Original articleGene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets
Introduction
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of joints followed by destruction of cartilage and bone. The precise etiology of RA is not completely understood, but a large body of studies indicates that pro-inflammatory cytokines such as TNFα and IL-1β are significantly involved in the pathophysiology of joint inflammation and bone destruction in RA [1]. Biologic agents, especially anti-TNF agents, have produced significant and clinically relevant improvements in patients with active RA and appear to be very effective therapeutic molecules for RA patients. Although the use of anti-TNF agents has revolutionized the treatment of RA, a substantial proportion (up to one-third) of RA patients treated with anti-TNF agents fail to achieve a satisfactory clinical response [2]. Therefore, a strategy for predicting which patients will and will not respond prior to initiation of therapy is needed to prevent disease progression, unnecessary side effects, and high costs for non-responders. Although baseline demographics and clinical characteristics, including age, sex, disease duration, Disease Activity Score (DAS28), Health Assessment Questionnaire score, and concurrent use of disease-modifying anti-rheumatic drugs (DMARDs), have been extensively studied, potential prediction markers of therapeutic responsiveness have not yet been discovered [3].
Pharmacogenetic studies have also been performed to discover determinants of efficacy of anti-TNF agents. Some studies have shown that polymorphisms in genes encoding TNFα, TNF receptors, the major histocompatibility complex region, and the Fcγ receptor IIIA (FCγRIIIA) are associated with a favorable response to anti-TNF therapy; however, these results were not consistent with those of other studies [3], [4].
Gene expression profiling with microarrays has become a standard method for identifying the genes and biological pathways that are associated with various complex diseases [5], [6]. Biomarkers identified by gene expression profiling can be used to make a diagnosis, monitor disease activity, and predict response to therapy. This technology has already been shown to identify gene expression profiles predictive of treatment response and clinical outcomes for some diseases [7], [8], [9], [10], [11]. Recently, several studies have attempted to identify gene expression profiles predicting the response to anti-TNF therapy in RA patients by using multiple gene expression microarrays [12], [13], [14], [15], [16], [17]. Although these studies have successfully identified gene expression signatures predicting the response to anti-TNF therapy, the expression profiles identified in these studies were not consistent with each other, and the genes reported by the authors of each study had little overlap. Herein, we attempt to identify differentially expressed genes (DEGs) that differ between responders and non-responders to anti-TNF therapy by:
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comparing gene signatures previously reported to predict response to anti-TNF therapy in RA patients;
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analyzing microarray datasets of these studies, which are available at the National Center for Biotechnology Information's Gene Expression Omnibus (GEO), using GEO2R;
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performing a meta-analysis of these datasets.
Section snippets
Data collection
We performed literature survey via Medline/PubMed searches to identify available studies that examined gene expression profiles of peripheral blood samples to predict treatment outcome of anti-TNF therapy in RA patients. We searched for a combination of KEY words such as “Infliximab”, “Adalimumab”, “Etanercept”, “Microarray”, “Gene expression profile”, “Anti-TNF” and “Rheumatoid arthritis” as both medical subject heading terms and text words. We also tried to identify additional studies by
Study characteristics
The analyses included 6 studies that used peripheral blood samples to identify gene expression profiles predicting response to anti-TNF therapy. The dataset of 1 of these studies was not deposited in the GEO database [15]. Therefore, the microarray dataset of this study was not included for individual analysis or meta-analysis. Detailed information of each study is described in Table 1. In the study of Lequerré et al., patients with very active disease (Disease Activity Score 28 > 5.1) were
Discussion
We performed a comprehensive literature search to identify microarray-based studies of gene expression profiles predicting response to anti-TNF therapy in RA patients and then attempted to find a DEG profile to discriminate between different response groups by combining the datasets of these studies. To our knowledge, this is the first study in which publicly available microarray datasets for anti-TNF response were combined and re-examined by means of the same analytical tool such as GEO2R and
Disclosure of interest
The authors declare that they have no conflicts of interest concerning this article.
Acknowledgement
This research was supported by a Korea University Grant.
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