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Whole-Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib
Chiara Bellocchi1, Ennio Giulio Favalli2, Gabriella Maioli2
1Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy.
Objective:
Patients with rheumatoid arthritis (RA) often fail to respond to therapies, including JAK inhibitors (JAKi), and treatment allocation is made via a trial-and-error strategy. A comprehensive analysis of responses to JAKi, including tofacitinib, by RNA sequencing (RNAseq) would allow the discovery of transcriptomic markers with a two-fold meaning: (1) an improved knowledge about the mechanisms of response to treatment (inference modeling) and (2) the definition of features that may be useful in treatment optimization and assignment (predictive modeling).
Methods:
Thirty-three patients with active RA were treated with a tofacitinib dose of 5 mg twice a day for 24 weeks and evaluated for EULAR Disease Activity Score in 28 joints using the C-reactive protein level response. Whole-blood RNA was collected before and after treatment to perform RNAseq transcriptome analysis. Linear models were used to determine differentially expressed genes (DEGs) (1) at baseline according to clinical responses and (2) in the pre-post comparison after tofacitinib treatment and in relation to EULAR responses. The capability of DEGs to predict a successful treatment was tested via machine learning modeling after extensive internal validation.
Results:
Of 26 patients who completed the study (per-protocol analysis), 15 (57.7%) achieved good responses, and 7 (26.9%) and 4 (15.3%) had moderate and no responses, respectively. Overall, 273 baseline genes were significantly associated with the attainment of good responses, contributing to several pathways linked to the immune system or RA pathogenesis (eg, citrullination processes and the negative regulation of natural killer function). The expression of several molecules was reverted by tofacitinib when good responses were reached, including AKT3, GK5, KLF12, FCRL3, BIRC3, TSPOAP1, and P2RY10. Finally, we isolated 14 markers that singularly were capable of predicting the attainment of good responses, including, NKG2D, CD226, CLEC2D, and CD52.
Conclusion:
Whole-blood transcriptome analysis of patients with RA treated with tofacitinib identified genes whose expression may be relevant in prognostication and understanding the mechanisms of responses to therapy.
Insights
RNA sequencing identified 14 predictive markers for tofacitinib response in rheumatoid arthritis (RA) patients. These findings enhance understanding of JAK inhibitor mechanisms and aid in personalized treatment strategies for RA.
Area of Science:
- Immunology
- Genomics
- Pharmacogenomics
Background:
- Rheumatoid arthritis (RA) treatment often involves a trial-and-error approach for therapies like Janus kinase inhibitors (JAKi).
- Predictive biomarkers are needed to optimize treatment selection for RA patients.
- RNA sequencing (RNAseq) offers a powerful tool to analyze transcriptomic profiles for treatment response prediction.
Purpose of the Study:
- To identify transcriptomic markers associated with tofacitinib response in RA patients using RNAseq.
- To gain insights into the mechanisms of response to JAK inhibitors.
- To develop predictive models for treatment optimization in RA.
Main Methods:
- Whole-blood RNA was collected from 33 RA patients before and after 24 weeks of tofacitinib treatment.
- RNAseq was performed to analyze transcriptome changes.
- Linear models and machine learning were used to identify differentially expressed genes (DEGs) and predict treatment response.
Main Results:
- Of 26 completers, 15 (57.7%) had good responses to tofacitinib.
- 273 baseline genes were associated with good response, linked to immune pathways and RA pathogenesis.
- 14 predictive markers, including NKG2D and CD52, were identified for good treatment response.
Conclusions:
- Whole-blood transcriptome analysis reveals key genes associated with tofacitinib response in RA.
- Identified gene expression patterns can aid in prognostication and understanding treatment mechanisms.
- These findings support the development of personalized treatment strategies for RA patients.
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