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  • Pharmacological induction of interferon type I activity following treatment with rituximab determines clinical response in rheumatoid arthritis

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Basic and translational research
Extended report
Pharmacological induction of interferon type I activity following treatment with rituximab determines clinical response in rheumatoid arthritis
  1. Saskia Vosslamber1,
  2. Hennie G Raterman2,
  3. Tineke C T M van der Pouw Kraan3,
  4. Marco W J Schreurs1,
  5. B Mary E von Blomberg1,
  6. Michael T Nurmohamed4,
  7. Willem F Lems2,4,
  8. Ben A C Dijkmans2,4,
  9. Alexandre E Voskuyl2,
  10. Cornelis L Verweij1,2
  1. 1Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
  2. 2Department of Rheumatology, VU University Medical Center, Amsterdam, The Netherlands
  3. 3Department of Molecular Cell Biology and Immunology, VU University Medical Center, Amsterdam, The Netherlands
  4. 4Department of Rheumatology, Reade/Jan van Breemen Research Institute, Amsterdam, The Netherlands
  1. Correspondence to Cornelis L Verweij, VU University Medical Center, Inflammatory Disease Profiling, CCA260, PO Box 7057, 1007MB Amsterdam, The Netherlands; c.verweij{at}vumc.nlSV and HGR contributed equally to this work

Abstract

Objective Despite the fact that rituximab depletes B cells in all treated patients with RA, not all patients show a favourable clinical response. The goal of this study was to provide insight into pharmacological changes in peripheral blood that are associated with clinical response to rituximab.

Methods Gene expression profiling was performed on peripheral blood RNA of 13 patients with RA (test group) using Illumina HumanHT beadchip microarrays. An independent group of nine patients was used for validation using TaqMan quantitative PCR. Clinical responder status was determined after 6 months using change in 28-joint Disease Activity Score (ΔDAS28) and European League Against Rheumatism (EULAR) response criteria. Significance analysis of microarrays and ontology analysis were used for data analysis and interpretation.

Results Pharmacogenomic analyses demonstrated marked interindividual differences in the pharmacological responses at 3 and 6 months after start of treatment with rituximab. Interestingly, only differences in the regulation of type I interferon (IFN)-response genes after 3 months correlated with the ΔDAS28 response. Good responders (∆DAS>1.2; n=7) exhibited a selective increase in the expression of type I IFN-response genes, whereas this activity was unchanged or hardly changed in non-responders (∆DAS<1.2; n=6) (p=0.0040 at a cut-off of 1.1-fold induction). Similar results were obtained using EULAR response criteria. These results were validated in an independent cohort of nine patients (five non-responders and four responders, p=0.0317).

Conclusions A good clinical response to rituximab in RA is associated with a selective drug-induced increase in type I IFN-response activity in patients with RA. This finding may provide insight in the biological mechanism underlying the therapeutic response to rituximab.

https://doi.org/10.1136/ard.2010.147199

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    Introduction

    Rheumatoid arthritis (RA) is a systemic autoimmune disease characterised by chronic inflammation of the joints that may cause permanent cartilage and bone destruction. Although the aetiology is still unknown it is assumed that the interplay between genetic background, immunological and environmental factors is important in disease pathogenesis.1

    No curative treatment is currently available, and patients are subjected to a prolonged course of treatment. Because of the increased insight in the role of proinflammatory mediators and cellular principles of immune tolerance a number of therapeutic options for RA have been developed and are currently available.2 3 However, a major challenge in the successful treatment of RA is disease heterogeneity, which is also presented in the diverse effects of treatment responses.4

    One of the treatment strategies is targeting of B cells making use of rituximab, a chimaeric monoclonal antibody directed against the B cell marker CD20. This treatment was shown to be highly effective for suppression of disease activity in RA.5,–,7 Clinical studies have demonstrated that in analogy to anti-tumour necrosis factor (TNF) therapies not all patients show a favourable response to rituximab treatment.8 Despite the fact that rituximab directly depletes specific B cell populations in all patients treated, the existence of interindividual differences in clinical outcome of rituximab treatment has raised questions regarding the mechanism of action.9 10 In order to provide insight in the biological basis for the clinical response towards rituximab we evaluated the pharmacological effects of rituximab in relation to the clinical outcome using genome-wide gene expression technology in whole blood of patients with RA.

    Materials and methods

    Patient recruitment

    Patients with established RA according to the revised American College of Rheumatology (ACR) criteria for the diagnosis of RA11 were consecutive recruited from two rheumatology outpatient clinics in The Netherlands (VU University Medical Center and Reade/Jan van Breemen Institute, Amsterdam). Inclusion criteria for this study are according to the guidelines of the Dutch Society for Rheumatology for treatment with rituximab, that is, active disease status despite previous treatment with methotrexate and one other disease-modifying antirheumatic drugs (DMARDs) and despite TNF-blocking agents, unless contraindicated in the opinion of the treating doctor. The study was approved by the local medical ethics committees and patients provided written informed consent. Patients had a delay of at least 4 weeks between last dose of TNF blocker and first injection of rituximab.

