The gene expression profile in the synovium as a predictor of the clinical response to infliximab treatment in rheumatoid arthritis

Abstract

Background: Although the use of TNF inhibitors has fundamentally changed the way rheumatoid arthritis (RA) is treated, not all patients respond well. It is desirable to facilitate the identification of responding and non-responding patients prior to treatment, not only to avoid unnecessary treatment but also for financial reasons. In this work we have investigated the transcriptional profile of synovial tissue sampled from RA patients before anti-TNF treatment with the aim to identify biomarkers predictive of response.

Methodology/principal findings: Synovial tissue samples were obtained by arthroscopy from 62 RA patients before the initiation of infliximab treatment. RNA was extracted and gene expression profiling was performed using an in-house spotted long oligonucleotide array covering 17972 unique genes. Tissue sections were also analyzed by immunohistochemistry to evaluate cell infiltrates. Response to infliximab treatment was assessed according to the EULAR response criteria. The presence of lymphocyte aggregates dominated the expression profiles and a significant overrepresentation of lymphocyte aggregates in good responding patients confounded the analyses. A statistical model was set up to control for the effect of aggregates, but no differences could be identified between responders and non-responders. Subsequently, the patients were split into lymphocyte aggregate positive- and negative patients. No statistically significant differences could be identified except for 38 transcripts associated with differences between good- and non-responders in aggregate positive patients. A profile was identified in these genes that indicated a higher level of metabolism in good responding patients, which indirectly can be connected to increased inflammation.

Conclusions/significance: It is pivotal to account for the presence of lymphoid aggregates when studying gene expression patterns in rheumatoid synovial tissue. In spite of our original hypothesis, the data do not support the notion that microarray analysis of whole synovial biopsy specimens can be used in the context of personalized medicine to identify non-responders to anti-TNF therapy before the initiation of treatment.

Conflict of interest statement

Competing Interests: P.P. Tak has served as a consultant for Abbott, Amgen, Centocor, Schering-Plough, UCB, and Wyeth. Patients were treated with infliximab, a commercial antibody marketed by Schering-Plough. There are no patents, marketed products, or products in development related to this research. Also, the use of infliximab does not alter the adherence of this work to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1. Lymphocyte aggregates.

The presence of lymphocyte aggregates was assessed on anti-CD3-stained sections. Aggregates were counted and graded by size. Aggregate size was assessed by counting the number of cells in a radius starting from an estimated center of the aggregate. Aggregate size was then classified as grade 1 (1–5 cells in the radius [panel B]), grade 2 (5–10 cells in the radius [panel C]), or grade 3 (>10 cells in the radius [panel D]). Tissue sections with no lymphocyte aggregates were graded as 0 (panel A).

Figure 2. Hierarchical cluster of patients using features with high variation irrespective of response group.

Patients were clustered in a hierarchical dendrogram using features with a log2-ratio (ratio = sample intensity/reference intensity) interquartile range >1 (1321 features in total). Abbreviations in the dendrogram: PXX = Patient number; G,  = Good responder; M = Moderate responder; N = Non responder; L+/− = presence of large lymphocyte aggregates; S+/−presence of small aggregates. Colors in the dendrograms: Red = patients positive for either small or large lymphocyte aggregates; Blue = patients negative for small and large lymphocyte aggregates; Black = patients with no lymphocyte aggregate assessment.