Immunoinformatics approach to predict T and B cell epitope of NY-BR-1 breast cancer antigen

Abstract

Breast cancer is one of the most common cancers in female around the world. Although many patients with breast cancer can be rendered free of disease with standard therapy such as surgery, radiation, and chemotherapy, some patients will have their disease recur. The identification of specific tumor antigens has significantly advanced in the field of tumor immunology, in particular, the development of cancer vaccines. The purpose of a vaccine is to induce an antigen-specific immune response that will result in disease prevention. Peptide based vaccines are one of the interesting strategies for developing vaccine against breast cancer. The use of synthetic peptides in vaccines offers practical advantages such as relative ease of construction and production, chemical stability, and a lack of infectious or oncogenic potential. T -cell epitopes were predicted from the consensus sequence of NY-BR-1 breast cancer antigen with the help of immunoinformatic tools for MHC Class I and Class II. Immunogenic peptides were generated by finding overlapping predicted epitopes. Twenty and six immunogenic peptides containing epitopes were identified for MHC Class I and II respectively. Finally we found two peptides which contained both MHC class I and class II T-cell epitopes. We have also conducted study for prediction of B-cell epitopes using immunoinformatic tools. Eleven immunogenic peptides containing B cell epitopes were finally selected. Interestingly, one immunogenic peptide was identified which contains MHC Class I restricted T-cell epitopes and a B-cell epitope. Immunogenic peptides which are finally selected based on the present study can be further evaluated to assess the immunogenic response and can become desirable vaccine candidates.