Shaw Vikram R, Patel Jay, Varra Vamsi
School of Medicine, Baylor College of Medicine, Houston, TX, USA.
Department of Dermatology, Baylor College of Medicine, Houston, TX, 77030, USA.
Arch Dermatol Res. 2025 Mar 26;317(1):638. doi: 10.1007/s00403-025-04158-2.
Precision medicine is a topic of growing interest in psoriasis. Many novel biologics are now available to clinicians and identifying who will be a responder or non-responder to a given biologic prior to treatment is an exciting area of inquiry with strong potential clinical utility. In the present study, we use an interpretable classification and regression tree (CART) model to predict week 12 PASI75 and PASI90 response to brodalumab treatment based on clinical variables and transcriptomic data from lesional biopsy tissue samples. We identify KRT16 normalized RNA expression levels and BMI as pre-treatment predictors of a PASI75 response and FERMT1, HLA_DQA1, TMPRSS11D, and S100P normalized RNA expression levels as pre-treatment predictors of a PASI90 response. The CART models demonstrated strong AUC values for the PASI75 (0.90) and PASI90 (0.88) analyses. Taken together, focused transcriptomics has the potential to be used clinically for the pre-treatment prediction of treatment response.
精准医学是银屑病领域中一个日益受到关注的话题。现在临床医生有许多新型生物制剂可供使用，在治疗前确定谁会对某种特定生物制剂产生反应或无反应，是一个令人兴奋的研究领域，具有很强的临床应用潜力。在本研究中，我们使用一种可解释的分类回归树（CART）模型，基于临床变量和来自皮损活检组织样本的转录组数据，预测第12周时对布罗达单抗治疗的PASI75和PASI90反应。我们确定角蛋白16（KRT16）标准化RNA表达水平和体重指数（BMI）作为PASI75反应的治疗前预测指标，而富含脯氨酸的肌动蛋白结合蛋白1（FERMT1）、人白细胞抗原DQA1（HLA_DQA1）、跨膜丝氨酸蛋白酶11D（TMPRSS11D）和S100钙结合蛋白P（S100P）标准化RNA表达水平作为PASI90反应的治疗前预测指标。CART模型在PASI75（0.90）和PASI90（0.88）分析中显示出很强的曲线下面积（AUC）值。综上所述，聚焦转录组学有潜力在临床上用于治疗反应的治疗前预测。
