Open Access Research article

The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients

Hennie G Raterman1, Saskia Vosslamber2, Sander de Ridder2, Michael T Nurmohamed3, Willem F Lems13, Maarten Boers134, Mark van de Wiel4, Ben AC Dijkmans13, Cornelis L Verweij12* and Alexandre E Voskuyl1

  • * Corresponding author: Cornelis L Verweij c.verweij@vumc.nl

  • † Equal contributors

Author Affiliations

1 Department of Rheumatology, VU University medical center, de Boelelaan 1117, Amsterdam, 1081HV, the Netherlands

2 Department of Pathology, VU University medical center, de Boelelaan 1118, Amsterdam, 1081HV, Amsterdam, the Netherlands

3 Department of Rheumatology, Jan van Breemen Research Institute|Reade, Jan van Breemenstraat 2, Amsterdam, 1056AB, The Netherlands

4 Department of Epidemiology and Biostatistics, VU University medical center, de Boelelaan 1117, Amsterdam, 1081HV, the Netherlands

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Arthritis Research & Therapy 2012, 14:R95  doi:10.1186/ar3819

Published: 27 April 2012

Abstract

Introduction

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.

Methods

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).

Results

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.

Conclusions

This study demonstrates clinical utility for the use of baseline IRG expression levels as a predictive biomarker for non-response to RTX in RA.