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. 2022 Jul 5;18(7):e1010204.
doi: 10.1371/journal.pcbi.1010204. eCollection 2022 Jul.

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

Bodhayan Prasad  1 , Cathy McGeough  1 , Amanda Eakin  1 , Tan Ahmed  1 , Dawn Small  2 , Philip Gardiner  2 , Adrian Pendleton  3 , Gary Wright  3 , Anthony J Bjourson  1 , David S Gibson  1 , Priyank Shukla  1
Affiliations

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

Bodhayan Prasad et al. PLoS Comput Biol. .

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.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: A UK-wide patent application has been filed by the Ulster University; UK Application No. 2208371.1, patent pending. All the aspects of this manuscript are covered in this patent application.

Figures

Fig 1
Fig 1. Principal component analysis (PCA) plot of rheumatoid arthritis patients (n = 89) using 352 plasma protein Normalised Protein Expression (NPX) values reveals two molecular sub-classes or endotypes with respect to positive and negative third principal component (PC3) values.
Endotype 1 is with PC3 values > 0 and endotype 2 is with PC3 values < 0. Each data point represents a patient, where size of the dot is proportional to the disease activity score (DAS) of the patient at baseline.
Fig 2
Fig 2
(A) Computational pipeline for the development of plasma protein signature. PEA = Protein Expression Analysis, LoD = Limit of Detection, QC = Quality Control, k-NN = k Nearest Neighbour, AUC = Area Under the Curve. (B) The Machine Learning (ML) schema. 5-fold nested cross-validation (CV) followed for building the classifier for response to anti-tumour necrosis factor (anti-TNF) treatment in rheumatoid arthritis (RA) patients.
Fig 3
Fig 3
(A) Feature importance of top 30 proteins along with significant demographic and clinical features, viz. gender and base line disease activity score (BLDAS). (B) Area Under the Curve (AUC) of training and test set vs. number of protein features. A set of 17 proteins along with gender and BLDAS gave the maximum mean AUC of 0.86 on test set without decreasing the training set’s AUC. Receiver Operator Characteristics (ROC) for the 5-fold cross-validation using gender, BLDAS, and 17 protein features of (C) training sets and corresponding (D) test sets.
Fig 4
Fig 4
(A) Effect sizes (ES) or beta coefficients of regression vs. feature importance, i.e. fraction of 500 models, the feature appeared. (B) Boxplot of model score of each patient. NR = Non-responder, R = Responder. (C) Protein-Protein Interaction (PPI) network obtained from STRING database for 17 featured proteins. The size of the cell depicts the degree of the node i.e. number of connection with the other proteins, whereas the edge thickness represents the STRING database’s interaction scores. ES = effect size, as presented in Table 3. (D) Pearson’s correlation coefficient plot of 17 feature proteins. The size of circle depicts the -log10(p-value) of the correlation.
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Grants and funding

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.