Background: The establishment of effective adaptive immunity is critical for the long-term control of all human pathogens. Vaccination remains to be the most efficient means of preventing infection. However, individuals with compromised immunity due to age or other comorbidities fail to effectively respond to vaccination. Immunotherapeutic approaches offer alternative options to enhance T cell immunity in immunocompromised individuals. Adoptive cell therapy (ACT) is emerging as a powerful immunotherapeutic approach and has proven effective in treating several virus-associated compilations. Our group has recently demonstrated ACT in treating diseases with several virus-associated complications [1-3]. In this study, we have utilized this expertise in developing ACT against COVID-19 for immunocompromised individuals.
Method: In-vitro approach: Peripheral blood mononuclear cells (PBMC) of recovered COVID-19 participants were stimulated with SARS-CoV-2 peptide pools and cultured in the presence of Interleukin-2 (IL-2) for two weeks. The expanded T cell cultures were then restimulated with peptide pools or single peptides to identify the T cell epitope. In-silico approach: The immune epitope database (IEDB) tool was utilized to understand T cell epitope conservation and NetMHCpan 4.1 server was used to predict T cells across SARS-CoV-2 antigens.
Result: Data from 60 COVID-19-recovered donors demonstrates the production of 52 CD4+ and 24 CD8+ T cell responses. A high level of conservation was observed across identified T cell determinants to VOC strains. We have validated the In-vitro immunogenic peptide response to In-silico epitope prediction, whereby 30-40% of our identified CD8+ T cell responses were not predicted to be a strong binder across the prediction tool.
Conclusion: Identified T cell determinants from this study formed the basis for the manufacturing of ACT for immunocompromised individuals. The comparison of in-silico prediction and the in-vitro identification of peptides gave us a clear idea on the limitation and reliability of the prediction tools.