Candidate epitope identification using peptide property models: application to cancer immunotherapy.
Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.
Sung
Myong-Hee
MH
Molecular Statistics and Bioinformatics Section, Biometric Research Branch, National Cancer Institute, National Institutes of Health, 6130 Executive Blvd. EPN 8146, MSC 7434, Bethesda, MD 20892, USA. sungm@mail.nih.gov
Simon
Richard
R
D000818:Animals; D019496:Cancer Vaccines / Q000276:immunology / Q000378:metabolism / Q000627:therapeutic use*; D018604:Epitope Mapping / Q000379:methods*; D006801:Humans; D018448:Models, Immunological; D009369:Neoplasms / Q000276:immunology* / Q000517:prevention & control*; D010446:Peptide Fragments / Q000276:immunology* / Q000378:metabolism / Q000627:therapeutic use*; D011485:Protein Binding / Q000276:immunology
D019496:Cancer Vaccines; D010446:Peptide Fragments