Use of CTEP portfolio compounds to counteract phenotype conversion in GBM

总结/摘要 尽管在基础科学、临床试验、药物开发和技术进步方面做出了巨大努力， 手术和放射肿瘤学，胶质母细胞瘤仍然是不可治愈的，总生存率的改善 边缘化。虽然放射治疗仍然是胶质母细胞瘤最有效的治疗选择之一，但它不能 随着时间的推移控制疾病。这表明，迫切需要新的联合疗法， 改善患有这种疾病患者的放射治疗结果。本文中概述的研究 我们的建议是基于一个假设，这是由我们广泛的初步数据和严格的出版支持。 文献中的数据。总的假设是，基于生物标志物的药物选择预测协同致死 GIC和胶质母细胞瘤大块肿瘤细胞群的联合治疗， GBM表型转换，并允许放射治疗的个性化优化。这三个目标 研究将使用一种创新的工具来追踪GIC及其后代， 利用UCLA和NIH/NCI CTEP药物组合的独特资源和专业知识。 目标1将确定NCI CTEP组合中干扰辐射诱导表型的化合物 在胶质母细胞瘤中的转化，并开发预测协同致死率的生物标志物谱， 辐射目标2中的研究将优化体内联合治疗。最后，目标3，将使用患者化身 在胶质母细胞瘤的PDX模型中验证基于生物标志物的药物选择的研究。

SUMMARY/ABSTRACT Despite a tremendous effort in basic science, clinical trials, drug development, and technical advances in surgery and radiation oncology, glioblastoma remains incurable and improvements in overall survival have been marginal. While radiotherapy is still one of the most effective treatment options for glioblastoma, it cannot control the disease over time. This suggests that novel combination therapies are desperately needed to improve radiation treatment outcome for patients suffering from this disease. The studies outlined in this proposal are based on a hypothesis that is backed by our extensive preliminary data and rigorous published data in the literature. The overall hypothesis is that biomarker-based drug selection predicts synergistic lethality of combination therapies in GICs and glioblastoma bulk tumor cell populations, prevents radiation-induced GBM phenotype conversion and allows for individualized optimization of radiotherapy. The three aims of this study will address this aspect of glioma biology using an innovative tool to track GICs and their progeny, while leveraging the unique resources and expertise available at UCLA and the NIH/NCI CTEP portfolio of drugs. Aim 1 will identify compounds in the NCI CTEP portfolio that interfere with radiation-induced phenotype conversion in glioblastoma and develop biomarker profiles predictive of synergistic lethality in combination with radiation. Studies in Aim 2 will optimize combination therapies in vivo. Finally, Aim 3, will use patient avatar studies to validate biomarker-based drug selection in PDX models of glioblastoma.