Department of Hematology, Istituto Superiore di Sanità, Rome, Italy.
Mol Cancer Res. 2013 Jun;11(6):676-85. doi: 10.1158/1541-7786.MCR-12-0690. Epub 2013 May 1.
The NCI-60 cell line set is likely the most molecularly profiled set of human tumor cell lines in the world. However, a critical missing component of previous analyses has been the inability to place the massive amounts of "-omic" data in the context of functional protein signaling networks, which often contain many of the drug targets for new targeted therapeutics. We used reverse-phase protein array (RPPA) analysis to measure the activation/phosphorylation state of 135 proteins, with a total analysis of nearly 200 key protein isoforms involved in cell proliferation, survival, migration, adhesion, etc., in all 60 cell lines. We aggregated the signaling data into biochemical modules of interconnected kinase substrates for 6 key cancer signaling pathways: AKT, mTOR, EGF receptor (EGFR), insulin-like growth factor-1 receptor (IGF-1R), integrin, and apoptosis signaling. The net activation state of these protein network modules was correlated to available individual protein, phosphoprotein, mutational, metabolomic, miRNA, transcriptional, and drug sensitivity data. Pathway activation mapping identified reproducible and distinct signaling cohorts that transcended organ-type distinctions. Direct correlations with the protein network modules involved largely protein phosphorylation data but we also identified direct correlations of signaling networks with metabolites, miRNA, and DNA data. The integration of protein activation measurements into biochemically interconnected modules provided a novel means to align the functional protein architecture with multiple "-omic" data sets and therapeutic response correlations. This approach may provide a deeper understanding of how cellular biochemistry defines therapeutic response. Such "-omic" portraits could inform rational anticancer agent screenings and drive personalized therapeutic approaches.
NCI-60 细胞系集是世界上最具分子特征的人类肿瘤细胞系集之一。然而,以前分析的一个关键缺失部分是无法将大量的“组学”数据置于功能蛋白质信号网络的背景下,这些网络通常包含许多新的靶向治疗药物的靶点。我们使用反相蛋白阵列(RPPA)分析来测量 135 种蛋白质的激活/磷酸化状态,总共分析了近 200 种与细胞增殖、存活、迁移、粘附等相关的关键蛋白质同工型。我们将信号数据聚集到 6 个关键癌症信号通路的相互连接的激酶底物的生化模块中:AKT、mTOR、表皮生长因子受体(EGFR)、胰岛素样生长因子-1 受体(IGF-1R)、整合素和细胞凋亡信号。这些蛋白质网络模块的净激活状态与可用的个体蛋白质、磷酸化蛋白质、突变、代谢组学、miRNA、转录和药物敏感性数据相关。通路激活映射确定了可重复且独特的信号群集,这些群集超越了器官类型的区别。与蛋白质网络模块的直接相关性主要涉及蛋白质磷酸化数据,但我们还确定了信号网络与代谢物、miRNA 和 DNA 数据的直接相关性。将蛋白质激活测量值整合到生化相互连接的模块中为将功能蛋白质结构与多个“组学”数据集和治疗反应相关性对齐提供了一种新方法。这种方法可以更深入地了解细胞生物化学如何定义治疗反应。这种“组学”图谱可以为合理的抗癌药物筛选提供信息,并推动个性化治疗方法。