Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept

Rheumatoid arthritis (RA) is an autoimmune disease of unknown aetiology that is characterized by recruitment and activation of inflammatory cells, synovial hyperplasia, and destruction of cartilage and bone. The proinflammatory cytokine TNFα is a key mediator in the pathogenesis of RA [1]. Etanercept (Enbrel^®; Wyeth, Cambridge, MA, USA), a soluble TNFα receptor immunoglobulin fusion protein, has been recognized as a potent biological that neutralizes TNFα [2–4]. Clinical studies on the efficacy of TNFα-blocking agents clearly show that about 30% of patients receiving this expensive therapy are nonresponders [3, 5]. Although many efforts have been made to identify biomarkers for therapy response [6], no clinical or single laboratory marker exists today that allows a prediction of TNFα therapy efficacy in the individual patient. This lack of biomarker includes the newly identified specific serological marker for RA – antibodies to cyclic citrullinated peptides [7, 8] – as well as genetic markers [9–12].

A number of studies have shown that the expression of individual proteins – particularly cytokines such as TNFα, IL-1β, IL-6 and IFNγ [13, 14], chemokines like IL-8 and MCP1, as well as matrix metalloproteinases such as MMP1 and MMP3 [15, 16] – changes during etanercept therapy. These studies were limited to a small number of genes and their corresponding proteins, and were not able to identify new markers for characterizing disease activity or to determine discriminatory markers for the prediction of therapy outcome. Van der Pouw and coworkers [17] used gene expression profiling of synovial tissue to identify subsets of RA based on molecular criteria; see also Glocker and colleagues [18].

Lequerre and colleagues described changes in gene expression signatures of mononuclear cells in RA patients 3 months after the start of treatment that were correlated with the treatment response to another TNFα inhibitor, infliximab, in combination with methotrexate [19]. They reported a significant decrease of transcript levels of eight genes regulated by TNFα-dependent pathways in nonresponders, whereas transcript levels in responders did not change significantly but were slightly increased. The effects of infliximab treatment on the long-term changes of gene expression pattern of synovial tissue and their potential to predict the outcome of infliximab-treated RA patients was investigated by Lindberg and coworkers [20]. Differentially expressed genes were involved in processes such as chemotaxis, immune function, signal transduction and inflammatory responses. The value of tissue biopsies is still under debate, and biopsies repeated in quick succession are not feasible.

The present study uses global transcriptome analysis to determine RNA expression signatures in peripheral blood cells that specify the response to anti-TNFα therapy within the first days of treatment. The objective of our approach is to discover predictive markers by analysing gene sets that are distinctly regulated in the first 3 days after anti-TNF (etanercept) administration. This short time interval was chosen to identify initially perturbed gene expression not influenced by possible changes in comedication and environmental factors occurring during longer follow-up.

We report the application of established DNA array technology (Affymetrix^®; St. Clara, CA, USA) to monitor changes in the expression levels of mononuclear cells from peripheral blood during etanercept treatment. Among about 14,500 genes, 42 candidate genes were found suitable for use as prognostic markers for the therapeutic outcome. Using supervised learning methods, pairs and triplets derived from these genes were found to have a high prognostic value – reflected by prediction accuracies of over 89% for seven gene pairs and of 95% for 10 specific gene triplets.

The goal of the present study was to identify reliable biomarkers for predicting therapy outcomes in RA patients treated with the TNFα-blocking agent etanercept. Changes of the pre-existing gene activities were monitored following the neutralization of TNFα. The Affymetrix microarray technique produced reliable semiquantitative results confirmed by comparing real-time RT-PCR results of selected genes with Affymetrix microarray results.

