Modelling Circulating Tumour Cells for Personalised Survival Prediction in Metastatic Breast Cancer

Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct) through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET). In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy.
We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.

导管癌（Ductal carcinoma）是女性最常见的癌症之一，其主要致死原因为转移灶形成。转移的发生源于癌细胞从原发肿瘤位点（乳腺导管）迁移，经血管外渗后启动转移过程。本研究提出一种多隔室模型，可模拟乳腺导管、循环系统及骨骼内肿瘤细胞的动态变化。通过分支过程模型，我们阐释了转移性乳腺癌相关的四大核心标志物（EPCAM、CD47、CD44与MET）与患者生存时间之间的关联。该模型特别纳入循环肿瘤细胞的基因表达谱，以预测个体化生存概率。此外，为分析治疗诱导的动态变化，我们还纳入了双膦酸盐类药物的给药场景——此类药物可减少循环肿瘤细胞的生成及其在血管内的存活能力。

我们通过量化侵袭骨组织所需的细胞驱动突变数目，分析了循环肿瘤细胞对疾病进展的影响。本模型可用于设计干预方案，通过调整循环肿瘤细胞种群以改变患者的个体化生存概率，且该模型可推广至其他癌症转移的动态研究中。