Biomarkers of AIT: Models of prediction of efficacy.

Abstract

Allergic rhinitis is an IgE-mediated inflammation that remains a clinical challenge, affecting 40% of the UK population with a wide range of severity from nasal discomfort to life-threatening anaphylaxis. It can be managed by pharmacotherapeutics and in selected patients by allergen immunotherapy (AIT), which provides long-term clinical efficacy, especially during peak allergy season. However, there are no definitive biomarkers for AIT efficacy. Here, we aim to summarize the key adaptive, innate, humoral, and metabolic advances in biomarker identification in response to AIT. Mechanisms of efficacy consist of an immune deviation towards T_H1-secreting IFN-γ, as well as an induction of IL10⁺ cT_FR and T_REG have been observed. T_H2 cells undergo exhaustion after AIT due to chronic allergen exposure and correlates with the exhaustion markers PD-1, CTLA-4, TIGIT, and LAG3. IL10⁺ DC_REG expressing C1Q and STAB are induced. KLRG1⁺ IL10⁺ ILC2 were shown to be induced in AIT in correlation with efficacy. B_REG cells secreting IL-10, IL-35, and TGF-β are induced. Blocking antibodies IgG, IgA, and IgG4 are increased during AIT; whereas inflammatory metabolites, such as eicosanoids, are reduced. There are multiple promising biomarkers for AIT currently being evaluated. A panomic approach is essential to better understand cellular, molecular mechanisms and their correlation with clinical outcomes. Identification of predictive biomarkers of AIT efficacy will hugely impact current practice allowing physicians to select eligible patients that are likely to respond to treatment as well as improve patients' compliance to complete the course of treatment.