GSE138746
Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [RNA-Seq]
Objectives: To predict response prior to anti-TNF treatment and comprehensively understand the mechanism how patients respond differently to anti-TNF treatment in rheumatoid arthritis (RA). Methods: 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 was studied. After 6-month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response-associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti-TNF treatment and were further validated by a follow-up study. Results: Transcriptional signatures in ADA and ETN responders are divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes upregulated in CD4+ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differential methylation positions (DMPs) of responders to ETN but not to ADA are majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusions: Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti-TNF treatment. Overall design: From the observational (BiOCURA) cohort, we selected 80 patients with RA, treated with ADA (38) or ETN(42) and a follow-up period of at least 6 months. Blood was drawn shortly before treatment initiation in a Lithium-heparinized tube (BD Vacutainer®). We first isolated peripheral blood mononuclear cells (PBMCs) from these blood samples prior to ADA or ETN treatments. And then genome-wide gene expression were performed on these PBMCs, using RNA-sequencing. RNA-sequencing on CD14+ monocytes and CD4+ T cells isolated from the PBMCs were performed to gain more insights into different anti-TNF response. After 6-month treatment, patients' responses were evaluated according to EULAR criteria.