scholarly journals Prediction of Therapeutic Responses to Tocilizumab in Patients With Rheumatoid Arthritis: Biomarkers Identified by Analysis of Gene Expression in Peripheral Blood Mononuclear Cells Using Genome-Wide DNA Microarray

2014 ◽  
Vol 66 (6) ◽  
pp. 1421-1431 ◽  
Author(s):  
Yoshie Sanayama ◽  
Kei Ikeda ◽  
Yukari Saito ◽  
Shin-ichiro Kagami ◽  
Mieko Yamagata ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Dna Microarray ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Peripheral Blood Mononuclear ◽  
Genome Wide ◽  
Blood Mononuclear Cells ◽  
Therapeutic Responses

scholarly journals Early Prediction of Clinical Response to Anti-TNF Treatment using Multi-omics and Machine Learning in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Niyaz Yoosuf ◽  
Mateusz Maciejewski ◽  
Daniel Ziemek ◽  
Scott A. Jelinsky ◽  
Lasse Folkersen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Machine Learning ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Learning Models ◽  
Peripheral Blood Mononuclear ◽  
Blood Mononuclear Cells ◽  
Machine Learning Models

Abstract ObjectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for rheumatoid arthritis patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The present study was aimed to detect biomarkers and expression signatures of treatment response to TNF inhibition.MethodsPeripheral blood mononuclear cells were obtained from 39 patients collected before anti-TNF treatment initiation (day 0) and after three months. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in peripheral blood mononuclear cells, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry, and the level of proteins in plasma. Finally, using biological measurements, we run machine learning models to predict non-response. ResultsThe gene expression analysis in baseline samples revealed notably a higher expression of the gene EPPK1 in future responders. We also detected the suppression of genes and proteins following treatment, including suppression of expression of the gene, T-cell inhibitor CHI3L1, and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomics data showed high predictive utility (ROC AUC ± SEM: 0.81 ± 0.17) in classifying non-response to anti-TNF treatment in RA.ConclusionsOur integrative multi-omics analyses identified new biomarkers for prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.

Sign in / Sign up

Export Citation Format

Share Document