Jeffrey Rothstein, James Berry, Clive Svendsen, Leslie Thompson, Steven Finkbeiner, Jennifer Van Eyk, Ernest Fraenkel, Merit Cudkowicz, Nicholas Maragakis, Dhruv Sareen, Raquel Norel, Victoria Dardov, Alyssa Coyne, Aaron Frank, Andrea Matlock
Answer ALS is a comprehensive multi-omics approach to ALS to ascertain, at a population level, the various clinical-molecular- biochemical subtypes of sporadic ALS. This national program enrolled 1046 ALS and ALS/FTD patients along with a cohort of 100 matched control patients followed longitudinally over at least one year. A smartphone-based app was employed to collect deep clinical data including fine motor activity, speech, breathing and linguistics/cognition. Analytics of the speech patterns revealed a strong correlation between clinical progression indices and speech. In parallel, blood-derived iPS motor neurons were generated from each patient and the cells underwent multi-omics analytics including whole genome sequencing, RNA transcriptomics, ATAC-Seq and proteome along with quality assurance standards. HIPPA compliant cloud data bases were employed to store all data. There are more than 6 billion clinical and molecular data points per patient generated in the program. The program was designed, and patient consented, to be open access to all clinical, biological and molecular data as well as public release of all generated iPS cell lines. A web portal is available to academics as well as commercial researchers. The ultimate intent of this data is for the generation of Integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease including subgroup identification. Overall, this community based clinical and science program provides for the identification of distinct reliably identifiable subgroups among the sporadic and familial patients and the great utility in iPS based approaches to disease pathophysiology and therapy discovery. Although the data is ALS centric, given the large number of both ALS and control data sets, it would also be enormously useful to others studying frontotemporal dementia, Alzheimer’s, Parkinson’s disease and others.
