Tao W, Concepcion AN, Vianen M, et al. - Researchers sought to predict response prior to anti‐TNF treatment and determine, in detail, the mechanism how cases respond differently to anti‐TNF treatment in rheumatoid arthritis (RA). They studied gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4+ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy. Evaluation of treatment response was done after 6‐month according to the EULAR criteria of disease response. They performed differential expression and methylation analyses to determine the response‐linked transcriptional and epigenetic signatures. ADA and ETN responders showed divergent transcriptional signatures in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The machine learning models to prognosticate the response to ADA and ETN employing differential genes attained overall accuracy of 85.9% and 79%, respectively. The models employing DMPs attained overall accuracy of 84.7% and 88% for ADA and ETN, respectively. Observations thereby support the accurate predictive value of machine learning models based on these molecular signatures for response before ADA and ETN treatment, paving the path towards personalized anti‐TNF treatment.