Frontiers in Immunology (Aug 2020)
Predicting Success of Allergen-Specific Immunotherapy
Abstract
The immune response to antigens is a key aspect of immunology, as it provides opportunities for therapeutic intervention. However, the induction of immunological tolerance is an evolving area that is still not sufficiently understood. Allergen immunotherapy (AIT) is a disease-modulating therapy available for immunoglobulin E (IgE)-mediated airway diseases such as allergic rhinitis or allergic asthma. This disease-modifying effect is not only antigen driven but also antigen specific. The specificity and also the long-lasting, often life-long symptom reduction make the therapy attractive for patients. Additionally, the chance to prevent the onset of asthma by treating allergic rhinitis with AIT is important. The mechanism and, in consequence, therapy guiding biomarker are still in its infancy. Recent studies demonstrated that the interaction of T, B, dendritic, and epithelial cells and macrophages are individually contributing to clinical tolerance and therefore underline the need for a system to monitor the progress and success of AIT. As clinical improvement is often accompanied by decreases in numbers of effector cells in the tissue, analyses of cellular responses and cytokine pattern provide a good insight into the mechanisms of AIT. The suppression of type-2 immunity is accompanied by decreased levels of type-2 mediators such as epithelial CCL-26 and interleukin (IL)-4, IL-13 produced by T cells that are constituting the immune memory and are increasingly controlled by regulatory T and B cells following AIT. Immune tolerance is also associated with increased production of type-1 mediators like interferon-gamma, tissue-homeostating factors like indoleamine 2,3-dioxygenase (IDO) expressed by macrophages and dendritic cells. Although these individual genes were convincingly demonstrated to play a role immune tolerance, they do not predict therapy outcomes of AIT on an individual level. Therefore, combinations or ratios of gene expression levels are a promising way to achieve predictive value and definition of helpful biomarker.
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