Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis. (27th June 2021)

• Record Type: Journal Article
• Authors: Yoosuf, Niyaz; Maciejewski, Mateusz; Ziemek, Daniel; Jelinsky, Scott A; Folkersen, Lasse; Müller, Malin; Sahlström, Peter; Vivar, Nancy; Catrina, Anca; Berg, Louise; Klareskog, Lars; Padyukov, Leonid; Brynedal, Boel
• Is Part Of: Rheumatology. Volume 61:Number 4(2022)
• Page Start: 1680
• Page End: 1689
• Publication Date: 2021-06-27
• Subjects: rheumatoid arthritis -- anti-TNF -- methotrexate -- treatment response -- inflammation -- peripheral blood mononuclear cells; Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/keab521
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: Objectives: Advances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition. Methods: Peripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. 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 PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment. Results: The gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA. Conclusions: Our integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.