ObjectiveBiologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.MethodsAn epigenome‐wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis‐acting single‐nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients.ResultsFive positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P = 1.39 × 10−8 and cg26401028, P = 1.69 × 10−8). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P = 2.63 × 10−7 and P = 1.05 × 10−6, respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P = 0.03; n = 56) and in the independent replication cohort (P = 0.003; n = 1,204).ConclusionWe identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low‐density lipoprotein receptor–related protein 1. Additional replication experiments in independent sample collections are now needed.

Objective. Approximately 30-40% of rheumatoid arthritis (RA) patients who are initially started on low-dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples from RA patients at 2 time points (pretreatment and 4 weeks following initiation of MTX), to identify gene expression biomarkers of the MTX response.Methods. RA patients who were about to commence treatment with MTX were selected from the Rheumatoid Arthritis Medication Study. Using European League Against Rheumatism (EULAR) response criteria, 42 patients were categorized as good responders and 43 as nonresponders at 6 months following the initation of MTX treatment. Data on whole blood transcript expression were generated, and supervised machine learning methods were used to predict a EULAR nonresponse. Models in which transcript levels were included were compared to models in which clinical covariates alone (e.g., baseline disease activity, sex) were included. Gene network and ontology analysis was also performed.Results. Based on the ratio of transcript values (i.e., the difference in log 2 -transformed expression values between 4 weeks of treatment and pretreatment), a highly predictive classifier of MTX nonresponse was developed using L2-regularized logistic regression (mean ± SEM area under the receiver operating characteristic [ROC] curve [AUC] 0.78 ± 0.11). This classifier was superior to models that included clinical covariates (ROC AUC 0.63 ± 0.06). Pathway analysis of gene networks revealed significant overrepresentation of type I interferon signaling pathway genes in nonresponders at pretreatment (P = 2.8 × 10 −25 ) and at 4 weeks after treatment initiation (P = 4.9 × 10 −28 ).Conclusion. Testing for changes in gene expression between pretreatment and 4 weeks post-treatment initiation may provide an early classifier of the MTX treatment response in RA patients who are unlikely to benefit from MTX over 6 months. Such patients should, therefore, have their treatment escalated more rapidly, which would thus potentially impact treatment pathways. These findings emphasize the importance of a role for early treatment biomarker monitoring in RA patients started on MTX.

Background: Defining regulatory mechanisms through which noncoding risk variants influence the cell-mediated pathogenesis of immune-mediated disease (IMD) has emerged as a priority in the post-genome-wide association study era. Objectives: With a focus on rheumatoid arthritis, we sought new insight into genetic mechanisms of adaptive immune dysregulation to help prioritize molecular pathways for targeting in this and related immune pathologies.