Circulating TNF-like protein 1A (TL1A) is elevated early in rheumatoid arthritis and depends on TNF.

BACKGROUND: The tumor necrosis factor (TNF) superfamily cytokine TNF-like protein 1A (TL1A) and its receptor DR3 are essential for diverse animal models of autoimmune disease and may be pathogenic in rheumatoid arthritis (RA). However, the relationship of TL1A to disease duration, activity, and response to anti-TNF and other therapies in RA is not clear. METHODS: We measured soluble TL1A in synovial fluid (SF), serum, or plasma from RA first-degree relatives (FDRs) and in early RA and established disease. We measured the effects of anti-TNF and methotrexate (MTX) therapy on circulating TL1A from multiple independent RA treatment trials. We also determined the ability of a blocking anti-TL1A antibody to inhibit clinical disease and articular bone destruction in the murine collagen-induced arthritis (CIA) model of human RA. RESULTS: Soluble TL1A was specifically elevated in the blood and SF of patients with RA compared to patients with other diseases and was elevated early in disease and in at-risk anti-cyclic citrullinated peptide (CCP) (+) first-degree relatives (FDRs). Therapeutic TNF inhibition reduced serum TL1A in both responders and non-responders, whereas TL1A declined following MTX treatment only in responders. In murine CIA, TL1A blockade was clinically efficacious and reduced bone erosions. CONCLUSIONS: TL1A is specifically elevated in RA from early in the disease course and in at-risk FDRs. The decline in TL1A after TNF blockade suggests that TL1A levels may be a useful biomarker for TNF activity in RA. These results support the further investigation of the relationship between TL1A and TNF and TL1A blockade as a potential therapeutic strategy in RA.

Subcutaneous golimumab for children with active polyarticular-course juvenile idiopathic arthritis: results of a multicentre, double-blind, randomised-withdrawal trial.

OBJECTIVE: This report aims to determine the safety, pharmacokinetics (PK) and efficacy of subcutaneous golimumab in active polyarticular-course juvenile idiopathic arthritis (polyJIA). METHODS: In this three-part randomised double-blinded placebo-controlled withdrawal trial, all patients received open-label golimumab (30 mg/m2 of body surface area; maximum: 50 mg/dose) every 4 weeks together with weekly methotrexate during Part 1 (weeks 0-16). Patients with at least 30% improvement per American College of Rheumatology Criteria for JIA (JIA ACR30) in Part 1 entered the double-blinded Part 2 (weeks 16-48) after 1:1 randomisation to continue golimumab or start placebo. In Part 3, golimumab was continued or could be restarted as in Part 1. The primary outcome was JIA flares in Part 2; secondary outcomes included JIA ACR50/70/90 responses, clinical remission, PK and safety. RESULTS: Among 173 patients with polyJIA enrolled, 89.0% (154/173) had a JIA ACR30 response and 79.2%/65.9%/36.4% demonstrated JIA ACR50/70/90 responses in Part 1. At week 48, the primary endpoint was not met as treatment groups had comparable JIA flare rates (golimumab vs placebo: 32/78=41% vs 36/76=47%; p=0.41), and rates of clinical remission were comparable (golimumab vs placebo: 10/78=12.8% vs 9/76=11.8%). Adverse event and serious adverse event rates were similar in the treatment groups during Part 2. Injection site reactions occurred with <1% of all injections. PK analysis confirmed adequate golimumab dosing for polyJIA. CONCLUSION: Although the primary endpoint was not met, golimumab resulted in rapid, clinically meaningful, improvement in children with active polyJIA. Golimumab was well tolerated, and no unexpected safety events occurred. CLINICAL TRIAL REGISTRATION: NCT01230827; Results.

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations.

BACKGROUND: Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis. RESULTS: We combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells. CONCLUSIONS: We highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.

Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies.

MOTIVATION: Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. RESULTS: We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. CONCLUSIONS: We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by the expansion of NGS technologies.

Modular analysis of peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in tumor necrosis factor responders.

OBJECTIVE: To establish whether the analysis of whole-blood gene expression is useful in predicting or monitoring response to anti-tumor necrosis factor (anti-TNF) therapy in patients with rheumatoid arthritis (RA). METHODS: Whole-blood RNA (using a PAXgene system to stabilize whole-blood RNA in the collection tube) was obtained at baseline and at 14 weeks from 3 independent cohorts, consisting of a combined total of 240 RA patients who were beginning therapy with anti-TNF. We used an approach to gene expression analysis that is based on modular patterns of gene expression, or modules. RESULTS: Good and moderate responders according to the European League Against Rheumatism criteria exhibited highly significant and consistent changes in multiple gene expression modules after 14 weeks of therapy, as demonstrated by hypergeometric analysis. Strikingly, nonresponders exhibited very little change in any modules, despite exposure to TNF blockade. These patterns of change were highly consistent across all 3 cohorts, indicating that immunologic changes after TNF treatment are specific to the combination of both drug exposure and responder status. In contrast, modular patterns of gene expression did not exhibit consistent differences between responders and nonresponders at baseline in the 3 study cohorts. CONCLUSION: These data provide evidence that using gene expression modules related to inflammatory disease may provide a valuable method for objective monitoring of the response of RA patients who are treated with TNF inhibitors.