Early prediction of clinical response to anti-TNF treatment using multi-Omics and machine learning in rheumatoid arthritis
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Abstract
Objectives: Advances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition. Methods: Peripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment. Results: The gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA. Conclusions: Our integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.
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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 [43]
The Discussion (page not provided) says that identifying predictive biomarkers is challenging and that the authors’ model requires independent validation before clinical use. This supports the narrower point that their proposed predictor is not established for clinical practice, but the paper does not state that no single biomarker has been reported in a real-life setting across rheumatoid arthritis treatment. (LLM, conf=0.78)
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- Authors
- Yoosuf, N., Maciejewski, M., Ziemek, D., Jelinsky, S. A., Folkersen, L., Müller, M., Sahlström, P., Vivar, N., Catrina, A., Berg, L., Klareskog, L., Padyukov, L., & Brynedal, B.
- Affiliations
- Karolinska University Hospital (SE)
- Pfizer (United States) (US)
- Danish National Genome Center, Copenhagen, Denmark
- Karolinska Institutet (SE)
- Corresponding author
- Yoosuf, Niyaz
- ORCID
- Venue
- Rheumatology
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-06-27
- Volume
- 61
- Issue
- 4
- Pages
- 1680-1689
- ISSN
- 1462-0324 (ISSN-L)
- 1462-0332
- PMC
- PMC8996791
- OpenAlex
- W3142652285
- Cited by
- 64
- References
- 55
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 11 · 2023: 9 · 2022: 9 · 2021: 2
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Lara D. Veeken (publishedVersion, cc-by-nc)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Novo Nordisk and Pfizer. L.P. was supported by a grant from the Swedish Council
- 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, anti-TNF therapy, treatment response prediction, biomarkers, YKL-40, peripheral blood mononuclear cells, gene expression signatures, plasma proteins, multi-omics, machine learning, female patients
- MeSH terms
- Machine Learning, Tumor Necrosis Factor Inhibitors, Arthritis, Rheumatoid, Female, Humans, Leukocytes, Mononuclear, Methotrexate, Tumor Necrosis Factor-alpha, Biomarkers, Treatment Outcome, Antirheumatic Agents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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