Advancing personalized immunotherapy for melanoma: Integrating immunoinformatics in multi-epitope vaccine development, neoantigen identification via NGS, and immune simulation evaluation

Abstract

The use of cancer vaccines represents a promising avenue in cancer immunotherapy. Advances in next-generation sequencing (NGS) technology, coupled with the development of sophisticated analysis tools, have enabled the identification of somatic mutations by comparing genetic sequences between normal and tumor samples. Tumor neoantigens, derived from these mutations, have emerged as potential candidates for therapeutic cancer vaccines. In this study, raw NGS data from two melanoma patients (NCI_3903 and NCI_3998) were analyzed using publicly available SRA datasets from NCBI to identify patient-specific neoantigens. A comprehensive pipeline was employed to select candidate peptides based on their antigenicity, immunogenicity, physicochemical properties, and toxicity profiles. These validated epitopes were utilized to design multi-epitope chimeric vaccines tailored to each patient. Peptide linkers were employed to connect the epitopes, ensuring optimal vaccine structure and function. The two-dimensional (2D) and three-dimensional (3D) structures of the chimeric vaccines were predicted and refined to ensure structural stability and immunogenicity. Furthermore, molecular docking simulations were conducted to evaluate the binding interactions between the vaccine chimeras and the HLA class I receptors, confirming their potential to elicit a robust immune response. This work highlights a personalized approach to cancer vaccine development, demonstrating the feasibility of utilizing neoantigen-based immunoinformatics pipelines to design patient-specific therapeutic vaccines for melanoma.