    Treatment and clinical evaluation

    Patients received rituximab 1000 mg intravenously on days 1 and 15, in combination with clemastine (2 mg intravenously), methylprednisolone (100 mg intravenously) and acetaminophen 1000 mg orally, as premedication. At 4 weeks after the first infusion and from 12 weeks on every 3 months the 28-joint Disease Activity Score (DAS28) was assessed for disease activity status and blood was drawn. The use of concomitant DMARDs, prednisolone or non-steroidal anti-inflammatory drugs during the study duration was permitted. Response to treatment was classified at 6 months following the start of treatment according to European League Against Rheumatism (EULAR) response and to change to DAS28 (ΔDAS28).12

    Blood sampling and RNA isolation

    For RNA isolation, 2.5 ml blood was drawn in PAXgene tubes (PreAnalytiX, Hilden, Germany) and stored at −20°C. After overnight thawing at room temperature total RNA was isolated using Bio robot MDX (Qiagen Benelux BV, Venlo, The Netherlands) according to the manufacturer's instructions (PAXgene Blood RNA Mdx kit). Samples were cleaned using Qiagen RNA MinElute (Qiagen, Venlo, The Netherlands). Total RNA concentration was measured using the NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, Delaware, USA) and RNA purity and integrity was verified using lab-on-chip technology (Agilent 2100 Bioanalyzer; Agilent, Palo Alto, California, USA).

    Microarray analysis

    The Illumina TotalPrep RNA amplification kit (Ambion, Austin, Texas, USA) was used to synthesize biotin-labelled cRNA from 500 ng total RNA. A total of 750 ng of biotinylated cRNA was hybridised onto the HumanHT-12 v3 Expression BeadChip (Illumina, San Diego, California, USA).

    Amplification and hybridisation were performed at the outsourcing company ServiceXS (Leiden, The Netherlands). Bead summary intensities were log2 transformed and normalised using quantile normalisation.13 14

    Calculation of interferon (IFN) type I response scores

    Expression of six IFN-response genes (IFI44, IFI44L, HERC5, RSAD2, LY6E and Mx1) was quantitated using the log2 transformed and quantile normalised expression values. IFN type I response scores were calculated as the mean of the expression values of the six IFN response genes for every single patient and at every single timepoint.

    cDNA synthesis and quantitative real-time PCR

    RNA (0.5 μg) was reverse transcribed into cDNA using a Revertaid H-minus cDNA synthesis kit (MBI Fermentas, St Leon-Rot, Germany). Quantitative real-time PCR was performed using an ABI Prism 7900HT Sequence detection system (Applied Biosystems, Foster City, California, USA). Gene expression levels were determined using TaqMan Gene expression assays following manufacturer's guidelines. To calculate arbitrary values of mRNA levels and to correct for differences in primer efficiencies for each gene a standard curve was constructed. Expression levels of target genes were quantified relative to the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) housekeeping gene.

    Flow cytometry

    To determine the relative amount of peripheral T and B lymphocytes, whole blood was stained for 30 min with monoclonal antibodies conjugated to fluorescein isothiocyanate (FITC), phycoerythrin (PE), peridinin chlorophyll protein (PerCP) and allophycocyanin (APC), and directed against lymphocyte subset-associated surface molecules. Four colour antibody combinations used were (FITC/PE/PerCP/APC): CD3/CD8/CD45/CD4 and CD3/CD16+56/CD45/CD19 (all from BD Biosciences, San Jose, California, USA). Subsequently, red cells were lysed (Lysing Solution; BD Biosciences) and lymphocyte subsets were analysed by flow cytometry on a standard four-colour fluorescence-activated cell scanner (FACSCalibur; BD Biosciences). Data were analysed using CellQuest Pro software (BD Biosciences). Care was taken to analyse only viable cellular events based on light scatter properties. All analyses were performed on lymphocytes, based on bright CD45 staining and low sideward scatter.

    Statistical analysis

    Statistical analysis on microarray data was performed using Significance Analysis of Microarray (SAM), version 3.09.15 Two class paired analysis using SAM at a false discovery rate (FDR) of less than 5% between pretreatment and post-treatment data was applied to identify genes that were significantly changed in expression after rituximab treatment. Cluster analysis was used for the categorisation of coordinately differentially expressed genes.16 TreeView was used to visualise differentially expressed genes. Gene Set Enrichment Analysis (GSEA; http://www.broad.mit.edu/gsea) was used for pathway analysis.17 18 It uses gene set permutation to adjust for multiple testing, indicated by a FDR. A minimal gene set size of 15 genes per pathway was applied, and pathways with a p value of <0.05 and a FDR of <0.05 were considered significant. A total of 282 pathways from Biocarta (http://www.biocarta.com) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg) were applied in this analysis. In addition, we incorporated the IFN-response gene set from Baechler et al19 (genes with at least a fivefold upregulation in peripheral blood mononuclear cell after treatment with type I IFN).

