title: Discovery in context: leveraging multidimensional glioblastoma datasets to identify targetable regulatory networks.
abstract: The Cancer Genome Atlas (TCGA) promises to transform the treatment of patients with cancer by identifying new drug targets. However, extracting mechanistically insightful, therapeutically actionable information from complex multidimensional datasets remains a significant challenge. In this issue of Cancer Discovery, Genovese and colleagues apply a context-dependent modeling algorithm to the glioblastoma TCGA datasets and couple it with functional genetic screens and experimental validation to identify a novel, and potentially targetable, microRNA-mediated regulatory pathway.
authors.affiliation: Ludwig Institute for Cancer Research, University of California at San Diego, La Jolla, California, USA.
authors.forename: Ivan
authors.initials: I
authors.lastname: Babic
authors.forename: Paul S
authors.initials: PS
authors.lastname: Mischel
mesh_terms: D000818:Animals; D005909:Glioblastoma / Q000235:genetics*; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D016212:Transforming Growth Factor beta / Q000378:metabolism*
chemical_list: C522425:MIRN34 microRNA, human; D035683:MicroRNAs; D016212:Transforming Growth Factor beta