Enderling Heiko, Chaplain Mark A J, Hahnfeldt Philip
Center of Cancer Systems Biology, Caritas St. Elizabeth's Medical Center, Tufts University School of Medicine, 736 Cambridge Street, Boston, MA 02135, USA.
Acta Biotheor. 2010 Dec;58(4):341-53. doi: 10.1007/s10441-010-9111-z. Epub 2010 Jul 24.
Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.
癌症是一种复杂的疾病，需要在许多不同层面进行研究：在亚细胞层面识别与该疾病相关的基因、蛋白质和信号通路；在细胞层面识别例如细胞间黏附及通讯机制；在组织层面研究稳态破坏以及与原发组织的相互作用或转移灶的形成；最后在系统层面探索其整体影响，例如通过恶病质机制。已有人提出数学模型来识别在各个感兴趣尺度下构成动态变化和事件基础的关键机制，并且现在人们越来越关注能够跨越不同尺度的多尺度模型。随着越来越多生物学数据的可得以及跨学科研究的增加，理论模型正成为预测疾病起源和病程的合适工具。然而，癌症模型的最终目标是启发我们对致癌过程的认识，并协助设计能够降低死亡率和提高患者生活质量的治疗方案。癌症的传统治疗方法是，对于局部肿瘤分别采用手术联合放疗，对于晚期癌症则采用全身治疗。尽管放疗被广泛用作治疗手段，但大多数治疗方案是基于经验知识，而非基于对复杂的治疗反应生长动力学模型的预测。建模研究未能转化应用于临床的部分原因可能在于语言障碍，而晦涩难懂的模型呈现及难以获取的参数设置使这一障碍更加突出。在此，我们讨论一些关于将易于处理的动态肿瘤生长模型与辐射反应模型相结合的想法，使用生物学上可获取的参数，以提供一个更直观且可利用的框架，用于理解放射治疗的复杂性及治疗失败原因。