    For ontology analysis of gene sets identified by cluster analysis we used MetaCore Pathway analysis, using the MetaCore Ontology tools, developed by GeneGo (St Joseph, Michigan, USA; http://www.genego.com/). Data mining in MetaCore is based on a manually curated database of human protein–protein, protein–DNA interactions, transcription factors, signalling pathways and metabolic pathways. Calculation of statistical significance are based on p values which are defined as the probability of a given number of genes from the input list to match a certain number of genes in the functional GeneGo Ontology categories.

    Differences in gene expression levels of IFN type I response score between patients with a ∆DAS28>1.2 versus ∆DAS28 <1.2 or between EULAR good versus moderate versus non-responders were analysed using Student unpaired t test or Mann–Whitney U test were appropriate. Correlations between ∆DAS28 and induction of IFN type I response score (ratio t3/t0) were tested using Pearson correlation test.

    Results

    Pharmacological response to rituximab treatment in RA

    To determine the pharmacological effects of rituximab we analysed the changes in peripheral blood gene expression profiles of 13 patients with RA at baseline, and 3 and 6 months following the start of treatment. Baseline characteristics are shown in table 1. To search for single genes that were significantly regulated in all patients after 3 months of treatment with rituximab we applied two-class paired analysis using SAM at a FDR of less than 5% between pretreatment and post-treatment (table 2). The analysis revealed 16 B cell specific genes that were significantly downregulated in all patients, confirming observations by others of an effective and selective B cell depletion after 3 months of treatment.9 20 21 All patients reached a comparable low level of expression of B cell related genes, indicative that the pharmacological depletion at 3 months was reached irrespective of the clinical response status. Accordingly, pathway level analysis using GSEA, identified ‘B cell-mediated immunity’ as the only significantly downregulated pathway (p=0.0020). At 6 months following treatment only six of the B cell related genes were still significantly downregulated. At both time points, no genes were significantly upregulated. These data are indicative for a gradual rise in B cell markers from 3 to 6 months following the start of treatment. These findings were confirmed by CD19 based fluorescence-activated cell sorting analysis (data not shown). Altogether, this indicates that under the influence of rituximab only changes in B cell related processes were consistently regulated in all patients.

    View this table:
    Table 1

    Baseline characteristics rituximab-treated patients with rheumatoid arthritis (RA)

    View this table:
    Table 2

    Genes that are significantly downregulated by peripheral blood cells during rituximab treatment at 3 months (t=3) and 6 months (t=6) after the start of treatment

    Variation in the pharmacological response to rituximab between patients with RA

    Given the heterogeneous nature of RA and the relative low number of differentially regulated genes in the group-based analysis we questioned how consistent the pharmacological response to rituximab was between patients with RA. Therefore, we analysed the pharmacological effects of each individual patient by comparison of the ratio of the post-treatment (3 months) versus pretreatment expression level for each gene (log2 ratios). To search for differences in the pharmacological response between patients, 154 genes were identified that revealed at least a twofold difference in the rituximab-induced response in at least 3 patients. Cluster analysis categorised these 154 genes in 6 clusters (figure 1A–F) that were differentially regulated by rituximab among patients with RA. MetaCore network analysis (supplementary table 1) revealed that cluster A is characterised by the expression of genes involved in IFN signalling (eg, MxA, IFIT3, IFI44, RSAD2) and relates to antiviral actions of IFN. Genes mainly involved in translation (eg, EEF1A1, RPL15, RPL39, RPS3a), macrophage migration inhibitory factor (MIF) and T cell receptor signalling (eg, CD8, ITK) and natural killer (NK) cell cytotoxicity (eg, ITPR3, CD2) were characteristic of cluster B. Cluster C represents genes involved in B cell immunology (eg, CD79a, CD79b, CD19, Igk chain V). Genes involved in extracellular matrix (ECM) remodelling (eg, CTSG) and iron transport (eg, LTF, LCN2) are characteristic for cluster D. Cluster E involves genes related to several processes including chemotaxis (eg, CXCR1, CXCR2, FPR1), cell adhesion and angiogenesis. Finally, cluster F consisted of genes involved in cytoskeleton and coagulation. Essentially, similar genes are regulated at the 3–6 month time period, albeit that the extent of the changes was not related to the 0–3 month time period.

    Figure 1
    Figure 1

    Cluster diagrams of genes that were differentially regulated by rituximab between patients with rheumatoid arthritis (RA). A. Unsupervised (2-way) hierarchical cluster analysis of induced gene expression levels (ratio t3/t0) of a set of 154 genes revealed a marked interindividual variation in the pharmacological response to rituximab between patients with RA. A total of six clusters of genes were differently regulated at 3 months following the start of rituximab treatment between patients. Pathway level analysis revealed that the clusters contained genes related to type I interferon (IFN) biology (cluster A), protein translation, macrophage migration inhibitory factor (MIF) and T cell receptor (TCR) signalling and natural killer (NK) cell cytotoxicity (cluster B), B cell immunology (cluster C), extracellular matrix (ECM) modelling and connective tissue degradation (cluster D), chemotaxis, adhesion and S100 family proteins (cluster E). Cluster F consisted of many genes with unknown function, which together could not be classified into a pathway. B. Supervised (one-way) cluster analysis revealed a set of type I IFN-response genes associated with clinical outcome. Patients were stratified based on changes in Disease Activity Score (∆DAS) at 6 months after the start of treatment. C. Cluster of type I IFN-response genes, which is related to clinical responder status.

