VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Abstract

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accurate prediction and prioritization of highly immunogenic and optimized MHC-I and MHC-II T-cell epitopes for cancer vaccine development and immunotherapy. Utilizing a curated dataset of experimentally validated epitopes and employing sophisticated machine learning techniques, VaxOptiML features three models: epitope prediction from target sequences, personalized HLA typing, and prioritization the predicted epitopes based on immunogenicity scores. Our rigorous data extraction, cleaning, and feature extraction processes, coupled with model building, yield exceptional accuracy, sensitivity, specificity, and F1 score, surpassing existing prediction methods. Comprehensive visual representations underscore VaxOptiML's robustness and efficacy in accelerating epitope discovery and vaccine design for cancer immunotherapy. Deployed via Streamlit for public use, VaxOptiML enhances accessibility and usability for researchers and clinicians, demonstrating significant potential in cancer immunotherapy.