Molecular profiling of RA synovium predicts biologic responses Save
Machine learning analysis of pre-treatment synovial tissue biopsy from rheumatoid arthritis (RA) patients starting either etanercept (ETN), tocilizumab (TCZ) and rituximab (RTX) revealed high accuracy prediction of 16 week outcomes unique to each biologic.
As nearly 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, there is the need for predictive biomarkers.
In the precision-medicine STRAP trial, that enrolled from 208 RA patients, RNA-sequencing (RNA-Seq), of pre-treatment synovial tissue, was used to identify gene response signatures in those treatd with either ETN, TCZ or RTX.
By week 16, machine learning models applied were able to predict responses (DAS28-ESR < 3.2) to ETN, TCZ and RTX with an area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively.
The predictive ability was also independently validated in the R4RA clinical trial cohort treated with either TCZ or RTX, yielding an AUC of 0.713 and 0.786 respectively.
This study shows how deep molecular profiling of RA synovial tissue might be incorporated into clinical decision making and assist in chosing biologic therapies in the future.
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The author has no conflicts of interest to disclose related to this subject
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