Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy
Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy
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DOI：
10.1073/pnas.1703355114
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发表时间：
2017-08-01
影响因子：
11.1
通讯作者：
Gevertz, Jana L.
中科院分区：
文献类型：
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作者：
Barish, Syndi;Ochs, Michael F.;Gevertz, Jana L.
癌症是一种高度异质性的疾病，表现出空间和时间上的差异，这给设计强有力的治疗方法带来了挑战。在这里，我们提出了VEPART(虚拟扩展人口分析治疗的稳健性)技术作为一个平台，整合了实验数据、数学建模和统计分析，以确定稳健的最佳治疗方案。VEPART从样本总体的时间历程实验数据开始，以及适合从该样本总体聚合数据的数学模型。利用非参数统计，样本总体被放大并用于创建大量的虚拟总体。在VEPART的最后一步，通过识别和分析每个虚拟人群的最佳治疗方案(可能仅限于一组临床可实现的方案)来评估稳健性。作为概念的证明，我们应用VEPART方法研究了免疫刺激溶瘤病毒和树突状细胞疫苗治疗黑色素瘤小鼠模型的治疗反应的稳健性。我们的分析(I)表明，实验使用的治疗方案的每个调度变量都是脆弱的(不稳健的)，并且(Ii)发现了剂量空间的替代区域(较低的溶瘤病毒剂量，较高的树突状细胞剂量)，对于该区域存在稳健的最优方案。
Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.