    Altogether, these analyses show that pharmacological responses in patients with RA under the influence of rituximab treatment are highly heterogeneous between patients.

    Pharmacodynamics in relation to clinical response

    Next we investigated the pharmacological differences between patients in relation to clinical response. Therefore, patients were stratified based on ∆DAS at 6 months after the start of treatment in good responders (∆DAS>1.2; n=7) and non-responders (∆DAS <1.2; n=6) (figure 1B,C). Subsequently, we performed a cluster analysis using the set of 154 genes to search for genes that were differentially regulated by rituximab between responders and non-responders. Remarkably, the analysis revealed a selective increase in the expression of only cluster A genes (type I IFN-response genes) at 3 months following the start of rituximab treatment in those patients who had a good clinical response, whereas the ones with a similar or decreased expression of these genes exhibited a poor response. This association was most prominent for genes that constitute a subcluster of six genes consisting of RSAD2, IFI44, IFI44L, HERC5, LY6E and Mx1, which were used for further analyses.

    Subsequently, we investigated the relationship between the rituximab-induced changes in the expression of this gene set and the clinical response status. Therefore, the expression levels of IFI44, IFI44L, HERC5, RSAD2, LY6E and Mx1 were averaged for each individual patient at each time point to reach an IFN type I response score. Rituximab-induced changes in the IFN type I response score over the 3 month time period, expressed as the ratio at 3 months versus the baseline score (ratio t3/t0), were compared between the responders and non-responders and revealed a significant increase in the IFN type I response score in the responders compared to the non-responders (p=0.0492, figure 2A). Excluding patients who were seronegative from the analysis yielded essentially similar results (p=0.05, data not shown). Division of patients into two groups based on a cut-off of 1.1-fold induction (0.15 log2 based) of IFN type I response activity resulted in a clear separation of good responders (high ΔDAS) and non-responders (low ΔDAS) (p=0.0040, figure 2B). In addition, a trend was observed for the correlation between improvement in ΔDAS and the increase in IFN type I response score (ratio t3/t0) (p=0.09, data not shown). Accordingly, similar results were observed when response status was assessed by the EULAR response criteria in good responders (n=4), intermediate responders (n=4) and non-responders (n=5) (p=0.048, figure 2C). The change in IFN-response score during rituximab treatment negatively correlated with the corresponding baseline level, although no significance was reached (p=0.0576 and R=−0.53). The IFN-response score returned to baseline values at 6 months after the start of treatment (figure 3).

    Figure 2
    Figure 2

    Differential regulation of type I interferon (IFN)-response gene activity upon rituximab treatment in relation to clinical responder status. The expression levels of type I IFN-response gene activity were determined by cDNA microarray and quantitative (q)PCR analysis in peripheral blood cells of patients with rheumatoid arthritis (RA) before (t=0) and 3 months after (t=3) rituximab treatment. For each patient the expression of the IFN type I response gene activity at baseline (t0) and after 3 months after the start of rituximab treatment (t3) was determined and the ratio (t3/t0, log2 space) was determined. Data are shown as box plots; each box showed the 25th to 75th percentiles. A. The expression levels of 6 type I IFN-response genes (IFI44, IFI44L, HERC5, RSAD2, LY6E and Mx1) were determined by cDNA microarray analysis in peripheral blood cells of 13 patients with RA at t0 and t3. Student t test analysis revealed a significant increase in the expression of the type I IFN-response score (ratio t3/t0) in responders compared to non-responders based on changes in Disease Activity Score (∆DAS) > or <1.2 (p=0.0492). B,C. Patients were divided into two groups based on rituximab induced changes in type I IFN-response gene (IFI44, IFI44L, HERC5, RSAD2, LY6E and Mx1) activity (ratio t3/t0) at a cut-off of 1.1-fold (=0.15 log2 based). The groups were compared to each other with respect to ΔDAS improvement (B) or European League Against Rheumatism (EULAR) responder status (C). The 1.1-fold (=0.15 log2 based) cut-off point marked a significant difference between responders and non-responders (p=0.0040 for ΔDAS and p=0.0336 for EULAR responder status). D. The expression levels of RSAD2 were determined by qPCR in peripheral blood cells from patients with RA of an independent validation cohort (n=9) at t0 and t3. For each patient the level induction (ratio t3/t0, log2 space) was calculated. Mann–Whitney U test analysis revealed a significant increase in the expression of RSAD2 in responders compared to non-responders based on ΔDAS > or <1.2 (p=0.0317).

