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

Abstract

Introduction: About 30% of rheumatoid arthritis patients fail to respond adequately to TNFalpha-blocking therapy. There is a medical and socioeconomic need to identify molecular markers for an early prediction of responders and nonresponders.

Methods: RNA was extracted from peripheral blood mononuclear cells of 19 rheumatoid arthritis patients before the first application of the TNFalpha blocker etanercept as well as after 72 hours. Clinical response was assessed over 3 months using the 28-joint-count Disease Activity Score and X-ray scans. Supervised learning methods were applied to Affymetrix Human Genome U133 microarray data analysis to determine highly selective discriminatory gene pairs or triplets with prognostic relevance for the clinical outcome evinced by a decline of the 28-joint-count Disease Activity Score by 1.2.

Results: Early downregulation of expression levels secondary to TNFalpha neutralization was associated with good clinical responses, as shown by a decline in overall disease activity 3 months after the start of treatment. Informative gene sets include genes (for example, NFKBIA, CCL4, IL8, IL1B, TNFAIP3, PDE4B, PPP1R15A and ADM) involved in different pathways and cellular processes such as TNFalpha signalling via NFkappaB, NFkappaB-independent signalling via cAMP, and the regulation of cellular and oxidative stress response. Pairs and triplets within these genes were found to have a high prognostic value, reflected by prediction accuracies of over 89% for seven selected gene pairs and of 95% for 10 specific gene triplets.

Conclusion: Our data underline that early gene expression profiling is instrumental in identifying candidate biomarkers to predict therapeutic outcomes of anti-TNFalpha treatment regimes.

Figure 1

Gene expression changes of a representative predictive gene pair. Shown is the pair PTPRD [205712_at], TNFAIP3 [202643_s_at]. The pair is presented in Table 4 with a prediction accuracy of 90.5% determined using the support vector machine algorithm (signal log ratios for t₁ versus t₀: (○) 12 responders and (●) seven nonresponders, defined due to clinical response; bars denote the confidence intervals of the signal log ratios). Patient 16 was classified as a nonresponder based on gene expression data, but as a responder from clinical status.

Figure 2

Gene expression changes of a representative predictive gene triplet. The triplet of genes TNFAIP3, PDE4B, RAPGEF1 is shown. The triplet is presented in Table 4 with a prediction accuracy of 95.8% determined using support vector machines (signal log ratios for t₁ versus t₀: (○) 12 responders and (●) seven nonresponders).

Figure 3

Visualization of the inferred dynamic gene regulatory network for the responder group. Each gene is represented by a node, and gene regulatory interactions are shown by directed edges. Solid lines, activating effects; dashed lines, inhibitory effects. The hypothesized network was reconstructed from quantitative real-time RT-PCR data by the modified LASSO method.