Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis.
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 expressi...
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2012
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oai:doaj.org-article:f82ca101767b4a1f92e2ffe26a4e189b2021-11-18T07:24:37ZValidation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis.1932-620310.1371/journal.pone.0033199https://doaj.org/article/f82ca101767b4a1f92e2ffe26a4e189b2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22457743/?tool=EBIhttps://doaj.org/toc/1932-6203So 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.Erik J M ToonenChristian GilissenBarbara FrankeWietske KievitAgnes M EijsboutsAlfons A den BroederSimon V van ReijmersdalJoris A VeltmanHans SchefferTimothy R D J RadstakePiet L C M van RielPilar BarreraMarieke J H CoenenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 3, p e33199 (2012) |
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| topic |
Medicine R Science Q |
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Medicine R Science Q Erik J M Toonen Christian Gilissen Barbara Franke Wietske Kievit Agnes M Eijsbouts Alfons A den Broeder Simon V van Reijmersdal Joris A Veltman Hans Scheffer Timothy R D J Radstake Piet L C M van Riel Pilar Barrera Marieke J H Coenen Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| description |
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. |
| format |
article |
| author |
Erik J M Toonen Christian Gilissen Barbara Franke Wietske Kievit Agnes M Eijsbouts Alfons A den Broeder Simon V van Reijmersdal Joris A Veltman Hans Scheffer Timothy R D J Radstake Piet L C M van Riel Pilar Barrera Marieke J H Coenen |
| author_facet |
Erik J M Toonen Christian Gilissen Barbara Franke Wietske Kievit Agnes M Eijsbouts Alfons A den Broeder Simon V van Reijmersdal Joris A Veltman Hans Scheffer Timothy R D J Radstake Piet L C M van Riel Pilar Barrera Marieke J H Coenen |
| author_sort |
Erik J M Toonen |
| title |
Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| title_short |
Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| title_full |
Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| title_fullStr |
Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| title_full_unstemmed |
Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. |
| title_sort |
validation study of existing gene expression signatures for anti-tnf treatment in patients with rheumatoid arthritis. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2012 |
| url |
https://doaj.org/article/f82ca101767b4a1f92e2ffe26a4e189b |
| work_keys_str_mv |
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