Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Toward more efficient clinical trials for amyotrophic lateral sclerosis.

More than 30 phase II or III clinical trials have been carried out in amyotrophic lateral sclerosis (ALS). Only riluzole, however, has been shown to extend survival and/or time to tracheostomy. Many early ALS trials lacked solid pharmacodynamic and pharmacokinetic data for the treatment being tested, challenging the interpretation of the efficacy and pathway relevance. Understanding of the genetics and pathophysiology of ALS has improved considerably in the past decade, but biomarkers of disease activity remain lacking. A more efficient approach to early phase clinical trials is needed to accelerate the identification of useful agents for ALS. Here we summarize our current thinking about phase II design options and the potential benefits of a clinical trial network for phase II trials in ALS.