Identification of biomarker sets for predicting the efficacy of sublingual immunotherapy against pollen-induced allergic rhinitis

Sublingual immunotherapy (SLIT) is effective against allergic rhinitis, although a substantial proportion of individuals is refractory. Herein, we describe a predictive modality to reliably identify SLIT non-responders (NRs). We conducted a 2-year clinical study in 193 adult patients with Japanese c...

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Bibliographic Details
Published in:International immunology 2017-06, Vol.29 (6), p.291-300
Main Authors: Gotoh, Minoru, Kaminuma, Osamu, Nakaya, Akihiro, Katayama, Kazufumi, Motoi, Yuji, Watanabe, Nobumasa, Saeki, Mayumi, Nishimura, Tomoe, Kitamura, Noriko, Yamaoka, Kazuko, Okubo, Kimihiro, Hiroi, Takachika
Format: Article
Language:English
Subjects:
Adult
Algorithms
Allergens - immunology
Allergens - therapeutic use
Antigens, Plant - immunology
Antigens, Plant - therapeutic use
Biomarkers - metabolism
Cluster Analysis
Cryptomeria - immunology
Cytokines - metabolism
Female
Humans
Immunoglobulin G - blood
Interleukin-12 - metabolism
Male
Middle Aged
Pollen - immunology
Rhinitis, Allergic - diagnosis
Rhinitis, Allergic - immunology
Rhinitis, Allergic - therapy
Severity of Illness Index
Sublingual Immunotherapy - methods
Th1-Th2 Balance
Treatment Outcome
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Summary:Sublingual immunotherapy (SLIT) is effective against allergic rhinitis, although a substantial proportion of individuals is refractory. Herein, we describe a predictive modality to reliably identify SLIT non-responders (NRs). We conducted a 2-year clinical study in 193 adult patients with Japanese cedar pollinosis, with biweekly administration of 2000 Japanese allergy units of cedar pollen extract as the maintenance dose. After identifying high-responder (HR) patients with improved severity scores and NR patients with unchanged or exacerbated symptoms, differences in 33 HR and 34 NR patients were evaluated in terms of peripheral blood cellular profiles by flow cytometry and serum factors by ELISA and cytokine bead array, both pre- and post-SLIT. Improved clinical responses were seen in 72% of the treated patients. Pre-therapy IL-12p70 and post-therapy IgG1 serum levels were significantly different between HR and NR patients, although these parameters alone failed to distinguish NR from HR patients. However, the analysis of serum parameters in the pre-therapy samples with the Adaptive Boosting (AdaBoost) algorithm distinguished NR patients with high probability within the training data set. Cluster analysis revealed a positive correlation between serum Th1/Th2 cytokines and other cytokines/chemokines in HR patients after SLIT. Thus, processing of pre-therapy serum parameters with AdaBoost and cluster analysis can be reliably used to develop a prediction method for HR/NR patients.
ISSN:0953-8178
1460-2377
DOI:10.1093/intimm/dxx034