ArticleRheumatology and therapy2021
A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study.
Article in Rheumatology and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- Use of a Molecular Signature Response Classifier to Inform Treatment Selection Improves Clinical Disease Activity Among Patients with Rheumatoid Arthritis Initiating a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug.Arthritis care & research · 2026Article
- Diagnostic, prognostic and therapeutic biomarkers in rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using Machine Learning Analysis of Whole-Blood Transcriptomics Data.Arthritis & rheumatology (Hoboken, N.J.) · 2025Article
- Immune-cell profiling to guide stratification and treatment of patients with rheumatic diseases.Nature reviews. Rheumatology · 2025Review
- Whole-Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib.ACR open rheumatology · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Poor prognostic factors and unmet needs in rheumatoid arthritis.Rheumatology (Oxford, England) · 2025Review
- Multi-omics identification of immune-related biomarkers predicting tofacitinib response in rheumatoid arthritis.Frontiers in immunology · 2025Article
- Prediction of ineffectiveness of biological drugs using machine learning and explainable AI methods: data from the Austrian Biological Registry BioReg.Arthritis research & therapy · 2024Article
- Decision Impact Analysis to Measure the Influence of Molecular Signature Response Classifier Testing on Treatment Selection in Rheumatoid Arthritis.Rheumatology and therapy · 2024Article
- Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry.Rheumatology and therapy · 2023Article
- Commentary on Cohen et al.: Role of Clinical Factors in Precision Medicine Test to Predict Nonresponse to TNFi Therapies in Rheumatoid Arthritis.Rheumatology and therapy · 2023Article
- The Economic Benefit of Remission for Patients with Rheumatoid Arthritis.Rheumatology and therapy · 2022Article
- A Response to: Letter to the Editor Regarding A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study.Rheumatology and therapy · 2022Article
- Considering Immuno-autonomics in Stratifying Successful Treatment of Rheumatoid Arthritis with Tumor Necrosis Factor Inhibition: Comment on Cohen et al. Rheumatol Ther 2021 June 19.Rheumatology and therapy · 2022Article
- Guided therapy selection in rheumatoid arthritis using a molecular signature response classifier: an assessment of budget impact and clinical utility.Journal of managed care & specialty pharmacy · 2021Article
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTimely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.
methodsThe protein-protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.
resultsThe 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0-8.3, p value 0.0001). Odds ratios (3.4-8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3-26.6 by ACR, DAS28-CRP, and CDAI metrics.
conclusionThe MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.
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