Glioblastoma (GBM) is the most common type of malignant adult brain tumor. It is, unfortunately, also a very aggressive form of cancer. Treatment options are limited, and prognosis is poor, with a median survival of just one year.
There is great need for new, effective therapies for this disease. Developing these therapies requires accurate preclinical disease models, as our ability to study this and other primary human brain diseases in humans is very limited, for both practical and ethical reason. We need systems that recapitulate the biology of these diseases to identify disease vulnerabilities, devise novel therapies, and establish the efficacy of these therapies. Human trials can then be pursued after we are confident that the therapy is beneficial.
Dr. Ray Monnat in the University of Washington Departments of Laboratory Medicine and Pathology and Genome Sciences explained, “Making headway on complex human disease challenges such as glioblastoma (GBM), the most common malignant primary human brain tumor, requires disease models. The best models are disease-derived and representative, well-characterized, ideally fast and comparatively inexpensive, and sufficiently versatile to support many different disease-modeling protocols.”
He added, “Glioma stem cell (GSC) cultures meet many of these requirements, and when handled appropriately can capture and propagate key GBM molecular and cellular features.” GSC cultures are derived from GBM surgical resection tissue and enriched in stem cell populations, allowing them to continually repopulate and survive long-term in defined laboratory conditions. They are a great model for GBM because they maintain many of the key characteristics of the GBM tumors from which they originate.
In a recent study published in Scientific Reports, Dr. Monnat and colleagues at the University of Washington and across the Cancer Consortium developed and characterized four new GSC cultures, derived from four unrelated adult GBM patients. Characterization of these GSC cultures documented genetic, proteomic and phenotypic features that relate to therapy success or failure.