Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.
Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.
复制标题
DOI：
10.1158/0008-5472.can-10-0460
复制
发表时间：
2010-09-01
期刊：
影响因子：
11.2
通讯作者：
Ram PT
中科院分区：
文献类型：
--
作者：
Iadevaia S;Lu Y;Morales FC;Mills GB;Ram PT
靶向治疗在抑制癌细胞增殖方面具有巨大的前景。然而，单独靶向蛋白可以通过旁路机制和调节环的激活来补偿。因此，设计最佳的治疗组合必须考虑到细胞中复杂的动态网络。在这项研究中，我们分析了胰岛素样生长因子（IGF-1）在MDA-MB 231乳腺癌细胞系的信号网络。我们使用反相蛋白质阵列来测量IGF-1刺激后蛋白质磷酸化的瞬时变化。我们开发了一个计算程序，集成质量作用模型与粒子群优化训练模型对实验数据和推断未知的模型参数。训练的模型用于预测靶向单个信号蛋白如何改变网络的其余部分，并确定最小程度地增加网络中其他蛋白质磷酸化的药物组合。对建模预测的实验测试表明，最佳药物组合抑制细胞信号传导和增殖，而非最佳抑制剂组合增加了非靶向蛋白的磷酸化，并拯救了细胞免于细胞死亡。这里描述的综合方法是有用的实验干预策略，可以优化药物组合，发现新的药理学癌症治疗的目标。
Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass-action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, while non-optimal combination of inhibitors increased phosphorylation of non-targeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.