Department of Electrical Engineering, Stanford University School of Medicine, Stanford, CA 94305, USA.
Comput Math Methods Med. 2013;2013:802512. doi: 10.1155/2013/802512. Epub 2013 Mar 20.
The dependence on the overexpression of a single oncogene constitutes an exploitable weakness for molecular targeted therapy. These drugs can produce dramatic tumor regression by targeting the driving oncogene, but relapse often follows. Understanding the complex interactions of the tumor's multifaceted response to oncogene inactivation is key to tumor regression. It has become clear that a collection of cellular responses lead to regression and that immune-mediated steps are vital to preventing relapse. Our integrative mathematical model includes a variety of cellular response mechanisms of tumors to oncogene inactivation. It allows for correct predictions of the time course of events following oncogene inactivation and their impact on tumor burden. A number of aspects of our mathematical model have proven to be necessary for recapitulating our experimental results. These include a number of heterogeneous tumor cell states since cells following different cellular programs have vastly different fates. Stochastic transitions between these states are necessary to capture the effect of escape from oncogene addiction (i.e., resistance). Finally, delay differential equations were used to accurately model the tumor growth kinetics that we have observed. We use this to model oncogene addiction in MYC-induced lymphoma, osteosarcoma, and hepatocellular carcinoma.
对单一癌基因的过度表达的依赖构成了分子靶向治疗的一个可利用的弱点。这些药物可以通过靶向驱动癌基因来产生显著的肿瘤消退，但常常会复发。了解肿瘤对癌基因失活的多方面反应的复杂相互作用是肿瘤消退的关键。很明显，一系列细胞反应导致了肿瘤的消退，而免疫介导的步骤对于防止复发至关重要。我们的综合数学模型包括肿瘤对癌基因失活的各种细胞反应机制。它允许对癌基因失活后事件的时间进程及其对肿瘤负担的影响进行正确的预测。我们的数学模型的许多方面已被证明对于重现我们的实验结果是必要的。这些方面包括许多异质的肿瘤细胞状态，因为遵循不同细胞程序的细胞具有截然不同的命运。这些状态之间的随机转换对于捕获逃避癌基因成瘾（即耐药性）的影响是必要的。最后，延迟微分方程被用于准确地模拟我们观察到的肿瘤生长动力学。我们用它来模拟 MYC 诱导的淋巴瘤、骨肉瘤和肝细胞癌中的癌基因成瘾。
