Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohort
Patients with amyotrophic lateral sclerosis (ALS), a rare fatal neurodegenerative disease, face a lengthy diagnostic process, and, although most survive only 2 to 4 years from diagnosis, lack information regarding their specific anticipated disease course due to a lack of prognostic tools. Although ALS is a heterogeneous disease of varied etiology, peripheral immune system dysfunction is ubiquitous, reflected by an altered whole blood transcriptome. Herein, we profiled whole blood gene expression by RNA sequencing in a large cohort of ALS cases versus controls. Several machine learning classifiers trained on our gene expression dataset predicted case-control status and survival, and integration analysis with external cohorts led to the identification of drug candidates. Data available... (for more see dbGaP study page.)