Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis
SourcePLoS One, 7, 3, (2012), article e33199
Article / Letter to editor
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Epidemiology, Biostatistics & HTA
SubjectDCN PAC - Perception action and control IGMD 3: Genomic disorders and inherited multi-system disorders; IGMD 2: Molecular gastro-enterology and hepatology; IGMD 3: Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory; IGMD 3: Genomic disorders and inherited multi-system disorders NCEBP 1: Molecular epidemiology; IGMD 5: Health aging / healthy living N4i 1: Pathogenesis and modulation of inflammation; NCEBP 2: Evaluation of complex medical interventions; NCEBP 2: Evaluation of complex medical interventions N4i 4: Auto-immunity, transplantation and immunotherapy; NCMLS 1: Infection and autoimmunity N4i 4: Auto-immunity, transplantation and immunotherapy; NCMLS 6: Genetics and epigenetic pathways of disease IGMD 3: Genomic disorders and inherited multi-system disorders; NCMLS 6: Genetics and epigenetic pathways of disease ONCOL 3: Translational research
So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response.
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