A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis

Abstract

Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA. Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses. Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response ( P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab ( P = 0.0015) and infliximab ( P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab ( P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism ( P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells ( P = 0.041). Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

“…At the cellular and tissue level, RA is characterized by the chronic infiltration of immune cells in the synovial membrane (9). To understand biologic processes associated with anti-TNF response, several transcriptomic and epigenetic studies have been conducted using the synovium and blood from patients with RA (10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Those studies have demonstrated that transcriptomic and epigenetic profiling have the potential to predict response to anti-TNF therapies before treatment.…”

Persistence of Mast Cell-Positive Synovitis in Early Rheumatoid Arthritis Following Treatment With Conventional Synthetic Disease Modifying Anti-Rheumatic Drugs

“…In fact, although a few study have looked at synovial membrane factors as predictors of response to csDMARDs ( Vieira-Sousa, 2011 ) and at the effect of treatment on synovitis ( Haringman, 2005 ), none to our knowledge include the analysis of MCs. In recent years, the attention has switched to biologic treatment, and a number of publications described both histological and molecular signatures that can predict treatment response to biologics ( Dennis et al., 2014 ; Aterido et al., 2019 ). However, to date, the relevance of synovial MCs in relation to response to conventional or biologic DMARDs has not been explored.…”

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

“…This knowledge will facilitate the identification of biomarkers and drug discovery and contribute to the development of precision therapy for RA ( 401 ). Besides, another study performed a sequential multi-omics analysis integrating transcriptomics and genomics to identify gene signatures associated with the response to anti-TNF therapy in RA patients ( 402 ). Using transcriptomic data from the RA synovium, thirteen gene co-expressed modules were found to be associated with anti-TNF response.…”