Immunoinformatics design of a multi-epitope peptide-based ovarian cancer vaccine targeting Mucin1

Abstract

Ovarian cancer is the most fatal gynecological malignancy and the fifth main cause of cancer deaths in women in the developed world. In Indonesia alone, new cases of ovarian cancer reach the number of 14,896 cases each year according to the Global Cancer Observatory. Immunotherapy using cancer vaccines is a promising treatment. Cancer vaccine works by activating cytotoxic T-cells that are responsible for cancer cell elimination and helper T-cells that are important for cytokine production. T-cells recognize peptides epitopes derived from the cancer antigen which is presented by HLA molecules on the surface of the cancer cells. We combined immunoinformatics with an in-silico vaccine design to construct a Mucin1-based cancer vaccine for the Indonesian population. The sequence of Mucin1 protein was obtained from NCBI databases and evaluated for CTL epitopes using netCTLpan and HTL epitopes using netMHCIIpan. The epitopes were predicted to bind to HLA alleles of the Indonesian population. B cell epitopes were predicted by the Bepipred server in IEDB. A total of ten T-cell epitopes were chosen for vaccine construct based on the highest immunogenicity scores (0.09837 - 0.19463), IFN gamma epitope score (0.3072045 -1.0216963), and population coverage score (99.62%). Five immunogenic B cell epitopes were also included. Two vaccine constructs were made by incorporating maltose-binding protein from Bacillus sp. and E. coli as an adjuvant. The epitopes were linked together using appropriate linkers. Vaccine constructs evaluation by Vaxijen, AntigenPro, AllerTop, and Protparam showed that both constructs are antigenic, non-allergenic, and have good stability.