Signal transduction networks in cancer: quantitative parameters influence network topology.
Signal transduction networks in cancer: quantitative parameters influence network topology.
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DOI：
10.1158/0008-5472.can-09-3234
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发表时间：
2010-03-01
期刊：
影响因子：
11.2
通讯作者：
Klinke DJ 2nd
中科院分区：
文献类型：
--
作者：
Klinke DJ 2nd
固定拓扑的网络用于总结对小区内信令信息流的集体理解(即规范的信令网络)。此外，这些规范的信号网络被用来解释观察到的蛋白质活性或表达的致癌变化如何改变癌细胞中的信息流。然而，在信号网络中创建一个新的分支(即非规范边缘)为细胞提供了一种获得癌症特征的机制。本研究的目的是以与ErbB1信号网络相关的早期信号事件的数学模型为例，仅基于蛋白质表达的变化来评估受体酪氨酸激酶(RTK)信号网络中非规范边缘的存在。标准蛋白-RTK复合体(如Grb2-ErbB1和Shc-ErbB1)的丰度被用来建立一个与细胞增殖的配体依赖变化相关的阈值。在现有数据的情况下，使用经验贝叶斯方法估计了与这一阈值相关的不确定性。利用在一组乳腺癌细胞系中观察到的蛋白质表达的变异性，该模型被用于评估非典型边缘(例如，IRS1-ErbB1)是否超过阈值，并识别可能观察到这种非典型边缘的细胞系。综上所述，模拟表明细胞内信号转导网络的拓扑结构受到定量参数的影响，如蛋白质表达和结合亲和力。此外，这种非规范途径的形成不仅仅是由于细胞表面受体的过度表达，而是受到多蛋白复合体所有成员的过度表达的影响。癌细胞中信号蛋白表达的多变量变化可能激活非规范途径，并可能重新连接细胞内的信号网络。正常细胞和癌细胞表现出几乎相同的信号回路。癌细胞为了获得增殖优势而调整了迂回路线。这些模拟表明，蛋白质表达的多变量变化通过在RTK信号网络中形成新的非规范边缘，在信号电路中产生细微的差异。识别这些电路中预测的细微差异可能有助于揭开癌症的分子基础。
Networks of fixed topology are used to summarize the collective understanding of the flow of signaling information within a cell (i.e., canonical signaling networks). Moreover, these canonical signaling networks are used to interpret how observed oncogenic changes in protein activity or expression alter information flow in cancer cells. However, creating a novel branch within a signaling network (i.e., a non-canonical edge) provides a mechanism for a cell to acquire the hallmark characteristics of cancer. The objective of this study was to assess the existence of a non-canonical edge within a receptor tyrosine kinase (RTK) signaling network based upon variation in protein expression alone, using a mathematical model of the early signaling events associated with ErbB1 signaling network as an illustrative example. The abundance of canonical protein-RTK complexes (e.g., Grb2-ErbB1 and Shc-ErbB1) were used to establish a threshold that was correlated with ligand-dependent changes in cell proliferation. Given the available data, the uncertainty associated with this threshold was estimated using an empirical Bayesian approach. Using the variability in protein expression observed among a collection of breast cancer cell lines, this model was used to assess whether a non-canonical edge (e.g., Irs1-ErbB1) exceeds the threshold and to identify cell lines where this non-canonical edge is likely to be observed. Taken together, the simulations suggest that the topology of signal transduction networks within cells is influenced by quantitative parameters, such as protein expression and binding affinity. Moreover, forming this non-canonical pathway was not due solely to over-expression of the cell-surface receptor, but was influenced by over-expression of all members of the multi-protein complex. Multivariate alterations in expression of signaling proteins in cancer cells may activate non-canonical pathways and may re-wire the signaling network within a cell. Normal cells and cancer cells exhibit almost the same signaling circuitry. Cancer cells tweak the circuity for proliferative advantage. These simulations suggest that multivariate changes in protein expression create subtle differences in signaling circuitry by forming new non-canonical edges in RTK signaling networks. Identifying these predicted subtle differences in circuitry may help unravel the molecular basis of cancer.