Multiparameter computational modeling of tumor invasion.
Multiparameter computational modeling of tumor invasion.
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DOI：
10.1158/0008-5472.can-08-3834
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发表时间：
2009-05-15
期刊：
影响因子：
11.2
通讯作者：
Cristini V
中科院分区：
文献类型：
--
作者：
Bearer EL;Lowengrub JS;Frieboes HB;Chuang YL;Jin F;Wise SM;Ferrari M;Agus DB;Cristini V
肿瘤学中的临床结果预测是治疗选择的指导原则。大量的定性经验证据将疾病进展与肿瘤形态学、组织病理学、侵袭和相关分子现象联系起来。然而，在这一进程中，每个已知参数的定量贡献仍然难以捉摸。数学建模可以提供量化控制生长、预后和治疗结果的变量之间的联系的能力。我们应用生物学建立的多尺度数学模型来识别和量化与临床和形态学表型相关的肿瘤生物学和分子特性，并证明肿瘤生长和侵袭是受生物物理学规律支配的可预测过程，并受表型、基因型和微环境参数异质性的调节。这种异质性驱使更具侵袭性的克隆在中央肿瘤块内和中央肿瘤块外的细胞基质梯度上迁移和增殖，同时通常还诱导细胞粘附的丧失。该模型预测，该过程触发了总体形态学不稳定性，导致肿瘤通过单个细胞、细胞链、链或分离的簇浸润到相邻组织中，产生所见的典型形态学模式，例如，在多形性胶质母细胞瘤的组织病理学中。该模型进一步预测，这些不同的浸润形态对应于由异质性调节的肿瘤进展的不同阶段。通过量化肿瘤边界形态和侵袭性表型之间的联系，这项工作提供了一个定量的工具，肿瘤进展和诊断/预后应用的研究。这建立了一个框架，用于监测系统扰动，以制定治疗策略和与预后的临床结果的相关性。
Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability for quantifying the connection between variables governing growth, prognosis and treatment outcome. We apply a biologically founded, multi-scale, mathematical model to identify and quantify tumor biologic and molecular properties relating to clinical and morphological phenotype and to demonstrate that tumor growth and invasion are predictable processes governed by biophysical laws, and regulated by heterogeneity in phenotypic, genotypic, and microenvironmental parameters. This heterogeneity drives migration and proliferation of more aggressive clones up cell substrate gradients within and beyond the central tumor mass, while often also inducing loss of cell adhesion. The model predicts that this process triggers a gross morphologic instability that leads to tumor invasion via individual cells, cell chains, strands or detached clusters infiltrating into adjacent tissue producing the typical morphologic patterns seen, e.g., in the histopathology of glioblastoma multiforme. The model further predicts that these different morphologies of infiltration correspond to different stages of tumor progression regulated by heterogeneity. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis.