The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients.
The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients.
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DOI：
10.1186/ar3819
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发表时间：
2012-04-27
影响因子：
4.9
通讯作者：
Voskuyl AE
中科院分区：
文献类型：
--
作者：
Raterman HG;Vosslamber S;de Ridder S;Nurmohamed MT;Lems WF;Boers M;van de Wiel M;Dijkmans BA;Verweij CL;Voskuyl AE
B细胞去除疗法对肿瘤坏死因子阻断药治疗失败的类风湿关节炎(RA)患者是有效的。然而，大约40%到50%的利妥昔单抗(RTX)治疗的RA患者的反应很差。我们调查了基线基因表达水平是否可以区分临床上对RTX无应答和有应答的患者。在连续14名服用RTX(测试队列)的RA患者中，使用Illumina®HumanHT珠芯片对全血RNA进行了基因表达谱分析。有监督的聚类分析被用来识别在基线下应答者和无应答者之间差异表达的基因，这是基于28个关节疾病活动评分(Δ)和6个月后欧洲风湿病联盟(EULAR1.2)反应标准的差异。在一个独立的验证队列(n=26)中，用实时定量聚合酶链式反应测量感兴趣基因，并使用受试者工作特征(ROC)曲线测试其预测值。全基因组微阵列分析显示，在RTX治疗开始之前，RA患者之间的外周血细胞存在显著差异。在这里，我们证明了只有由干扰素I型反应基因LY6E、HERC5、IFI44L、ISG15、MXA、MXB、EPSTI1和RSAD2代表的由I型干扰素网络基因组成的簇与ΔDAS28和EULAR反应结果相关(分别为P=0.0074和P=0.0599)。基于8个IRG，计算干扰素评分，达到0.82的曲线下面积，在26名患者的独立验证队列中，使用根据Δ标准的接收者操作员特征(ROC)曲线分析来区分无应答者和应答者。高级分类器分析产生了三个IRG集，达到了87%的AUC。类似的研究结果也适用于EULAR无反应标准。这项研究证明了使用基线IRG表达水平作为预测类风湿关节炎对RTX无反应的生物标志物的临床实用价值。
B cell depletion therapy is efficacious in rheumatoid arthritis (RA) patients failing on tumor necrosis factor (TNF) blocking agents. However, approximately 40% to 50% of rituximab (RTX) treated RA patients have a poor response. We investigated whether baseline gene expression levels can discriminate between clinical non-responders and responders to RTX. In 14 consecutive RA patients starting on RTX (test cohort), gene expression profiling on whole peripheral blood RNA was performed by Illumina® HumanHT beadchip microarrays. Supervised cluster analysis was used to identify genes expressed differentially at baseline between responders and non-responders based on both a difference in 28 joints disease activity score (ΔDAS28 < 1.2) and European League against Rheumatism (EULAR) response criteria after six months RTX. Genes of interest were measured by quantitative real-time PCR and tested for their predictive value using receiver operating characteristics (ROC) curves in an independent validation cohort (n = 26). Genome-wide microarray analysis revealed a marked variation in the peripheral blood cells between RA patients before the start of RTX treatment. Here, we demonstrated that only a cluster consisting of interferon (IFN) type I network genes, represented by a set of IFN type I response genes (IRGs), that is, LY6E, HERC5, IFI44L, ISG15, MxA, MxB, EPSTI1 and RSAD2, was associated with ΔDAS28 and EULAR response outcome (P = 0.0074 and P = 0.0599, respectively). Based on the eight IRGs an IFN-score was calculated that reached an area under the curve (AUC) of 0.82 to separate non-responders from responders in an independent validation cohort of 26 patients using Receiver Operator Characteristics (ROC) curves analysis according to ΔDAS28 < 1.2 criteria. Advanced classifier analysis yielded a three IRG-set that reached an AUC of 87%. Comparable findings applied to EULAR non-response criteria. This study demonstrates clinical utility for the use of baseline IRG expression levels as a predictive biomarker for non-response to RTX in RA.
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DOI：
10.1093/rheumatology/keq159
发表时间：
2010-10
期刊：
Rheumatology (Oxford, England)
影响因子：
--
作者：
Magnusson M;Brisslert M;Zendjanchi K;Lindh M;Bokarewa MI
通讯作者：
Bokarewa MI
影响因子：
5.5
作者：
Cantaert, Tineke;van Baarsen, Lisa G.;Baeten, Dominique L.
通讯作者：
Baeten, Dominique L.
影响因子：
--
作者：
Emery, P;Fleischmann, R;Shaw, TM
通讯作者：
Shaw, TM
影响因子：
158.5
作者：
Edwards, JCW;Szczepanski, L;Shaw, T
通讯作者：
Shaw, T
影响因子：
--
作者：
Cohen, Stanley B.;Emery, Paul;Totoritis, Mark C.
通讯作者：
Totoritis, Mark C.