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Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers
Author(s) -
Guan Yuanfang,
Zhang Hongjiu,
Quang Daniel,
Wang Ziyan,
Parker Stephen C. J.,
Pappas Dimitrios A.,
Kremer Joel M.,
Zhu Fan
Publication year - 2019
Publication title -
arthritis and rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.106
H-Index - 314
eISSN - 2326-5205
pISSN - 2326-5191
DOI - 10.1002/art.41056
Subject(s) - rheumatoid arthritis , tumor necrosis factor alpha , drug , medicine , immunology , tumor necrosis factor α , oncology , bioinformatics , pharmacology , biology
Objective Accurate prediction of treatment responses in rheumatoid arthritis ( RA ) patients can provide valuable information on effective drug selection. Anti–tumor necrosis factor (anti‐ TNF ) drugs are an important second‐line treatment after methotrexate, the classic first‐line treatment for RA . However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti‐ TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response. Methods Using data on patient demographics, baseline disease assessment, treatment, and single‐nucleotide polymorphism ( SNP ) array from the Dialogue on Reverse Engineering Assessment and Methods ( DREAM ): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints ( DAS 28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross‐validated using data from 1,892 RA patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features. Results In the cross‐validation tests, our method predicted changes in DAS 28 (Δ DAS 28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting Δ DAS 28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti‐ TNF treatments. Genetic SNP biomarkers showed small contributions in the prediction when added to the clinical models. This was the best‐performing model in the DREAM Challenge. Conclusion The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.