基于 PDGF 的胶质母细胞瘤数学模型揭示了最佳的放疗剂量方案。
Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules.
机构信息
Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Brain Tumor Center, Department of Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
出版信息
Cell. 2014 Jan 30;156(3):603-616. doi: 10.1016/j.cell.2013.12.029.
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:
胶质母细胞瘤（GBM）是最常见和恶性的原发性脑肿瘤，采用手术、化疗和放疗进行积极治疗。尽管进行了这种治疗，但复发是不可避免的，而且在过去 50 年中，生存率的提高微乎其微。最近的研究表明，GBM 表现出异质性和分化状态的不稳定性，以及这些状态对辐射的不同敏感性。在这里，我们采用了一种迭代的理论和实验相结合的策略，考虑到肿瘤细胞的异质性和动态获得的辐射抗性，以预测不同辐射方案的有效性。使用该模型，我们确定了两种输送方案，通过利用辐射抗性的动态不稳定性，预计将显著提高疗效。这些方案使小鼠的存活率得到了提高。我们的跨学科方法也可能适用于其他接受放疗治疗的人类癌症类型，因此可能为显著提高肿瘤学治疗的主要手段的有效性奠定基础。
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