Bild Andrea H, Yao Guang, Chang Jeffrey T, Wang Quanli, Potti Anil, Chasse Dawn, Joshi Mary-Beth, Harpole David, Lancaster Johnathan M, Berchuck Andrew, Olson John A, Marks Jeffrey R, Dressman Holly K, West Mike, Nevins Joseph R
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708, USA.
Nature. 2006 Jan 19;439(7074):353-7. doi: 10.1038/nature04296. Epub 2005 Nov 6.
The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
致癌状态的发展是一个复杂的过程,涉及多个独立突变的积累,这些突变导致对细胞生长和细胞命运控制至关重要的细胞信号通路失调。多项研究已证明,利用基于DNA微阵列的基因表达特征来定义癌症亚型、疾病复发情况以及对特定疗法的反应的能力。各种研究还证明了利用基因表达谱分析致癌途径的潜力。在此,我们表明可以识别出反映多种致癌途径激活状态的基因表达特征。当在几大组人类癌症中进行评估时,这些基因表达特征可识别肿瘤中途径失调的模式以及与疾病预后的临床相关关联。跨多个途径组合基于特征的预测可识别途径失调的协同模式,从而区分特定癌症和肿瘤亚型。基于途径特征对肿瘤进行聚类可进一步确定各个患者亚组的预后,表明致癌途径失调模式是致癌表型发展的基础,并反映特定癌症的生物学特性和预后。癌细胞系中途径失调的预测还显示可预测对靶向该途径成分的治疗药物的敏感性。将途径失调与对靶向该途径成分的治疗药物的敏感性联系起来,为利用这些致癌途径特征指导靶向治疗的应用提供了机会。