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An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis

BACKGROUND: TNF alpha blockade agents like infliximab are actually the treatment of choice for those rheumatoid arthritis (RA) patients who fail standard therapy. However, a considerable percentage of anti-TNF alpha treated patients do not show a significant clinical response. Given that new therapi...

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Autores principales: Julià, Antonio, Erra, Alba, Palacio, Carles, Tomas, Carlos, Sans, Xavier, Barceló, Pere, Marsal, Sara
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762038/
https://www.ncbi.nlm.nih.gov/pubmed/19847310
http://dx.doi.org/10.1371/journal.pone.0007556
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author Julià, Antonio
Erra, Alba
Palacio, Carles
Tomas, Carlos
Sans, Xavier
Barceló, Pere
Marsal, Sara
author_facet Julià, Antonio
Erra, Alba
Palacio, Carles
Tomas, Carlos
Sans, Xavier
Barceló, Pere
Marsal, Sara
author_sort Julià, Antonio
collection PubMed
description BACKGROUND: TNF alpha blockade agents like infliximab are actually the treatment of choice for those rheumatoid arthritis (RA) patients who fail standard therapy. However, a considerable percentage of anti-TNF alpha treated patients do not show a significant clinical response. Given that new therapies for treatment of RA have been recently approved, there is a pressing need to find a system that reliably predicts treatment response. We hypothesized that the analysis of whole blood gene expression profiles of RA patients could be used to build a robust predictor to infliximab therapy. METHODS AND FINDINGS: We performed microarray gene expression analysis on whole blood RNA samples from RA patients starting infliximab therapy (n = 44). The clinical response to infliximab was determined at week 14 using the EULAR criteria. Blood cell populations were determined using flow cytometry at baseline, week 2 and week 14 of treatment. Using complete cross-validation and repeated random sampling we identified a robust 8-gene predictor model (96.6% Leave One Out prediction accuracy, P = 0.0001). Applying this model to an independent validation set of RA patients, we estimated an 85.7% prediction accuracy (75–100%, 95% CI). In parallel, we also observed a significantly higher number of CD4+CD25+ cells (i.e. regulatory T cells) in the responder group compared to the non responder group at baseline (P = 0.0009). CONCLUSIONS: The present 8-gene model obtained from whole blood expression efficiently predicts response to infliximab in RA patients. The application of the present system in the clinical setting could assist the clinician in the selection of the optimal treatment strategy in RA.
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spelling pubmed-27620382009-10-22 An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis Julià, Antonio Erra, Alba Palacio, Carles Tomas, Carlos Sans, Xavier Barceló, Pere Marsal, Sara PLoS One Research Article BACKGROUND: TNF alpha blockade agents like infliximab are actually the treatment of choice for those rheumatoid arthritis (RA) patients who fail standard therapy. However, a considerable percentage of anti-TNF alpha treated patients do not show a significant clinical response. Given that new therapies for treatment of RA have been recently approved, there is a pressing need to find a system that reliably predicts treatment response. We hypothesized that the analysis of whole blood gene expression profiles of RA patients could be used to build a robust predictor to infliximab therapy. METHODS AND FINDINGS: We performed microarray gene expression analysis on whole blood RNA samples from RA patients starting infliximab therapy (n = 44). The clinical response to infliximab was determined at week 14 using the EULAR criteria. Blood cell populations were determined using flow cytometry at baseline, week 2 and week 14 of treatment. Using complete cross-validation and repeated random sampling we identified a robust 8-gene predictor model (96.6% Leave One Out prediction accuracy, P = 0.0001). Applying this model to an independent validation set of RA patients, we estimated an 85.7% prediction accuracy (75–100%, 95% CI). In parallel, we also observed a significantly higher number of CD4+CD25+ cells (i.e. regulatory T cells) in the responder group compared to the non responder group at baseline (P = 0.0009). CONCLUSIONS: The present 8-gene model obtained from whole blood expression efficiently predicts response to infliximab in RA patients. The application of the present system in the clinical setting could assist the clinician in the selection of the optimal treatment strategy in RA. Public Library of Science 2009-10-22 /pmc/articles/PMC2762038/ /pubmed/19847310 http://dx.doi.org/10.1371/journal.pone.0007556 Text en Julià et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Julià, Antonio
Erra, Alba
Palacio, Carles
Tomas, Carlos
Sans, Xavier
Barceló, Pere
Marsal, Sara
An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title_full An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title_fullStr An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title_full_unstemmed An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title_short An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
title_sort eight-gene blood expression profile predicts the response to infliximab in rheumatoid arthritis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762038/
https://www.ncbi.nlm.nih.gov/pubmed/19847310
http://dx.doi.org/10.1371/journal.pone.0007556
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