Correction to: Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data
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SUBMITTER: Rehberg M
PROVIDER: S-EPMC8572238 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionIn rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers.MethodsThe decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg.ResultsIn the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the "rule" that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab.ConclusionsWell-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited.Clinical trial registrationNCT01061736, NCT02332590, NCT01709578, NCT01768572.
| S-EPMC8572308 | biostudies-literature
Project description:In the last few decades, strategies for the management of rheumatoid arthritis (RA) have been increasingly oriented toward more comprehensive control of the disease, taking into account even RA extra-articular manifestations, comorbidities, and the patient's perception about the disease. The need for improving the shared decision-making process suggested by European League Against Rheumatism recommendations is leading to an increasing interest in the role of patient-reported outcomes (PROs) beside the usual more objective criteria for defining clinical response based on disease-activity composite indices. Measurement of such PROs as pain or fatigue may be significantly influenced by mood disorders often complicating RA, the pathogenesis of which is deeply interconnected with phlogistic processes mediated by proinflammatory cytokines. IL6 is a pleiotropic mediator involved in neuroendocrine and neuropsychological processes, besides its well known effects on immune, cardiovascular, and metabolic systems. Therefore, there is a growing body of evidence about the efficacy of IL6 blockade in PRO improvement in RA patients. Sarilumab is a monoclonal antibody binding both soluble and membrane-bound IL6R?, inhibiting the IL6-mediated signaling pathway with favorable efficacy and safety profile. This review analyzes the importance of PROs in strategies for the management of RA and the pathogenic mechanisms linking IL6 with the patient's perception of the disease. Moreover, the main findings from sarilumab randomized controlled trials are summarized in detail, emphasizing the potential role of this IL6 blocker in the holistic treatment of RA.
| S-EPMC6108331 | biostudies-literature
Project description:ObjectiveSarilumab, as monotherapy or in combination with conventional synthetic DMARDs, such as MTX, has demonstrated improvement in clinical outcomes in patients with RA. The primary objective of this post hoc analysis was to compare the efficacy of sarilumab (200 mg every 2 weeks) monotherapy (MONARCH study) with that of sarilumab and MTX combination therapy (MOBILITY study) at week 24.MethodsThe endpoints assessed were mean change from baseline in the Clinical Disease Activity Index (CDAI), 28-joint Disease Activity using CRP (DAS28-CRP), CRP, haemoglobin (Hb), pain visual analogue scale (VAS) and Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue. Least square (LS) mean change from baseline (95% CI) at week 24 for all endpoints was compared between the treatment arms for adjusted comparisons.ResultsThis analysis included 184 patients on sarilumab monotherapy and 399 patients on sarilumab plus MTX. Differences (P < 0.05) were observed in ethnicity, region, body mass index group, rheumatoid factor, anti-cyclic citrullinated peptide antibodies, swollen joint count, CRP, CDAI and oral glucocorticoid use between these treatment groups. After adjusting for these differences in a mixed-effect model repeated measure, LS mean change from baseline for all assessments was similar between the treatment groups with overlapping CIs: CDAI, -28.79 vs -26.21; DAS28-CRP, -2.95 vs -2.81; CRP, -18.31 vs -16.46; Hb, 6.59 vs 8.09; Pain VAS, -33.62 vs -31.66; FACIT-Fatigue, 9.90 vs 10.24.ConclusionThis analysis demonstrated that the efficacy of sarilumab monotherapy was similar to that of sarilumab and MTX combination therapy.
| S-EPMC9157062 | biostudies-literature
Project description:BackgroundWe sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples.MethodsWe compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs.ResultsSynovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82.ConclusionsH&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.
| S-EPMC9979511 | biostudies-literature
Project description:OBJECTIVE:In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning. METHODS:Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes. RESULTS:Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others. CONCLUSION:Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.
| S-EPMC6336443 | biostudies-literature
Project description:Background and objectiveSarilumab binds to the interleukin-6 receptor with high affinity, inhibiting cis and trans signaling by interleukin-6. Sarilumab has demonstrated efficacy and safety in patients with rheumatoid arthritis. The objective of this study was to develop a population-pharmacokinetic model using data from 1770 patients with rheumatoid arthritis across phase I-III studies.MethodsPotential covariates were identified using a stepwise forward-addition and backward-deletion strategy, and the final model was evaluated by visual predictive check and bootstrap methods.ResultsSarilumab pharmacokinetics is described by a two-compartment model with first-order absorption and parallel linear and nonlinear Michaelis-Menten elimination. A subcutaneous dose of sarilumab 200 mg every 2 weeks resulted in more pronounced saturation of the nonlinear clearance pathway over the dosing interval than 150 mg every 2 weeks. Steady-state exposure (area under the plasma concentration-time curve from day 0 to day 14) increased twofold with dose escalation from 150 to 200 mg every 2 weeks. Body weight, anti-drug antibody status, sarilumab drug product, sex, creatinine clearance, albumin, and baseline C-reactive protein levels were identified as significant covariates according to the predefined statistical significance criteria in stepwise covariate searches. The main intrinsic source of pharmacokinetic variability in exposure was body weight. Compared with a typical 71-kg patient, the area under the plasma concentration-time curve from day 0 to day 14 was 20-23% lower for an 83-kg patient and 20-25% higher for a 62-kg patient.ConclusionsThese findings, combined with the safety and efficacy data, indicated limited clinical relevance of body-weight effect on sarilumab exposure. No adjustment in sarilumab dose is required for body weight or any other demographics assessed.
| S-EPMC6856490 | biostudies-literature
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
2014-02-21 | GSE55235 | GEO
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.
2014-02-21 | E-GEOD-55235 | biostudies-arrayexpress
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:BackgroundBone metabolism is disrupted in rheumatoid arthritis (RA); however, the bone metabolic signature of RA is poorly known. The objective of the study is to further characterize the bone metabolic profile of RA and compare it to psoriatic arthritis (PsA), systemic sclerosis (SSc) and healthy controls.MethodsWe did a cross-sectional case-control study on consecutively enrolled patients and age-matched controls. We collected clinical characteristics, serum biomarkers related to bone metabolism and Bone Mineral Density (BMD). A multiple correlation analysis using Spearman's rank correlation coefficient was conducted within the RA patient group to investigate associations between biomarker levels and clinical variables. Machine learning (ML) models and Principal Component Analysis (PCA) was performed to evaluate the ability of bone biomarker profiles to differentiate RA patients from controls.ResultsWe found significantly lower BMD in RA patients compared to PsA, and Systemic Sclerosis SSc groups. RA patients exhibited higher Dkk1, sclerostin and lower P1nP and B-ALP levels compared to controls. No significant differences in CTX levels were noted. Correlation analysis revealed associations between bone biomarkers and clinical variables. PCA and ML highlighted distinct biomarker patterns in RA which can effectively discriminated bone biomarkers profile in RA from controls.ConclusionOur study helped uncover the distinct bone profile in RA, including changes in bone density and unique biomarker patterns. These findings enhance our comprehension of the intricate links between inflammation, bone dynamics, and RA activity, offering potential insights for diagnostic and therapeutic advancements in managing bone involvement in this challenging condition.
| S-EPMC10665911 | biostudies-literature