Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Area of Science:

• Immunology and Bioinformatics
• Computational vaccinology
• Oncology

Background:

• Therapeutic cancer vaccines aim to induce tumor regression through immunotherapy.
• Identifying specific neoepitopes is crucial for developing effective cancer vaccines.
• Computational methods are increasingly used to predict and accelerate neoepitope discovery.

Purpose of the Study:

• To systematically review and analyze recent computational methods for neoepitope prediction in therapeutic vaccine development.
• To outline critical steps, strengths, and limitations of current neoepitope prediction programs.
• To assess the validation and clinical translation of computationally predicted neoepitopes.

Main Methods:

• A scoping review following the PRISMA-ScR extension was conducted.
• Searches were performed in Scopus, PubMed, Web of Science, and Science Direct using keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, tumor.
• Forty-nine articles published between 2012 and 2024 were synthesized and analyzed.

Main Results:

• Most studies focus on predicting epitopes binding to MHC class I molecules in solid tumors; MHC class II prediction is underexplored.
• Mutect2 (variant calling) and NetMHCpan (epitope prediction) are commonly used tools, with artificial/convolutional neural networks being preferred prediction methods.
• Random forest algorithms are favored for prioritizing immunogenic epitopes; 55% of studies included in vitro/in vivo validation, and some clinical trials showed tumor remission.

Conclusions:

• Computational neoepitope prediction shows high performance, but challenges remain, including the use of microbiome databases for training.
• Integrating advanced bioinformatics tools with rigorous validation is essential for developing personalized neoepitope-based therapeutic cancer vaccines.
• Neoepitope-based vaccines hold significant promise for personalized cancer immunotherapy, with demonstrated potential for clinical benefit and tumor regression.