OBJECTIVE: The aim of this prospective multicenter study was to identify biomarkers that can be used to predict therapeutic responses to tocilizumab in patients with rheumatoid arthritis (RA). METHODS: We recruited patients with RA who were treated with tocilizumab for the first time, and determined therapeutic responses at 6 months. In the training cohort (n = 40), gene expression in peripheral blood mononuclear cells (PBMCs) at baseline was analyzed using genome-wide DNA microarray, with 41,000 probes derived from 19,416 genes. In the validation cohort (n = 20), expression levels of the candidate genes in PBMCs at baseline were determined using real-time quantitative polymerase chain reaction (qPCR) analysis. RESULTS: We identified 68 DNA microarray probes that showed significant differences in signal intensity between nonresponders and responders in the training cohort. Nineteen putative genes were selected, and a significant correlation between the DNA microarray signal intensity and the qPCR relative expression was confirmed in 15 genes. In the validation cohort, a significant difference in relative expression between nonresponders and responders was reproduced for 3 type I interferon response genes (IFI6, MX2, and OASL) and MT1G. Receiver operating characteristic curve analysis of models incorporating these genes showed that the maximum area under the curve was 0.947 in predicting a moderate or good response to tocilizumab in the validation cohort. CONCLUSION: Using genome-wide DNA microarray analyses, we identified candidate biomarkers that can be used to predict therapeutic responses to tocilizumab in patients with RA. These findings suggest that type I interferon signaling and metallothioneins are involved in the pathophysiology of RA.
OBJECTIVE: The aim of this prospective multicenter study was to identify biomarkers that can be used to predict therapeutic responses to tocilizumab in patients with rheumatoid arthritis (RA). METHODS: We recruited patients with RA who were treated with tocilizumab for the first time, and determined therapeutic responses at 6 months. In the training cohort (n = 40), gene expression in peripheral blood mononuclear cells (PBMCs) at baseline was analyzed using genome-wide DNA microarray, with 41,000 probes derived from 19,416 genes. In the validation cohort (n = 20), expression levels of the candidate genes in PBMCs at baseline were determined using real-time quantitative polymerase chain reaction (qPCR) analysis. RESULTS: We identified 68 DNA microarray probes that showed significant differences in signal intensity between nonresponders and responders in the training cohort. Nineteen putative genes were selected, and a significant correlation between the DNA microarray signal intensity and the qPCR relative expression was confirmed in 15 genes. In the validation cohort, a significant difference in relative expression between nonresponders and responders was reproduced for 3 type I interferon response genes (IFI6, MX2, and OASL) and MT1G. Receiver operating characteristic curve analysis of models incorporating these genes showed that the maximum area under the curve was 0.947 in predicting a moderate or good response to tocilizumab in the validation cohort. CONCLUSION: Using genome-wide DNA microarray analyses, we identified candidate biomarkers that can be used to predict therapeutic responses to tocilizumab in patients with RA. These findings suggest that type I interferon signaling and metallothioneins are involved in the pathophysiology of RA.
Authors: Dana E Orange; Vicky Yao; Kirsty Sawicka; John Fak; Mayu O Frank; Salina Parveen; Nathalie E Blachere; Caryn Hale; Fan Zhang; Soumya Raychaudhuri; Olga G Troyanskaya; Robert B Darnell Journal: N Engl J Med Date: 2020-07-16 Impact factor: 91.245
Authors: Theresa Wampler Muskardin; Priyanka Vashisht; Jessica M Dorschner; Mark A Jensen; Beverly S Chrabot; Marlena Kern; Jeffrey R Curtis; Maria I Danila; Stacey S Cofield; Nancy Shadick; Peter A Nigrovic; E William St Clair; Clifton O Bingham; Richard Furie; William Robinson; Mark Genovese; Christopher C Striebich; James R O'Dell; Geoffrey M Thiele; Larry W Moreland; Marc Levesque; S Louis Bridges; Peter K Gregersen; Timothy B Niewold Journal: Ann Rheum Dis Date: 2015-11-06 Impact factor: 19.103
Authors: Ty M Thomson; Reynald M Lescarbeau; David A Drubin; Daphna Laifenfeld; David de Graaf; David A Fryburg; Bruce Littman; Renée Deehan; Aaron Van Hooser Journal: BMC Med Genomics Date: 2015-06-03 Impact factor: 3.063
Authors: Tamarah D de Jong; Saskia Vosslamber; Marjolein Blits; Gertjan Wolbink; Mike T Nurmohamed; Conny J van der Laken; Gerrit Jansen; Alexandre E Voskuyl; Cornelis L Verweij Journal: Arthritis Res Ther Date: 2015-03-23 Impact factor: 5.156