AIMS: Transient CD20+ B-cell depletion with rituximab is an effective treatment for rheumatoid arthritis (RA). However, there is a subgroup of patients that do not show significant clinical response to rituximab and for these patients, other modes of treatment are preferred. Finding biomarkers for drug response in RA has immense potential for improving treatment and lowering healthcare costs for treating RA patients by facilitating the optimization of their pharmacotherapy. In the present study, we report on gene expression profiles of three different blood cell types in rituximab responders and nonresponder RA patients identifying new candidate genes associated with rituximab response. MATERIALS & METHODS: Transcriptional profiles of whole-blood, CD4+ T cells and B cells were analyzed from nine female patients (mean age 53 +/- 11 years) with active RA disease (DAS28 > 5.1), starting rituximab therapy using Illumina (CA, USA) gene-expression microarrays. Whole-blood RNA was extracted using the PAXgene system (PreAnalytix, Hombrechtikon, Switzerland) whilst the lymphocyte RNA was obtained following cell isolation using negative selection. Flow cytometry analysis was performed to determine whole blood subpopulations, as well as the lymphocyte isolation purity. A whole-genome expression profiling was performed on the RNA samples prepared from the three blood cell populations using the Illumina Human 6 Beadchip array system version 1 (Illumina). From the group of statistically significant genes showing differential expression in rituximab responders compared with nonresponder RA patients, we selected a group of candidate genes that were subsequently validated in the same RNA samples using TaqMan real-time PCR assays. RESULTS: Several genes were identified whose level of expression is associated significantly with the response to rituximab in all three blood cell types evaluated (multiple-test corrected p-value < 0.05). Real-time PCR-validated genes include ARG1 (1.6-fold downregulated in responders) and TRAF1 (1.4-fold upregulated in responders) genes in whole blood and TLR4 (1.3-fold upregulated in responders) in CD4+ T cells. CONCLUSIONS: The present study is the first gene expression microarray analysis reporting on biomarkers of the clinical response to rituximab in RA in blood cells. Following validation in larger cohorts, the identified genes may serve as biomarkers for treatment choice in RA.
AIMS: Transient CD20+ B-cell depletion with rituximab is an effective treatment for rheumatoid arthritis (RA). However, there is a subgroup of patients that do not show significant clinical response to rituximab and for these patients, other modes of treatment are preferred. Finding biomarkers for drug response in RA has immense potential for improving treatment and lowering healthcare costs for treating RA patients by facilitating the optimization of their pharmacotherapy. In the present study, we report on gene expression profiles of three different blood cell types in rituximab responders and nonresponder RA patients identifying new candidate genes associated with rituximab response. MATERIALS & METHODS: Transcriptional profiles of whole-blood, CD4+ T cells and B cells were analyzed from nine female patients (mean age 53 +/- 11 years) with active RA disease (DAS28 > 5.1), starting rituximab therapy using Illumina (CA, USA) gene-expression microarrays. Whole-blood RNA was extracted using the PAXgene system (PreAnalytix, Hombrechtikon, Switzerland) whilst the lymphocyte RNA was obtained following cell isolation using negative selection. Flow cytometry analysis was performed to determine whole blood subpopulations, as well as the lymphocyte isolation purity. A whole-genome expression profiling was performed on the RNA samples prepared from the three blood cell populations using the Illumina Human 6 Beadchip array system version 1 (Illumina). From the group of statistically significant genes showing differential expression in rituximab responders compared with nonresponder RA patients, we selected a group of candidate genes that were subsequently validated in the same RNA samples using TaqMan real-time PCR assays. RESULTS: Several genes were identified whose level of expression is associated significantly with the response to rituximab in all three blood cell types evaluated (multiple-test corrected p-value < 0.05). Real-time PCR-validated genes include ARG1 (1.6-fold downregulated in responders) and TRAF1 (1.4-fold upregulated in responders) genes in whole blood and TLR4 (1.3-fold upregulated in responders) in CD4+ T cells. CONCLUSIONS: The present study is the first gene expression microarray analysis reporting on biomarkers of the clinical response to rituximab in RA in blood cells. Following validation in larger cohorts, the identified genes may serve as biomarkers for treatment choice in RA.
Authors: Hennie G Raterman; Saskia Vosslamber; Sander de Ridder; Michael T Nurmohamed; Willem F Lems; Maarten Boers; Mark van de Wiel; Ben A C Dijkmans; Cornelis L Verweij; Alexandre E Voskuyl Journal: Arthritis Res Ther Date: 2012-04-27 Impact factor: 5.156
Authors: Elena V Tchetina; Anastasya N Pivanova; Galina A Markova; Galina V Lukina; Elena N Aleksandrova; Andrey P Aleksankin; Sergey A Makarov; Aleksandr N Kuzin Journal: Arthritis Date: 2016-01-24
Authors: A N Burska; K Roget; M Blits; L Soto Gomez; F van de Loo; L D Hazelwood; C L Verweij; A Rowe; G N Goulielmos; L G M van Baarsen; F Ponchel Journal: Pharmacogenomics J Date: 2014-03-04 Impact factor: 3.550
Authors: Samantha Louise Smith; Stephen Eyre; Annie Yarwood; Kimme Hyrich; Ann W Morgan; A G Wilson; John Isaacs; Darren Plant; Anne Barton Journal: Arthritis Res Ther Date: 2015-12-14 Impact factor: 5.156