Differential expression of rituximab responders vs. non responders on 3 different blood cell types

New and effective therapeutical options are available for the treatment of Rheumatoid Arthritis. One of such treatments is rituximab, and chimeric anti-CD20 antibody that selectively depletes the CD20+ B cell subpopulation. Similar to established anti-TNF alpha therapies, there is a subgroup of RA patients that do not experience significant clinical response. Therefore, one of the major necessities in actual RA therapeutical management is to identify reliable predictors of the response to this therapies. In the present study we have evaluated 3 blood cell types (i.e. whole blood, isolated B cells and isolated CD4 T cells) using microarray gene expression profiling to identify their potential use as biomarkers for rituximab response. In all three tissues evaluated, we have identified statistically significant differentially expressed genes. The most relevant candidates have been reevaluated using RealTime PCR. These genes were: TRAF1 and arginase 1 in whole blood, Toll-Like Receptor 4 (TLR4) in CD4+ T cells and AT-rich interactive domain 3A (ARID3A) in B cells. In the present study we have demonstrated the potential of different blood cell types for the prediction of the response to rituximab. In particular, we have found a set of relevant candidate genes that could be the basis for future treatment response prediction. We have recruited 9 RA patients starting immunotherapy with rituximab. The same day of the first infusion we obtained the three different cell type samples: whole blood (preserved in PaxGene tubes), B cells (using negative selection) and CD4+ T cells (using negative selection). At week 24 of treatment we determined the clinical response using the RelDas score. All tissue samples were processed in parallel (i.e. RNA extraction and microarray profiling with Illumina WG6 Beadchip) in order to minimize the technical variability. The three datasets were normalized separately using quantile normalization. After performing differential expression analysis between Responders and Non-Responders we identified significantly expressed genes in all three cell types. The most significant gene candidates were validated using the Taqman Real-Time PCR assays.