Advances in Bioinformatics Techniques to Predict Neoantigen: Exploring Tumor Immune Microenvironment and Transforming Data into Therapeutic Insights

The incorporation of bioinformatics into the prediction of neoantigens has greatly enhanced cancer immunotherapy by improving the understanding of tumor-specific antigens that can trigger targeted immune responses. This review emphasizes the vital role of bioinformatics in identifying neoantigens, which are unique antigens arising from somatic mutations, and their significance in customizing cancer treatments like therapeutic vaccines and T-cell therapies. It critically examines advanced sequencing technologies, such as whole-genome (WGS) and whole-exome sequencing (WES), for their role in assessing mutations that lead to neoantigen production. The review also discusses innovative computational methods, including artificial intelligence (AI), machine learning (ML), and deep learning (DL), for their effectiveness in predicting immunogenic neoantigens and tailoring personalized therapies. Case studies illustrate the successes achieved through these bioinformatics advancements, showcasing their potential in developing personalized vaccines that address the specific genetic makeup of tumors. Despite challenges like tumor heterogeneity and the complexities of data analysis, ongoing advancements.