Raw Data from the Experiments: Western Blot Intensities and Coulter Counter Counts from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

Estrogen 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 estrogen 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 estrogen 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 estrogen 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.

雌激素受体（ER）阳性乳腺癌对多种临床应用的靶向治疗方案具有响应性。然而，持续使用任意一种靶向治疗往往会导致肿瘤对该疗法产生耐药性。本研究的最终目标是通过数学建模优化交替治疗方案，该方案既能抑制肿瘤增殖，又能延缓耐药性的产生。为此，我们在为期7天的时间进程中，检测了ER阳性MCF-7乳腺癌细胞中关键蛋白的表达水平与细胞增殖情况。本次实验采用的内分泌治疗方案包括两种：一是雌激素剥夺法，该方法模拟芳香化酶抑制剂的作用效果；二是氟维司群（fulvestrant），一种ER降解剂。我们利用这些实验数据对基于ER信号通路与细胞周期关键相互作用的数学模型进行了校准。结果表明，经校准后的模型能够预测氟维司群与雌激素剥夺联合治疗的效果。此外，我们证实仅需通过检测cMyc与高磷酸化RB1这两种关键蛋白的表达水平，并仅调整与该药物相关的模型参数，即可将帕博西尼（palbociclib）这一新药纳入模型。校准后的模型便可预测雌激素剥夺与帕博西尼联合治疗的效果。本研究还展示了该模型在探索治疗方案方面的潜力：通过不依赖单一疗法，即可实现抑制肿瘤增殖并延缓耐药性的目标。