Differential escape of HCV from CD8+ T cell selection pressure between China and Germany depends on the presenting HLA class I molecule

Immune escape pathways from the HBV core18-27 CD8 T cell response are driven by individual HLA class I alleles

Background and aims

There is growing interest in T cell-based immune therapies for a functional cure of chronic HBV infection including check-point inhibition, T cell-targeted vaccines or TCR-grafted effector cells. All these approaches depend on recognition of HLA class I-presented viral peptides. The HBV core region 18-27 is an immunodominant target of CD8+ T cells and represents the prime target for T cell-based therapies. Here, a high-resolution analysis of the core18-27 specific CD8+ T cell and the selected escape pathways was performed.

Methods

HLA class I typing and viral sequence analyses were performed for 464 patients with chronic HBV infection. HBV-specific CD8+ T-cell responses against the prototype and epitope variants were characterized by flow cytometry.

Results

Consistent with promiscuous presentation of the core18-27 epitope, antigen-specific T cells were detected in patients carrying HLA-A*02:01, HLA-B*35:01, HLA-B*35:03 or HLA-B*51:01. Sequence analysis confirmed reproducible selection pressure on the core18-27 epitope in the context of these alleles. Interestingly, the selected immune escape pathways depend on the presenting HLA-class I-molecule. Although cross-reactive T cells were observed, some epitope variants achieved functional escape by impaired TCR-interaction or disturbed antigen processing. Of note, selection of epitope variants was exclusively observed in HBeAg negative HBV infection and here, detection of variants associated with significantly greater magnitude of the CD8 T cell response compared to absence of variants.

Conclusion

The core18-27 epitope is highly variable and under heavy selection pressure in the context of different HLA class I-molecules. Some epitope variants showed evidence for impaired antigen processing and reduced presentation. Viruses carrying such escape substitutions will be less susceptible to CD8+ T cell responses and should be considered for T cell-based therapy strategies.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.