Wang, Nan, Song, Jia, Sun, Shi‐Ran, Zhu, Ke‐Zhang, Li, Jing‐Xian, Wang, Zhi‐Chao, Guo, Cui‐Lian, Xiang, Wen‐Xuan, Tong, Yun‐Long, Zeng, Ming, Wang, Heng, Xu, Xiao‐Yan, Yao, Yin, and Liu, Zheng
Background: Identifying predictive biomarkers for allergen immunotherapy response is crucial for enhancing clinical efficacy. This study aims to identify such biomarkers in patients with allergic rhinitis (AR) undergoing subcutaneous immunotherapy (SCIT) for house dust mite allergy. Methods: The Tongji (discovery) cohort comprised 72 AR patients who completed 1‐year SCIT follow‐up. Circulating T and B cell subsets were characterized using multiplexed flow cytometry before SCIT. Serum immunoglobulin levels and combined symptom and medication score (CSMS) were assessed before and after 12‐month SCIT. Responders, exhibiting ≥30% CSMS improvement, were identified. The random forest algorithm and logistic regression analysis were used to select biomarkers and establish predictive models for SCIT efficacy in the Tongji cohort, which was validated in another Wisco cohort with 43 AR patients. Results: Positive SCIT response correlated with higher baseline CSMS, allergen‐specific IgE (sIgE)/total IgE (tIgE) ratio, and frequencies of Type 2 helper T cells, Type 2 follicular helper T (TFH2) cells, and CD23+ nonswitched memory B (BNSM) and switched memory B (BSM) cells, as well as lower follicular regulatory T (TFR) cell frequency and TFR/TFH2 cell ratio. The random forest algorithm identified sIgE/tIgE ratio, TFR/TFH2 cell ratio, and BNSM frequency as the key biomarkers discriminating responders from nonresponders in the Tongji cohort. Logistic regression analysis confirmed the predictive value of a combination model, including sIgE/tIgE ratio, TFR/TFH2 cell ratio, and CD23+ BSM frequency (AUC = 0.899 in Tongji; validated AUC = 0.893 in Wisco). Conclusions: A T‐ and B‐cell signature combination efficiently identified SCIT responders before treatment, enabling personalized approaches for AR patients. [ABSTRACT FROM AUTHOR]