    Figure 3
    Figure 3

    Pharmacodynamics of the interferon (IFN) type I-response activity during rituximab treatment reveals marked differences between responders and non-responders. Shown are pharmacodynamic measurements in 13 patients with rheumatoid arthritis (RA) of a set of 6 type I IFN response genes (IFI44, IFI44L, HERC5, RSAD2, LY6E and Mx1) (left y axis) and B cell counts based on CD19 cytometry (right y axis) at baseline, 3 (t3) and 6 months (t6). Patients were stratified in responders and non-responders based on changes in Disease Activity Score (∆DAS) (A) and European League Against Rheumatism (EULAR) (B) criteria. Pharmacodynamic analyses of the IFN type I-response activity during rituximab treatment revealed marked differences between responders and non-responders for baseline IFN-response activity (ΔDAS p=0.0052; EULAR p=0.016, respectively) and the rituximab induced increase in IFN type I activity at 3 months (ratio t3/t0) (ΔDAS p=0.049; EULAR p=0.048, respectively). The change in IFN-response gene activity during rituximab treatment negatively correlated with the corresponding baseline level, although no significance was reached (p=0.0576=and R=−0.53).

    We also studied the relationship between the type I IFN signature and clinical and laboratory parameters that are listed in table 1. This analysis revealed no associations between the baseline type I IFN-response activity and laboratory parameters. This is in line with results from other studies that reported on the absence of a relationship between the type I IFN signature and clinical and laboratory parameters.20,–,22 Also no significant associations were observed when we studied the relationship between IFN type I response activity and erythrocyte sedimentation rate/C reactive protein as continuous variables at baseline and at 3 and 6 months. The IFN t3/t0 ratio did show a significant correlation with the decrease in CRP at 3 months (t3/t0 ratio) (Pearson r=-0.6095), p=0.0270) (data not shown). Moreover, no association of the time between the last anti-TNF injection and start of rituximab treatment (at least 4 weeks), and baseline IFN score or induction (ratio t3/t0) was observed, indicative that our results are not influenced by differences in the delay between treatments.

    To provide further evidence that differential regulation of type I IFN-response activity during rituximab is associated with the clinical response status, we tested an independent cohort of nine patients (five non-responders and four responders based on ΔDAS). Therefore, we measured the expression of RSAD2, which is a representative IFN type I response gene that has an excellent correlation with the mean expression IFN type I score at baseline and 3 months after the start of treatment (Pearson r=0.97; p≤0.0001). Analysis of only RSAD2 expression data (ratio t3/t0) for the analysis of pharmacological differences between ∆DAS28-based responders and non-responders in the test cohort revealed essentially similar results compared to usage of the six genes set (p=0.0406). Analysing the t3/t0 ratio of RSAD2 in the validation cohort revealed a significant increase in the responders compared to the non-responders after 3 months of treatment (p=0.0317, figure 2D). Moreover, a correlation was observed between improvement of ΔDAS and increase in RSAD2 expression (ratio t3/t0) (p=0.045). These results confirm that differential regulation of type I IFN-response activity during rituximab is associated with the clinical response status.

    Thus, whereas rituximab depletes B cells in all patients treated irrespective of their clinical response, our data show that a drug-induced increase of type I IFN-response activity is associated with clinical response (figure 3).

    Discussion

    Pharmacogenomic analyses demonstrated that despite the overall decrease in the expression of B cell markers, patients with RA exhibited interindividual differences in their pharmacological responses upon rituximab treatment. Among these we observed a difference in the pharmacodynamics of only one gene signature during rituximab treatment between responders and non-responders. This signature represents type I IFN-response genes. Responders exhibited an increase in IFN type I-response activity after 3 months treatment with rituximab, whereas the IFN type I-response activity was unchanged or hardly changed during treatment in the non-responders. The differential response correlated with baseline levels of IFN type I-response genes, which were low in responders and high in non-responders. These findings lead us to conclude that an increase in IFN type I-response activity during rituximab treatment is associated with a favourable response and may provide insight in the biological mechanism underlying the therapeutic response.

    RA is a heterogeneous condition that is reflected by a heterogeneous response to treatment. As an exponent of that we observed that periodic analyses of the patients' blood revealed rituximab-induced interindividual differences in the gene expression profiles. Our results revealed six gene signatures that are differentially regulated after rituximab treatment between the patients. These signatures contained gene sets that represent several distinct biological processes, including IFN-response gene activity, humoral immunity, cytotoxic T and NK cell immunity and chemotaxis. The rituximab induced expression of genes in cluster B (T and NK cell mediated immunity) was inversely correlated with the expression of genes involved in ECM remodelling and connective tissue degradation (cluster D) (r=−0.62, p<0.0233) and cluster E (chemotaxis, cell adhesion) (r=−0.96, p=<0.0001). For patients with systemic lupus erythematosus (SLE) treated with rituximab an increase in NK cell activity was found for patients who exhibited a sustained response.23 Such an association between an increased NK cell activity and clinical outcome could not be found for RA (data not shown). Differences in the reduction of B cell numbers have been reported by Dass and colleagues,24 who claimed a significant association between less reduction and a poor clinical outcome. However, our data, based on cytometric and gene expression analysis, did not reveal significant differences in the expression of B cell markers and genes between responders and non-responders at 3 and 6 months after the start of treatment.

