Ummanni Ramesh, Mannsperger Heiko A, Sonntag Johanna, Oswald Marcus, Sharma Ashwini K, König Rainer, Korf Ulrike
Division of Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.
Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, 07747 Jena, Germany.
Biochim Biophys Acta. 2014 May;1844(5):950-9. doi: 10.1016/j.bbapap.2013.11.017. Epub 2013 Dec 19.
The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.
采用反相蛋白质阵列(RPPA)方法,对84种非小细胞肺癌(NSCLC)细胞系中的71种癌症相关蛋白和磷酸化蛋白进行定量分析,并监测选定的受体酪氨酸激酶、PI3K/AKT和MEK/ERK1/2信号传导、细胞周期控制、细胞凋亡及DNA损伤的激活状态。利用监督和非监督数据分析方法,结合蛋白质组学数据,分析了NSCLC细胞系的其他信息,如转录组数据、基因组畸变和药物敏感性。首先,蛋白质组学数据的非监督分析表明,紧密聚集在一起的蛋白质反映了众所周知的信号传导模块,如PI3K/AKT和RAS/RAF/ERK信号传导、细胞周期调控和细胞凋亡。然而,发现EGFR、ERBB2、RAF、RAS、TP53和PI3K的突变分散在不同的信号通路簇中。仅EGFR和/或ERBB2扩增的细胞系在蛋白质组学水平上紧密聚集在一起,但在转录组学水平上并非如此。其次,监督数据分析显示,对抗EGFR药物的敏感性通常与高水平的EGFR磷酸化相关性更好,而不是与EGFR本身的丰度相关。RB的高水平磷酸化和AURKA的高丰度被确定为可能预测对极光激酶抑制剂VX680敏感性的候选指标。所示实例表明,RPPA方法为靶向蛋白质组学提供了一个有用的平台,具有发现生物标志物的巨大潜力。本文是名为:生物标志物:蛋白质组学挑战的特刊的一部分。