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OP0339 Identification of a transcriptomic signature correlated with modified rodnan skin score (MRSS) in patients with diffuse cutaneous systemic sclerosis
  1. I Agueusop1,
  2. S Illiano2,
  3. C Rocher2,
  4. E Boitier3,
  5. J Murphy4,
  6. Y Allanore5,
  7. CP Denton6,
  8. O Distler7,
  9. D Khanna8,
  10. F Benderitter2
  1. 1Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
  2. 2Sanofi R&D, Chilly-Mazarin
  3. 3Sanofi R&D, Alfortville, France
  4. 4sanofi, Bridgewater, United States
  5. 5Paris Descartes University, Paris, France
  6. 6Centre for Rheumatology, Royal Free Hospital, London, United Kingdom
  7. 7Department of Rheumatology, University Hospital Zurich, Zurich, Switzerland
  8. 8Scleroderma program, University of Michigan, Ann Arbor, United States

Abstract

Background To support internal compound development in systemic sclerosis, a study was performed to identify an mRSS signature in a longitudinal approach by analyzing skin biopsies.

Objectives Identification of a gene signature that could be used as a quantitative surrogate marker for the mRSS independent of any treatment.

Methods 77 forearm skin biopsies from 32 patients at baseline, and from the same patients after 8 weeks of treatment with SAR100842 (a LPA1 antagonist) or placebo (N=30) and after an additional 16 weeks of treatment with SAR100842 (N=15) in a phase 2 trial, were collected. Total RNA was extracted with the RNeasy® Fibrous Tissue Mini kit according to the manufacturer's instructions. Total RNA was quantified by spectrofluormetry and qualified by capillary electrophoresis using Agilent Bioanalyzer 2100. Whole transcriptome analysis was performed using Affymetrix chips. Genes highly correlated (Pearson's correlation) with the mRSS were identified at each treatment visit. A signature was identified as a set of genes whose expression levels correlated consistently either positively or negatively with the mRSS at all study visits regardless of treatment group. The correlation value between the genes and the mRSS at baseline had to be >0.5 or < -0.5.The association between mRSS and the single composite marker obtained was investigated. A multivariate analysis of the correlation between the identified genes was performed using the median polish algorithm and PCA. The gene signature underwent pathway analysis using QIAGEN's Ingenuity Pathway Analysis (IPA).

Results This methodology led to the identification of 64 genes considered for the signature and viewed as a single composite marker that was highly correlated to the mRSS. A principal component analysis was computed and the first component explaining the maximum variance in the signature was highly correlated to the mRSS at baseline and week 8. This correlation was confirmed with the median polish algorithm (Pearson's correlation coefficient of -0.75 and -0.73 respectively). The most significant disease and disorder biological functions associated with the mRSS signature genes were related to immunological diseases. A significant enrichment was also detected for genes associated with inflammatory response and connective tissue disorders with p-values from 2.98E-05 to 2.47E-02.

Conclusions An mRSS signature was identified using skin biopsies in SSc patients. Some of these genes (i.e. IRF7, THBS1, COMP or BANK1) have been published using similar approaches in other sets of SSc patients (1), which supports our results. The functional categories of this signature are characteristic for scleroderma pathology reflecting autoimmunity, vasculopathy, inflammation and fibrosis. This mRSS signature needs to be validated in a larger set of SSc patients including assessment of change over time.

References

  1. Mahoney et al. PLOS Computational Biology 2015: Vol 11; 1–20.

References

Disclosure of Interest I. Agueusop: None declared, S. Illiano: None declared, C. Rocher: None declared, E. Boitier: None declared, J. Murphy: None declared, Y. Allanore Grant/research support from: BMS, Genentech-Roche, Inventiva, Pfizer, sanofi, Consultant for: Actelion, Bayer, Biogen, Genetech-Roche, Galapagos, Medac, Pfizer, Sanofi, Servier, UCB, C. Denton Consultant for: Actelion, Bayer, GSK, CSL Behring, Merck-Serono, Genentech-Roche, Inventiva, Sanofi-Aventis, O. Distler Grant/research support from: Actelion, Bayer, Boehringer Ingelheim, Pfizer, Sanofi, Consultant for: 4 D Science, Actelion, Active Biotec, Bayer, BiogenIdec, BMS, Boehringer Ingelheim, ChemomAb, EpiPharm, espeRare foundation, Genentech/Roche, GSK, Inventiva, Lilly, medac, Mepha, MedImmune, Mitsubishi Tanabe Pharma, Pharmacyclics, Pfizer, Sanofi, Serodapharm, Sinoxa, AbbVie, iQone Healthcare, Mepha, D. Khanna Grant/research support from: Bayer, BMS, Genentech/Roche, Sanofi-Aventis, NIH K24AR063120, Consultant for: Actelion, Bayer, Covis, Cytori, EMD Serono, Genentech/Roche, Gilead, GSK, Sanofi-Aventis, F. Benderitter: None declared

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