Skehan P
Growth. 1986 Winter;50(4):496-515.
The in vivo population dynamics of 248 literature data bases of normal and neoplastic growth processes were examined by computer velocity analysis. The growth of most tumors was remarkably normal. When abnormalities did occur, they tended to be infrequent and subtle. Tumor growth was highly regulated, not unregulated Exponential growth was rare, and the kinetics of both normal tissues and tumors were predominantly deceleratory. With both normal tissues and tumors, the most intense period of growth inhibition usually occurred early in development. In general, growth was rapid only at small size. There was no evidence that tumors as a group grew faster than normal tissues. A small subclass of tumors exhibited a specific proliferative abnormality. Their growth did not stop entirely at large size, but instead continued indefinitely at a very slow basal rate. The majority of tumors did not exhibit this anomaly, however. Eight of the data bases permitted a kinetic analysis of the inhibitory mechanisms underlying growth inhibition. Six behaved as if their growth were governed by a tissue sizer, that is, by a negative feedback inhibition that strongly correlated with tumor size. The seventh behaved as if its growth were regulated by a biological time-keeper, and the final tumor as if by a stochastic mechanism such as random arrest or terminal differentiation. The analysis indicated that many tumors were completely normal in their growth governance policies and control mechanisms, and suggested that in some instances neoplastic transformation might be a disease of tissue neogenesis resulting from an altered or inappropriate cellular recognition process. The ability of 18 different equations to model in vivo growth data was examined. Accurate modeling required an equation to be objectively selected from a menu of alternatives by statistical criteria. All but one of the equations provided a best-fit to at least one data base, and all of the equations were inferior models of some data bases. The Spillman and inverse Nth root equations provided the greatest number of best-fits. The Gompertz equation was a good growth model for normal tissues, but mediocre for tumors.
通过计算机速度分析研究了248个正常和肿瘤生长过程文献数据库的体内种群动态。大多数肿瘤的生长明显正常。当确实出现异常时，往往不常见且不明显。肿瘤生长受到高度调节，并非不受调节。指数生长很少见，正常组织和肿瘤的动力学主要是减速的。对于正常组织和肿瘤，生长抑制最强烈的时期通常发生在发育早期。一般来说，只有在体积较小时生长才迅速。没有证据表明肿瘤作为一个整体比正常组织生长得更快。一小类肿瘤表现出特定的增殖异常。它们的生长在体积较大时不会完全停止，而是以非常缓慢的基础速率无限期持续。然而，大多数肿瘤并未表现出这种异常。其中8个数据库允许对生长抑制背后的抑制机制进行动力学分析。6个表现得好像其生长受组织大小调节器控制，也就是说，受与肿瘤大小密切相关的负反馈抑制控制。第7个表现得好像其生长受生物钟调节，最后一个肿瘤则好像受随机机制如随机停滞或终末分化调节。分析表明，许多肿瘤在其生长调控策略和控制机制方面完全正常，并表明在某些情况下，肿瘤转化可能是由于细胞识别过程改变或不适当而导致的组织新生疾病。研究了18种不同方程对体内生长数据的建模能力。准确建模需要根据统计标准从一系列备选方程中客观地选择一个方程。除了一个方程外，所有方程都至少对一个数据库提供了最佳拟合，并且所有方程对某些数据库来说都是较差的模型。斯皮尔曼方程和倒数第N次方根方程提供的最佳拟合数量最多。冈珀茨方程是正常组织的良好生长模型，但对肿瘤来说则一般。
