Daniel Brat, MD, PhD
Professor, Pathology; Feinberg School of Medicine
Project 1. We investigate mechanisms of progression to glioblastoma (GBM), the highest grade astrocytoma, including genetics, hypoxia, and angiogenesis. Progression is characterized by tumor necrosis, severe hypoxia and microvascular hyperplasia, a type of angiogenesis. We proposed that vaso-occlusion and intravascular thrombosis within a high grade glioma results in hypoxia, necrosis and hypoxia-induced microvascular hyperplasia in the tumorâs periphery, leading to neoplastic expansion outward. We are determining if transcriptional profiles, signaling networks and therapeutic targets vary within this altered micro-environment, and have also begun investigations involving the enrichment of glioma stem cells and influx of tumor associated macrophages. Project 2. We study mechanisms that confer specialized biologic properties to glioma stem cells (GSC) in GBM, including their ability to divide asymmetrically and their ability to home to hypoxic micro-environments. The Drosophila brain tumor (brat) gene normally regulates asymmetric cellular division and neural progenitor differentiation in the CNS of flies and, when mutated, leads to a massive brain containing only neuroblastic cells with tumor-like properties. We study the human homolog of Drosophila brat, Trim3, for its role in regulating asymmetric cell division and stem-like properties in GSCs. We are currently investigating those gene products that antagonize the tumor promoting effects of Brat/Trim3 loss on brain tumor development and are focused on CDK5/p35. Project 3. We initiated an In Silico Center for Brain Tumor Research to investigate the molecular correlates of pathologic, radiologic and clinical features of gliomas using pre-existing databases, especially the cancer genome atlas project (TCGA). Using molecular datasets and image analysis algorithms, we study whether tumor cell morphology or elements of the tumor micro-environment, such as tumor necrosis, angiogenesis, or inflammatory infiltrates, correlate with molecular profiles or clinical behavior in TCGA gliomas. We are using deep learning computational methods to investigate molecular data and imaging features that are associated with patient outcomes.