异质微环境中侵袭性肿瘤生长的元胞自动机模型所产生的涌现行为
Emergent Behaviors from A Cellular Automaton Model for Invasive Tumor Growth in Heterogeneous Microenvironments
AI总结：
本文提出一种纳入机械作用、基质降解和氧/营养梯度的元胞自动机模型，模拟异质微环境中的侵袭性肿瘤生长，再现树突状侵袭并揭示肿瘤—宿主耦合机制。
摘要：
理解肿瘤侵袭和转移对于基础癌症研究与临床实践都具有至关重要的意义。体外实验已经证实，恶性肿瘤的侵袭性生长以树突状侵袭分支为特征，这些分支由从原发肿瘤团块发出的肿瘤细胞链构成。以往的肿瘤模拟绝大多数集中于非侵袭性（或增殖性）生长。侵袭性细胞链的形成及其与原发肿瘤团块和宿主微环境的相互作用仍未得到充分理解。在此，我们提出一种新颖的元胞自动机（CA）模型，使人们能够在异质性宿主微环境中高效模拟侵袭性肿瘤生长。通过考虑多种微观尺度的肿瘤—宿主相互作用，包括肿瘤细胞与肿瘤基质之间的短程机械相互作用、侵袭性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，我们的CA模型预测了侵袭性肿瘤丰富的生长动力学谱系和涌现行为。除了稳健地再现树突状侵袭生长的显著特征（例如最小阻力和分支内同型吸引）之外，我们还预测了原发肿瘤团块与侵袭性细胞的生长动力学之间非平凡的耦合。此外，我们表明宿主微环境的性质能够显著影响肿瘤形态和生长动力学，强调了理解肿瘤—宿主相互作用的重要性。我们的CA模型所具备的能力表明，成熟的计算机模拟工具最终可用于临床情境，以预测肿瘤进展并提出个体化的最佳治疗策略。
英文摘要：
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 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 and intrabranch homotype attraction, we also predict nontrivial coupling of 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 well-developed in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
