Oestrogen receptor (ER)-positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either oestrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and oestrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of oestrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.

中文翻译：

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乳腺癌细胞响应内分泌治疗和 Cdk4/6 抑制的数学模型

雌激素受体 (ER) 阳性乳腺癌对临床上使用的许多靶向治疗有反应。不幸的是，任何靶向治疗的持续应用通常会导致对治疗的抵抗。我们的最终目标是使用数学模型来优化交替疗法，不仅可以减少增殖，还可以避免耐药性。为此，我们在 7 天的时间内测量了 ER+ MCF-7 乳腺癌细胞中关键蛋白和增殖的水平。治疗包括内分泌治疗，或者是雌激素剥夺，它模仿芳香酶抑制剂的作用，或者是氟维司群，一种 ER 降解剂。这些数据用于校准基于 ER 信号和细胞周期之间关键相互作用的数学模型。我们表明，校准模型能够预测氟维司群和雌激素剥夺的联合治疗。此外，我们表明我们可以通过仅测量两种关键蛋白质 cMyc 和过度磷酸化的 RB1 并仅调整与药物相关的参数来将新药 palbociclib 添加到模型中。然后，该模型能够预测雌激素剥夺和 palbociclib 的联合治疗。我们展示了该模型探索通过不依赖任何一种疗法来限制增殖和阻止耐药性的方案的潜力。然后，该模型能够预测雌激素剥夺和 palbociclib 的联合治疗。我们展示了该模型探索通过不依赖任何一种疗法来限制增殖和阻止耐药性的方案的潜力。然后，该模型能够预测雌激素剥夺和 palbociclib 的联合治疗。我们展示了该模型探索通过不依赖任何一种疗法来限制增殖和阻止耐药性的方案的潜力。