Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
Proc Natl Acad Sci U S A. 2013 Aug 27;110(35):14266-71. doi: 10.1073/pnas.1300619110. Epub 2013 Aug 12.
Physical properties of the microenvironment influence penetration of drugs into tumors. Here, we develop a mathematical model to predict the outcome of chemotherapy based on the physical laws of diffusion. The most important parameters in the model are the volume fraction occupied by tumor blood vessels and their average diameter. Drug delivery to cells, and kill thereof, are mediated by these microenvironmental properties and affected by the diffusion penetration distance after extravasation. To calculate parameter values we fit the model to histopathology measurements of the fraction of tumor killed after chemotherapy in human patients with colorectal cancer metastatic to liver (coefficient of determination R(2) = 0.94). To validate the model in a different tumor type, we input patient-specific model parameter values from glioblastoma; the model successfully predicts extent of tumor kill after chemotherapy (R(2) = 0.7-0.91). Toward prospective clinical translation, we calculate blood volume fraction parameter values from in vivo contrast-enhanced computed tomography imaging from a separate cohort of patients with colorectal cancer metastatic to liver, and demonstrate accurate model predictions of individual patient responses (average relative error = 15%). Here, patient-specific data from either in vivo imaging or histopathology drives output of the model's formulas. Values obtained from standard clinical diagnostic measurements for each individual are entered into the model, producing accurate predictions of tumor kill after chemotherapy. Clinical translation will enable the rational design of individualized treatment strategies such as amount, frequency, and delivery platform of drug and the need for ancillary non-drug-based treatment.
肿瘤微环境的物理特性会影响药物渗透到肿瘤中的程度。在这里，我们开发了一个数学模型，根据扩散的物理定律预测化疗的结果。该模型中最重要的参数是肿瘤血管所占的体积分数及其平均直径。药物输送到细胞并杀死细胞是由这些微环境特性介导的，并且受到血管外渗后扩散渗透距离的影响。为了计算参数值，我们将模型拟合到人类结直肠癌肝转移患者化疗后肿瘤杀伤的组织病理学测量值（决定系数 R(2) = 0.94）。为了在不同的肿瘤类型中验证模型，我们从胶质母细胞瘤输入了患者特异性模型参数值；该模型成功预测了化疗后肿瘤杀伤的程度（R(2) = 0.7-0.91）。为了进行前瞻性临床转化，我们从另一组结直肠癌肝转移患者的活体对比增强计算机断层扫描成像中计算了血容量分数参数值，并证明了该模型对个体患者反应的准确预测（平均相对误差= 15%）。在这里，来自活体成像或组织病理学的患者特异性数据驱动模型公式的输出。将从每个个体的标准临床诊断测量中获得的值输入到模型中，可以准确预测化疗后的肿瘤杀伤程度。临床转化将能够合理设计个体化治疗策略，如药物的剂量、频率和输送平台，以及辅助非药物治疗的需求。
