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

Authors:
Jaume Bonet, PhD
Jaume Bonet, PhD
Ecole Polytechnique Fédérale de Lausanne
Post-doc
Lausanne, Vaud | Switzerland

Nat Commun 2016 08 23;7:12460. Epub 2016 Aug 23.

Sage Bionetworks, Seattle, Washington 98115, USA.

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 (h(2)=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.

Download full-text PDF

Source
http://dx.doi.org/10.1038/ncomms12460DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996969PMC
August 2016
117 Reads
12 Citations
10.742 Impact Factor

Publication Analysis

Top Keywords

anti-tnf treatment
12
predicting anti-tnf
8
rheumatoid arthritis
8
genetic contribution
8
treatment
6
observed formally
4
non-responders treatment
4
data predicting
4
snp data
4
treatment efficacy
4
efficacy patients
4
covering wide
4
patients performed
4
predictive performance
4
wide range
4
utility snp
4
performance relative
4
community-based assessment
4
relative standard
4
accuracy observed
4

References

(Supplied by CrossRef)

A Gibofsky et al.
Am. J. Manag. Care 2012

IB McInnes et al.
N. Engl. J. Med. 2011

FB Vincent et al.
Ann. Rheum. Dis. 2013

CA Wijbrandts et al.
Ann. Rheum. Dis. 2008

PP Tak et al.
Rheumatology 2012

J Cui et al.
PLoS Genet. 2013

EA Stahl et al.
Nat. Genet. 2010

NR Wray et al.
Genome Res. 2007

NR Wray et al.
Nat. Rev. Genet. 2013

HD Daetwyler et al.
PLoS ONE 2008

JC Costello et al.
Nat. Biotechnol. 2014

Similar Publications