Integrating biocomputational techniques for vaccine development for glioblastoma multiforme: a possible way of enhancing precision

Abstract

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with poor survival despite multimodal therapy. Tumor heterogeneity, immune evasion, and recurrence limit the effectiveness of current treatments, necessitating novel therapeutic strategies. Vaccine-based immunotherapy aims to induce tumor-specific immune responses but has been challenged by antigen variability and an immunosuppressive tumor microenvironment. Biocomputational techniques have transformed vaccine development by enabling precise identification of immunogenic epitopes and neoantigens. Methods such as reverse vaccinology, immunoinformatics, and artificial intelligence facilitate the rational design of multi-epitope and personalized vaccines. Integration of multi-omics data further enhances target selection and therapeutic precision. Despite these advances, challenges including limited predictive accuracy and translational barriers persist. Overall, biocomputationally driven vaccine design offers a promising pathway toward precision immunotherapy and improved outcomes in GBM.