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* Department of Dermatology, Clinical Research Group Tumor Immunology, Institute of Biochemistry and Bioinformatics, Charité-Universitätsmedizin Berlin, and Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, Berlin, Germany, Institut für Chemie, und Biologie Johann-Wolfgang-Goethe Universität Frankfurt/Main, Frankfurt, Germany; and ¶ Klinik und Poliklinik für Neurochirurgie, Klinik und Poliklinik für Neurologie, Universitätsklinikum Münster, Münster, Germany
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.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
1 This work was supported by Deutsche Forschungsgemeinschaft Grants KFO 0050, FOR 299/2-1,2, and KL 427/11-1,2; CallistoGen Grant ANN-TAA01; and Deutsche Krebshilfe Grant 10-1898-Sp1.
2 A.B. and F.O.L. have contributed equally to the work.
3 Current address: IMS Health GmbH and Co. OHG, Hahn straße 30-32, 60528 Frankfurt/Main, Germany.
4 Address correspondence and reprint requests to Dr. Peter Walden, Department of Dermatology, Charité Campus Mitte, Clinical Research Group Tumor Immunology, Charité-Universitätsmedizin Berlin, Schumannstrasse 20/21, 10117 Berlin, Germany. E-mail address: peter.walden{at}charite.de
5 Abbreviations used in this paper: TAA, tumor-associated Ag; ANN, artificial neural network; ICC, intracellular cytokine; TATE, tumor-associated T cell epitope; TRP-2, tyrosinase-related protein 2; RT, room temperature; BIMAS, BioInformatics and Molecular Analysis Section.
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