Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach
Citation:
Kueffner, R., Zach, N., Bronfeld, M., Norel, R., Atassi, N., Balagurusamy, V., Di Camillo, B., Chio, A., Cudkowicz, M., Dillenberger, D., Garcia-Garcia, J., Hardiman, O., Hoff, B., Knight, J., Leitner, M.L., Li, G., Mangravite, L., Norman, T., Wang, L., Xiao, J., Fang, W.-C., Peng, J., Yang, C., Chang, H.-J., Stolovitzky, G., Alkallas, R., Anghel, C., Avril, J., Bacardit, J., Balser, B., Balser, J., Bar-Sinai, Y., Ben-David, N., Ben-Zion, E., Bliss, R., Cai, J., Chernyshev, A., Chiang, J.-H., Chicco, D., Corriveau, B.A.N., Dai, J., Deshpande, Y., Desplats, E., Durgin, J.S., Espiritu, S.M.G., Fan, F., Fevrier, P., Fridley, B.L., Godzik, A., Golinska, A., Gordon, J., Graw, S., Guo, Y., Herpelinck, T., Hopkins, J., Huang, B., Jacobsen, J., Jahandideh, S., Jeon, J., Ji, W., Jung, K., Karanevich, A., Koestler, D.C., Kozak, M., Kurz, C., Lalansingh, C., Larrieu, T., Lazzarini, N., Lerner, B., Lesinski, W., Liang, X., Lin, X., Lowe, J., Mackey, L., Meier, R., Min, W., Mnich, K., Nahmias, V., Noel-Macdonnell, J., O'donnell, A., Paadre, S., Park, J., Polewko-Klim, A., Raghavan, R., Rudnicki, W., Saghapour, E., Salomond, J.-B., Sankaran, K., Sendorek, D., Sharan, V., Shiah, Y.-J., Sirois, J.-K., Sumanaweera, D.N., Usset, J., Vang, Y.S., Vens, C., Wadden, D., Wang, D., Wong, W.C., Xie, X., Xu, Z., Yang, H.-T., Yu, X., Zhang, H., Zhang, L., Zhang, S., Zhu, S. Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach, Scientific Reports, 2019, 9, 690Download Item:
Abstract:
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.
Author's Homepage:
http://people.tcd.ie/hardimao
Author: Hardiman, Orla
Type of material:
Journal ArticleCollections
Series/Report no:
Scientific Reports;9;
1;
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Full text availableKeywords:
Neurodegenerative diseases, Therapeutic development, Amyotrophic lateral sclerosis (ALS), Patient clustersDOI:
http://dx.doi.org/10.1038/s41598-018-36873-4Metadata
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