ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Prasad, B. et al. (2022) ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients. PLoS Computational Biology, 18(7), e1010204. (doi: 10.1371/journal.pcbi.1010204) (PMID:35788746) (PMCID:PMC9321399)

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

• Item Type: Articles
• Additional Information: Funding: BP acknowledges support of ViceChancellor’s Research Scholarship (VCRS), Ulster University. AJB acknowledges support from the European Union Regional Development Fund (ERDF), EU Sustainable Competitiveness Programme for Northern Ireland & the Northern Ireland Public Health Agency (HSC R&D) and Ulster University. PS acknowledges support from the Innovate UK NxNW ICURe programme.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Prasad, Dr Bodhayan
• Authors: Prasad, B., McGeough, C., Eakin, A., Ahmed, T., Small, D., Gardiner, P., Pendleton, A., Wright, G., Bjourson, A. J., Gibson, D. S., and Shukla, P.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences
• Journal Name: PLoS Computational Biology
• Publisher: Public Library of Science
• ISSN: 1553-734X
• ISSN (Online): 1553-7358
• Copyright Holders: Copyright © 2022 Prasad et al.
• First Published: First published in PLoS Computational Biology 18(7):e1010204
• Publisher Policy: Reproduced under a Creative Commons license