Dataset Information

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

ABSTRACT:

Background

Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients.

Methods

Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set.

Findings

Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients.

Interpretation

Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways.

Funding

Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

SUBMITTER: Lim AJW

PROVIDER: S-EPMC8808170 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

Lim Ashley J W AJW Lim Lee Jin LJ Ooi Brandon N S BNS Koh Ee Tzun ET Tan Justina Wei Lynn JWL Chong Samuel S SS Khor Chiea Chuen CC Tucker-Kellogg Lisa L Leong Khai Pang KP Lee Caroline G CG

EBioMedicine 20220110

<h4>Background</h4>Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-S ...[more]

PMID: 35022146

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Project description:BackgroundMethotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment in patients with RA are lacking. We aimed to identify clinical predictors of response to methotrexate in patients with rheumatoid arthritis (RA) using machine learning methods.MethodsRandomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus methotrexate were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline and 12 and 24 weeks were included. Latent class modeling of methotrexate response was performed. The least absolute shrinkage and selection operator (LASSO) and random forests methods were used to identify predictors of response.ResultsA total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: "good responders" and "poor responders." Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA), and Health Assessment Questionnaire (HAQ) score were the top predictors of good response using LASSO (area under the curve [AUC] 0.79) and random forests (AUC 0.68) in the external validation set. DAS28-ESR ≤ 7.4, ACPA positive, and HAQ ≤ 2 provided the highest likelihood of response. Among patients with 12-week DAS28-ESR > 3.2, ≥ 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR ≤ 3.2 at 24 weeks.ConclusionsWe have developed and externally validated a prediction model for response to methotrexate within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cutoffs for clinical decision-making.

Project description:BackgroundFatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.MethodsFifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.ResultsThe highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.ConclusionsThe results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

Project description:ObjectivesMethotrexate is considered to be first-line therapy for rheumatoid arthritis (RA). However, a substantial proportion of treated patients do not achieve the desired goals of therapy. This analysis aimed to identify predictors of insufficient response to methotrexate in patients with early RA.MethodsThe Optimal Protocol for Treatment Initiation with Methotrexate and Adalimumab (OPTIMA) and PREMIER studies in patients with RA for <1 and <3 years, respectively, examined the efficacy of methotrexate and adalimumab in methotrexate-naive patients. This post hoc analysis included patients for whom initial methotrexate monotherapy was not successful after 6 months. Candidate predictors of insufficient response and clinically relevant radiographic progression (CRRP) included demographics, baseline disease characteristics and time-averaged disease variables over a 12-week interval. In OPTIMA, adalimumab was added to therapy after insufficient treatment response; in PREMIER, initial methotrexate therapy was continued; clinical, functional and radiologic outcomes were assessed after 1 year.ResultsBaseline 28-joint Disease Activity Score based on C-reactive protein (DAS28(CRP)) and time-averaged DAS28(CRP) over 4, 8 and 12 weeks were the strongest predictors of insufficient response to methotrexate and CRRP. Addition of adalimumab to methotrexate therapy was associated with better clinical, functional and radiographic outcomes after 1 year compared with continuing on methotrexate monotherapy.ConclusionsIn patients with early RA, baseline disease characteristics and early disease activity can predict response to methotrexate treatment and radiographic progression at 6 months. The addition of adalimumab at 6 months after methotrexate failure is associated with improved outcomes. These results support treatment-to-target strategies and timely adaptation of therapy in patients with early RA.Trial registration numberNCT00420927, NCT00195663; Post-results.

Dataset Information

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

Background

Methods

Findings

Interpretation

Funding

Publications

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

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