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建立循环肿瘤细胞模型用于转移性乳腺癌的个性化生存预测。

Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer.

作者信息

Ascolani Gianluca, Occhipinti Annalisa, Liò Pietro

机构信息

University of Cambridge, Computer Laboratory, Cambridge, United Kingdom.

出版信息

PLoS Comput Biol. 2015 May 15;11(5):e1004199. doi: 10.1371/journal.pcbi.1004199. eCollection 2015 May.

Abstract

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.

摘要

导管癌是女性中最常见的癌症之一,主要死因是转移灶的形成。转移灶的形成是由癌细胞从原发肿瘤部位(乳腺导管)通过血管迁移并外渗引发转移所致。在此,我们提出一个多房室模型,该模型模拟了乳腺导管、循环系统和骨骼中肿瘤细胞的动态变化。通过一个分支过程模型,我们描述了生存时间与主要参与转移性乳腺癌的四种标志物(EPCAM、CD47、CD44和MET)之间的关系。特别是,该模型考虑了循环肿瘤细胞的基因表达谱来预测个性化的生存概率。我们还纳入了双膦酸盐类药物的给药情况,这类药物可减少循环肿瘤细胞的形成及其在血管中的存活,以便分析治疗引起的动态变化。我们分析了循环肿瘤细胞对疾病进展的影响,提供了侵袭骨组织所需的细胞驱动突变的定量测量。我们的模型能够设计干预方案,通过改变循环肿瘤细胞群体来改变患者特异性的生存概率,并且可以扩展到其他癌症转移动态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/795d/4433130/c4ce4bf1f00f/pcbi.1004199.g001.jpg

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