Next-Gen mRNA Vaccine Design: Precision Targeting of MUC۱ for Colorectal Cancer with Population-Specific HLA Optimization

This study presents the computational design of an mRNA vaccine targeting MUC۱, a tumor-associated antigen overexpressed in ۸۰% of colorectal cancer (CRC) cases, specifically optimized for the Iranian population. Through comprehensive bioinformatics analysis, we identified the ۹-mer peptide epitope SVSDVPFPF (residues ۱۱۴۰-۱۱۴۸ of MUC۱) as the optimal candidate due to its high binding affinity to prevalent Iranian HLA class I alleles (IC۵۰ = ۲۳ nM for HLA-B*۳۵:۰۱), cross-reactivity with HLA class II molecules (percentile rank <۱ for HLA-DRB۱*۱۱:۰۱), and strong immunogenicity (VaxiJen score = ۰.۶۹). The epitope demonstrated superior antigen processing characteristics, with a proteasomal cleavage score of ۱.۰۷ and TAP transport efficiency of ۱.۲, yielding a total processing score of ۰.۹۱. Population coverage analysis confirmed ۱۰۰% coverage (۹۵% CI: ۹۹.۲-۱۰۰%) across Iranian subpopulations by targeting high-frequency HLA alleles, including HLA-A*۲۴:۰۲ (۱۸.۷%), HLA-B*۳۵:۰۱ (۱۷.۲%), and HLA-C*۰۴:۰۱ (۱۵.۹%). The vaccine construct incorporated optimized elements such as a ۱۵-nt ۵' UTR from clinically validated Moderna platforms, a GCCGCCACC Kozak sequence, an IgE signal peptide, and a ۱۰۰-nt poly(A) tail with ۳' UTR stability elements. Secondary structure prediction revealed excellent thermodynamic stability (minimum free energy = -۵۲.۷۸ kcal/mol) with ۶۲.۱۹% ensemble diversity, ensuring efficient translation and immune recognition. This design offers broad applicability by targeting consistent MUC۱ overexpression rather than patient-specific mutations, while its population-specific HLA targeting addresses regional disparities in cancer immunotherapy access. Future work includes preclinical validation in humanized mouse models