Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Expression profiling of rheumatoid arthritis patients treated with anti-TNF

ABSTRACT: Expression profiles of anti-TNF responders were compared to profiles of anti-TNF non-responders in order to identify an expression signature for anti-TNF response In total 42 patients were treated with anti-TNF. RNA was isoloated from white blood cells and anti-TNF responders (n=18) were compared to nonresponders (n=24) regarding expression profiles

ORGANISM(S): Homo sapiens

SUBMITTER: Erik Toonen

PROVIDER: E-GEOD-33377 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

Dataset Information

Expression profiling of rheumatoid arthritis patients treated with anti-TNF

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