> Details

Kueffner, Robert; Zach, Neta; Bronfeld, Maya; Norel, Raquel; Atassi, Nazem; Balagurusamy, Venkat; Di Camillo, Barbara; Chio, Adriano; Cudkowicz, Merit; Dillenberger, Donna; Garcia-Garcia, Javier; Hardiman, Orla; Hoff, Bruce; Knight, Joshua; Leitner, Melanie L.; Li, Guang; Mangravite, Lara; Norman, Thea; Wang, Liuxia; Alkallas, Rached; Anghel, Catalina; Avril, Jeanne; Bacardit, Jaume; Balser, Barbara; [...]

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

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  • Media type: E-Article
  • Title: Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach
  • Contributor: Kueffner, Robert; Zach, Neta; Bronfeld, Maya; Norel, Raquel; Atassi, Nazem; Balagurusamy, Venkat; Di Camillo, Barbara; Chio, Adriano; Cudkowicz, Merit; Dillenberger, Donna; Garcia-Garcia, Javier; Hardiman, Orla; Hoff, Bruce; Knight, Joshua; Leitner, Melanie L.; Li, Guang; Mangravite, Lara; Norman, Thea; Wang, Liuxia; Alkallas, Rached; Anghel, Catalina; Avril, Jeanne; Bacardit, Jaume; Balser, Barbara; [...]
  • imprint: Springer Science and Business Media LLC, 2019
  • Published in: Scientific Reports
  • Language: English
  • DOI: 10.1038/s41598-018-36873-4
  • ISSN: 2045-2322
  • Keywords: Multidisciplinary
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:p>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 &gt;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.</jats:p>
  • Access State: Open Access