Halasz Melinda, Kholodenko Boris N, Kolch Walter, Santra Tapesh
Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Sci Signal. 2016 Nov 22;9(455):ra114. doi: 10.1126/scisignal.aae0535.
Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for intervention. As a proof of principle, we analyzed a perturbation data set targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in a panel of colorectal cancer cells. Our computational approach predicted cell line-specific network rewiring. In particular, feedback inhibition of insulin receptor substrate 1 (IRS1) by the kinase p70S6K was predicted to confer resistance to EGFR inhibition, suggesting that disrupting this feedback may restore sensitivity to EGFR inhibitors in colorectal cancer cells. We experimentally validated this prediction with colorectal cancer cell lines in culture and in a zebrafish (Danio rerio) xenograft model.
信号转导网络在癌细胞中常常被重新布线。识别这些改变将有助于实现更有效的癌症治疗。我们开发了一个计算框架，该框架可以从嘈杂且不完整的扰动响应数据中识别、重建并对这些重新布线的网络进行机制建模，然后预测潜在的干预靶点。作为原理验证，我们分析了一组结肠癌细胞中针对表皮生长因子受体（EGFR）和胰岛素样生长因子1受体（IGF1R）通路的扰动数据集。我们的计算方法预测了细胞系特异性的网络重新布线。特别是，激酶p70S6K对胰岛素受体底物1（IRS1）的反馈抑制被预测会赋予对EGFR抑制的抗性，这表明破坏这种反馈可能会恢复结肠癌细胞对EGFR抑制剂的敏感性。我们在培养的结肠癌细胞系以及斑马鱼（Danio rerio）异种移植模型中通过实验验证了这一预测。
