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Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid
Chae-Yeon Yu1,2, Hye-Soon Lee3, Young Bin Joo3
1Department of Biology, Kyung Hee University, Seoul, Republic of Korea.
Objective:
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.
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 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.
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 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.
Conclusion:
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.
Insights
Researchers identified key genes and immune pathways linked to patient response to anti-tumor necrosis factor (anti-TNF) biologics in rheumatoid arthritis (RA). These findings could lead to biomarkers for predicting treatment success in RA patients.
Area of Science:
- Genomics and Immunology
- Biomarker Discovery
- Rheumatoid Arthritis Therapeutics
Background:
- Anti-tumor necrosis factor (anti-TNF) biologics are standard treatments for rheumatoid arthritis (RA).
- A significant number of RA patients exhibit inadequate responses to anti-TNF therapy.
- Predictive biomarkers for anti-TNF response in RA are currently lacking.
Purpose of the Study:
- To identify biomarkers associated with treatment response in RA patients receiving anti-TNF biologics.
- To utilize longitudinal transcriptomic data from independent RA cohorts for biomarker discovery.
- To understand the molecular mechanisms underlying differential responses to anti-TNF therapy.
Main Methods:
- Analysis of RNA sequencing data from peripheral blood cells of Korean and Caucasian RA cohorts.
- Comparison of gene expression changes between excellent and null responders post-anti-TNF treatment.
- Application of weighted correlation network, immune cell composition, and key driver analyses.
Main Results:
- Identified 305 response-associated genes with differential expression patterns between responders and non-responders.
- Discovered 41 key driver genes within four immune-related networks (Type I IFN signaling, myeloid activation, B cell activation, NK cell cytotoxicity).
- Developed transcriptomic response scores that correlated significantly with clinical outcomes in independent patient cohorts.
Conclusions:
- This study elucidates treatment-induced transcriptomic signatures specific to anti-TNF drug response in RA.
- Identified gene and network-level alterations provide insights into differential patient responses.
- The findings pave the way for developing predictive biomarkers for anti-TNF therapy in rheumatoid arthritis.
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