Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers
Open accessAbstract
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.
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No single established biomarker for predicting treatment response in rheumatoid arthritis has been reported in a real-life setting.
“Despite several attempts, including machine learning utilisation,42–44 no single established biomarker has been reported in a real-life setting.45”
From Diurnal production of cortisol and prediction of treatment response in rheumatoid arthritis: a 6-month, real-life prospective cohort study (2024) · cites this paper as [42]
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- Authors
- Guan, Y., Zhang, H., Quang, D., Wang, Z., Parker, S. C. J., Pappas, D. A., Kremer, J. M., & Zhu, F.
- Affiliations
- University of Michigan (US)
- Columbia University Irving Medical Center (US)
- Center for Rheumatology (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Guan, Yuanfang
- ORCID
- Venue
- Arthritis & Rheumatology
- Publisher
- Wiley
- Published
- 2019-11-04
- Volume
- 71
- Issue
- 12
- Pages
- 1987-1996
- ISSN
- 2326-5191 (ISSN-L)
- 2326-5205
- OpenAlex
- W2964201896
- Cited by
- 137
- References
- 59
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 18 · 2023: 20 · 2022: 23 · 2021: 36 · 2020: 11 · 2019: 1
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Arthritis & Rheumatology (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61802360)
- Topics
- Health Sciences › Medicine › Rheumatology › Rheumatoid Arthritis Research and Therapies
- Health Sciences › Medicine › Hematology › Autoimmune and Inflammatory Disorders Research
- Health Sciences › Medicine › Complementary and alternative medicine › Natural Compounds in Disease Treatment
- Keywords
- rheumatoid arthritis, treatment response prediction, DAS28, Gaussian process regression, single nucleotide polymorphisms, genetic biomarkers, clinical biomarkers, personalized treatment selection
- MeSH terms
- Certolizumab Pegol, Etanercept, Adalimumab, Infliximab, Machine Learning, Arthritis, Rheumatoid, Female, Genetic Markers, Humans, Male, Methotrexate, Middle Aged, Predictive Value of Tests, Regression Analysis, Severity of Illness Index, Tumor Necrosis Factor-alpha, Reproducibility of Results, Normal Distribution, Treatment Outcome, Outcome Assessment, Health Care, Antirheumatic Agents, Polymorphism, Single Nucleotide
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