Dataset Information


Transcription profiling of human whole blood from rheumatoid arthritis patients

ABSTRACT: The whole blood was collected pre-treatment from rheumatoid arthritis patients starting the anti_TNF therapy. All patients were naïve to anti_TNFs. The disease activity was measured using the DAS28 score at the pre-treatment visit1 (DAS28_v1) and 14 weeks after treatment visit3 (DAS28_v3). The response to the therapy was evaluated using the EULAR [European League Against Rheumatism] definition of the response. The objective of the data analysis was to identify gene expression coorelating with response as well as to identify genes that differentiate responders versus non-responders pre-treatment. The results of this investigation identified 8 trainscripts that predict responders vs. non-responders with 89% accuracy. Experiment Overall Design: Patients' response to anti-TNF was assessed using EULAR score and patients were classified as responders, moderate responders and non-responders. Genes correlating with the response status have been identified.

ORGANISM(S): Homo sapiens  

SUBMITTER: Jadwiga Bienkowska  

PROVIDER: E-GEOD-15258 | ArrayExpress | 2009-09-18



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Convergent Random Forest predictor: methodology for predicting drug response from genome-scale data applied to anti-TNF response.

Bienkowska Jadwiga R JR   Dalgin Gul S GS   Batliwalla Franak F   Allaire Normand N   Roubenoff Ronenn R   Gregersen Peter K PK   Carulli John P JP  

Genomics 20090820 6

Biomarker development for prediction of patient response to therapy is one of the goals of molecular profiling of human tissues. Due to the large number of transcripts, relatively limited number of samples, and high variability of data, identification of predictive biomarkers is a challenge for data analysis. Furthermore, many genes may be responsible for drug response differences, but often only a few are sufficient for accurate prediction. Here we present an analysis approach, the Convergent R  ...[more]

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