By applying a newly implemented criterion that takes into account the confidence intervals of the signal log ratios of gene expression [25] (see Additional file 1), 42 candidate genes (46 probe sets) were found to be differentially regulated following a single application of etanercept (Table 2). The early downregulation of expression levels secondary to TNFα neutralization includes genes involved in different pathways and cellular processes such as TNFα signalling via NFκB (TNFAIP3, NFKBIA), NFκB-independent signalling via cAMP (PDE4B), and in the regulation of cellular and oxidative stress response (PPP1R15A, DDIT4, CROP, adrenomedullin, MnSOD). The differential expression of this gene set was associated with distinct clinical responses as evinced by changes in overall disease activities 3 months after the start of treatment. The majority of the identified genes (40 probe sets) were found to be downregulated in responders compared with nonresponders. The differential expression of 27 probe sets was confirmed to be significant using a resampling method. Most importantly, changes in the expression profiles of these selected genes, particularly of pairs or triplets of genes detected 3 days after the start of treatment, were identified as being closely associated with the outcome of therapy (Additional file 1, Tables S3a, S3b). Flow cytometry analysis ruled out that changes of the expression pattern within the first 3 days of treatment were due to an altered cellular distribution of peripheral blood cells.

Two patients (patients 2 and 16) who were not predicted properly were classified as outliers by correlating clinical data and gene expression changes. Patient 2 presents a highly destructive RA, making it difficult to distinguish joint destructions in RA from destructions due to secondary osteoarthritis. Patient 16 displays the highest DAS28 score of the cohort, making it difficult to classify the patient as responder when reaching a DAS28 of 5.9, which is exceptionally high. The stratification of these two cases is hampered in their overall assessment by the limitation of tools such as the DAS28.

In contrast to changes in gene expression pattern in the first days of treatment, gene expression signatures at a single time point, here at baseline, were not reliable in predicting the clinical outcome. Diversities between RA patients on the genetic, molecular and clinical levels [17] evinced by the presence of autoantibodies (rheumatoid factor, anti-cyclic citrullinated peptide antibodies) [26] probably underline the difficulty to predict therapy outcome solely based on pretreatment expression profiles. Eventually, the differences seen in transcriptional responses to etanercept administration might either reflect the state or type of the RA disease or describe epigenomic/genomic variabilities within the patient cohort.

The reconstructed dynamic network representing responders (Figure 3) indicates that not only TNFα may play a significant role in the response to TNFα inhibitors such as etanercept. IL-6-related functionalities seem to play a key role in the responder model, while TNFα-related mechanisms are underscored in nonresponders. The functional dynamics of TNFα and IL-6 might be crucial for the outcome of an etanercept therapy. In biological terms, functionalities of anti-TNFα responses observed in nonresponding patients in comparison with responding patients might emerge due to a differential dynamic regulation of TNFα and of TNFα-dependent target gene expression, possibly also flanked by TNFα-independent mechanisms.

Responders show complex network functions of cytokines including IL-6-mediated, IL-1-mediated, and IL-8-mediated activities. Once TNFα signals are therapeutically downregulated, cytokines such as IL-6 and IL-8 become visible, possibly modulating and eventually attenuating TNF-driven inflammatory processes. This observation is in line with reports on the pleiotropic/anti-inflammatory actions of IL-6 [27], which demonstrated the role of endogenous IL-6 in controlling the levels of proinflammatory cytokines in acute inflammatory responses. The particular role of IL-6 in inflammatory conditions such as RA is presently considered in therapeutic interventions that target IL-6 or its receptor [28].

Differential changes in the expression pattern following anti-TNFα treatment can most probably be attributed to the presence of genetic heterogeneities within the group of RA patients, suggesting the presence of polymorphisms (single nucleotide polymorphisms) and/or epigenetic differences (DNA methylation patterns) in the identified genes. These polymorphisms – found in regulatory gene elements of central cytokines or downstream cascades – or the combination of single nucleotide polymorphisms as well as other types of genetic variations within these differentially regulated or associated genes, such as copy number variations, might possibly turn out to be responsible for mediating therapeutic responses as observed. This hypothesis is supported by findings that some population differences in gene expressions are attributable to allele frequency differences, in particular at regulatory polymorphisms [29].

The present findings demonstrate that it is possible to predict the response of RA patients to anti-TNFα therapy at an early stage of treatment with likelihood >89% (95%) based on differentially expressed gene pairs or gene triplets. By knowing gene sets differentially regulated by TNFα-blocking therapy, additional epigenetic/genetic marker information might be obtained to circumvent the necessity of conducting cost-intensive expression studies. Along these lines, the real challenge of the listed predictory gene sets (pairs and triplets) is to validate in prospectively designed clinical trials the true accuracy and clinical value of this approach in selecting patients that profit most from a TNFα-blocking therapy.