Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder which causes the death of motor neurons that control voluntary muscles. The loss of motor neurons leads to progressive muscle weakening and paralysis and on average patients will survive only 3-5 years from symptom onset 1 . Despite being known for over 150 years, we only have limited understanding of the biological mechanisms underlying ALS and existing therapeutic options merely extend survival by a few months 2,3 . One of the biggest challenges in ALS treatment and research today is the wellestablished heterogeneity of the disease 4,1 ; ALS patients can have widely different patterns of disease manifestation and progression, and genetic analyses suggest heterogeneity of the underlying biological mechanisms as well 5,6,7,8 . This heterogeneity has detrimental effects on clinical trial planning and interpretation, as it might mask drug effects 3 , on attempts to uncover disease mechanisms, and on clinical care, as it increases uncertainty about prognosis and makes treatment course planning challenging. Thus, successfully stratifying ALS patients into clinically meaningful sub-groups can be of great value for advancing the development of effective treatments and achieving better care for ALS patients.Early classification systems for ALS patients were based on clinical presentation of the disease and were intended for ascertainment of an ALS diagnosis, but were limited in their ability to predict disease prognosis or suggest underlying disease mechanisms 9,10,4 . More recent attempts of ALS patient classification focused on prediction of clinical outcomes but were often limited by small sample sizes lacking the highly needed detailed characterization of patient subgroups 11,12,13,14 . In the current study, we sought to use the power of state of the art machine learning algorith...
