Designing a novel multi-epitope vaccine against Glioblastoma cancer based on immunoinformatics approaches

Abstract

Abstract Glioblastoma multiforme (GBM) stands as the prevalent and highly aggressive malignant primary brain tumor affecting adults. Presently, treatment approaches commonly involve surgery, followed by chemotherapy, or more frequently, radiotherapy. Nevertheless, the median survival of patients remains slightly above one year. Hence, the demand for innovative curative treatments for GBM is crucial. The analysis of GBM cells has played a significant role in identifying various molecules that serve as targets for immunotherapy-based approaches. These include EGFR/EGFRvIII, IDH R132H, H3 K27M, WT1, and TERT. Immunoinformatics methods offer a dual advantage of cost-effectiveness and convenience, leveraging in-silico simulations to significantly reduce development timelines. In this study, we employ immunoinformatics techniques to create an innovative multi-epitope vaccine aimed at preventing GBM. Utilizing complicated immunoinformatics approaches, we successfully predicted distinct epitopes for cytotoxic T lymphocytes (CTLs) and helper T lymphocytes (HTLs). Following that, Through the utilization of appropriate linkers and adjuvants, we created the multi-epitope vaccine by integrating all conserved epitopes. The ultimate vaccine demonstrated antigenicity, non-allergenicity, and stability. Next, we utilized predictions, refinements, and evaluations to determine the 3D configuration of the vaccine. To reveal the interactions between the vaccine and the immune receptor TLR4, we carried out molecular docking and dynamic simulations. Finally, to guarantee that the vaccine protein was fully expressed, the sequence of the designed vaccine was adjusted and in-silico cloning was conducted. In conclusion, the molecule developed in this study shows promise as a potential vaccine option against GBM tumors. However, further research is necessary to thoroughly assess its safety and efficacy.