Epidermal Growth Factor Receptor as a Therapeutic Target in Glioblastoma

Abstract

Glioblastoma represents one of the most challenging problems in neurooncology. Among key elements driving its behavior is the transmembrane epidermal growth factor receptor family, with the first member epidermal growth factor receptor (EGFR) centered in most studies. Engagement of the extracellular domain with a ligand activates the intracellular tyrosine kinase (TK) domain of EGFR, leading to autophosphorylation and signal transduction that controls proliferation, gene transcription, and apoptosis. Oncogenic missense mutations, deletions, and insertions in the EGFR gene are preferentially located in the extracellular domain in glioblastoma and cause constitutive activation of the receptor. The mutant EGFR may also transactivate other cell surface molecules, such as additional members of the EGFR family and the platelet-derived growth factor receptor, which ignite signaling cascades that synergize with the EGFR-initiated cascade. Because of the cell surface location and increased expression of the receptor along with its important biological function, EGFR has triggered much effort for designing targeted therapy. These approaches include TK inhibition, monoclonal antibody, vaccine, and RNA-based downregulation of the receptor. Treatment success requires that the drug penetrates the blood-brain barrier and has low systemic toxicity but high selectivity for the tumor. While the blockade of EGFR-dependent processes resulted in experimental and clinical treatment success, cells capable of using alternative signaling ultimately escape this strategy. A combination of interventions targeting tumor-specific cell surface regulators along with convergent downstream signaling pathways will likely enhance efficacy. Studies on EGFR in glioblastoma have revealed much information about the complexity of gliomagenesis and also facilitated the development of strategies for targeting drivers of tumor growth and combination therapies with increasing complexity.