Expression levels of selected genes can predict individual rheumatoid arthritis patient response to tumor necrosis factor alpha blocker treatment

Daphna Paran, Yoav Smith, Shlomo Pundak, Uri Arad, David Levartovsky, Ilana Kaufman, Jonathan Wollman, Victoria Furer, Adi Broyde, Ofir Elalouf, Dan Caspi, Sara Pel, Ori Elkayam

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Rheumatoid arthritis (RA) patients have many therapeutic options; however, tools to predict individual patient response are limited. The Genefron personal diagnostic kit, developed by analyzing large datasets, utilizes selected interferon stimulated gene expressions to predict treatment response. This study evaluates the kit’s prediction accuracy of individual RA patients’ response to tumor necrosis alpha (TNFα) blockers. Methods: A retrospective analysis was performed on RA patients reported in published datasets. A prospective analysis assessed RA patients, before and 3 months after starting a TNFα blocker. Clinical response was evaluated according to EULAR response criteria. Blood samples were obtained before starting treatment and were analyzed utilizing the kit which measures expression levels of selected genes by quantitative real time polymerase chain reaction (PCR). ROC analysis was applied to the published datasets and the prospective data. Results: The Genefron kit analysis of retrospective data predicted the response to a TNFα blocker in 53 of 61 RA patients (86.8% accuracy). In the prospective analysis, the kit predicted the response in 16 of 18 patients (89% accuracy) achieving a EULAR moderate response, and in 15 of 18 patients achieving a EULAR good response (83.3% accuracy). ROC analysis applied to the two published datasets yielded an AUC of 0.89. ROC analysis applied to the prospective data yielded an AUC of 0.83 (sensitivity–100%, specificity–75%) The statistical power obtained in the prospective study was.9. Conclusion: The diagnostic kit predicted the response to TNFα blockers in a high percentage of patients assessed retrospectively or prospectively. This personal kit may guide selection of a suitable biological drug for the individual RA patient.

Original languageEnglish
Pages (from-to)1777-1783
Number of pages7
JournalCurrent Medical Research and Opinion
Volume34
Issue number10
DOIs
StatePublished - 3 Oct 2018

Keywords

  • Rheumatoid arthritis
  • personal gene expression
  • prediction of response

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