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 and Priyank Shukla

PLOS Computational Biology, 2022, vol. 18, issue 7, 1-20

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. 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.Author summary: Rheumatoid arthritis (RA) is a chronic disease, characterised by joint pain, damage and disability. It is known to affect at least 1% of European population. It can be addressed in a high proportion of patients by timely use of targeted biologic treatments. But, biologic treatments continue to rank among the highest grossing drugs. Adalimumab (a biologic drug) for example, alone generated 20 billion US dollars of revenue worldwide in 2018. Additionally, European countries with limited resources, place volume controls on reimbursed medicines. A cheaper prognostic test for biologic response can help clinicians prescribe treatments to those who will receive benefit and also rationalise expensive treatments. In this study we have proposed an informative plasma protein signature, consisting of 17 proteins, and have developed a ML based classifier ATRPred (Anti-TNF Response Predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy. With this work we have tried to help clinicians to optimise treatment selection, reduce spend on biologics in unresponsive patients and overall improve quality of life for non-responsive RA patients. Our study has also identified endotypes or molecular sub-classes of RA using plasma protein profiles. These endotypes did not show difference in the responsiveness towards the anti-TNF, however they may be helpful in understanding of the disease and response to other treatments, going forward.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010204

DOI: 10.1371/journal.pcbi.1010204

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