Computational Prediction of Tumor-Specific Antigens as Potential Vaccine Candidates against Germ-line Mutations in Endometrial Cancer

Abstract

Endometrial cancer is the fourth most common cancer in women. It arises from the endometrium and accompanied by the abnormal growth of the cells. Sign and symptoms include pelvic pain and abnormal vaginal bleeding. It has two categories. Type 1 tumors are estrogen-dependent and they have mutations in PTEN, PIK3CA while Type 2 tumors are more sensitive and have mutations in TP53. Overactivation of the signaling pathway (PI3K) results in anti-apoptosis. Here, this study aims to identify Tumor-Specific Antigen for germline mutations in endometrial cancer which can be used as a potential vaccine candidate. The germline mutations data are obtained from cancer gene census of the cosmic database. Genes mutating with crucial role in endometrial cancer are considered. Peptides libraries are generated using peptide design library. Human leukocyte antigen alleles are identified for the peptide library through NetMHC. Binding affinities of alleles with peptide are determined. Linear regression is performed to generate graphs. PTEN, TP53, PIK3CA, KRAS, and CTNNB1 proved to have critical role. About 575 overlapping peptide libraries are generated and each peptide has a length of 18-20 amino acids. Approximately 58 HLAs are identified, having strong interactions with HLAs. Regression analysis shows that the no. of mutations are directly associated with a binding affinity of peptides. From this, we suggest that the identified TSA can be used as personalized peptide vaccines that directly target the mutated genes in endometrial cancer. This research work can be used in the laboratories for further validation.