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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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Author summary We applied a systems modelling approach combining mechanistic modelling and biological experimentation to identify effective drug combinations for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with no approved targeted treatment. The model predicted and prioritized the synergistic combinations as confirmed by experimental data, demonstrating the power of this approach. Moreover, analysis of clinical data of TNBC patients and patient-specific modelling simulation enabled us to stratify the patients into subgroups with distinct susceptibility to specific drug combinations, and thus defined a subset of patient that could benefit from the combined treatments.
机译:作者摘要我们应用了结合机械建模和生物学实验的系统建模方法,以识别三阴性乳腺癌(TNBC)的有效药物组合,TNBC是一种未经批准的靶向治疗方法,是乳腺癌的一种侵袭性亚型。如实验数据所证实的,该模型预测并确定了协同组合的优先次序,证明了这种方法的强大功能。此外,对TNBC患者的临床数据的分析和特定于患者的模型模拟使我们能够将患者分为对特定药物组合具有不同易感性的亚组,从而定义了可以从联合治疗中受益的一部分患者。

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