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一种结合纵向肿瘤大小数据和组织学生物标志物的小鼠血管肿瘤生长模型。

A model of vascular tumour growth in mice combining longitudinal tumour size data with histological biomarkers.

机构信息

INRIA, Project-team NUMED, Ecole Normale Supérieure de Lyon, 46 allée d'Italie, 69007 Lyon Cedex 07, France.

出版信息

Eur J Cancer. 2011 Feb;47(3):479-90. doi: 10.1016/j.ejca.2010.10.003. Epub 2010 Nov 10.

Abstract

Optimising the delivery of antiangiogenic drugs requires the development of drug-disease models of vascular tumour growth that incorporate histological data indicative of cytostatic action. In this study, we formulated a model to analyse the dynamics of tumour progression in nude mice xenografted with HT29 or HCT116 colorectal cancer cells. In 30 mice, tumour size was periodically measured, and percentages of hypoxic and necrotic tissue were assessed using immunohistochemistry techniques on tumour samples after euthanasia. The simultaneous analysis of histological data together with longitudinal tumour size data prompted the development of a semi-mechanistic model integrating random effects of parameters. In this model, the peripheral non-hypoxic tissue proliferates according to a generalised-logistic equation where the maximal tumour size is represented by a variable called 'carrying capacity'. The ratio of the whole tumour size to the carrying capacity was used to define the hypoxic stress. As this stress increases, non-hypoxic tissue turns hypoxic. Hypoxic tissue does not stop proliferating, but hypoxia constitutes a transient stage before the tissue becomes necrotic. As the tumour grows, the carrying capacity increases owing to the process of angiogenesis. The model is shown to correctly predict tumour growth dynamics as well as percentages of necrotic and hypoxic tissues within the tumour. We show how the model can be used as a theoretical tool to investigate the effects of antiangiogenic treatments on tumour growth. This model provides a tool to analyse tumour size data in combination with histological biomarkers such as the percentages of hypoxic and necrotic tissue and is shown to be useful for gaining insight into the effects of antiangiogenic drugs on tumour growth and composition.

摘要

优化抗血管生成药物的递送需要开发将血管肿瘤生长的组织学数据纳入细胞抑制作用的药物-疾病模型。在这项研究中,我们构建了一个模型来分析裸鼠中 HT29 或 HCT116 结直肠癌细胞异种移植瘤的肿瘤进展动力学。在 30 只小鼠中,定期测量肿瘤大小,并在安乐死后通过免疫组织化学技术评估肿瘤样本中缺氧和坏死组织的百分比。将组织学数据与纵向肿瘤大小数据的同时分析促使我们开发了一个整合参数随机效应的半机械模型。在该模型中,周边非缺氧组织根据广义逻辑方程进行增殖,其中最大肿瘤大小由一个称为“承载能力”的变量表示。整个肿瘤大小与承载能力的比值用于定义缺氧应激。随着这种应激的增加,非缺氧组织会变得缺氧。缺氧组织不会停止增殖,但缺氧是组织坏死前的一个短暂阶段。随着肿瘤的生长,由于血管生成过程,承载能力增加。该模型被证明可以正确预测肿瘤生长动力学以及肿瘤内坏死和缺氧组织的百分比。我们展示了如何将该模型用作理论工具来研究抗血管生成治疗对肿瘤生长的影响。该模型提供了一种工具,可以结合组织学生物标志物(如缺氧和坏死组织的百分比)分析肿瘤大小数据,并且被证明对于深入了解抗血管生成药物对肿瘤生长和组成的影响很有用。

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