An Artificial Intelligence Model for Longitudinal Assessment of TCR Repertoires in SARS-CoV-2 Vaccine Recipients

Abstract

T cells play a crucial role in reducing disease severity during SARS-CoV-2 infection and in shaping long-term immune memory. However, the precise molecular immune responses, particularly involving T-cell receptor (TCR) repertoire changes after full vaccination, and the use of TCR analysis to evaluate vaccine efficacy, remain incompletely understood. In this study, we developed the DeepAir-Cov19 model (AUC=0.94), a large language model tailored to identify SARS-CoV-2-specific TCRs, and observed significant differences in the TCR profiles between antibody-negative and antibody-positive populations before and after vaccination, indicating that the immune status of pre-vaccine recipients can directly assess the efficacy of vaccination. Notably, SARS-CoV-2-specific TCRs expanded to peak levels after the second dose and remained detectable in most subjects up to 10 months post-vaccination. Meanwhile, we also identified three specific V genes, 20 V-J combinations, and 3 epitopes associated with these responses. Finally, by leveraging vaccine-specific TCRs as novel biomarkers, we developed a vaccine efficacy model that predicts antibody levels with a mean AUC of 0.96. These findings underscore the accuracy of the large model in predicting SARS-CoV-2-specific TCRs and reveal a strong correlation between SARS-CoV-2-specific TCR responses and antibody levels. This highlights the complementary and synergistic roles of T cells and antibodies in providing vaccine-mediated protection. Our results offer valuable insights into the longitudinal dynamics of SARS-CoV-2-specific TCRs and illustrate the potential for developing potency evaluation models based on these TCR insights.