[论文解读] The Impact of Competition Between Cancer Cells and Healthy Cells on Optimal Drug Delivery

Heyrim Cho, Doron Levy|arXiv (Cornell University)|Jun 19, 2018

Mathematical Biology Tumor Growth参考文献 102被引用 9

一句话总结

本研究构建了一个整合癌细胞与健康细胞之间细胞竞争及对细胞毒性药物和靶向药物持续耐药性的数学模型。结果表明，在轻度竞争环境下，联合治疗优于单药治疗；而在高度竞争环境中，单独靶向治疗更有效，最优治疗效果取决于治疗前的耐药水平和治疗时机。

ABSTRACT

Cell competition is recognized to be instrumental to the dynamics and structure of the tumor-host interface in invasive cancers. In mild competition scenarios, the healthy tissue and cancer cells can coexist. When the competition is aggressive, competitive cells, the so called super-competitors, expand by killing other cells. Novel cytotoxic drugs and molecularly targeted drugs are commonly administered as part of cancer therapy. Both types of drugs are susceptible to various mechanisms of drug resistance, obstructing or preventing a successful outcome. In this paper, we develop a cancer growth model that accounts for the competition between cancer cells and healthy cells. The model incorporates resistance to both cytotoxic and targeted drugs. In both cases, the level of drug resistance is assumed to be a continuous variable ranging from fully-sensitive to fully-resistant. Using our model we demonstrate that when the competition is moderate, therapies using both drugs are more effective compared with single drug therapies. However, when cancer cells are highly competitive, targeted drugs become more effective. In this case, therapies that are initiated with a targeted drug and are exposed to it for a sufficiently long time are shown to have better outcomes. The results of the study stress the importance of adjusting the therapy to the pre-treatment resistance levels. We conclude with a study of the spatiotemporal propagation of drug resistance in a competitive setting, verifying that the same conclusions hold in the spatially heterogeneous case.

研究动机与目标

• 研究癌细胞与健康细胞之间的竞争如何影响化疗和靶向治疗的疗效。
• 将药物耐药性建模为连续性状而非二元状态，以更贴近生物学现实。
• 在不同竞争强度下，确定最优的药物给药策略——单药、交替或联合治疗。
• 评估治疗时机和耐药性动态对肿瘤复发和治疗成功的影响。
• 在空间异质环境中验证结果，以反映体内肿瘤的复杂性。

提出的方法

• 建立一组偏微分方程，用于描述包含竞争项的肿瘤细胞与健康细胞动态。
• 为细胞毒性药物和靶向药物引入连续耐药变量，范围从完全敏感到完全耐药。
• 采用空间扩展模型，模拟耐药性和肿瘤生长的时空传播。
• 在不同竞争水平（a = 0.2 与 a = 0.8）下，比较单药、交替和联合治疗的治疗效果。
• 应用最优控制理论概念，识别最小化癌细胞负荷的切换时间。
• 在具有不规则药物分布的二维空间模型中验证结果，以评估对异质性的鲁棒性。

实验结果

研究问题

• RQ1 癌细胞与健康细胞之间的竞争水平如何影响细胞毒性药物和靶向药物治疗的有效性？
• RQ2 在何种条件下联合治疗优于单药治疗以抑制肿瘤生长？
• RQ3 药物切换时机是否显著影响治疗结果，特别是在高度竞争环境中？
• RQ4 与二元耐药模型相比，连续耐药建模在预测治疗成功方面有何差异？
• RQ5 同质模型中的结论是否在空间异质性肿瘤微环境中依然成立？

主要发现

• 在轻度竞争（a = 0.2）下，联合治疗和交替治疗显著延迟复发，相较于单药治疗，其中交替治疗表现出更优的抑制效果。
• 在强烈竞争（a = 0.8）下，单独靶向治疗优于细胞毒性治疗，且延长暴露时间可改善疗效。
• 无论竞争水平如何，单药治疗均因预先存在的耐药性而持续导致强烈复发。
• 线性耐药模型对切换时间更敏感，次优时机可能导致治疗效果劣于单药治疗。
• 在治疗期间采用短周期交替治疗在整体上最有效抑制肿瘤负荷，尤其在低竞争环境中。
• 空间模拟结果证实，关键发现——在轻度竞争中联合/交替治疗占优，而在高度竞争中靶向治疗更优——在异质环境中依然成立。