The immunogenetic mechanisms of response to biologic drugs in rheumatoid arthritis

该项目将包括确定遗传、人口统计学和临床因素，以及与类风湿性关节炎生物治疗反应相关的免疫细胞类型。背景：类风湿性关节炎（RA）是一种病因不明的自身免疫性疾病。病程和对治疗的反应部分由基因决定。由于缺乏对类风湿性关节炎病理生理学的了解，导致了在生物药物处方上的反复试验，30%的患者没有反应。最近已经发现了一些与RA病因相关的致病性免疫细胞类型[2,3,4,5]，但它们在治疗反应中的作用尚不清楚。目的：评价遗传易感性/严重性多态性和免疫细胞亚群（如CD4+ T淋巴细胞亚群）对RA易感性、临床亚表型和对生物治疗的反应的共同贡献。方法：我们将使用世界上最大的接受生物制剂治疗的RA患者前瞻性队列，BRAGGSS队列和国家健康志愿者库（NRHV）。300名BRAGGSS患者和150名NRHV患者的以下数据将在该项目开始时提供：a)人口统计学和临床患者特征，公司对治疗的反应；B)免疫表型（外周血），通过2个质量和3个流式细胞仪检测（淋巴细胞和骨髓细胞亚群的水平和功能，包括最近发表的[2,3,4,5]）；C)全基因组遗传图谱。免疫表型将使用无偏聚类算法进行分析，以不可知性地定义细胞聚类（FlowSOM, tSNE）。线性混合模型将用于与疾病结果的关联检验（如MASC[5]）或共变邻域分析（CNA）。遗传风险评分和细胞簇之间的关联将定义细胞数量性状位点（cQTL）；中介分析和相互作用试验将评估它们对疾病的影响（易感性、严重程度或对治疗的反应）。

The project will consist in identifying genetic, demographic and clinical factors, as well as immune cell types associated with response to biologic treatment in rheumatoid arthritis. Background: Rheumatoid arthritis (RA) is an autoimmune disease of unknown aetiology. Disease course and response to treatment are partially genetically determined [1]. Our lack of understanding of RA pathophysiology results in a trial and error in the prescription of biologic drugs with 30% of patients failing to respond. A few pathogenic immune cell types involved in the aetiology of RA have been recently identified [2,3,4,5], but their role in treatment response is unknown.Aim: To evaluate the joint contribution of genetic susceptibility/severity polymorphisms and immune cell subsets (f.e. CD4+ T lymphocyte subsets) on RA susceptibility, clinical subphenotypes and response to biologic treatments.Methods: We will use the world's largest prospective cohort of RA patients undergoing treatment with biologics, the BRAGGSS cohort, and the National Repository of Healthy Volunteers (NRHV). The following data on 300 BRAGGSS patients and 150 NRHV individuals will be available at the start of this project: a) demographic and clinical patients' characteristics, inc. response to treatment; b) immunophenotypes (peripheral blood) as determined by 2 mass and 3 flow cytometry panels (level and functions of lymphocyte and myeloid cell subsets, inc. those recently published [2,3,4,5]); c) genome-wide genetic profiles. Immunophenotypes will be analysed using unbiased clustering algorithms to define cellular clusters agnostically (FlowSOM, tSNE). Linear mixed models will be used for association testing with disease outcome (f.e. MASC [5]) or covarying neighborhood analysis (CNA). The association between genetic risk scores and cellular clusters will define cellular Quantitative Trait Loci (cQTL); mediation analysis and interaction testing will assess their effect on disease (susceptibility, severity or response to treatment).