Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

“…We showed that the SNP data contribute very little information to the prediction achieved using clinical covariates only, in accord with previous studies of cardiovascular disease (Morris et al, 2016) as well as of treatment response in RA (Sieberts et al, 2016). We showed that the SNP data contribute very little information to the prediction achieved using clinical covariates only, in accord with previous studies of cardiovascular disease (Morris et al, 2016) as well as of treatment response in RA (Sieberts et al, 2016).…”

“…All three methods achieved a (reasonable) identical prediction accuracy as measured by the correlation (0.44), the slope (0.98) and the PMSE (1.27; Supporting Information Figure 4). This indicates that most of the information about prediction comes from the nongenetic predictors, supporting the conclusions (in relation to prediction of change in DAS28) from a previous anti-TNF study (Sieberts et al, 2016).…”

Polymorphisms in STAT4, PTPN2, PSORS1C1 and TRAF3IP2 Genes Are Associated with the Response to TNF Inhibitors in Patients with Rheumatoid Arthritis

“…Candidate genes encoding proteins involved in the immune response have been fairly investigated to search for a possible association with TNF-i response in several autoimmune diseases, including RA [33–36]. However, validated genomic biomarkers currently do not significantly allow the identification of non-responders before treatment in RA [15]. …”

“…Moreover, RA disease duration, disease activity, functional status, presence of autoantibodies [rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA)], and previous therapies can influence drug response [8–11]. Genetic inter-individual variability can also contribute to the differences in the response to treatment: some single nucleotide polymorphisms (SNPs) showed an association with bDMARDs response and might be useful for prediction, although few associations have been replicated [12–15]. …”

“…Some genes already known to be involved in RA susceptibility [16] could also be involved in the variability of the response to tumor necrosis factor (TNF)-inhibitors (TNF-i) drugs [15]. Among the known loci associated with RA, the signal transducer and activator of transcription 4 ( STAT4 ) could be one of most interesting candidate genes to study in relation to drug response [17].…”