    Our data reveal that rituximab-induced differential regulation of only the IFN-response genes correlated with the ΔDAS28 and EULAR outcome. Regarding the pharmacodynamics of rituximab in relation to the type I IFN activity, two interesting observations were made in this study. First, non-responders displayed an activated type I IFN system prior to the start of treatment, which remains active during treatment. Second, good responders have low or absent IFN-response activity at baseline and develop IFN-response activity during 3 months of treatment that is comparable to that of non-responders. The correlation between baseline type I IFN levels and clinical response is in line with previous findings.20

    Concerning the correlation between baseline level and clinical outcome, a simple explanation could be that the pathogenesis in IFNhigh patients is less dependent on B cells compared to IFNlow patients. Alternatively, one might speculate that a high IFN activity is associated with protection from depletion of pathogenic B cells, especially located in tissues, due to concomitant increased expression of B cell survival factors such as B cell activating factor (BAFF) and B lymphocyte stimulator (BLyS).25 Additional effects of IFNs on B cell differentiation, such as in situ differentiation in CD20 negative plasma blasts,26 could contribute to diminish the effects of anti-CD20 depletion, which processes remain unseen in the peripheral blood compartment.

    With respect to the increase of IFN type I activity we know that regulation of type I IFN production and the consecutive IFN-response activity is initially mediated via the Toll-like receptors, which are triggered by exogenous (infectious) agents and endogenous agents, such as nucleic acids and apoptotic/necrotic material.27 Hence, subsequent release from apoptotic/necrotic material from depleted CD20+ B cells may promote IFN production and release, which might selectively take place in the IFNlow patients. Thus, interindividual differences in the regulatory processes involved in the IFN biology, such as mentioned above, may be held responsible for the rituximab induced in the divergent regulation of IFN type I activity. Such differences could have its origin in genetic variation in the type I IFN biology. Single nucleotide polymorphisms in several key components of the type I IFN system (such as interferon regulatory factor 5 (IRF5), tyrosine kinase 2 (Tyk2) and signal transducer and activator of transcription 4 (STAT4)) have recently been identified to play a role in the differential of the IFN activity in for example, SLE.28 29

    The pharmacological induction of type I IFN activity could be an important factor in the ameliorative effect of B cell depletion treatment in RA. Such a role for type I IFN activity in RA is highlighted by Treschow et al,30 who showed that IFNβ deficiency prolonged experimental arthritis. Additional evidence for a beneficial effect of type I IFNs in RA has been provided by de Hooge et al31 who demonstrated that STAT1 deficiency resulted in exacerbation of experimental arthritis. Moreover, transfer of IFN-competent FLS was able to ameliorate arthritis in IFNβ-deficient recipients.30 However, although treatment with recombinant IFNβ revealed promising results in experimental arthritis, treatment of patients with RA with IFNβ has been unsuccessful so far, which may be due to issues with dosing and pharmacokinetics.32 Conversely, a pharmacological increase in the type I IFN activity by rituximab may lead to disease progression and/or an increase in disease activity in IFN type I-driven diseases such as SLE. Recent randomised, placebo-controlled trials of rituximab failed to meet their primary or secondary clinical end points for renal and non-renal SLE.33 Our data suggest that rituximab might be less effective in those patients with SLE who experience an increase in their type I IFN response activity levels during rituximab treatment.

    A relation between the dynamics of the IFN system and clinical responsiveness has also been reported for infliximab, one of the TNF blockers.34 Data from an explorative study in RA revealed that an increase in IFN-response activity during treatment with infliximab is associated with a poor clinical outcome. The dynamics of this response do not seem to correlate with the baseline IFN-response activity as was seen for rituximab. Moreover, the response pattern in relation to clinical outcome that was observed for infliximab is not in line with that for rituximab. Recent observations indicate that TNF blockade involves complex crosstalk between TNF and IFN that may exert stimulating and inhibiting effects on IFN-response activity.35 36 Unlike rituximab, the differential effects on type I IFN-response activity associated with infliximab are believed to be mainly linked to intrinsic differences in this crosstalk. As a consequence, in the context of infliximab treatment the increased IFN system seems to accompany maintenance of inflammatory activity. This is in line with earlier observations that reveal an association between synovial inflammation and synovial IFN-response gene activity.37 Overall, these findings indicate that the dynamics of the type I IFN-response activity might have different clinical consequences in RA depending on the specific biological, thus consequently type of modulated immune pathway, used.

    In conclusion, our data provide insight in the pharmacological features associated with the clinical outcome of rituximab. An increase in type I IFN during treatment correlated with a favourable clinical outcome in RA. Further studies are required to define the underlying mechanism for this response association.

