Jul 2026· Journal of Immunology· Vol 215· 0 citations
Abstract
Understanding vaccine durability is key to designing immunizations with long-term efficacy. Leveraging the Immune Signatures Data Resource, a compendium of transcriptomic and immunological responses from 1405 healthy adults (18+ years) across 24 vaccines, we investigated shared immune mechanisms underlying durable antibody responses. The dataset spans live (yellow fever, smallpox), recombinant viral-vector (Ebola), inactivated (influenza), and glycoconjugate (pneumococcal) vaccines.
Data preprocessing included imputation, normalization, and alignment across post-vaccination time points. We applied advanced machine learning (ML) frameworks to predict antibody immunogenicity and durability. Feature selection for high-dimensional, low-sample-size multi-omics datasets was performed using HSIC Lasso to identify predictors of antibody responses. Selected features served as input to ensemble, regularized regression, and gradient-boosting models (e.g. DT, RF, LASSO, XGB, CatBoost). We compared single-target and multi-output approaches, evaluating stacked, chained, and wrapper-based strategies, and implemented multi-layer neural networks to capture complex relationships among immune features.
Post-vaccination time points explained ∼15% of the total variance, indicating shared immune kinetics across vaccine types, while age and sex contributed minimally. Gradient-boosting and multi-output modeling approaches achieved the highest predictive accuracy across vaccines, highlighting the value of integrating correlated outcomes. Neural network models similarly captured complex, nonlinear immune signatures, albeit with reduced explainability.
Conserved transcriptional modules, particularly interferon-signaling and plasmablast-related pathways, emerged as strong predictors of antibody durability. This integrative ML framework enables identification of key immune signatures critical for developing vaccines with durable responses, advancing data-driven strategies for systems vaccinology.
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Computational and Systems Immunology (COMP)
Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.
Pramod Shinde, Lisa Willemsen, Jiyeun Lee et al.· bioRxiv· 0 citations
This work is the first to use a multi-modal foundation model for neoantigen vaccines, overcoming the tumor-type-specific constraints of conventional approaches and facilitating joint modelling of immunogenicity and clinical efficacy, thereby offering an AI decision engine for precision cancer vaccine design that is applicable to all cancer types.
Gang Liu, Jia Wang, Jia Zhu· Global Health Care· 1 citation
The ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications is developed.
Smita Krishnaswamy, J. Rocha, Hiren Madhu et al.· Journal of Immunology· 0 citations
The current role of AI is summarized across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition, and the application of modern computational frameworks.
P. Brlek, J. Kolić, L. Bulić et al.· Frontiers in Genetics· 0 citations
Background The management of multidrug-resistant HIV-1 in patients experiencing virologic failure remains a critical clinical challenge. Traditional linear scoring systems often fail to adequately capture the complex evolutionary dynamics between the virus, host immunity, and antiretroviral regimens. This study aims to construct and validate a prediction model in line with the TRIPOD+AI statement to quantify the multidimensional, nonlinear interactions among “virus-host-drug” and to guide individualized clinical salvage therapy. Methods This retrospective cohort study integrated de-identified data from 18 clinical trials in the Stanford HIV Drug Resistance Database (n = 6,844). Seven machine learning algorithms were compared with traditional models. Model evaluation metrics included area under the receiver operating characteristic curve (AUC), Brier score, and calibration curves. TreeSHAP quantified feature contributions and interactions. Ablation studies (DeLong’s test) evaluated the incremental predictive value of integrating virological, immunological, and treatment history domains. The algorithmic fairness of the model across populations with different immune statuses and viral loads was evaluated through subgroup analysis, and the Effective Sample Size (ESS) was introduced to assess individual prediction uncertainty. Results The XGBoost model best predicted 24-week virologic suppression (AUC: 0.887), significantly outperforming the baseline model (AUC: 0.816) with excellent calibration (Brier score: 0.096). Ablation studies confirmed that the integrated model significantly outperformed partial models restricted to single feature domains (all P < 0.05). SHAP interaction analysis revealed a significant modification effect of baseline CD4+ T cell count on the predictive weight of viral load; meanwhile, a temporal decay in drug resistance test results was observed, significantly diminishing the negative predictive weight of a heavy treatment history. Reclassification analysis showed that the XGBoost model corrected 61.90% of actual failures misclassified by the baseline model, demonstrating a significant net clinical benefit (Net Reclassification Improvement: 0.490). Clinical fairness checks confirmed that the model performed stably in subgroups with severe immune compromise and high viral loads, without showing systematic bias. Conclusion The developed XGBoost model overcomes the limitations of traditional linear scoring and achieves precise prediction of HIV salvage therapy outcomes by quantifying immune modulatory effects and therapeutic exhaustion markers. This model acts as a clinical safeguard to identify ineffective treatments while maintaining algorithmic fairness across patient severities. The developed web-based calculator and risk stratification system help clinicians optimize resource allocation and advance novel drug use in complex resistance scenarios, promoting evidence-based HIV precision medicine practices.
Defu Yuan, Yangyang Liu, Shanshan Liu et al.· Frontiers in Immunology· 0 citations
Seasonal influenza A viruses cause significant global morbidity each year. Although vaccination remains the primary preventive strategy, effectiveness is often reduced by antigenic drift. This challenge is particularly pronounced for influenza A(H3N2), which has required eight vaccine updates over the past decade. Here, we present a computational framework to engineer broadly reactive influenza A(H3N2) vaccines, using protein language models to generate novel hemagglutinin (HA) sequences and a machine learning model to predict antigenic distance from circulating strains. In a proof-of-concept study, seven HA candidates designed using sequence data from 2013–2018 were evaluated in mice against contemporary and subsequently circulating viruses. Two candidates elicited protective levels of reactive antibodies, robust H3-specific antibody-secreting cell responses, and cross-neutralization against contemporary clades and drifted 2019-2020 strains. These findings demonstrate that an integrated generation–selection strategy can enhance vaccine coverage across current and future A(H3N2) seasons and may be applicable to other influenza subtypes.
Victoria R. Howard, James D. Allen, Matthew H. Thomas et al.· bioRxiv· 0 citations
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