Mathematical modeling of pdgf-driven glioblastoma reveals optimized radiation dosing schedules

Kevin Leder, Ken Pitter, Quincey Laplant, Dolores Hambardzumyan, Brian D. Ross, Timothy A. Chan, Eric C. Holland, Franziska Michor

Research output: Contribution to journalArticlepeer-review

142 Scopus citations


Glioblastomas (GBMs) are the most common and malignant primary brain tumors and are aggressively treated with surgery, chemotherapy, and radiotherapy. Despite this treatment, recurrence is inevitable and survival has improved minimally over the last 50 years. Recent studies have suggested that GBMs exhibit both heterogeneity and instability of differentiation states and varying sensitivities of these states to radiation. Here, we employed an iterative combined theoretical and experimental strategy that takes into account tumor cellular heterogeneity and dynamically acquired radioresistance to predict the effectiveness of different radiation schedules. Using this model, we identified two delivery schedules predicted to significantly improve efficacy by taking advantage of the dynamic instability of radioresistance. These schedules led to superior survival in mice. Our interdisciplinary approach may also be applicable to other human cancer types treated with radiotherapy and, hence, may lay the foundation for significantly increasing the effectiveness of a mainstay of oncologic therapy. PaperClip

Original languageEnglish (US)
Pages (from-to)603-616
Number of pages14
Issue number3
StatePublished - Jan 30 2014

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