    References

    1. ↵
      1. Firestein GS
      . Evolving concepts of rheumatoid arthritis. Nature 2003;423:356–61.
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Feldmann M,
      2. Maini SR
      . Role of cytokines in rheumatoid arthritis: an education in pathophysiology and therapeutics. Immunol Rev 2008;223:7–19.
      OpenUrlCrossRefPubMedWeb of Science
    3. ↵
      1. Smolen JS,
      2. Aletaha D,
      3. Koeller M,
      4. et al
      . New therapies for treatment of rheumatoid arthritis. Lancet 2007;370:1861–74.
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Verweij CL
      . Predicting the future of anti-tumor necrosis factor therapy. Arthritis Res Ther 2009;11:115.
      OpenUrlCrossRefPubMed
    5. ↵
      1. Cohen SB,
      2. Emery P,
      3. Greenwald MW,
      4. et al
      . Rituximab for rheumatoid arthritis refractory to anti-tumor necrosis factor therapy: Results of a multicenter, randomized, double-blind, placebo-controlled, phase III trial evaluating primary efficacy and safety at twenty-four weeks. Arthritis Rheum 2006;54:2793–806.
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Edwards JC,
      2. Szczepanski L,
      3. Szechinski J,
      4. et al
      . Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis. N Engl J Med 2004;350:2572–81.
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      1. Emery P,
      2. Fleischmann R,
      3. Filipowicz-Sosnowska A,
      4. et al
      . The efficacy and safety of rituximab in patients with active rheumatoid arthritis despite methotrexate treatment: results of a phase IIB randomized, double-blind, placebo-controlled, dose-ranging trial. Arthritis Rheum 2006;54:1390–400.
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      1. Caporali R,
      2. Caprioli M,
      3. Bobbio-Pallavicini F,
      4. et al
      . Long term treatment of rheumatoid arthritis with rituximab. Autoimmun Rev 2009;8:591–4.
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Cambridge G,
      2. Stohl W,
      3. Leandro MJ,
      4. et al
      . Circulating levels of B lymphocyte stimulator in patients with rheumatoid arthritis following rituximab treatment: relationships with B cell depletion, circulating antibodies, and clinical relapse. Arthritis Rheum 2006;54:723–32.
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Edwards JC,
      2. Leandro MJ,
      3. Cambridge G
      . B lymphocyte depletion in rheumatoid arthritis: targeting of CD20. Curr Dir Autoimmun 2005;8:175–92.
      OpenUrlPubMed
    11. ↵
      1. Arnett FC,
      2. Edworthy SM,
      3. Bloch DA,
      4. et al
      . The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988;31:315–24.
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Prevoo ML,
      2. van't Hof MA,
      3. Kuper HH,
      4. et al
      . Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum 1995;38:44–8.
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      1. Bolstad BM,
      2. Irizarry RA,
      3. Astrand M,
      4. et al
      . A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003;19:185–93.
      OpenUrlAbstract/FREE Full Text
    14. ↵
      1. Rainer J,
      2. Sanchez-Cabo F,
      3. Stocker G,
      4. et al
      . CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis. Nucleic Acids Res 2006;34:W498–503.
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Tusher VG,
      2. Tibshirani R,
      3. Chu G
      . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001;98:5116–21.
      OpenUrlAbstract/FREE Full Text
    16. ↵
      1. Eisen MB,
      2. Spellman PT,
      3. Brown PO,
      4. et al
      . Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998;95:14863–8.
      OpenUrlAbstract/FREE Full Text
    17. ↵
      1. Mootha VK,
      2. Lindgren CM,
      3. Eriksson KF,
      4. et al
      . PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34:267–73.
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Subramanian A,
      2. Tamayo P,
      3. Mootha VK,
      4. et al
      . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545–50.
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Baechler EC,
      2. Batliwalla FM,
      3. Karypis G,
      4. et al
      . Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci USA 2003;100:2610–15.
      OpenUrlAbstract/FREE Full Text
    20. ↵
      1. Thurlings RM,
      2. Boumans M,
      3. Tekstra J,
      4. et al
      . Relationship between the type I interferon signature and the response to rituximab in rheumatoid arthritis patients. Arthritis Rheum 2010;62:3607–14.
      OpenUrlCrossRefPubMedWeb of Science
    21. ↵
      1. Cantaert T,
      2. van Baarsen LG,
      3. Wijbrandts CA,
      4. et al
      . Type I interferons have no major influence on humoral autoimmunity in rheumatoid arthritis. Rheumatology (Oxford) 2010;49:156–66.
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. van der Pouw Kraan TC,
      2. Wijbrandts CA,
      3. van Baarsen LG,
      4. et al
      . Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann Rheum Dis 2007;66:1008–14.
      OpenUrlAbstract/FREE Full Text
    23. ↵
      1. Reis EA,
      2. Athanazio DA,
