Higher baseline global leukocyte DNA methylation is associated with MTX non-response in early RA patients

Gosselt, Helen R.; Zelst, Bertrand D. van; Rotte, Maurits C F J de; Hazes, Johanna M. W.; Jonge, Robert de; Heil, Sandra G.

doi:10.1186/s13075-019-1936-5

Cited by 33 publications

(32 citation statements)

References 19 publications

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“…It is noteworthy that the number of cases in the TCZ-monotherapy group was limited ( N = 16), however the ROC curve for this group still had a power of 86%. We may have also missed new features that could potentially improve the prediction regarding MTX (combination) therapy (e.g., global DNA methylation [ 34 ]) because we were limited to data availability in all included cohorts. However, the clinical features currently included in the model are often readily available and commonly assessed, which eases the implementation of the model into clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

Gosselt

Verhoeven

Bulatović-Ćalasan

et al. 2021

JPM

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The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response.

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Section: Discussionmentioning

confidence: 99%

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

Gosselt

Verhoeven

Bulatović-Ćalasan

et al. 2021

JPM

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“…DNA was extracted from EDTA whole blood using the MagNA Pure Compact Nucleic Acid Isolation Kit I, following manufacturer’s protocol (cat.no: 03730964001, Roche Molecular Biochemicals®) [ 10 ][. DNA concentrations were assessed using a NanoDrop ND-1000 Spectrophotometer with DNA-50 default settings (NanoDrop Technologies, Wilmington, DE, USA).…”

Section: Methodsmentioning

confidence: 99%

“…DNA methylation can be actively de-methylated through the oxidation by Ten-eleven-Translocation (TET) enzymes to 5-hydroxymethylcytosine (5hmC) which can also act as an epigenetic modifier itself [3,4]. DNA methylation and hydroxymethylation have previously been associated with several diseases like cancer, heart disease, autoimmune diseases, and neurological disorders [5][6][7][8][9][10], and are therefore potential biomarkers for disease onset, progression, or response to medication [11]. DNA is required for epigenetic studies, which is mainly extracted from blood samples that have been collected from patients over several years and has therefore been stored for different time periods prior to the start of a study.…”

Section: Introductionmentioning

confidence: 99%

Global DNA (hydroxy)methylation is stable over time under several storage conditions and temperatures

et al. 2020

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Background: Epigenetic markers are often quantified and related to disease in stored samples. While, effects of storage on stability of these markers have not been thoroughly examined. In this longitudinal study, we investigated the influence of storage time, material, temperature, and freeze-thaw cycles on stability of global DNA (hydroxy)methylation. Methods: EDTA blood was collected from 90 individuals. Blood (n = 30, group 1) and extracted DNA (n = 30, group 2) were stored at 4°C, −20°C and −80°C for 0, 1 (endpoint blood 4°C), 6, 12 or 18 months. Additionally, freeze-thaw cycles of blood and DNA samples (n = 30, group 3) were performed over three days. Global DNA methylation and hydroxymethylation (mean ± SD) were quantified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with between-run precision of 2.8% (methylation) and 6.3% (hydroxymethylation). Effects on stability were assessed using linear mixed models. Results: global DNA methylation was stable over 18 months in blood at −20°C and −80°C and DNA at 4°C and −80°C. However, at 18 months DNA methylation from DNA stored at −20°C relatively decreased −6.1% compared to baseline. Global DNA hydroxymethylation was more stable in DNA samples compared to blood, independent of temperature (p = 0.0131). Stability of global DNA methylation and hydroxymethylation was not affected up to three freeze-thaw cycles. Conclusion: Global DNA methylation and hydroxymethylation stored as blood and DNA can be used for epigenetic studies. The relevance of small differences occuring during storage depend on the expected effect size and research question.

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“…MTX can also reverse DNA hypomethylation in T cells, B cells, and monocytes in patients with RA ( de Andres et al, 2015 ). Furthermore, patients with higher baseline global DNA methylation levels in RA exhibit lower MTX response ( Gosselt et al, 2019 ). MTX can also reduce MTR expression in rheumatoid nodules, affecting remethylation mediated by MTR and MTRR , indicating the important roles of DNA methylation in RA pathogenesis and drug response mechanism ( Houlder et al, 2017 ).…”

Section: Epigenetic Modules Mediated By Methylation Are Promising Ra mentioning

confidence: 99%

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

Guo

Chang

et al. 2020

Front. Genet.

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Rheumatoid arthritis (RA) is a complex disease triggered by the interaction between genetics and the environment, especially through the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, the available evidence shows that genetic diversity and environmental exposure cannot explain all the clinical characteristics and heterogeneity of RA. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of RA, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adaptive immune cell differentiation and migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic-mediated regulation of immune-related genes and inflammation pathways explains the dynamic expression network of RA. In this review, we summarize the comprehensive evidence to show that methylation of DNA and histones is significantly involved in the pathogenesis of RA and could be applied as a promising biomarker in the disease progression and drug-response prediction. We also explain the advantages and challenges of the current epigenetics research in RA. In summary, epigenetic modules provide a possible interface through which genetic and environmental risk factors connect to contribute to the susceptibility and pathogenesis of RA. Additionally, epigenetic regulators provide promising drug targets to develop novel therapeutic drugs for RA. Finally, DNA methylation and histone modifications could be important features for providing a better RA subtype identification to accelerate personalized treatment and precision medicine.

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Higher baseline global leukocyte DNA methylation is associated with MTX non-response in early RA patients

Cited by 33 publications

References 19 publications

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

Global DNA (hydroxy)methylation is stable over time under several storage conditions and temperatures

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

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