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

Bodhayan Prasad, Cathy McGeough, Amanda Eakin, Tan Ahmed, Dawn Small, Philip Gardiner, Adrian Pendleton, Gary Wright, Anthony J Bjourson, David S Gibson, Priyank Shukla

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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.
Original languageEnglish
Article numbere1010204
Pages (from-to)1-20
Number of pages20
JournalPLoS Computational Biology
Volume18
Issue number7
DOIs
Publication statusPublished (in print/issue) - 5 Jul 2022

Bibliographical note

Funding Information:
BP acknowledges support of Vice-Chancellor’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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Prasad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

  • Antirheumatic Agents
  • Arthritis, Rheumatoid
  • Biological Products
  • Clinical Decision-Making
  • Humans
  • Machine Learning
  • Quality of Life
  • Tumor Necrosis Factor Inhibitors
  • Tumor Necrosis Factor-alpha
  • Arthritis, Rheumatoid/drug therapy
  • Biological Products/therapeutic use
  • Tumor Necrosis Factor Inhibitors/therapeutic use
  • Antirheumatic Agents/therapeutic use

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