Project description: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 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. All patients were hybridized in a dual channel reference design where the reference was always labeled with cy3 and the samples with cy5. In total, RNA was extracted from synovial biopsies of 62 patients. 18 patients were good responding, 30 moderate responding and 14 non-responding. Universal Human Reference RNA from Stratagene was used as reference. All samples were amplified with the RiboAmp II kit before labeling and hybridization.

Project description: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 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.

Project description:Objective: use comprehensive molecular profiling to understand the molecular mechanisms that affect clinical response to anti-TNF therapy in rheumatoid arthritis (RA) and to identify predictive markers to differentiate good responders and non-responders. Methods: two independent cohorts of 40 and 36 biologic-naïve RA patients were selected from the Corrona (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) CERTAIN registry and categorized by EULAR response criteria. Whole-blood RNA and plasma samples from baseline and after 3 months of anti-TNF treatment were profiled using RNA-seq, shotgun proteomics and glycopeptide analysis. A cell type-specific transcriptional data analysis was applied to RNA-seq data to evaluate the impact of the most common immune cell sub-populations. Results: a treatment-related molecular signature was identified that showed a high level of correlation (ρ=0.62; permutation p<0.01) between cohorts. Treatment led to a reduction of neutrophils, independent of the status of response. Gene expression differences between good responders and non-responders at baseline did not manifest statistically significant concordance genome-wide between the two cohorts. However, a cell type-specific analysis indicated increased representation of innate cell type signatures in good responders and, conversely, increased expression of adaptive cell type signatures in non-responders at baseline in both cohorts. This result was confirmed by applying the cell-type specific analysis to other publicly available RA datasets. Evaluation of the neutrophil to lymphocyte ratio (NLR) at baseline in the remaining patients (n=1962) from the CERTAIN database using a logistic regression model further confirmed the observation (odds ratio of good/moderate response = 1.20 [95% CI = 1.03 – 1.41; p = 0.02]). Conclusion: differences in innate/adaptive immune cell type composition at baseline may be a major contributor to response to anti-TNF treatment within the first 3 months of therapy.

Project description:TNF antagonists are routinely used in severe rheumatoid arthritis (RA) patients who failed conventional DMARD therapy. According to large clinical trials, the three available drugs (adalimumab, infliximab and etanercept) display similar effects in terms of efficacy, tolerability and side effects. These studies also indicate that about 25% of RA patients treated with TNF-antagonists do not display any significant clinical improvement. The aim of this study was to investigate global molecular patterns in synovial biopsies from RA patients obtained 12 weeks after initiation of adalimumab therapy. All patients had rheumatoid arthritis (RA), according to the American College of rheumatology criteria for the diagnosis of RA. They had active disease at the time of initiation of adalimumab therapy and were resistant to conventional therapy. They all had erosive changes imaged on conventional x-rays of the hands and/or feet. All patients were treated with disease-modifying antirheumatic drugs (DMARD’s), 23 with methotrexate (median dose 15 mg/week, range 7.5 – 20 mg/week), and 2 with leflunomide (20 mg/day); 18 of them were treated with low-dose steroids (prednisolone ≤ 7.5 mg/day). Six patients had been included in double-blind clinical trials before inclusion in the present study (1 in a Golimumab versus placebo trial, 3 in a MapKinase inhibitor versus placebo trial and 2 in a TACE-inhibitor versus placebo trial). These trials were stopped at least 3 months prior to initiation of TNF-blocking therapy. All drug dosages were stable from at least 3 months prior to initiation of TNF blocking therapy until completion of the study. No steroid injections were allowed during the duration of the study. Adalimumab therapy was initiated at a dosage of 40 mg subcutaneously every other week. Disease activity at baseline and 12 weeks after initiation of therapy (T12) was evaluated using DAS(28)-CRP (3- and 4-variables) scores, and response to therapy was assessed according to the EULAR response criteria that categorize patients in responders (good- or moderate-) and non- (or poor-) responders based on changes in DAS activity between T0 and T12 and absolute DAS values at T12. Synovial biopsies were obtained by needle-arthroscopy of knee of the patients at T12. The aim of the study was to compare gene expression profiles in synovial tissue of RA patients who responded versus not responded to adalimumab therapy.

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Synovial inflammatory pathways characterize anti-TNF-responsive rheumatoid arthritis patients

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