Frontiers in immunology · 2026
Cellular signatures of immune dysregulation in inborn errors of immunity: development of a quantitative immune balance score.
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Inan Dilan, Bildik Hacer Neslihan, Ciftcier Busra, Akarsu Aysegul, Ocak Melike, Bolat Aslihan Berra, Cagdas Deniz, Tezcan Ilhan, Halacli Sevil Oskay
Аннотация
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.
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.