Article: article from journal or magazin.
In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes.
The Journal of biological chemistry
Publication types: Comparative Study ; Journal Article
Publication Status: ppublish
Publication Status: ppublish
Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-specific T cells often are tumoricidal. However, identifying tumor-specific T-cell epitopes is a major challenge. A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this approach is unclear. Here, we used the human tumor antigen NY-ESO-1 (ESO) and the human leukocyte antigen variant HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer peptides and assessed their binding, kinetic complex stability, and immunogenicity in A2-transgenic mice and on peripheral blood mononuclear cells from ESO-vaccinated melanoma patients. We found that 19 of the peptides strongly bound to A2, 10 of which formed stable A2-peptide complexes and induced CD8(+) T cells in A2-transgenic mice. However, only 5 of the peptides induced cognate T cells in humans; these peptides exhibited strong binding and complex stability and contained multiple large hydrophobic and aromatic amino acids. These results were not predicted by in silico algorithms and provide new clues to improving T-cell epitope identification. In conclusion, our findings indicate that only a small fraction of in silico-predicted A2-binding ESO peptides are immunogenic in humans, namely those that have high peptide-binding strength and complex stability. This observation highlights the need for improving in silico predictions of peptide immunogenicity.
Animals, Antigens, Neoplasm/chemistry, Antigens, Neoplasm/genetics, Antigens, Neoplasm/metabolism, Antigens, Neoplasm/therapeutic use, Artificial Intelligence, Cancer Vaccines/genetics, Cancer Vaccines/immunology, Cancer Vaccines/metabolism, Cancer Vaccines/therapeutic use, Cells, Cultured, Computational Biology, Epitopes, Expert Systems, HLA-A2 Antigen/chemistry, HLA-A2 Antigen/genetics, HLA-A2 Antigen/metabolism, Humans, Immunogenicity, Vaccine, Melanoma/immunology, Melanoma/metabolism, Melanoma/pathology, Melanoma/therapy, Membrane Proteins/chemistry, Membrane Proteins/genetics, Membrane Proteins/metabolism, Membrane Proteins/therapeutic use, Mice, Knockout, Mice, Transgenic, Models, Immunological, Neoplasm Proteins/chemistry, Neoplasm Proteins/genetics, Neoplasm Proteins/metabolism, Neoplasm Proteins/therapeutic use, Oligopeptides/chemistry, Oligopeptides/metabolism, Peptide Fragments/chemistry, Peptide Fragments/genetics, Peptide Fragments/metabolism, Peptide Fragments/therapeutic use, Protein Refolding, Protein Stability, Reproducibility of Results, T-Lymphocytes, Cytotoxic/immunology, T-Lymphocytes, Cytotoxic/metabolism, T-Lymphocytes, Cytotoxic/pathology, Vaccines, Synthetic/genetics, Vaccines, Synthetic/immunology, Vaccines, Synthetic/metabolism, Vaccines, Synthetic/therapeutic use, T-cell, T-cell receptor (TCR), cancer therapy, epitope mapping, major histocompatibility complex (MHC), transgenic mice, viral protein
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