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The study was done to predict the response before the anti-TNF therapy in RA. It was performed to know the mechanism with which patients react in a different way to anti-TNF treatment in RA.
From a recent study it
was found out that machine learning models supporting the molecular signatures can
precisely calculate the reaction before the adalimumab or etanercept treatment.
They can accurately determine the response to Adalimumab and Etanercept therapy in rheumatoid
arthritis (RA) patients.
The study was done to
predict the response before the anti-TNF therapy in RA. It was performed to
know the mechanism with which patients react in a different way to anti-TNF
treatment in RA.
The gene expression and
DNA methylation profiling on PBMC, CD4+ T cells, and monocytes from eighty RA patients before
the initiation of adalimumab or etanercept therapy was considered.
To estimate the response
after six months of therapy, EULAR criteria was used. The methylation analyses
and differential expression were done to recognize the response-associated
epigenetic and transcriptional signatures. Machine learning models were built
with the help of these signatures to calculate the reaction before the
administration of anti-TNF treatment. Then, a follow-up study confirmed them.
Transcriptional signatures in adalimumab or etanercept responders were different in PBMCs (peripheral blood mononuclear cell). The TNF signaling pathway was enriched with the genes upregulated in CD4+ T cells of adalimumab responders. The accuracy of 85.9% and 79% was achieved with the help of machine learning models to calculate the response to adalimumab or etanercept, respectively. The models using differentially methylated positions attained the accuracy of 84.7% and 88% for adalimumab or etanercept, respectively.
Table-
Overall accuracy in prediction of response using differentially methylated positions
The machine learning
models supporting the molecular signatures could precisely calculate the
reaction before the adalimumab or etanercept treatment.
Arthritis & Rheumatology
Multi-omics and machine learning accurately predicts clinical response to Adalimumab and Etanercept therapy in patients with rheumatoid arthritis
Weiyang Tao et al.
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