Using genome-by environment interactions to design surrogate biomarkers of therapeutic response for smaller clinical trials
More than 5% of funded NIH extramural grants (5,000 grants/yr) involve biomarker development (REPORTER search 9/2018), yet the FDA has qualifies a handful of biomarkers yearly (https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/ucm409960.htm). Many analytical methods assume that a single molecular measurement should provide guidance for diagnostic, prognosis and treatment. This monomolecular biomarker model is particularly well-suited for monogenic disorders anThe talk will focus on the discovery of interacting molecular dynamics involved for the development of systems-level biomarkers (multimolecular biomarkers). Specifically, we will propose biological assays that determine a clinically-relevant response to identify responsive systems-level biomarkers (SLBs). Different from conventional classifiers derived from “static panels of molecular measurements“, SLBs are designed ab initio from systems biology responses and machine learning modeling of genome-scale network dynamics. When trained on I will demonstrate its application in a genome-by-environment classifier of future hospitalization of asthmatic children designed from in vitro cellular responses to rhinovirus.
Talk will be at 3:20