Integrative mapping of preexisting influenza immune landscapes predicts vaccine response
Stephanie Hao, … , Thushan I. de Silva, Adriana Tomic
Stephanie Hao, … , Thushan I. de Silva, Adriana Tomic
Published July 15, 2025
Citation Information: J Clin Invest. 2025;135(18):e189300. https://doi.org/10.1172/JCI189300.
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Clinical Research and Public Health Clinical Research Immunology Virology

Integrative mapping of preexisting influenza immune landscapes predicts vaccine response

Abstract

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 prevaccination and postvaccination 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 3 distinct immunophenotypic profiles driven by baseline immunity: (a) CD8+ T cell responders with strong preexisting immunity boosting memory T cell responses; (b) mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (c) systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how preexisting 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).

Authors

Stephanie Hao, Ivan Tomic, Benjamin B. Lindsey, Ya Jankey Jagne, Katja Hoschler, Adam Meijer, Juan Manuel Carreño Quiroz, Philip Meade, Kaori Sano, Chikondi Peno, André G. Costa-Martins, Debby Bogaert, Beate Kampmann, Helder Nakaya, Florian Krammer, Thushan I. de Silva, Adriana Tomic

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Figure 5

Baseline immune features and pathway-level determinants of LAIV responder profiles.

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Baseline immune features and pathway-level determinants of LAIV responde...
(A–C) Polar plots illustrating scaled median expression of immune pathways across 3 responder groups: (A) CD8+ T cell responders (group 1, green); (B) mucosal responders (group 2, orange); and (C) systemic, broad influenza A virus responders (group 3, purple). (D) Combined radar plot showing integrated immune pathway signatures across the 3 responder groups, highlighting intergroup differences in pathway activation. (E) SHAP (SHapley Additive exPlanations) summary plots showing the contribution of baseline features to model predictions for each responder group (CD8+ T cell responders, group 1, green; mucosal responders, group 2, orange; and systemic, broad influenza A virus responders, group 3, purple). The intercept represents the baseline prediction before feature contributions. All other factors include the combined effect of features not displayed in the top 10 contributors. Prediction (purple bar) is the final probability derived by summing the intercept, top 10 feature contributions, and all other factors. Feature impacts are color coded as follows: green (positive, 1) increases the likelihood of belonging to the group, and red (negative, –1) decreases it. The top 10 features are ranked by their contribution to the prediction, providing insights into key drivers of LAIV response profiles. (F) The decision tree depicts the splits made at each node based on immune feature thresholds. Splits are chosen to maximize class separation, with fitted class probabilities displayed as group 1 (CD8+ T cell responders, green), group 2 (mucosal responder, orange), and group 3 (systemic, broad influenza A virus responders, purple) for each terminal node. The coverage percentage represents the proportion of observations falling under each rule. Nodes are labeled with thresholds and the conditions that define group separation, with terminal nodes representing the predicted group and associated probabilities.