Sanga Sandeep, Frieboes Hermann B, Zheng Xiaoming, Gatenby Robert, Bearer Elaine L, Cristini Vittorio
Department of Biomedical Engineering, University of Texas, Austin, TX 78712, USA.
Neuroimage. 2007;37 Suppl 1(Suppl 1):S120-34. doi: 10.1016/j.neuroimage.2007.05.043. Epub 2007 Jun 7.
Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.
经验证据和理论研究表明，表型，即细胞和分子尺度的动态变化，包括由于微环境因素导致的增殖率和粘附性，以及控制肿瘤生长和侵袭性的基因表达，也决定了肿瘤整体尺度的形态。使用传统的临床和实验方法及可观测指标，难以量化这些联系对疾病进展和预后的相对影响。因此，无法通过手术切除和化疗对高度恶性和侵袭性癌症（如胶质母细胞瘤）进行成功的个体化治疗，且总体预后较差。需要一种确定性的、可量化的方法来理解表型与肿瘤形态之间的联系。在此，我们批判性地评估了近期为实现这种理解而进行的计算建模工作（例如连续体模型、离散模型和细胞自动机模型）的优缺点。基于这一评估，我们回顾了一种多尺度的，即从分子尺度到肿瘤整体尺度的，基于质量守恒和其他物理定律（如反应扩散系统中所采用的）的数学和计算“第一原理”方法。模型变量描述了肿瘤行为的已知特征，跨尺度的参数和函数关系则来源于体外、体内和离体生物学研究。我们回顾了这种方法的可行性，一旦将其与肿瘤成像以及肿瘤活检或细胞培养数据相结合，应该能够通过量化潜在动态变化与形态特征之间的关系来预测肿瘤生长和治疗结果。特别是，对该数学模型的形态稳定性分析表明，肿瘤 - 宿主界面处的肿瘤细胞模式受细胞增殖、粘附和其他表型特征的调节：肿瘤边界的组织病理学信息可输入到数学模型中，并用作表型诊断工具，以预测肿瘤细胞对周围组织的集体和个体侵袭。这种方法还提供了一种确定性地测试新型和假设性治疗策略对肿瘤行为影响的手段。
