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首页> 美国卫生研究院文献>PLoS Computational Biology >Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer
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Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer

机译：EGFR-PYK2-c-Met相互作用网络的系统建模可预测三联阴性乳腺癌的协同药物组合并确定其优先级

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Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.
机译：有效靶向癌细胞的药物组合的预测是癌症治疗的关键挑战，特别是对于三阴性乳腺癌（TNBC），这是一种没有有效靶向治疗的高度侵袭性乳腺癌亚型。由于信号通路网络可以严格控制癌细胞的行为，因此对信号通路活动和串扰的分析可以帮助预测有效的药物组合和患者的合理分层，从而带来治疗和预后价值。先前我们已经证明非受体酪氨酸激酶PYK2是EGFR和c-Met的下游效应子，并在基底样TNBC中证明了其串扰信号。在这里，我们应用了系统建模方法，并开发了整合的EGFR-PYK2-c-Met信号网络的机制模型，以识别和优先考虑TNBC的有效药物组合。通过实验数据验证的模型预测结果表明，针对网络中枢节点的六种潜在药物组合（包括EGFR，c-Met，PYK2和STAT3），EGFR和PYK2的共同靶向以及较小的EGFR和c -Met产生最强的协同作用。重要的是，共同靶向EGFR和PYK2的协同作用与开关样细胞增殖相关反应相关。此外，使用TNBC患者的公共基因表达数据对患者特异性模型进行的模拟导致将患者预测性分层为对特定药物组合表现出不同易感性的亚组。这些结果表明，机械系统建模是针对单个患者进行有效组合治疗的合理设计，预测和优先排序的有效方法，从而为针对TNBC和其他肿瘤类型的个性化治疗提供了具体步骤。

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