Information dynamics in carcinogenesis and tumor growth
Information dynamics in carcinogenesis and tumor growth
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DOI：
10.1016/j.mrfmmm.2004.04.018
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发表时间：
2004-12-21
影响因子：
2.3
通讯作者：
Frieden, BR
中科院分区：
文献类型：
--
作者：
Gatenby, RA;Frieden, BR
信息的存储和传输对正常细胞和转化细胞的功能至关重要。我们使用信息论和蒙特卡罗理论的方法来分析信息在癌症发生中的作用。我们的分析表明，在恶性表型的体细胞进化过程中，基因组突变的积累降低了细胞内的信息。然而，这种退化受到达尔文主义体细胞生态学的限制，在达尔文体细胞生态学中，突变克隆只有在突变提供选择性生长优势时才会增殖。在这种环境中，通常会减少细胞增殖的基因，如肿瘤抑制基因或分化基因，会遭受最大的信息退化。相反，那些促进增殖的基因，如癌基因，是保守的，或者只表现出功能突变的收益。这些限制保护了大多数细胞群体免受突变子引起的灾难性的跨膜熵梯度丧失的影响，从而避免了细胞死亡。肿瘤发生过程中受限信息降解的动力学导致肿瘤基因组渐近于最小信息状态，临床表现为去分化和不受约束的增殖。超常物理信息(EPI)理论表明，改变的信息流从癌细胞到其环境将在体内表现为指数为1.62的指数型肿瘤生长。这一预测只是基于这样的假设，即肿瘤细胞处于绝对信息量最小，能够“自由场”生长，即它们不受外部生物参数的限制。这一预测与几项研究非常吻合，这些研究表明，小型人类乳腺癌的指数为1.72+/-0.24，呈幂规律增长。仅从EPI成功地推导出癌症生长的解析表达式就支持了这样的概念模型，即癌变是一个受约束的信息降解过程，而恶性细胞是最小的信息系统。EPI理论还预测，临床观察到的肿瘤的估计年龄受到均方根误差约30%的影响。这是由于信息丢失和组织解体造成的，可能表现为实验观察到的生长模式中的随机变量滞后阶段。肿瘤大小和年龄之间的差异可能会对基于早期发现小肿瘤的筛查效果施加根本限制。由于从环境到转化细胞的扰动信息流，蒙特卡罗方法被应用于预测统计的肿瘤生长，与EPI分析无关。根据EPI方法的研究结果，提出了一个“最简单的”蒙特卡罗模型，即肿瘤生长源于一个最小的复杂机制。大量模拟的结果表明：(A)由于关键基因片段的突变，大约40%的种群无法在前两代存活；但(B)那些存活下来的种群将经历与独立EPI方法预测的增长率相同的幂函数增长。这两种截然不同的方法解决这一问题的共识有力地支持了这样一种观点，即肿瘤细胞在癌变过程中退化到信息最少的状态，并且信息动力学与肿瘤的发展和生长密切相关。(C)2004爱思唯尔B.V.保留所有权利。
The storage and transmission of information is vital to the function of normal and transformed cells. We use methods from information theory and Monte Carlo theory to analyze the role of information in carcinogenesis. Our analysis demonstrates that, during somatic evolution of the malignant phenotype, the accumulation of genomic mutations degrades intracellular information. However, the degradation is constrained by the Darwinian somatic ecology in which mutant clones proliferate only when the mutation confers a selective growth advantage. In that environment, genes that normally decrease cellular proliferation, such as tumor suppressor or differentiation genes, suffer maximum information degradation. Conversely, those that increase proliferation, such as oncogenes, are conserved or exhibit only gain of function mutations. These constraints shield most cellular populations from catastrophic mutator-induced loss of the transmembrane entropy gradient and, therefore, cell death. The dynamics of constrained information degradation during carcinogenesis cause the tumor genome to asymptotically approach a minimum information state that is manifested clinically as dedifferentiation and unconstrained proliferation.Extreme physical information (EPI) theory demonstrates that altered information flow from cancer cells to their environment will manifest in-vivo as power law tumor growth with an exponent of size 1.62. This prediction is based only on the assumption that tumor cells are at an absolute information minimum and are capable of "free field" growth that is, they are unconstrained by external biological parameters. The prediction agrees remarkably well with several studies demonstrating power law growth in small human breast cancers with an exponent of 1.72+/-0.24. This successful derivation of an analytic expression for cancer growth from EPI alone supports the conceptual model that carcinogenesis is a process of constrained information degradation and that malignant cells are minimum information systems. EPI theory also predicts that the estimated age of a clinically observed tumor is subject to a root-mean square error of about 30%. This is due to information loss and tissue disorganization and probably manifests as a randomly variable lag phase in the growth pattern that has been observed experimentally. This difference between tumor size and age may impose a fundamental limit on the efficacy of screening based on early detection of small tumors.Independent of the EPI analysis, Monte Carlo methods are applied to predict statistical tumor growth due to perturbed information flow from the environment into transformed cells. A "simplest" Monte Carlo model is suggested by the findings the EPI approach that tumor growth arises out of a minimally complex mechanism. The outputs of large numbers of simulations show that (a) about 40% of the populations do not survive the first two-generations due to mutations in critical gene segments; but (b) those that do survive will experience power law growth identical to the predicted rate obtained from the independent EPI approach. The agreement between these two very different approaches to the problem strongly supports the idea that tumor cells regress to a state of minimum information during carcinogenesis, and that information dynamics are integrally related to tumor development and growth. (C) 2004 Elsevier B.V. All rights reserved.