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A9.5 Identification and Validation of a Protein Combination Including S100A9 able to Predict the Response to the MTX/Etanercept Association in Rheumatoid Arthritis Patients
  1. A Obry1,2,
  2. T Lequerré1,
  3. J Hardouin2,
  4. O Boyer1,3,4,
  5. O Vittecoq1,4,
  6. P Cosette2
  1. 1INSERM 905, Institute for Biomedical Research, University of Rouen, Rouen
  2. 2UMR 6270 CNRS, Plate-Forme d’Analyse Protéomique PISSARO, Faculté des Sciences
  3. 3Department of Immunology, Rouen University Hospital
  4. 4Department of rheumatology, Rouen University Hospital


Background The number of biologic agents in Rheumatoid Arthritis (RA) is continuously increasing. However, clinicians observe that around 30 to 40% of treated patients fail to respond to TNFα blocking agents. One way to optimise the drug prescription is to identify predictive markers of drug responsiveness.

Objectives To identify a combination of serum proteins whose expression profile would predict the RA patients responses to the association of methotrexate (MTX) and etanercept (ETA) by mass spectrometry-based quantification methods and ELISA.

Methods A “cohort discovery phase” of 23 patients with active RA was treated by a subcutaneous injection of. The clinical efficacy of these drugs was evaluated with the DAS28 score after 6 months of treatment according to the EULAR response criteria. For proteomic analysis, a serum sample was collected in patients prior to treatment exposure. A “label free” approach on the whole proteome was performed by mass spectrometry on the 25 sera. Accordingly, the proteome of each sample was extracted and in-gel digested. The resulting peptides were analysed by LTQ Orbitrap® (ThermoFisher). Differential analysis between responder and non responder samples was performed with LCMS ProGenesis® (Nonlinear Dynamics). To validate these results a relative quantification of selected protein was performed on the second “cohort validation phase” by ELISA. The proteome of peripheral blood mononuclear cells (PBMC) from a second cohort of seven patients with similar characteristics has also been studied by the same label free approach.

Results The label free approach revealed 12 differentially expressed serum proteins according to patient response. This combination of proteins was used to build a Random Forest statistical model to predict the patient’s status. This model was validated by a blind test on a panel of seven patients. Moreover, these results have shown the protein S100A9 overexpression in both the serum and the PBMCs from responder’s patients and this expression was confirmed by ELISA.

Conclusions The label free approach has identified a combination of predictive markers of response to MTX treatment/ETA. Thus, using sera samples collected in patients prior to treatment exposure, it is possible to predict response to treatment with a small error. These proteins represent interesting candidate biomarkers of response that must be validated in a larger population. Already identified as a diagnostic and prognostic biomarker of RA, the S100A9 protein has been identified as a predictive biomarker of response both in serum and in PBMCs.

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