THERAPEUTIC TARGETS PREDICTION OF TUMOR SPECIFIC ANTIGEN FOR VACCINE DESIGN

Abstract

The difficulty of treating cancer lies in their similarity with normal cells in terms of antigen expression. Cancer cells are transformed cells that gains immortality by mutagenic transformation of normal cells. The cross reactivity of surface antigens on tumor cells and normal cells makes them difficult to be identified by immune system. However, T cytotoxic cells, NK cells and dendritic cells play important role in immune response to tumor cells. T cell epitopes of tumor specific antigens were predicted as vaccine candidates so as to increase immunity against tumor cell. Tumor cells express tumor specific antigen peptide on their surfaces which are recognised by T-cytotoxic cells and hence give immune response against tumor. Dendritic cell phagocytose the tumor cell and present their peptides to the Thelper cell and hence result in tumor rejection. MHC class I binding prediction tool and CD8+ immunogenicity prediction tool were used to predict class I epitopes. MHC class II binding prediction tool and CD4+ immunogenicity prediction tool were used to predict class II epitopes. Population coverage of the epitopes of both MHC I and MHC II binding peptide was calculated in Indian population. Top ten population covered epitopes were selected for each antigen. Epitopes were predicted for ACTN4, BRAF, CAMEL, CASP5, CASP8, CDC27, CDK4, CDKN2A, CTNNB1, EEF2, FLT3, GPNMB, HSPA1B, KRAS, MUM1, PAPOLG, TPI1, UBXD5. A combinatorial epitope prediction would be effective in designing multifactorial immune stimulation response against tumor specific antigen.