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Series GSE138653 Query DataSets for GSE138653
Status Public on Aug 24, 2020
Title Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [Methylation array]
Organism Homo sapiens
Experiment type Methylation profiling by genome tiling array
Summary 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 DNA methylation profiling was performed on these PBMCs, using Infinium MethylationEPIC BeadChip array (Illumina). After 6-month treatment, patients’ responses were evaluated according to EULAR criteria.
 
Contributor(s) Tao W, Concepcion AN, Vianen M, Marijnissen AC, Lafeber FP, Pandit A, Radstake TR
Citation(s) 32909363
Submission date Oct 09, 2019
Last update date Mar 10, 2021
Contact name Aridaman Pandit
Organization name University Medical Center Utrecht
Street address Heidelberglaan 100
City Utrecht
ZIP/Postal code 3584 CX
Country Netherlands
 
Platforms (1)
GPL21145 Infinium MethylationEPIC
Samples (80)
GSM4115518 DNA methylation_Etanercept_noResponse_01
GSM4115519 DNA methylation_Adalimumab_moderateResponse_02
GSM4115520 DNA methylation_Etanercept_noResponse_03
This SubSeries is part of SuperSeries:
GSE138747 Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis
Relations
BioProject PRJNA576678

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE138653_MatrixProcessed_Raw.csv.gz 276.5 Mb (ftp)(http) CSV
GSE138653_MatrixSignal.csv.gz 307.9 Mb (ftp)(http) CSV
GSE138653_RAW.tar 161.2 Mb (http)(custom) TAR
Processed data included within Sample table
Processed data are available on Series record
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