Marc BoninDepartment of Rheumatology and Clinical ImmunologyCharité University HospitalCharitéplatz 1D-10117 Berlin Germany

Tel: +49(0) 30 450 513 296Fax: +49(0) 30 450 513 968E-Mail: marc.bonin@charite.deWeb: www.charite-bioinformatik.de

Introduction:

Methods:

Results:

Conclusion:

www.charite-bioinformatik.de

Whole blood samples of 28 patients (test group) from the clinical trial HIT HARD were collected in PAXgene® Blood RNA Tubes (PreAnalytiX GmbH, Switzerland) prior to treatment with MTX. A second RA patient cohort (validation group, n=11) receiving MTX monotherapy was assembled from donors recruited at the Rheumatology Clinic of the Charité. Intracellular total RNA including miRNA was extracted according to the PAXgene® Blood miRNA Kit protocol (PreAnalytiX GmbH, Switzerland). Extracted total RNA samples were labeled using the FlashTag™ Biotin HSR RNA Labeling Kit for Affymetrix® GeneChip® miRNA Arrays (Genisphere, LLC., USA) and hybridized onto GeneChip® miRNA 2.0 Arrays (Affymetrix, Inc., USA) according to the manufacturer protocol. The generated signal data were background corrected (RMA), normalized (quantile) and summarized (median polish) utilizing the miRNA QC Tool v 1.1.1.0 (Affymetrix, Inc., USA). Biomarker candidates to predict MTX treatment effectiveness were determined in the well defined HIT HARD patient cohort and then validated in the second patient group recruited separately at the Rheumatology Clinic of the Charité (own observational group) to asses the applicability of the miRNA predictor set. Classification of RA patients into good (R), moderate (MR) and non-responders (NR) was performed based on the DAS28 and EULAR response criteria after three to four months of MTX treatment. Differential expression of human miRNAs among four non-responders versus 14 good responders of the HIT HARD test group was determined by calculating the fold changes (FC) and p-values (one-tailed Welch’s t-test) for the miRNA expression signals between the two groups. Only miRNAs that were detected (TRUE) in all patients were included in the analysis. Hierarchical clustering for visualization was performed using Genesis 1.7.6 (IGB TU-Graz, Austria).In a second step all responders and non-responders from the test and the validation group were used to predict MTX response with a nearest prototype classification model. A leave-one-out cross-validation was performed instead of splitting the data into training and test group.

In total PAXgene® whole blood samples of 39 RA patients were analyzed. The 28 patients enrolled in the HIT HARD clinical trial (test group) all showed active early RA with a DAS28 >5.1 and a disease duration less that one year prior to treatment. The 11 patients recruited separately at Rheumatology Clinic of the Charité (validation group) showed a disease duration of less than two years except for two moderate responders. Patients were selected which had received no or only low doses of steroids at the time of blood sampling preceding the MTX treatment to avoid highly changed expression patterns caused by high doses of steroids to distort the intended

Prediction of response to MTX therapy using microarray analyses allows reducing costs, preventing side effects and is an opportunity for effective „individualized medicine“. Next to mRNA expression analysis the investigation of miRNAs as posttranscriptional regulators could prove helpful to better understand physiological and pathological processes, to define miRNA biomarkers and predict response to medication.

Methotrexate (MTX) is the standard medication to treat Rheumatoid Arthritis (RA). It is generally efficient and affordable. Nevertheless, some patients suffer from significant adverse effects and about 30-40% of patients do not show adequate improvement under MTX treatment1. Therefore predictors for treatment response would be useful which enable the clinician to only treat patients that will benefit from MTX. Loss of valuable time and unnecessary side effects in MTX non-responders could thus be avoided. A novel and promising class of potential biomarkers are miRNAs which exhibit several advantages such as higher stability than mRNA and abundance in many easily accessible body fluids. microRNAs are short, single-stranded, non-coding RNAs that regulate a multitude of physiological and pathological processes through posttranscriptional repression of protein-coding genes. Goal of this study was to define miRNA biomarkers to predict response to methotrexate treatment in rheumatoid arthritis (RA) patients.

Contacts:

Prediction of Methotrexate TreatmentResponse in Rheumatoid

Arthritis via Affymetrix miRNA Microarray Profiling Marc Bonin, Stephan Peter, Karsten Mans, Carolin Sohnrey, Gerd-Rudiger Burmester,

Thomas Haupl, Bruno Stuhlmuller

Department of Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany

Fig. 1: Hierarchical cluster of the test group based on four miRNAs Patients of the HIT HARD group were clustered (euclidean distance, average linkage) based on four miRNAs statistically determined between good responders (R) and non-responders (NR) to MTX as potential response biomarkers. (MR: moderate responder)

Tab. 1: Patient characteristicsShown are the gender, age, disease duration and disease activity (DAS28) prior to MTX treatment for the analyzed patients of the HIT HARD group and our own observational group split up based on the response to MTX after 3-4 months (EULAR response criteria).

analysis. According to the EULAR response criteria 14 patients of the HIT HARD group were categorized as good responders, 10 patients as moderate responders and four patients as non-responders. The own observational group comprises four good responders, four moderate and four non-responders. In both groups responders and non-responders showed a comparable mean disease activity before MTX treatment (Tab. 1).

miRNA predictor selection and validation via Microarray analysis Only good responders and non-responders were included in the statistical analysis to select miRNA candidates predictive for patient response to MTX treatment. Moderate responders were excluded for the calculations. Comparing only the good responders and non-responders of the HIT HARD group four miRNA candidates were selected below a threshold of p<0.01 allowing a good discrimination of responders and non-responders by hierarchical clustering (Fig. 1). Only one out of 14 good responders is classified incorrectly as non-responder while all non-responders were identified correctly (Sensitivity: 100%, Specificity: 93%). Moderate responders which were included in the clustering are distributed among both of the distinguishable groups (Fig.1). While three of the miRNA candidates are higher expressed in non-responders, one shows a lower expression in this group compared to good responders. The four miRNA candidates were validated as predictive markers for MTX treatment ineffectiveness by clustering a separate group of RA patients (own observational group) according to the expression levels of the four candidate miRNAs prior to MTX treatment (Fig. 2). Hierarchical clustering again shows a good discrimination between responders and non-responders. All patients except one non-responder are classified correctly (Sensitivity: 75%, Specificity: 100%) Moderate responders again are divided between both main groups in the cluster (Fig. 2).

With reference to the MTX response prediction the leave-one-out crossvalidation showed a Sensitivity up to 65 % and a Specificityup to 95 %.

Fig. 2: Validation group Our own observational patient group was clustered (euclidean distance, average linkage) for validation based on the four miRNA candidates determined using the HIT HARD patient group. (R: good responder, MR: moderate responder, NR: non-responder to MTX)

The leave-one-out crossvalidation uses up to 428 genes for the prediction. (91 of the genes are in all of the different cross-validation groups (p-value < 0,04), 0-25% (249 genes), 26-50% (26 genes). 51-75% (28 genes), 76-99% (34 genes)). The overlap between this two different prediction models are two genes, which were used in all of the crossvalidation grpups.