Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules.
Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules.
复制标题
DOI：
10.1371/journal.pcbi.1009104
复制
发表时间：
2022-03
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
虽然获得性耐药性被认为是治疗许多类型癌症的关键挑战，但暴露后药物敏感性变化的动力学特征很差。大多数化疗方案要求定期重复给药，如果药物敏感性在类似的时间尺度上变化，则可以优化治疗间隔以改善治疗性能。理论工作表明，这种最佳的时间表是存在的，但实验证实已被阻碍的困难去卷积的同时发生在癌细胞群的死亡，适应和再生的过程。在这里，我们提出了一种方法，优化药物的时间表在体外通过迭代应用实验校准模型，并证明其能力，以表征动态变化的敏感性化疗阿霉素在三个乳腺癌细胞系进行治疗时间表不同的浓度，脉冲治疗之间的间隔，和顺序脉冲治疗的数量。通过自动成像纵向监测细胞群600-800小时，并且该数据用于校准癌症生长模型家族，每个模型由从表征抗性和敏感亚群的双指数模型导出的常微分方程系统组成。我们确定了一个模型，将一段时间的生长停滞在存活的细胞和延迟的化学敏感细胞的死亡，优于原来的双指数增长模型在赤池信息准则为基础的模型选择，并使用校准模型来量化每个药物的性能时间表。我们发现，治疗间隔是决定序贯给药方案性能的关键变量，并确定了每种细胞系的最佳再治疗时间，可延长再生长时间40%-239%，表明多柔比星暴露后化学敏感性变化的时间尺度允许通过改变治疗间隔来优化药物方案。获得性耐药性是癌症治疗失败的常见原因。多剂量化疗疗程的安排可能会影响获得性化疗耐药性的动态变化，并且药物方案优化可能会增加特定化疗药物对特定患者的有效性持续时间。在这里，我们提出了一种方法，实验优化体外药物方案，通过迭代轮的实验和计算分析，并证明该方法的能力，以提高性能的阿霉素治疗三个乳腺癌细胞系。具体而言，我们发现，药物暴露之间的间隔可以优化，同时保持药物浓度和治疗次数不变，这表明这可能是一个关键变量，探索在未来的药物方案优化工作。我们进一步使用这种方法的模型校准和选择过程中提取信息的阿霉素反应的基础生物学，并发现，细胞死亡和再生的延迟的掺入是必要的细胞生长数据的准确参数化。
While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval. Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.
登录
查看更多内容
HER2 neu 异质性的临床重要性。
影响因子：
0.8
作者：
Davila, Enrique;Amazon, Kip
通讯作者：
Amazon, Kip
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
3.3
作者：
Flusberg DA;Roux J;Spencer SL;Sorger PK
通讯作者：
Sorger PK
影响因子：
16.6
作者：
Brady-Nicholls, Renee;Nagy, John D.;Enderling, Heiko
通讯作者：
Enderling, Heiko
影响因子：
45.3
作者：
Bonadonna, G;Zambetti, M;Valagussa, P
通讯作者：
Valagussa, P