Candidate Markers for Stratification and Classification in Rheumatoid Arthritis
Candidate Markers for Stratification and Classification in Rheumatoid Arthritis
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DOI：
10.3389/fimmu.2019.01488
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发表时间：
2019-07-05
影响因子：
7.3
通讯作者：
Gavasso, Sonia
中科院分区：
文献类型：
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作者：
Bader, Lucius;Gullaksen, Stein-Erik;Gavasso, Sonia
风湿性关节炎（RA）是一种慢性自身免疫性炎症性疾病，其特征在于中小关节的滑膜炎，并且如果不早期和有效地治疗，则关节损伤和破坏。RA是一种异质性疾病，有多种治疗选择。促炎细胞因子肿瘤坏死因子（TNF）在RA的发病机制中起着重要作用，TNF抑制剂可有效抑制RA的炎症活动。目前，治疗决定主要基于药物学和经济考虑。然而，患者间对治疗的反应存在相当大的差异，这是一个挑战。缺乏更准确的患者分类和分层标记。本研究的目的是确定免疫细胞群中区分RA患者和健康供体的标志物，重点是TNF信号传导。我们采用了一组13个表型和10个功能标志物的质谱仪（CyTOF），以探索20名新诊断的未经治疗的RA患者和20名健康供体的未经刺激和TNF刺激的外周血单核细胞中的信号传导。在三个独立的分析管道中分析所得的高维数据，其特征在于数据清理、细胞亚群/聚类识别和统计方法的差异。所有三个分析管道都在免疫系统的先天臂（骨髓树突状细胞和经典单核细胞）和适应性臂（记忆CD 4（+）T细胞）的细胞中鉴定了p-p38、IkBa、p-cJun、p-NFkB和CD 86作为RA患者和健康供体之间的区分标志物。纳入标志物p-Akt和CD 120 b后，在基于基础信号和TNF诱导信号组合模型的回归模型中，20名RA患者中的18名和20名健康供体中的17名得到了正确分类。与健康个体相比，RA患者的一组功能标志物和特异性免疫细胞亚群的表达模式不同。这些特征可以支持疾病发病机制的研究，提供对TNF抑制剂治疗有反应和无反应的候选标志物，并有助于鉴定未来的治疗靶点。
Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease, characterized by synovitis in small- and medium-sized joints and, if not treated early and efficiently, joint damage, and destruction. RA is a heterogeneous disease with a plethora of treatment options. The pro-inflammatory cytokine tumor necrosis factor (TNF) plays a central role in the pathogenesis of RA, and TNF inhibitors effectively repress inflammatory activity in RA. Currently, treatment decisions are primarily based on empirics and economic considerations. However, the considerable interpatient variability in response to treatment is a challenge. Markers for a more exact patient classification and stratification are lacking. The objective of this study was to identify markers in immune cell populations that distinguish RA patients from healthy donors with an emphasis on TNF signaling. We employed mass cytometry (CyTOF) with a panel of 13 phenotyping and 10 functional markers to explore signaling in unstimulated and TNF-stimulated peripheral blood mononuclear cells from 20 newly diagnosed, untreated RA patients and 20 healthy donors. The resulting high-dimensional data were analyzed in three independent analysis pipelines, characterized by differences in both data clean-up, identification of cell subsets/clustering and statistical approaches. All three analysis pipelines identified p-p38, IkBa, p-cJun, p-NFkB, and CD86 in cells of both the innate arm (myeloid dendritic cells and classical monocytes) and the adaptive arm (memory CD4(+) T cells) of the immune system as markers for differentiation between RA patients and healthy donors. Inclusion of the markers p-Akt and CD120b resulted in the correct classification of 18 of 20 RA patients and 17 of 20 healthy donors in regression modeling based on a combined model of basal and TNF-induced signal. Expression patterns in a set of functional markers and specific immune cell subsets were distinct in RA patients compared to healthy individuals. These signatures may support studies of disease pathogenesis, provide candidate markers for response, and non-response to TNF inhibitor treatment, and aid the identification of future therapeutic targets.