Computational Design of Multi-Epitope Vaccine Candidates Targeting Reelin-Mediated Brain Metastases in HER2-Positive Breast Cancer

Brain metastases represent a critical clinical challenge in HER2-positive breast cancer (HER2+ BC), accounting for a disproportionately high share of disease-related mortality. Emerging evidence implicates Reelin protein (RELN) overexpression in facilitating blood-brain barrier penetration and cerebral colonization by HER2+ tumor cells, positioning RELN as a mechanistically relevant vaccine target. However, no prior studies have explored RELN-derived epitopes as vaccine candidates for this indication. Using an integrated immunoinformatic approach combining the Kolaskar-Tongaonkar antigenicity algorithm, ElliPro conformational epitope prediction, and NetCTL 1.2 cytotoxic T lymphocyte (CTL) prediction. We systematically characterized the epitope landscape of the RELN RR5-6 domain (725 residues; GenBank ID: 291463430). We identified twenty linear B-cell epitopes, four conformational B-cell epitopes (highest PI score: 0.999), and eleven CTL epitopes with prediction scores exceeding 0.75. Molecular docking of all CTL epitopes against HLA-A*02:01 (MHC class I) via PatchDock and FireDock yielded global binding energies ranging from −17.16 to −54.19 kcal/mol; CTL-epitope1 (47EEDSAMVFV55) demonstrated the most favorable binding energy (−54.19 kcal/mol), with critical interactions mediated by MHC residues Q155 and T73. Seven epitopes overlapped between B-cell and CTL predictions, suggesting potential for multi-arm immune activation. These findings provide a computational framework to prioritize RELN-derived candidates for in vitro and in vivo experimental validation, with translational implications for prophylactic and therapeutic vaccination strategies in HER2+ breast cancer brain metastases.