Cellular signatures of immune dysregulation in inborn errors of immunity: development of a quantitative immune balance score

This is the authors' abstract. We don't add key points for this paper.

Background Immune dysregulation, classified as distinct phenotypes within inborn errors of immunity (IEIs) and collectively known as primary immune regulatory disorders (PIRD) is increasingly recognized as a major contributor to morbidity; however, the quantitative cellular signatures identifying this state remain incompletely characterized. Objective We aimed to describe cellular changes underlying immune dysregulation and to develop an integrative biomarker framework that links inflammatory/regulatory imbalance with genetic background and clinical phenotypes. Methods We performed multiparametric flow cytometry and computational modeling in 39 genetically defined IEI patients (PIRD and non-PIRDs) and 17 age-matched healthy controls. Correlation networks, t-SNE, and FlowSOM clustering were applied to examine regulatory and inflammatory compartments. An immune dysregulation score (IDS) was derived as the log-ratio of inflammatory to regulatory subsets, and clinical dysregulation was quantified by a composite presence/absence score. Integrated models were evaluated using ROC, calibration, and decision curve analyses in accordance with TRIPOD recommendations. Results Patients showed significant alterations in immune networks, with consistent reductions in FOXP3 + Tregs and transitional Breg cells alongside expansions of BCL6 + , CD4 + ICOS + BCL6 + Tfh-like subsets, and activated CD8 + T cells. IDS clearly distinguished patients from controls (AUC = 0.75, 95% CI 0.60–0.90, p < 0.01) and correlated inversely with clinical z scores (r = –0.65, p = 2.0 × 10 -5 ). Subgroup analyses displayed elevated IDS in patients with genetically confirmed PIRDs whereas non-PIRDs showed IDS values comparable to controls. Integrating IDS with clinical features tiered patients into three clusters that consistent with the underlying genetic causes. Composite machine-learning models modestly improved stratification of mild and moderate cases, though severe phenotypes remained heterogeneous. Conclusion Our findings identify IDS as a novel quantitative biomarker that captures the regulatory–inflammatory imbalance underlying immune dysregulation, distinguishes PIRDs from non-PIRD IEIs, and provides a translational framework for patient stratification. IDS may inform longitudinal monitoring and guide targeted therapeutic interventions in immune dysregulation syndromes.