An efficient T-cell epitope discovery strategy using in silico prediction and the iTopia assay platform

Area of Science:

• Immunology
• Computational Biology
• Cancer Research

Background:

• T-cell epitope discovery is vital for developing immunotherapies, especially in cancer immunology.
• In silico epitope prediction is widely used but often yields false negatives and unreliable rankings.
• The iTopia Epitope Discovery System measures peptide binding and stability to MHC Class I molecules.

Purpose of the Study:

• To systematically investigate the value of combining in silico epitope prediction with the iTopia system.
• To develop a novel in silico selection strategy for T-cell epitopes.
• To identify and prioritize potential T-cell epitopes from human tumor-associated antigens.

Main Methods:

• Developed a novel in silico selection strategy based on MHC Class I binding, antigen uniqueness, and natural processing likelihood.
• Predicted and characterized 225 candidate T-cell epitopes and analogs using in silico methods and the iTopia system.
• Screened HLA-A2-restricted fragments for cell-mediated responses in transgenic mice.

Main Results:

• The iTopia binding assay showed limited informativeness, but the stability assay was a valuable screening tool.
• Identified thirteen novel T-cell epitopes and analogs from tumor-associated antigens (CEA, HER2, TERT).
• Additional potential epitopes were identified, laying groundwork for new anticancer immunotherapies.

Conclusions:

• The combination of in silico prediction and the iTopia-based assay is an accurate and efficient method for MHC Class I epitope discovery.
• This approach aids in identifying T-cell epitopes from tumor-associated antigens for novel anticancer immunotherapies.
• The stability assay component of iTopia is particularly valuable for experimental screening.