Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE138747 | GEO | 2020/08/24
REPOSITORIES: GEO
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Project description:Objectives: To predict response prior to anti-TNF treatment and comprehensively understand the mechanism how patients respond differently to anti-TNF treatment in rheumatoid arthritis (RA). Methods: Gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4+ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6-month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response-associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti-TNF treatment and were further validated by a follow-up study. Results: Transcriptional signatures in ADA and ETN responders are divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes upregulated in CD4+ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differential methylation positions (DMPs) of responders to ETN but not to ADA are majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusions: Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti-TNF treatment.
2020-08-24 | GSE138746 | GEO
Project description:Objectives: To predict response prior to anti-TNF treatment and comprehensively understand the mechanism how patients respond differently to anti-TNF treatment in rheumatoid arthritis (RA). Methods: Gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4+ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6-month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response-associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti-TNF treatment and were further validated by a follow-up study. Results: Transcriptional signatures in ADA and ETN responders are divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes upregulated in CD4+ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differential methylation positions (DMPs) of responders to ETN but not to ADA are majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusions: Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti-TNF treatment.
2020-08-24 | GSE138653 | GEO
Project description:ObjectiveTo predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment.MethodsGene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study.ResultsTranscription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models.ConclusionOur findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.
| S-EPMC7898388 | biostudies-literature
Project description:Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis
| PRJNA577031 | ENA
Project description:Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [Methylation array]
| PRJNA576678 | ENA
Project description:Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [RNA-Seq]
| PRJNA577035 | ENA
Project description:IntroductionWe previously reported a specific defect of rheumatoid arthritis (RA) monocyte polarization to anti-inflammatory M2-like macrophages related to increased miR-155 expression in all RA patients except those receiving adalimumab (ADA). In this longitudinal study, we examined whether different tumor necrosis factor inhibitors were able to restore monocyte polarization to M2-like macrophages and their effect on the transcriptomic signature.MethodsM2-like polarization induced by human serum AB was studied in 7 healthy donors and 20 RA patients included in the ABIRA cohort before and 3 months after starting ADA or etanercept (ETA). The differential gene expression of M2- and M1-related transcripts was studied in macrophage-derived monocytes after differentiation.ResultsAt baseline, RA monocytes showed a defect of polarization to M2-like macrophages as compared with healthy donor monocytes, which was negatively correlated with disease activity. M2-like polarization from circulating monocytes was restored only with ADA and not ETA treatment. The transcriptomic signature demonstrated downregulation of M2-related transcripts and upregulation of M1-related transcripts in active RA. In patients receiving ADA, the transcriptomic signature of M2-related transcripts was restored.ConclusionThis longitudinal study demonstrates that ADA but not ETA is able to restore the M2-like polarization of monocytes that is defective in RA.
| S-EPMC8904384 | biostudies-literature
Project description:ObjectiveTo compare drug survival on adalimumab, etanercept and infliximab in patients with rheumatoid arthritis (RA).MethodsPatients with RA (n=9139; 76% women; mean age 56 years) starting their first tumour necrosis factor (TNF) inhibitor between 2003 and 2011 were identified in the Swedish Biologics Register (ARTIS). Data were collected through 31 December 2011. Drug survival over up to 5 years of follow-up was compared overall and by period of treatment start (2003-2005/2006-2009; n=3168/4184) with adjustment for age, sex, education, period, health assessment questionnaire (HAQ), disease duration, concomitant disease modifying antirheumatic drug (DMARD) treatment and general frailty (using hospitalisation history as proxy).ResultsDuring 20 198 person-years (mean/median 2.2/1.7 years) of follow-up, 3782 patients discontinued their first biological (19/100 person-years; 51% due to inefficacy, 36% due to adverse events). Compared with etanercept, infliximab (adjusted HR 1.63, 95% CI 1.51 to 1.77) and adalimumab initiators had higher discontinuation rates (1.26, 95% CI 1.16 to 1.37), and infliximab had a higher discontinuation rate than adalimumab (1.28, 95% CI 1.18 to 1.40). These findings were consistent across periods, but were modified by time for adalimumab versus etanercept (p<0.001; between-drug difference highest the 1st year in both periods). The discontinuation rate was higher for starters in 2006-2009 than 2003-2005 (adjusted HR 1.12, 95% CI 1.04 to 1.20). The composition of 1-year discontinuations also changed from 2003-2005 vs 2006-2009: adverse events decreased from 45% to 35%, while inefficacy increased from 43% to 53% (p<0.001).ConclusionsDiscontinuation rates were higher for infliximab compared with adalimumab and etanercept initiators, and for adalimumab versus etanercept during the 1st year. Discontinuation rates increased with calendar period, as did the percentage discontinuations due to inefficacy.
| S-EPMC4316855 | biostudies-literature
Project description:Background and aimsAt present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR).Materials and methodsEMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model.ResultsTender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively.ConclusionA pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.
| S-EPMC7411125 | biostudies-literature
Project description:IntroductionTo evaluate the efficacy and safety of etanercept treatment in adult patients with moderate to severe rheumatoid arthritis (RA) who failed to respond (primary failure) or lost a satisfactory response (secondary failure) to adalimumab.MethodsAll patients discontinued prior adalimumab treatment and continued methotrexate with etanercept 50 mg once weekly for 24 weeks. The primary study endpoint was American College of Rheumatology 20% improvement criteria (ACR20) at week 12.ResultsEighty-five patients (mean age 56.6 years; female 80.0%) were evaluated for safety and 84 for efficacy. Thirty (35.7%) patients achieved ACR20 at week 12; the lower bound of the 95% confidence interval (CI; 25.6, 46.9) was greater than the prespecified goal of 24% based on previous research. Improvements from baseline in clinical outcomes and patient-reported outcomes were observed at each study visit. In planned subgroup analyses, patients with anti-adalimumab antibodies and secondary adalimumab failure had the highest ACR20 response to etanercept at week 12 (11/17 patients; 64.7%). Among the patients with secondary adalimumab failure, those with anti-adalimumab antibodies were fivefold more likely to have an ACR20 response to etanercept than those without anti-adalimumab antibodies (odds ratio 5.2; 95% CI 2.0, 13.5; P < 0.001). Adverse events were reported for 62 (72.9%) patients and were consistent with previous studies of etanercept. Most adverse events were mild or moderate in severity.ConclusionSwitching to etanercept is a therapeutic option in patients with RA who fail adalimumab treatment. The presence of anti-adalimumab antibodies may provide additional support for switching to etanercept, particularly in patients with secondary adalimumab failure.Trial registrationClinicalTrials.gov identifier, NCT01927757.
| S-EPMC5696291 | biostudies-literature