Identification of biomarker sets for predicting the efficacy of sublingual immunotherapy against pollen-induced allergic rhinitis
Identification of biomarker sets for predicting the efficacy of sublingual immunotherapy against pollen-induced allergic rhinitis
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DOI：
10.1093/intimm/dxx034
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发表时间：
2017-06-01
影响因子：
4.4
通讯作者：
Hiroi, Takachika
中科院分区：
文献类型：
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作者：
Gotoh, Minoru;Kaminuma, Osamu;Hiroi, Takachika
舌下免疫疗法(SIT)对过敏性鼻炎是有效的，尽管相当一部分人是难治的。在这里，我们描述了一种预测模式，以可靠地识别狭缝无反应者(NRS)。我们对193例日本雪松花粉症成人患者进行了为期两年的临床研究，每两周给药2000个日本过敏单位的雪松花粉提取物作为维持剂量。在确定严重程度评分改善的高应答者(HR)患者和症状无变化或加重的NR患者后，用流式细胞仪检测33例HR和34例NR患者的外周血细胞学，并用ELISA法和细胞因子珠阵列检测切开前和切开后的血清因子。在接受治疗的患者中，72%的患者临床反应有所改善。治疗前IL-12p70和治疗后IgG1水平在HR和NR患者之间有显著差异，尽管这些单独的参数不能区分NR和HR患者。然而，使用自适应增强(AdaBoost)算法对治疗前样本中的血清参数进行分析后，在训练数据集中区分了高概率的NR患者。聚类分析显示，HR患者术后血清T(H)1/T(H)2与其他细胞因子/趋化因子呈正相关。因此，用AdaBoost对治疗前血清参数进行处理和聚类分析可以可靠地用于HR/NR患者的预测方法。
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 T(h)1/T(h)2 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.