Computational Design of an mRNA Vaccine Targeting LRP6 for Triple-Negative Breast Cancer Therapy

Triple-negative breast cancer (TNBC) presents a poorer prognosis than other breast cancer subtypes, attributed to its aggressive nature and the lack of specific therapeutic interventions. TNBC has high recurrence rates and limited survival despite current therapies, emphasizing the critical need for improved treatment options. TNBC exhibits increased levels of LRP6 expression, which is linked to tumor-related features such as growth, metastasis, poor prognosis, resistance to chemotherapy, and invasion. Therefore, LRP6 offers a promising option for therapeutic intervention in breast cancer.

This research aims to use in silico and bioinformatics techniques to develop an mRNA vaccine that specifically targets the LRP6 antigen.

The final vaccine construct comprised 431 amino acids, with a molecular weight of 47.5 kDa, theoretical pI of 5.11, and an instability index of 38.3 indicating stability. Population coverage analysis showed broad global coverage of 99.04%. Molecular docking revealed strong binding affinities to immune receptors, including HLA-A0201 (-812.0), HLA-A0301 (-707.1), HLA-DRB1*0101 (-955.7), and TLR9 (-1339.5). Immune simulation predicted high titers of IgG1 antibodies, sustained memory B cell populations (> 200 by Day 365), elevated CD4+ T cells (> 3000), and robust IFN-γ responses. Codon optimization yielded a high CAI value of 0.94 and GC content of 58.37%, supporting efficient expression in human systems.

Collectively, these results suggest that the designed LRP6-targeted mRNA vaccine could induce durable humoral and cellular immunity against TNBC and warrants further experimental validation.