Abstract B055: A Genome-to-Vaccine: An Integrated Deep learning-Driven Neoantigen Identification and Multi-Epitope Vaccine Construction for Personalized Melanoma Immunotherapy

Abstract

Melanoma, a highly aggressive and therapy-resistant skin cancer, is increasingly treated with immunotherapy, driven by advances in molecular biology and cancer immunology. This study aimed to develop an integrated computational method that combines deep learning based somatic mutation detection, MHC-binding prediction, and reverse vaccinology to identify melanoma neoantigens and design an epitope-based vaccine construct. Paired tumor-normal whole-genome sequencing data were investigated using deep learning-based variant callers, to identify somatic mutations. RNA-seq data were used to validate transcript expression and extract mutant coding sequences. MHC class I binding affinity was evaluated using the network-based deep learning model, and high-affinity binders were assessed using Immunoinformatic filters. Multiple reverse vaccinology filters were then utilized to identify potential neoantigens. A total of 4,050 mutant epitopes were initially predicted, from which nine epitopes met all immunoinformatic selection criteria and were used to construct the multi-epitope vaccine (MEVC). These epitopes were linked using AAY linkers, while a TLR-4 agonist adjuvant was additionally attached via an EAAAK linker to enhance the immunogenicity of the vaccine construct. The final MEVC comprised 145 amino acids, with stable physicochemical properties, signifying improved cellular uptake and immune interaction. Structural modeling, and molecular dynamics simulations of 100-ns confirmed favorable stability and interaction between the vaccine construct and TLR-4. Furthermore, Immune simulation (C-IMMSIM) showed a balanced humoral and cellular immune response. Overall, the results suggest that the proposed MEVC is stable and immunogenic; however, experimental and preclinical validation is required to confirm these findings. Saba Ismail, Devin Atkin, Khaled Barakat. A Genome-to-Vaccine: An Integrated Deep learning-Driven Neoantigen Identification and Multi-Epitope Vaccine Construction for Personalized Melanoma Immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr B055.