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去分化脂肪肉瘤的药物协同筛选和网络建模确定 CDK4 和 IGF1R 为协同药物靶点。

Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets.

机构信息

1Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.

出版信息

Sci Signal. 2013 Sep 24;6(294):ra85. doi: 10.1126/scisignal.2004014.

DOI:10.1126/scisignal.2004014
PMID:24065146
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4000046/
Abstract

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.

摘要

去分化脂肪肉瘤(DDLS)是一种罕见但具有侵袭性的癌症,其靶向治疗的复发率高,反应率低。提高治疗效果可能需要联合使用靶向药物,以抵消多种异常的影响。为了确定可能的多组分治疗方法,我们在源自 DDLS 的细胞系中进行了组合药物筛选,并确定细胞周期蛋白依赖性激酶 4(CDK4)和胰岛素样生长因子 1 受体(IGF1R)为协同药物靶点。我们测量了对系统药物组合的多种蛋白质磷酸化和细胞活力,并得出了信号网络的计算模型。这些模型预测,观察到的 CDK4 和 IGF1R 抑制剂降低细胞活力的协同作用取决于 AKT 通路的活性。实验证实,联合抑制 CDK4 和 IGF1R 协同抑制 AKT 通路内蛋白质的激活。与这些发现一致,当将 CDK4 抑制与 AKT 或表皮生长因子受体(EGFR)(另一种类似于 IGF1R 激活 AKT 的受体)的抑制相结合时,也发现细胞活力协同降低。因此,从系统扰动的癌细胞的特定于上下文的蛋白质组学测量中得出的网络模型可能揭示癌症特异性信号机制,并有助于设计有效的联合治疗方法。

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