Laboratory of Population Genetics, and Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Insitute, National Institutes of Health, Bethesda, MD 20892, USA.
Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):3184-9. doi: 10.1073/pnas.1117872109. Epub 2012 Jan 30.
Metastatic disease is the proximal cause of mortality for most cancers and remains a significant problem for the clinical management of neoplastic disease. Recent advances in global transcriptional analysis have enabled better prediction of individuals likely to progress to metastatic disease. However, minimal overlap between predictive signatures has precluded easy identification of key biological processes contributing to the prometastatic transcriptional state. To overcome this limitation, we have applied network analysis to two independent human breast cancer datasets and three different mouse populations developed for quantitative analysis of metastasis. Analysis of these datasets revealed that the gene membership of the networks is highly conserved within and between species, and that these networks predicted distant metastasis free survival. Furthermore these results suggest that susceptibility to metastatic disease is cell-autonomous in estrogen receptor-positive tumors and associated with the mitotic spindle checkpoint. In contrast, nontumor genetics and pathway activities-associated stromal biology are significant modifiers of the rate of metastatic spread of estrogen receptor-negative tumors. These results suggest that the application of network analysis across species may provide a robust method to identify key biological programs associated with human cancer progression.
转移性疾病是大多数癌症患者死亡的主要原因，也是肿瘤疾病临床管理的一个重大难题。目前，在全球转录分析方面的进展使得我们能够更好地预测哪些患者可能会发展为转移性疾病。然而，预测性特征之间的微小重叠，使得难以确定导致促转移转录状态的关键生物学过程。为了克服这一限制，我们将网络分析应用于两个独立的人类乳腺癌数据集和三个用于定量分析转移的不同小鼠群体。对这些数据集的分析表明，网络的基因成员在物种内和物种间具有高度的保守性，并且这些网络可以预测远处无转移的生存。此外，这些结果表明，雌激素受体阳性肿瘤的转移性疾病易感性是细胞自主性的，并与有丝分裂纺锤体检查点有关。相比之下，非肿瘤遗传和途径活性相关的基质生物学是雌激素受体阴性肿瘤转移扩散速度的重要修饰因子。这些结果表明，跨物种应用网络分析可能为识别与人类癌症进展相关的关键生物学程序提供一种可靠的方法。
