Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Sci Transl Med. 2011 Oct 5;3(103):103ra99. doi: 10.1126/scitranslmed.3002018.
Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called "oncogene addiction." The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs. However, we reasoned that many of these inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of K-ras(G12D)--or MYC-induced lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used computational modeling based on ordinary differential equations (ODEs) to show that oncogene addiction could be modeled as differential changes in survival and death intracellular signals. Our mathematical model could be generalized to different imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-ras(G12D) and MYC), and several tumor types (lung and lymphoma). Our ODE model could predict the differential dynamics of several putative prosurvival and prodeath signaling factors [phosphorylated extracellular signal-regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the aggregate survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53⁻/⁻, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation. Then, using machine learning based on support vector machine, we applied quantitative imaging methods to human patients to predict both their EGFR genotype and their progression-free survival after treatment with the targeted therapeutic erlotinib. Hence, the consequences of oncogene inactivation can be accurately modeled on the basis of a relatively small number of parameters that may predict when targeted therapeutics will elicit oncogene addiction after oncogene inactivation and hence tumor regression.
癌症可以通过一种称为“癌基因成瘾”的现象表现出明显的肿瘤消退。预测肿瘤何时会表现出癌基因成瘾的能力将有助于开发靶向治疗。癌基因成瘾可能是许多细胞程序的结果。然而，我们推断，许多这些输入可能会集中在总体存活和死亡信号上。为了验证这一点，我们检查了条件性转基因模型的 K-ras（G12D）或 MYC 诱导的肺肿瘤和淋巴瘤，结合定量成像和增殖和凋亡信号生物标志物的原位分析。然后，我们使用基于常微分方程（ODE）的计算建模来表明，癌基因成瘾可以作为存活和死亡细胞内信号的差异变化来建模。我们的数学模型可以推广到不同的成像方法（计算机断层扫描和生物发光成像）、不同的癌基因（K-ras（G12D）和 MYC）和几种肿瘤类型（肺和淋巴瘤）。我们的 ODE 模型可以预测几种假定的生存和死亡信号因子（磷酸化细胞外信号调节激酶 1 和 2、Akt1、Stat3/5（信号转导和转录激活因子 3/5）和 p38）的差异动力学，这些因子有助于癌基因失活后总体存活和死亡信号。此外，我们可以预测特定遗传病变（p53⁻/⁻、Stat3-d358L 和 myr-Akt1）对癌基因失活后肿瘤消退的影响。然后，我们使用基于支持向量机的机器学习，将定量成像方法应用于人类患者，以预测他们的 EGFR 基因型和接受靶向治疗厄洛替尼治疗后的无进展生存期。因此，癌基因失活的后果可以基于相对较少的参数进行准确建模，这些参数可能预测靶向治疗在癌基因失活后何时会引发癌基因成瘾，从而导致肿瘤消退。
