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Nat Commun. 2016 Aug 23;7:12460. doi: 10.1038/ncomms12460.

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

Author information

1
Sage Bionetworks, Seattle, Washington 98115, USA.
2
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
3
Structural Bioinformatics Group (GRIB/IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Spain.
4
Center for Statistical Genetics, Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.
5
Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.
6
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.
7
Division of Rheumatology, Department of Medicine, Columbia University, New York, New York 10032, USA.
8
Corrona LLC, Southborough, Massachusetts 01772, USA.
9
Center for Complex Network Research, Northeastern University, Boston, Massachusetts 02115, USA.
10
Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
11
Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon 1749-016, Portugal.
12
IBM T.J. Watson Research Center, Yorktown Heights, New York, New York 10598, USA.
13
Department of Computer Science, Aalto University, Espoo 02150, Finland.
14
Helsinki Institute for Information Technology (HIIT), Esbo 02150, Finland.
15
MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau 77300, France.
16
Institut Curie, Paris 75248, France.
17
Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, INSERM U900, Paris 75248, France.
18
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki FI-00014, Finland.
19
Department of Mathematics and Statistics, University of Helsinki, Helsinki FI-00014, Finland.
20
Stanford Center for Biomedical Informatics, Stanford University, Stanford, California 94305, USA.
21
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5G OA5.
22
Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada M5G 0A4.
23
Department of Computer Science, University of Helsinki, Helsinki FI-00014, Finland.
24
Department of Integrative Structural and Computational Biology, The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, California 92037, USA.
25
Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna 1090, Austria.
26
Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA.
27
Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA.
28
Department of Paediatrics, Department of Immunology, Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada M5S 1A8.
29
Cell Biology, SickKids Research Institute, Toronto, Ontario, Canada M5G 0A4.
30
Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA.
31
Department of Medicine, New York University School of Medicine, New York, New York 10003, USA.
32
Department of Medicine, Division of Rheumatology, Albany Medical College, Albany, New York 12206, USA.
33
Department of Medicine, University of Nebraska Medical Center, Omaha, Nebraska 68198, USA.
34
National Data Bank for Rheumatic Diseases, Wichita, Kansas 67214, USA.
35
Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester M13 9PT, UK.
36
NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester Foundation Trust, Manchester M13 9WU, UK.
37
Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen 6525 GA, The Netherlands.
38
Department of Rheumatology, Université Paris-Sud, Orsay 91400, France.
39
APHP-Hôpital Bicêtre, Center of Immunology of Viral Infections and Autoimmune Diseases (IMVA) INSERM U1184, Paris 94276, France.
40
Department of Clinical Immunology and Rheumatology, Academic Medical Center/University of Amsterdam, Amsterdam 1105 AZ, The Netherlands.
41
Department of Medicine, Cambridge University, Cambridge CB2 1TN, UK.
42
Department of Rheumatology, Ghent University, Ghent 9000, Belgium.
43
GlaxoSmithKline, Stevenage SG1 2NY, UK.
44
Clinical Unit, GlaxoSmithKline, Cambridge CB2 0QQ, UK.
45
Department of Rheumatology, Leiden University Medical Centre, Leiden 2300 RC, The Netherlands.
46
Merck Research Labs, Merck and Co., Inc., Boston, Massachusetts 02115, USA.
47
Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.
48
Department of Medicine, Division of Rheumatology, Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, University of California San Francisco, San Francisco, California 94143, USA.
49
Division of Rheumatology and Clinical Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.
50
Department of Medicine, Rheumatology Unit, Karolinska Hospital and Karolinska Institutet, Solna, Stockholm 171 76, Sweden.
51
Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, New York 11030, USA.

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 (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.

PMID:
27549343
PMCID:
PMC4996969
DOI:
10.1038/ncomms12460
[Indexed for MEDLINE]
Free PMC Article

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