Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments.
Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments.
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DOI：
10.1371/journal.pcbi.1002314
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发表时间：
2011-12
影响因子：
4.3
通讯作者：
Torquato S
中科院分区：
文献类型：
--
作者：
Jiao Y;Torquato S
了解肿瘤的侵袭和转移对于肿瘤的基础研究和临床实践都是至关重要的。体外实验已经证实，恶性肿瘤的侵袭性生长的特征在于由源自原发性肿瘤块的肿瘤细胞链组成的树突状侵袭分支。以前的肿瘤模拟的优势集中在非侵入性（或增殖性）生长。侵袭性细胞链的形成及其与原发性肿瘤块和宿主微环境的相互作用还不清楚。在这里，我们提出了一种新的细胞自动机（CA）模型，使人们能够有效地模拟在异质性宿主微环境中的侵袭性肿瘤生长。通过考虑各种微观尺度的肿瘤-宿主相互作用，包括肿瘤细胞和肿瘤基质之间的短程机械相互作用，细胞外基质的降解，由侵袭性细胞和氧/营养梯度驱动的细胞运动，我们的CA模型预测了丰富的生长动力学谱和侵入性肿瘤的紧急行为。除了稳健地再现树突状细胞侵袭性生长的显著特征，如细胞的最小阻力路径和分支内同型吸引力，我们还预测了原发性肿瘤块和侵袭性细胞的生长动力学之间的非平凡耦合。此外，我们表明，宿主微环境的属性可以显着影响肿瘤的形态和生长动力学，强调了解肿瘤-宿主相互作用的重要性。我们的CA模型的能力表明，复杂的计算机工具最终可以用于临床情况下，预测肿瘤进展，并提出个性化的最佳治疗策略。本工作的目标是开发一个有效的基于单细胞的细胞自动机（CA）模型，使人们能够研究侵袭性实体肿瘤的生长动力学和形态学。最近的实验表明，高度恶性的肿瘤会发展出由肿瘤细胞组成的树突状分支，这些肿瘤细胞相互跟随，大规模侵入宿主微环境，最终导致癌症转移。以前的理论/计算癌症建模既没有解决这样的链状侵入性分支如何形成的问题，也没有解决它们如何与宿主微环境和原发性肿瘤相互作用的问题。我们的CA模型，它结合了各种微观尺度的肿瘤-宿主相互作用（例如，肿瘤细胞和肿瘤基质之间的机械相互作用、肿瘤细胞对细胞外基质的降解以及氧/营养物梯度驱动的细胞运动），可以稳健地再现实验观察到的侵入性肿瘤演变，并预测各种不同异质环境中的广泛的侵入性肿瘤生长动力学和紧急行为。我们的CA模型的进一步完善，最终可能导致开发一个强大的模拟工具，用于临床目的，能够预测肿瘤的进展，并提出个性化的最佳治疗策略。
Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies. The goal of the present work is to develop an efficient single-cell based cellular automaton (CA) model that enables one to investigate the growth dynamics and morphology of invasive solid tumors. Recent experiments have shown that highly malignant tumors develop dendritic branches composed of tumor cells that follow each other, which massively invade into the host microenvironment and ultimately lead to cancer metastasis. Previous theoretical/computational cancer modeling neither addressed the question of how such chain-like invasive branches form nor how they interact with the host microenvironment and the primary tumor. Our CA model, which incorporates a variety of microscopic-scale tumor-host interactions (e.g., the mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the tumor cells and oxygen/nutrient gradient driven cell motions), can robustly reproduce experimentally observed invasive tumor evolution and predict a wide spectrum of invasive tumor growth dynamics and emergent behaviors in various different heterogeneous environments. Further refinement of our CA model could eventually lead to the development of a powerful simulation tool for clinical purposes capable of predicting neoplastic progression and suggesting individualized optimal treatment strategies.
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影响因子：
2
作者：
Gevertz, Jana L.;Gillies, George T.;Torquato, Salvatore
通讯作者：
Torquato, Salvatore
影响因子：
8.5
作者：
Deisboeck, TS;Berens, ME;Chiocca, EA
通讯作者：
Chiocca, EA
影响因子：
64.5
作者：
CROSS, FR;TINKELENBERG, AH
通讯作者：
TINKELENBERG, AH
影响因子：
2
作者：
Gevertz, Jana L.;Torquato, Salvatore
通讯作者：
Torquato, Salvatore
影响因子：
11.2
作者：
Frieboes, HB;Zheng, X;Cristini, V
通讯作者：
Cristini, V