A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategies
A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategies
复制标题
乳腺癌表型异质性的数学模型及其对治疗策略的启示
DOI：
10.1098/rsif.2021.0803
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发表时间：
2022-01-26
影响因子：
3.9
通讯作者：
Thirumalai, D.
中科院分区：
文献类型：
--
作者：
Li, Xin;Thirumalai, D.
几乎所有癌症患者都不可避免地对靶向治疗产生耐药性。肿瘤内异质性是耐药性的主要原因。定量解释实验的数学模型对于理解肿瘤内异质性的起源非常有用，然后可以用来探索有效治疗的方案。在这里，我们开发了一个数学模型来研究乳腺癌的肿瘤内异质性，利用观察，HER 2+和HER 2-细胞可以对称或不对称分裂。我们的预测细胞分数的演变与单细胞实验定量一致。值得注意的是，从单个HER 2-细胞中出现的HER 2+细胞的殖民地大小（或反之亦然），其发生在约4个细胞的倍增中，也与实验结果一致，而无需调整模型中的任何参数。该理论解释了不同治疗方案下乳腺肿瘤反应的实验数据。然后，我们使用该模型来预测，不仅可以操纵两种药物的顺序，还可以操纵每种药物的治疗时间和肿瘤细胞的可塑性，以提高治疗效果。当数学模型与患者数据相结合时，可以很容易地探索广泛的参数，这可能为设计有效的治疗方法提供见解。
Inevitably, almost all cancer patients develop resistance to targeted therapy. Intratumour heterogeneity is a major cause of drug resistance. Mathematical models that explain experiments quantitatively are useful in understanding the origin of intratumour heterogeneity, which then could be used to explore scenarios for efficacious therapy. Here, we develop a mathematical model to investigate intratumour heterogeneity in breast cancer by exploiting the observation that HER2+ and HER2- cells could divide symmetrically or asymmetrically. Our predictions for the evolution of cell fractions are in quantitative agreement with single-cell experiments. Remarkably, the colony size of HER2+ cells emerging from a single HER2- cell (or vice versa), which occurs in about four cell doublings, also agrees with experimental results, without tweaking any parameter in the model. The theory explains experimental data on the responses of breast tumours under different treatment protocols. We then used the model to predict that, not only the order of two drugs, but also the treatment period for each drug and the tumour cell plasticity could be manipulated to improve the treatment efficacy. Mathematical models, when integrated with data on patients, make possible exploration of a broad range of parameters readily, which might provide insights in devising effective therapies.