Dataset Information

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

ABSTRACT: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n?=?116?=?44?+?15?+?30?+?27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

SUBMITTER: Thomson TM

PROVIDER: S-EPMC4455917 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

Thomson Ty M TM Lescarbeau Reynald M RM Drubin David A DA Laifenfeld Daphna D de Graaf David D Fryburg David A DA Littman Bruce B Deehan Renée R Van Hooser Aaron A

BMC medical genomics 20150603

<h4>Background</h4>Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.<h4>Methods</h4>We identified a gene expression classifi ...[more]

PMID: 26036272

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Project description:In 15 patients with rheumatoid arthritis the baseline expression (t=0) of type I IFN-regulated genes was determined in peripheral blood cells (PAXgene) using cDNA-microarrays and compared to levels one month after (t=1) anti-TNF treatment (infliximab). Therefore, we used 43K cDNA microarrays from the Stanford Functional Genomics Facility (http://microarray.org/sfgf/) printed on aminosilane-coated slides containing ~20.000 unique genes. Only one batch of arrays was used for all experiments. First DNA spots were UV-cross linked to the slide using 150-300 mJoules. Sample preparation and microarray hybridization were performed as described previously (1,2). Data storage and filtering was performed using the Stanford Microarray Database (http://genome-www5.stanford.edu//) as described previously. 1. van der Pouw Kraan TC, Wijbrandts CA, van Baarsen LG, Voskuyl AE, Rustenburg F, Baggen JM et al.: Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann Rheum Dis 2007, 66: 1008-1014. 2. van der Pouw Kraan TC, van Baarsen LG, Rustenburg F, Baltus B, Fero M, Verweij CL: Gene expression profiling in rheumatology. Methods Mol Med 2007, 136: 305-327. Abbreviations: - Bnrs are the different patients analyzed - PAX or Paxgene indicates the total RNA was isolated from peripheral blood obtained in PAXgene tubes - CRS: common reference sample: We made a common reference that consisted of a mixture of mRNAs isolated from 11 different cell lines (Perou CM, Jeffrey SS, van de RM et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999; 96:9212-9217) supplemented with RNA from rheumatoid synovial tissue, fibroblasts, and activated peripheral blood mononuclear cells (PBMCs). - PMT: photomultiplier tube: the sensitivity/intensity of the scanner can be adjusted to prevent spot saturation. Compound Based Treatment: anti-TNF (infliximab) reference_design

Dataset Information

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

Publications

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

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