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A7.20 Response to Infliximab Therapy can be Predicted using Distinct, Non-Overlapping Gene Panels of Peripheral Blood Gene Expression in Rheumatoid Arthritis and Crohn’s Disease
  1. Zoltán Szekanecz1,
  2. Bertalan Meskó2,
  3. Szilárd Poliska2,3,
  4. Andrea Váncsa1,
  5. Károly Palatka4,
  6. Zsolt Holló5,
  7. Gábor Zahuczky6,
  8. János Podani7,
  9. Attila Horváth6,
  10. László Steiner6,
  11. László Nagy2,3
  1. 1Department of Rheumatology, Institute of Medicine
  2. 2Department of Biochemistry and Molecular Biology
  3. 3Clinical Genomics Center
  4. 4Department of Gastroenterology, Institute of Medicine; University of Debrecen, Medical and Health Science Center, Debrecen, Hungary
  5. 5EGIS Pharmaceuticals Plc., Budapest, Hungary
  6. 6UD-GenoMed, Ltd., Debrecen, Hungary
  7. 7Institute of Biology, Loránd Eötvös University, Budapest, Hungary

Abstract

Background Biological therapies targeting tumour necrosis factor α (TNF-α) have been widely used to treat chronic inflammatory disorders including rheumatoid arthritis (RA) and Crohn’s disease (CD). As these treatment modalities are rather expensive, there is a need for biomarkers that may predict therapeutic responses. As RA and CD have similar pathogenic background, one may expect to detect overlapping gene panels in predicting the response to the same therapy by the same approach. There have been very few studies using simultaneous pharmacogenomic approach in two diseases.

Methods In this study, we performed peripheral blood global gene expression profiling followed by Canonical Variates Analysis (CVA) in CD and RA to identify gene sets that can differentiate responders from non-responders for infliximab therapy; and validated the results in an independent cohort by RT-QPCR.

Results In CD, global gene expression analysis of samples obtained at baseline resulted in a list of 48 probe sets differentiating responders from non-responders. Comparing baseline and week 2 samples resulted in a list of five genes including AQP9, IGKC, MGAM, MMP8 and TNFAIP6 that exert expression changes upon treatment. Similarly, in RA, analysis of baseline samples resulted in a list of 30 probe sets that differentiated responders from non-responders. Expression of 3 genes; AQP9, IGJ and TNFAIP6 showed significant difference between baseline and week 2 samples. Results were validated by RT-QPCR on independent patient cohorts in both diseases. CVA analysis yielded to 15 and 12 genes in RA and CD, respectively, showing the best discriminatory power between responders and non-responders in both diseases, however there was no overlap between these two gene lists.

Conclusions We provided two pieces of proof of concept evidence showing that 1) peripheral blood gene expression profiles are suitable for determining gene panels with the highest discriminatory power in order to differentiate responders and non-responders in a patient cohort in CD and RA; and 2) distinct, non-overlapping gene panels are required for the prediction of the responder status in CD and RA despite the fact that these conditions have similar pathogenic background. Application of such gene panels could solve unmet needs in the clinical settings by determining specific responses to expensive biological therapies.

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