The big data revolution in amyotrophic lateral sclerosis (ALS) has begun, bringing with it excitement and expectations for transforming diagnosis and patient care. Advances in computing power and computational methods, the explosion of high-throughput methods to probe nucleic acids, proteins, and other cellular molecules (“-omics”), the development of induced pluripotent stem cells (iPSCs), and rapid improvements in robotics and other technologies make it possible to query individual patient cells in unprecedented detail, and to link data from thousands of patients to see patterns of causation that have simply been unavailable before now.
Multiple large initiatives are currently underway to collect and analyze such data from ALS patients, all aimed at identifying causes of the disease, biomarkers, and treatments. These treatments will be guided by the precision medicine principles that the most effective treatments will be those that target the individual patient’s disease process and “take into account individual variability in genes, environment, and lifestyle for each person” (NIH Precision Medicine Initiative Cohort Program, 2015).
Precision medicine approaches that have brought on a new era of cancer therapeutics (Friedman et al., 2015) appear particularly promising in ALS, given the clinical and genetic heterogeneity of the disease (Cooper-Knock, J et al., 2014; Xu, SW et al., 2014; Sabatelli, M. et al. 2016): over a dozen genes, which fall into pathways as diverse as RNA processing, nuclear transport, mitochondrial dysfunction, and axonal transport, are associated with ALS, and striking differences exist in clinical manifestations with respect to age and site of onset, rate of progression, and impact on cognitive function.
A note on data sharing and integration: while each of the projects underway has the potential to advance the understanding of mechanisms and new targets in ALS, an even bigger opportunity will emerge from combining data sets to allow pooled analyses of many thousands of ALS patients. Among the biggest challenges facing the field in the near future will be the development of protocols for sharing data sets. “We need to develop a blueprint for data sharing in collaboration with data scientists that can help us design the correct infrastructure. Work we do now to plan for the integration of data from different projects will have major implications for how smoothly that work flows,” stated Bernard Muller, an entrepreneur and ALS patient who co-initiated Project MinE. “There are technical challenges, but these can be solved. Even more significant may be the legal hurdles for cross-border data-sharing, which will be needed to integrate data from all ongoing projects”. Work to overcome those hurdles is only now beginning, Mueller said. As noted below and in the project descriptions, project leaders are anticipating both the technical and legal challenges, and are in the process of planning the path forward. Many of the details are still being worked out as of the publication date of this article.
This article is provides an overview of the current landscape of big data/precision medicine projects in ALS, with a primary focus on projects in the United States. It contains information garnered from the program websites, as well as from interviews by the ALS Research Forum with the project directors or principal investigators. Regularly updated descriptions of each project on the ALS Research Forum provide further details about scope, progress, and resources that will be available to the research community. We aim for this to be a resource for the ALS research community, highlighting collaboration opportunities and emerging resources for advancing ALS research.
ADDITIONAL DATA RESOURCES FOR RESEARCHERS
Outlook Toward the Future: the outcome of all of these initiatives is intended to be a better understanding of the diverse disease mechanisms driving ALS, and an ability to identify which mechanisms are operating in an individual patient. The ability to subtype patients by mechanism will make clinical trial populations more homogenous, in turn allowing them to be shorter (because of less sample variation) and to more definitively test a mechanism-based therapy on patients whose disease is driven by that mechanism. Those efforts will be aided by other initiatives in the ALS clinical trial field, including a major push by Biogen to test and refine outcome measures beyond the ALS-FRS (see Feb 2016 news) and studies to define disease progression biomarkers to supplement clinical measures of disease progression.
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