Interferon-related gene expression in response to TNF inhibitor treatment in ankylosing spondylitis patients: a pilot study

• Published in: Rheumatology (Oxford, England) England), 2021-08, Vol.60 (8), p.3607-3616
• Main Authors: Harrison, Stephanie R, Burska, Agata N, Emery, Paul, Marzo-Ortega, Helena, Ponchel, Frederique
• Format: Article
• Language: English
• ISSN: 1462-0324; 1462-0332
• DOI: 10.1093/rheumatology/keaa817

Ankylosing spondylitis (AS) is a chronic inflammatory arthritis primarily affecting the spine and sacroiliac joints. TNF inhibitor (TNFi) drugs are recommended for patients not responding to NSAIDs; however, there is a significant need for biomarkers of response. IFN-regulated genes (IRGs) and other cytokines/chemokines are linked to autoimmune diseases and have been associated with treatment response. Our objective was to explore whether IRGs and cytokines/chemokines can be associated with response to TNFiagents in AS. Peripheral blood mononuclear cells were obtained from 26 AS patients who were to receive a TNFi (I, n = 15) or placebo (P, n = 11) at week 0 and week 22. Response (R)/non-response (NR) was defined as reduction in ASDAS ≥ 1.2 points or reduction in sacroiliac/vertebral MRI lesions. The expression of 96 genes was quantified using TaqMan assays. Finally, ELISA was used to measure IL-6 in serum samples from another 38 AS patients. Analysis of gene expression in 26 baseline samples segregated patients into four groups defined by a signature of 15 genes (mainly IRGs). ASDAS response was associated with one group independently of treatment received. We then analysed response to the TNFi (n = 15) and identified a 12-gene signature associated with MRI response. A third IRG signature was also associated with a reduction in IRGs expression post-TNFi samples (n = 10 pairs). Finally, decreased circulating IL-6 was associated with BASDAI-R. This pilot study suggests an association between IRG expression and response to TNFi in AS. These findings require validation in a larger cohort in order to construct predictive algorithms for patient stratification.