Division of Rheumatology, St. Vincent Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Computer Science, University of California at Davis, California, United States; Genome Center, University of California at Davis, California, United States.
Department of Computer Science, University of California at Davis, California, United States; Genome Center, University of California at Davis, California, United States.
Clin Immunol. 2019 May;202:1-10. doi: 10.1016/j.clim.2019.03.002. Epub 2019 Mar 1.
Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used to provide predictive insights on drug responses. A normalized compendium consisting of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets was build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of a predictive model of patient treatment response. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.
类风湿关节炎（RA）因患者异质性和变异性而具有治疗挑战性。在此，我们描述了一种新的整合方法，将不同患者队列的 RA 滑膜全基因组转录组谱进行整合，可以为药物反应提供预测性见解。构建了一个包含 256 个 RA 滑膜样本的标准化总集，这些样本涵盖了 11 个数据集的 11769 个基因的交集，并与源自 OA 患者和健康对照的类似数据集进行了比较。通过三种独立方法识别的差异表达基因（DEG）被输入功能网络分析，并随后根据非负矩阵分解方法对样本进行分组。RA 相关途径激活评分和四种机器学习分类技术支持生成患者治疗反应的预测模型。我们鉴定了 876 个上调的 DEG，其中包括 24 个已知的遗传风险因素和 8 个药物靶点。基于 DEG 的亚组分析揭示了 3 个具有独特 RA 相关途径活性特征的不同 RA 患者亚群。在英夫利昔单抗的情况下，我们构建了一个具有高度准确性的药物反应分类器，AUC/AUPR 为 0.92/0.86。实现这一性能的最具信息量的途径是 NFκB、FcεRI-TCR-和 TNF 信号通路。同样，HMMR、PRPF4B、EVI2A、RAB27A、MALT1、SNX6 和 IFIH1 基因的表达有助于预测患者的结局。标准化滑膜转录组总集的构建和分析可以为理解 RA 相关途径的参与和个体患者的药物反应提供有用的见解。
