Characterizing tyrosine phosphorylation signaling in lung cancer using SH2 profiling.
Characterizing tyrosine phosphorylation signaling in lung cancer using SH2 profiling.
复制标题
DOI：
10.1371/journal.pone.0013470
复制
发表时间：
2010-10-19
期刊：
影响因子：
3.7
通讯作者：
Haura EB
中科院分区：
文献类型：
--
作者：
Machida K;Eschrich S;Li J;Bai Y;Koomen J;Mayer BJ;Haura EB
酪氨酸激酶驱动许多人类癌症的增殖和存活。因此，分析肿瘤的酪氨酸磷酸化的整体状态可能提供丰富的信息，可用于肿瘤分类的预后和预测。然而，使用现有方法对大量人类癌症标本的酪氨酸磷酸化进行综合分析在技术上具有挑战性。我们使用一种称为SH2分析的磷酸化蛋白质组学方法来表征人肺癌细胞系中磷酸酪氨酸（pTyr）信号的整体状态。该方法量化了SH2结构域的磷酸化结合位点，细胞利用SH2结构域在信号传导过程中响应pTyr的变化。细胞可以根据SH2结合模式进行分组，其中一些簇与EGF受体（EGFR）或K-RAS突变状态相关。特异性SH2结构域的结合，最突出的是RAS通路激活剂Grb2和ShcA，与EGFR突变和对EGFR抑制剂厄洛替尼的敏感性相关。SH2结合模式也反映MET活化，可以识别由多种激酶驱动的细胞。用激酶抑制剂处理的细胞的pTyr反应提供了不同抑制机制的证据。这项研究说明了模块化蛋白质结构域及其蛋白质组学结合谱作为肿瘤分类和生物标志物鉴定的强大分子诊断工具的潜力。
Tyrosine kinases drive the proliferation and survival of many human cancers. Thus profiling the global state of tyrosine phosphorylation of a tumor is likely to provide a wealth of information that can be used to classify tumors for prognosis and prediction. However, the comprehensive analysis of tyrosine phosphorylation of large numbers of human cancer specimens is technically challenging using current methods. We used a phosphoproteomic method termed SH2 profiling to characterize the global state of phosphotyrosine (pTyr) signaling in human lung cancer cell lines. This method quantifies the phosphorylated binding sites for SH2 domains, which are used by cells to respond to changes in pTyr during signaling. Cells could be grouped based on SH2 binding patterns, with some clusters correlated with EGF receptor (EGFR) or K-RAS mutation status. Binding of specific SH2 domains, most prominently RAS pathway activators Grb2 and ShcA, correlated with EGFR mutation and sensitivity to the EGFR inhibitor erlotinib. SH2 binding patterns also reflected MET activation and could identify cells driven by multiple kinases. The pTyr responses of cells treated with kinase inhibitors provided evidence of distinct mechanisms of inhibition. This study illustrates the potential of modular protein domains and their proteomic binding profiles as powerful molecular diagnostic tools for tumor classification and biomarker identification.
登录
查看更多内容
影响因子：
158.5
作者：
Lynch, TJ;Bell, DW;Haber, DA
通讯作者：
Haber, DA
影响因子：
11.5
作者：
Brannan, Jennifer M.;Dong, Wenli;Johnson, Faye M.
通讯作者：
Johnson, Faye M.
DOI：
10.1007/978-1-59745-542-8_34
发表时间：
2009-01-01
期刊：
PROTEIN BLOTTING AND DETECTION: METHODS AND PROTOCOLS
影响因子：
--
作者：
Machida, Kazuya;Mayer, Bruce J.
通讯作者：
Mayer, Bruce J.
影响因子：
4.4
作者：
Balko JM;Potti A;Saunders C;Stromberg A;Haura EB;Black EP
通讯作者：
Black EP
DOI：
10.1073/pnas.0710370104
发表时间：
2007-12-26
影响因子：
11.1
作者：
Bean, James;Brennan, Cameron;Pao, William
通讯作者：
Pao, William