Dr. Brat directs a basic and translational research lab that investigates biologic progression mechanisms of glioblastoma (GBM), including genetics, hypoxia, glioma stem cells (GSC) and the tumor micro-environment (TME). Progression is characterized by tumor necrosis, severe hypoxia, angiogenesis, immune cell infiltration and GSC enrichment. Past work has indicated that intra-tumoral vaso-occlusion and intravascular thrombosis results in severe central hypoxia, initiating TME events that promote rapid neoplastic expansion outward. His lab uses neurospheres, organoids, mouse models and computational approaches to investigate glioma dynamics and are currently focused on mechanisms that enrich GSCs; cause influx and polarization of tumor associated macrophages (TAMs); and reverse glioma cell quiescence following therapy.

His lab also explores mechanisms that confer specialized biologic properties to glioma stem cells (GSC) in GBM, including their localization and specialization within specific TME niches, the ability undergo self-renewing division and divide asymmetrically and their transcriptional drivers. The Drosophila brain tumor (brat) normally regulates asymmetric cellular division in flies, interfacing with Notch and Hippo pathways. His lab studies Brat, Hippo and Notch signaling pathways for roles in regulating asymmetric cell division, self-renewing properties and transcriptional drivers of GSCs.

Dr. Brat has also used computational approaches to investigate molecular correlates of pathologic, radiologic and clinical features of gliomas using multi-omic, imaging and clinical databases, such as the cancer genome atlas project (TCGA), the glioma longitudinal analysis (GLASS) and single cell RNA sequencing data. Using image analysis algorithms and machine learning, he and collaborators have studied whether genomics or elements of the TME, such as tumor necrosis, angiogenesis, or immune cell infiltrates or functional states, correlate with molecular profiles or clinical behavior. Deep learning methods are used to uncover novel features associated with patient outcomes and to develop clinical decision support models.