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Küffner, R. ; Zach, N.* ; Norel, R.* ; Hawe, J.* ; Schoenfeld, D.* ; Wang, L.* ; Li, G.* ; Fang, L.* ; Mackey, L.* ; Hardiman, O.* ; Cudkowicz, M.* ; Sherman, A.* ; Ertaylan, G.* ; Grosse-Wentrup, M.* ; Hothorn, T.* ; van Ligtenberg, J.* ; Macke, J.H.* ; Meyer, T.* ; Schölkopf, B.* ; Tran, L.* ; Vaughan, R.* ; Stolovitzky, G.* ; Leitner, M.L.*

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.

Nat. Biotechnol. 33, 51-57 (2015)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Uric-acid Levels; Body-mass Index; Functional Rating-scale; Alsfrs-r Score; Disease Progression; Prognostic-factors; Parkinson-disease; Blood-pressure; Survival; Population
Language english
Publication Year 2015
Prepublished in Year 2014
HGF-reported in Year 2014
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Quellenangaben Volume: 33, Issue: 1, Pages: 51-57 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Bioinformatics and Systems Biology (IBIS)
POF-Topic(s) 30505 - New Technologies for Biomedical Discoveries
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503700-001
PubMed ID 25362243
DOI 10.1038/nbt.3051
WOS ID WOS:000347714200027
Scopus ID 84963940835
Erfassungsdatum 2014-11-17
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