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

• Published in: PLoS computational biology Vol. 18; no. 7; p. e1010204
• Main Authors: Prasad, Bodhayan, McGeough, Cathy, Eakin, Amanda, Ahmed, Tan, Small, Dawn, Gardiner, Philip, Pendleton, Adrian, Wright, Gary, Bjourson, Anthony J., Gibson, David S., Shukla, Priyank
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 05.07.2022; Public Library of Science (PLoS)
• Subjects: Arthralgia; Arthritis; Autoimmune diseases; Bioinformatics; Biological treatment; Biology and Life Sciences; Biomarkers; Clinical decision making; Computer and Information Sciences; Decision making; Dosage and administration; Drug therapy; Ethics; Health aspects; Health services; Learning algorithms; Machine learning; Medicine and Health Sciences; Normal distribution; Pain; Patients; Physical Sciences; Plasma; Protein expression; Proteins; Proteomics; Quality control; Quality of life; Research and Analysis Methods; Response data analysis; Rheumatoid arthritis; Rheumatology; Technology application; Thermal resistance; Tumor necrosis factor; Tumor necrosis factor inhibitors; Tumor necrosis factor-TNF; Tumors; Values; United Kingdom; United Kingdom > UK; Northern Ireland
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010204

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.