[论文解读] Investigating the development of chemotherapeutic drug resistance in cancer: A multiscale computational study

Gibin Powathil, Mark A. J. Chaplain+1 more|arXiv (Cornell University)|Jul 3, 2014

Mathematical Biology Tumor Growth参考文献 58被引用 14

一句话总结

这项多尺度计算研究采用混合个体基础模型，探究肿瘤内异质性及慢周期癌细胞如何驱动细胞周期介导的化疗耐药性。该模型表明，相位特异性药物会避开非靶向的细胞周期相位，导致耐药性慢周期亚群富集；同时显示，针对所有相位的序贯多剂量治疗方案能显著提高细胞杀灭率并减少耐药性出现。

ABSTRACT

Chemotherapy is one of the most important therapeutic options used to treat human cancers, either alone or in combination with radiation therapy and surgery. Recent studies have indicated that intra-tumoural heterogeneity has a significant role in driving resistance to chemotherapy in many human malignancies. Multiple factors including the internal cell-cycle dynamics and the external microenvironement contribute to the intra-tumoural heterogeneity. In this paper we present a hybrid, multiscale, individual-based mathematical model, incorporating internal cell-cycle dynamics and changes in oxygen concentration, to study the effects of delivery of several different chemotherapeutic drugs on the heterogeneous subpopulations of cancer cells with varying cell-cycle dynamics. The computational simulation results from the multiscale model are in good agreement with available experimental data and support the hypothesis that slow-cycling sub-populations of tumour cells within a growing tumour mass can induce drug resistance to chemotherapy and thus the use of conventional chemotherapy may actually result in the emergence of dominant, therapy-resistant, slow-cycling subpopulations of tumour cells. Our results indicate that the appearance of this chemotherapeutic resistance is mainly due to the inability of the administered drug to target all cancer cells irrespective of the stage in the cell-cycle they are in i.e. most chemotherapeutic drugs target cells in a particular phase/phases of the cell-cycle, and hence always spare some cancer cells that are not in the targeted cell-cycle phase/phases. The results also suggest that this cell-cycle-mediated drug resistance may be overcome by using multiple doses of cell-cycle, phase-specific chemotherapy that targets cells in all phases and its appropriate sequencing and scheduling.

研究动机与目标

• 探究肿瘤内异质性及慢周期亚群在介导化疗耐药性中的作用。
• 理解细胞周期特异性药物因相位依赖性靶向而无法彻底清除所有癌细胞的原因。
• 评估药物给药方案与序列对肿瘤细胞杀灭率及耐药性富集的影响。
• 开发一个整合细胞内周期动力学与细胞外微环境因素（如氧浓度）的预测性多尺度模型。
• 提供一个计算框架，用于设计克服内在耐药机制的个体化、自适应化疗方案。

提出的方法

• 基于Compucell3D框架开发了混合、多尺度、个体基础的数学模型，用于模拟肿瘤生长与药物反应。
• 该模型整合了详细的细胞周期内动力学，包含四个阶段（G1、S、G2、M），并考虑了细胞周期蛋白依赖性激酶与细胞周期抑制因子的调控。
• 通过引入细胞内突变，生成慢周期亚群，模拟静止期或G0样状态，具有更长的周期时间。
• 模拟了氧浓度梯度，以反映肿瘤微环境对细胞周期进展与药物疗效的影响。
• 药物效应被建模为相位特异性细胞毒性：G1相药物靶向处于G1期的细胞，S-G2-M药物靶向处于S、G2和M期的细胞。
• 模拟了多种给药方案，以评估给药序列与时间安排对总体细胞杀灭率及耐药性发展的影响。

实验结果

研究问题

• RQ1 肿瘤内异质性，特别是慢周期亚群，如何促进化疗耐药性？
• RQ2 由于细胞周期相位的差异，相位特异性化疗药物在多大程度上无法彻底清除所有癌细胞？
• RQ3 如何实现多款相位特异性药物的最佳序列与给药安排，以最大化肿瘤细胞杀灭率并最小化耐药性富集？
• RQ4 氧浓度梯度与微环境因素如何与细胞周期动力学相互作用，进而影响药物反应？
• RQ5 多尺度计算模型能否准确再现异质性肿瘤中耐药性出现的实验观察？

主要发现

• 慢周期亚群，特别是富集于G2/M相的细胞，由于其非增殖状态，对相位特异性化疗药物表现出内在耐药性。
• 仅靶向特定细胞周期相位的传统化疗方案无法清除慢周期细胞，导致治疗后其富集。
• 模拟结果显示，两剂G1相特异性药物可实现最高细胞杀灭率，而两剂S-G2-M相药物则导致最低杀灭率。
• G1相与S-G2-M相特异性药物的联合使用可实现最有效的细胞杀灭，证实全面靶向所有相位优于单一相位靶向。
• 该模型的预测与实验数据在定性上一致，显示慢周期亚群中G2/M相的占比增加。
• 结果支持需要采用个体化、自适应的治疗方案，综合考虑肿瘤异质性与细胞周期动力学，以改善临床疗效。