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.

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

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...

No abstract

Purpose: Clinical analysis and reporting of somatically acquired copy number abnormalities (CNAs) detected through next-generation sequencing (NGS) is time consuming and requires significant expertise. Interpretation is complicated by other classes of variants such as coding mutations and gene fusions. Recent guidelines for the clinical assessment of tumor CNAs harmonize and simplify the reporting criteria but did not directly address NGS-specific concerns or the need for a standardized and scalable protocol for CNA analysis. Methods: We developed a scalable NGS-derived CNA analysis protocol paired with a novel interactive web application, CNA Explorer and anaLyzer (CNAEL), to facilitate the rapid, scalable, and reproducible analysis and reporting of complex tumor-derived CNA profiles https://CNAEL.sema4.com. Results: Novel features of CNAEL include on-the-fly data rescaling to account for tumor ploidy, purity, and modal chromosomal copy number; integration of gene expression, coding, and fusion variants into review and automated genome-wide summarization to enable rapid reporting. We found that case curation times were significantly reduced when using CNAEL [median:7 mins, IQR = 4, 10.25] compared with our previous laboratory standard operating procedure [median: 61 mins, IQR = 23.75, 176,25] with p=4.631e-05. Conclusion CNAEL enables efficient and accurate clinical review and reporting of complex NGS-derived tumor copy number profiles.