Candidate Markers for Stratification and Classification in Rheumatoid Arthritis

Area of Science:

• Immunology
• Rheumatology
• Systems Biology

Background:

• Rheumatoid arthritis (RA) is a chronic autoimmune disease causing joint damage.
• Tumor necrosis factor (TNF) inhibitors are effective but patient response varies significantly.
• Lack of biomarkers hinders personalized treatment strategies for RA.

Purpose of the Study:

• To identify immune cell markers distinguishing RA patients from healthy donors.
• To investigate TNF signaling pathways in RA pathogenesis.
• To find potential biomarkers for predicting treatment response in RA.

Main Methods:

• Mass cytometry (CyTOF) was used to analyze peripheral blood mononuclear cells.
• A panel of 13 phenotyping and 10 functional markers were employed.
• Unstimulated and TNF-stimulated cells from 20 RA patients and 20 healthy donors were analyzed using three independent data analysis pipelines.

Main Results:

• Specific signaling markers (p-p38, IkBa, p-cJun, p-NFkB, CD86) were identified in both innate and adaptive immune cells.
• These markers effectively differentiated RA patients from healthy donors.
• Regression modeling using additional markers (p-Akt, CD120b) achieved high classification accuracy for RA patients.

Conclusions:

• Distinct immune cell expression patterns and signaling signatures were observed in RA patients.
• Identified markers may aid in classifying RA patients and predicting response to TNF inhibitor therapy.
• These findings could facilitate the development of targeted therapies for rheumatoid arthritis.