Cellular Signatures of Immune Dysregulation in Inborn Errors of Immunity: Development of a Quantitative Immune Balance Score

Dilan InanHacer Neslihan BildikBusra CiftcierAysegul AkarsuMelike OcakAslihan Berra BolatDeniz N. Cagdas AyvazIlhan TezcanSevil Oskay Halacli^*

• Hacettepe University, Ankara, Türkiye

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.