Objective: Anti-TNF biologics have been widely used to ameliorate disease activity in patients with rheumatoid arthritis (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.
Methods: RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analyzed to assess treatment-induced expression changes that differed between highly reliable excellent 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.
Results: 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 interferon 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.
Conclusions: 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.
Keywords: Rheumatoid arthritis; bioinformatics; biological therapy; statistics; transcriptome.
© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.