Abstract
In this talk I will present recent research performed in the Elahi Lab at the ICAHN School of Medicine at Mount Sinai on CADASIL which relies on machine learning approaches to unveil relevant signatures from large sets of proteomics data. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of vascular cognitive impairment and dementia. A genetic arteriolosclerotic disease, the molecular mechanisms driving vascular brain degeneration and decline remain unclear. With the goal of driving discovery of disease-relevant biological perturbations in CADASIL, we used machine learning approaches to extract proteomic disease signatures from large-scale proteomics generated from plasma collected from three distinct cohorts in US and Colombia: CADASIL-Early (N = 53), CADASIL-Late (N = 45), and CADASIL-Colombia (N = 71). We extracted molecular signatures with high predictive value for early and late-stage CADASIL and performed robust cross- and external-validation. We examined the biological and clinical relevance of our findings through pathway enrichment analysis and testing of associations with clinical outcomes. Our study represents a model for unbiased discovery of molecular signatures and disease biomarkers, combining non-invasive plasma proteomics with clinical data. We report on novel disease-associated molecular signatures for CADASIL, derived from the accessible plasma proteome, with relevance to vascular cognitive impairment and dementia.