Molecularly Targeted Therapies for Astrocytomas

  • Ian F. Pollack


Astrocytomas constitute the largest group of central nervous system (CNS) neoplasms during childhood, and incorporate tumors with diverse histological features and biological characteristics (Pollack 1994). Whereas patients with superficial low-grade gliomas are often cured with surgery alone, patients with deep-seated or malignant gliomas continue to have a suboptimal prognosis, despite recent improvements in surgery and adjuvant therapy. The prognosis remains particularly grim for patients with malignant gliomas, such as glioblastoma multiforme (GBM) and anaplastic astrocytoma, which generally lead to death within several years after diagnosis (Finlay et al. 1995). The poor response of these tumors to conventional therapies reflects a resistance of malignant glioma cells to undergo apoptosis in response to DNA damage, which may result from mutations of tumor suppressor and cell cycle control genes and aberrant activation of growth and survival signaling pathways. Although molecular pathways leading to tumorigenesis have been clearly established for adult malignant gliomas (Louis 1997), the involvement of such pathways in pediatric glial neoplasia remains inferential. For example, because in adult gliomas, platelet-derived growth factor receptor (PDGFR) and epidermal growth factor receptor (EGFR) have been observed to play important roles in tumor proliferation, and have therefore constituted logical targets for molecularly targeted therapies, such approaches have concurrently been explored in pediatric gliomas. This chapter reviews the molecular features of childhood gliomas associated with disease progression and prognosis, and discusses recent molecularly targeted therapeutic strategies.


Epidermal Growth Factor Receptor Malignant Glioma Epidermal Growth Factor Receptor Gene Malignant Glioma Cell Recurrent Malignant Glioma 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by NIH grant NSP0140923.


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of NeurosurgeryChildren’s Hospital of PittsburghPittsburghUSA

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