Predictive Modeling of Drug Response in Non-Hodgkin’s Lymphoma

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.

本研究将数学建模与活体小鼠实验相结合，以量化固有细胞层面与组织尺度生理因素在化疗耐药性中的相对作用——这类作用仅通过单一实验手段难以明确阐释。我们分别开展细胞培养实验与荷瘤小鼠实验，所用细胞系分别为药物敏感型（Eµ-myc/Arf-/-）与药物耐药型（Eµ-myc/p53-/-）淋巴瘤细胞系，用于校准并验证一个机制性数学模型。为该模型提供输入的参数包括肿瘤药物转运特性，例如血液体积分数、血管平均几何半径、药物扩散渗透距离，以及细胞水平的药物应答数据。模型结果显示，基于上述输入参数，可可靠预测小鼠体内的药物应答水平——该水平以肿瘤死亡体积占比表征。由此，基于细胞层面与组织尺度生理双维度贡献的淋巴瘤化疗疗效预测量化方法的原理验证得以确立。我们进一步证实，若已知某特定癌细胞系在化疗条件下的体外（in vitro）细胞毒性应答数据，该模型即可预测其体内（in vivo）治疗疗效。最终研究确定，组织血液体积分数是模型中最具敏感性的参数，同时也是化疗耐药性的核心影响因素。