Integrative mapping of pre-existing influenza immune landscapes predicts vaccine response

BACKGROUND. Predicting individual vaccine responses is a substantial public health challenge. We developed immunaut, an open-source, data-driven framework for systems vaccinologists to analyze and predict immunological outcomes across diverse vaccination settings, beyond traditional assessments.

METHODS. Using a comprehensive live attenuated influenza vaccine (LAIV) dataset from 244 Gambian children, immunaut integrated pre- and post-vaccination humoral, mucosal, cellular, and transcriptomic data. Through advanced modeling, our framework provided a holistic, systems-level view of LAIV-induced immunity.

RESULTS. The analysis identified three distinct immunophenotypic profiles driven by baseline immunity: (1) CD8 T-cell responders with strong pre-existing immunity boosting memory T-cell responses; (2) Mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (3) Systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how pre-existing immune landscapes and baseline features, such as mucosal preparedness and cellular support, quantitatively dictate vaccine outcomes.

CONCLUSION. Our findings emphasize the power of integrative, predictive frameworks for advancing precision vaccinology. The immunaut framework is a valuable resource for deciphering vaccine response heterogeneity and can be applied to optimize immunization strategies across diverse populations and vaccine platforms.

FUNDING. Wellcome Trust (110058/Z/15/Z); Bill & Melinda Gates Foundation (INV-004222); HIC-Vac consortium; NIAID (R21 AI151917); NIAID CEIRR Network (75N93021C00045).