canonical sequence motifs led to improved HLA binding and to improved capacity to stimulate T cells.
authors.affiliation: Department of Dermatology, Clinical Research Group Tumor Immunology, Charité-Universitätsmedizin Berlin, Germany.
authors.forename: Anne
authors.initials: A
authors.lastname: Bredenbeck
authors.forename: Florian O
authors.initials: FO
authors.lastname: Losch
authors.forename: Tumenjargal
authors.initials: T
authors.lastname: Sharav
authors.forename: Mathias
authors.initials: M
authors.lastname: Eichler-Mertens
authors.forename: Matthias
authors.initials: M
authors.lastname: Filter
authors.forename: Alireza
authors.initials: A
authors.lastname: Givehchi
authors.forename: Wolfram
authors.initials: W
authors.lastname: Sterry
authors.forename: Paul
authors.initials: P
authors.lastname: Wrede
authors.forename: Peter
authors.initials: P
authors.lastname: Walden
mesh_terms: D000938:Antigen-Presenting Cells / Q000276:immunology / Q000378:metabolism; D000951:Antigens, Neoplasm / Q000276:immunology / Q000378:metabolism / Q000627:therapeutic use; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology / Q000378:metabolism; D019496:Cancer Vaccines / Q000276:immunology* / Q000378:metabolism / Q000627:therapeutic use*; D002460:Cell Line; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods; D003601:Cytotoxicity Tests, Immunologic / Q000379:methods; D004797:Enzyme-Linked Immunosorbent Assay / Q000379:methods; D018984:Epitopes, T-Lymphocyte / Q000276:immunology* / Q000378:metabolism / Q000627:therapeutic use*; D015234:HLA-A Antigens / Q000096:biosynthesis / Q000276:immunology / Q000378:metabolism; D015789:HLA-A2 Antigen; D006801:Humans; D008545:Melanoma / Q000276:immunology* / Q000628:therapy*; D058950:Melanoma-Specific Antigens; D008562:Membrane Glycoproteins / Q000276:immunology / Q000378:metabolism / Q000627:therapeutic use; D009363:Neoplasm Proteins / Q000276:immunology* / Q000378:metabolism / Q000627:therapeutic use*; D010446:Peptide Fragments / Q000276:immunology / Q000378:metabolism; D011237:Predictive Value of Tests; D011485:Protein Binding / Q000276:immunology; D058951:gp100 Melanoma Antigen
chemical_list: D000951:Antigens, Neoplasm; D019496:Cancer Vaccines; D018984:Epitopes, T-Lymphocyte; D015234:HLA-A Antigens; C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; C072011:MAGEA1 protein, human; C420909:Mage-a2 antigen; D058950:Melanoma-Specific Antigens; D008562:Membrane Glycoproteins; D009363:Neoplasm Proteins; C548637:PMEL protein, human; D010446:Peptide Fragments; D058951:gp100 Melanoma Antigen