Background and Objective:Materials and Methods: Results: Conclusion: Samples were collected before Methotrexate treatment. The transcriptomes were determined by Affymetrix technology and the therapeutic response level by clinical follow-up in an observational study. To select potential molecular predictors and to compare the molecular classifiers between different clinical response groups, various R-packages were applied. Good to very good predictors could be identified by using the Limma and the Lasso algorithms in the mRNA transcriptoms, which enabled to classify nearly without an error by linear discrimination analysis (LDA). Between groups of genes determined by different selection methods an overlap up to 40% could be reached and hierachical clustering generated nearly perfect grouping. Nevertheless among mRNAs the heatmap patterns seemed to be in part heterogeneous. The analysis was repeated after splitting the samples into two groups with respect to the expression level of the gene HLA-DRB4 (Fig.3), a gene locus, which is genetically important for risk prediction in rheumatoid arthritis. Between the molecular predictors of response for the two groups of HLA-DRB4 positive and negative patients no overlap could be found. Also the overlap with the predictors of the combined group decreased notable. Similar results were observed when analysing the microRNA transcriptoms. Finally the samples were seperated into a test and a training set for an independent validation. Only the investigation of the groups spitted by HLA criteria showed adequate reproducibility whereas the combined group obviously generated unstable predictors. In summary, combining the advantages of different algorithms like Limma, Lasso and LDA for selecting and testing molecular predictors for clinical response increases the dignostic power of biomarkers. Nevertheless, appropriate characterization and splitting into distinct subgroups is essential to increase reproducibility and validity in biomarker development. Treatment of chronic arthritis is challenged by the need to adapt dosis or exchange therapeutic agents without prior knowlegde of the individual response characteristics. With genomewide screening of transcriptional activities in whole blood, we hope to identify molcular patterns that help to distinguish responders from non-responders prior to treatment and thus may support therapeutic decisions. Acknowledgement: BTCure IMI grant agreement no Figure 3 ArthroMark grant no 01EC1009A Prediction for successful treatment of methotrexate in rheumatoid arthritis with mRNA and miRNA microarraydata Pascal Schendel, Marc Bonin, Karsten Mans, Florian Heyl, Jekaterina Kokatjuhha, Sascha Johannes, Irene Ziska, Biljana Smiljanovic, Till Sörensen, Bruno Stuhlmüller, Thomas Häupl Department of Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany Figure 1 Marc Bonin Department of Rheumatology and Clinical Immunology Charité University Hospital Charitéplatz 1 D Berlin Germany Tel: +49(0) Fax: +49(0) Web: Contacts: number of transcripts transcript selection classification HLA-DRB4 Groups (mRNA): HLA-DRB4 negative: min. intensity: 4, max. intensity: 66 HLA-DRB4 positive: min. intensity: 1464, max. intensity: 5354 HLA-DRB4- positive Group with 19 miRNAs, after Kruskal-Wallis-Test. Figure 2 miRNA biomarker without grouping by HLA-DRB4 positive and negative. Figure 4 HLA-DRB4- negative Group with 17 miRNAs, after Kruskal-Wallis-Test. Figure 5 HLA-DRB4- positive Group with 5 miRNAs, after Kruskal-Wallis-Test & SVM. HLA-DRB4- negative Group with 4 miRNAs, after Kruskal-Wallis-Test. HLA-DRB4- positive Group with 3 miRNAs, after Lasso. (100 % overlap with Kruskal- Wallis-Test & SVM) HLA-DRB4- negative Group with 4 miRNAs, after Lasso. (75 % overlap with Kruskal-Wallis-Test & SVM) ROC of cross validation: HLA-DRB4- positive Group with 5 miRNAs, after Kruskal-Wallis-Test & SVM. ROC of cross validation: HLA-DRB4- negative Group with 4 miRNAs, after Kruskal-Wallis-Test. Reciever Operation Characteristic