      3. Lima I,
      4. et al
      . NK and NKT cell dynamics after rituximab therapy for systemic lupus erythematosus and rheumatoid arthritis. Rheumatol Int 2009;29:469–75.
      OpenUrlCrossRefPubMedWeb of Science
    24. ↵
      1. Dass S,
      2. Vital EM,
      3. Emery P
      . Rituximab: novel B-cell depletion therapy for the treatment of rheumatoid arthritis. Expert Opin Pharmacother 2006;7:2559–70.
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      1. Jego G,
      2. Pascual V,
      3. Palucka AK,
      4. et al
      . Dendritic cells control B cell growth and differentiation. Curr Dir Autoimmun 2005;8:124–39.
      OpenUrlCrossRefPubMed
    26. ↵
      1. Jego G,
      2. Palucka AK,
      3. Blanck JP,
      4. et al
      . Plasmacytoid dendritic cells induce plasma cell differentiation through type I interferon and interleukin 6. Immunity 2003;19:225–34.
      OpenUrlCrossRefPubMedWeb of Science
    27. ↵
      1. Kumar H,
      2. Kawai T,
      3. Akira S
      . Toll-like receptors and innate immunity. Biochem Biophys Res Commun 2009;388:621–5.
      OpenUrlCrossRefPubMedWeb of Science
    28. ↵
      1. Graham RR,
      2. Kozyrev SV,
      3. Baechler EC,
      4. et al
      . A common haplotype of interferon regulatory factor 5 (IRF5) regulates splicing and expression and is associated with increased risk of systemic lupus erythematosus. Nat Genet 2006;38:550–5.
      OpenUrlCrossRefPubMedWeb of Science
    29. ↵
      1. Sigurdsson S,
      2. Nordmark G,
      3. Garnier S,
      4. et al
      . A risk haplotype of STAT4 for systemic lupus erythematosus is over-expressed, correlates with anti-dsDNA and shows additive effects with two risk alleles of IRF5. Hum Mol Genet 2008;17:2868–76.
      OpenUrlAbstract/FREE Full Text
    30. ↵
      1. Treschow AP,
      2. Teige I,
      3. Nandakumar KS,
      4. et al
      . Stromal cells and osteoclasts are responsible for exacerbated collagen-induced arthritis in interferon-beta-deficient mice. Arthritis Rheum 2005;52:3739–48.
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. de Hooge AS,
      2. van de Loo FA,
      3. Koenders MI,
      4. et al
      . Local activation of STAT-1 and STAT-3 in the inflamed synovium during zymosan-induced arthritis: exacerbation of joint inflammation in STAT-1 gene-knockout mice. Arthritis Rheum 2004;50:2014–23.
      OpenUrlCrossRefPubMedWeb of Science
    32. ↵
      1. van Holten J,
      2. Plater-Zyberk C,
      3. Tak PP
      . Interferon-beta for treatment of rheumatoid arthritis? Arthritis Res 2002;4:346–52.
      OpenUrlCrossRefPubMedWeb of Science
    33. ↵
      1. Merrill JT,
      2. Neuwelt CM,
      3. Wallace DJ,
      4. et al
      . Efficacy and safety of rituximab in moderately-to-severely active systemic lupus erythematosus: the randomized, double-blind, phase II/III systemic lupus erythematosus evaluation of rituximab trial. Arthritis Rheum 2010;62:222–33.
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. van Baarsen LG,
      2. Wijbrandts CA,
      3. Rustenburg F,
      4. et al
      . Regulation of IFN response gene activity during infliximab treatment in rheumatoid arthritis is associated with clinical response to treatment. Arthritis Res Ther 2010;12:R11.
      OpenUrlCrossRefPubMed
    35. ↵
      1. Palucka AK,
      2. Blanck JP,
      3. Bennett L,
      4. et al
      . Cross-regulation of TNF and IFN-alpha in autoimmune diseases. Proc Natl Acad Sci USA 2005;102:3372–7.
      OpenUrlAbstract/FREE Full Text
    36. ↵
      1. Yarilina A,
      2. Park-Min KH,
      3. Antoniv T,
      4. et al
      . TNF activates an IRF1-dependent autocrine loop leading to sustained expression of chemokines and STAT1-dependent type I interferon-response genes. Nat Immunol 2008;9:378–87.
      OpenUrlCrossRefPubMedWeb of Science
    37. ↵
      1. van Baarsen LG,
      2. Wijbrandts CA,
      3. Timmer TC,
      4. et al
      . Synovial tissue heterogeneity in rheumatoid arthritis in relation to disease activity and biomarkers in peripheral blood. Arthritis Rheum 2010;62:1602–7.
      OpenUrlPubMedWeb of Science

    Supplementary materials

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    Footnotes

    • SV and HGR contributed equally to this work

    • Funding This study was partly supported by grants from the European Community's FP6 funding (AUTOCURE), MS-Research (grant no. 04-549 MS) and the Centre for Medical Systems Biology (CMSB, a centre of excellence from The Netherlands Genomics Initiative).

    • Competing interests MTN has received research and speaking fees from Roche. WFL is member of advisory board van Roche, Abbott and Shering Plough (MSD). BACD received speaking fees from Pfizer and Abbott. CLV is an inventor on a patent application on the predictive value of IFN type I response activity for the clinical outcome of B cell depletion treatment. SV, HGR, TCTMvdPK, MWJS, BMEvB and AEV have no competing interests to declare.

    • Ethics approval This study was conducted with the approval of the local medical ethics committee.

    • Provenance and peer review Not commissioned; externally peer reviewed.