Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis.

Abstract

Anti-TNF biologics have been widely used to ameliorate disease activity in patients with RA. However, a large fraction of patients show a poor response to these agents. Moreover, no clinically applicable predictive biomarkers have been established. This study aimed to identify response-associated biomarkers using longitudinal transcriptomic data in two independent RA cohorts. RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analysed to assess treatment-induced expression changes that differed between highly reliable excellent responders and null responders. Weighted correlation network, immune cell composition, and key driver analyses were performed to understand response-associated transcriptomic networks and cell types and their correlation with disease activity indices. In total, 305 response-associated genes showed significantly different treatment-induced expression changes between excellent and null responders. Co-expression network construction and subsequent key driver analysis revealed that 41 response-associated genes played a crucial role as key drivers of transcriptomic alteration in four response-associated networks involved in various immune pathways: type I IFN signalling, myeloid leucocyte activation, B cell activation, and NK cell/lymphocyte-mediated cytotoxicity. Transcriptomic response scores that we developed to estimate the individual-level degree of expression changes in the response-associated key driver genes were significantly correlated with the changes in clinical indices in independent patients with moderate or ambiguous response outcomes. This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels.