Integrated cross-species transcriptional network analysis of metastatic susceptibility
Integrated cross-species transcriptional network analysis of metastatic susceptibility
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DOI：
10.1073/pnas.1117872109
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发表时间：
2012-02-21
影响因子：
11.1
通讯作者：
Hunter, Kent W.
中科院分区：
文献类型：
--
作者：
Hu, Ying;Wu, Gang;Hunter, Kent W.
转移性疾病是大多数癌症死亡的近端原因，并且仍然是肿瘤性疾病的临床管理的重要问题。全球转录分析的最新进展使得能够更好地预测可能进展为转移性疾病的个体。然而，预测签名之间的最小重叠已经排除了有助于促转移转录状态的关键生物过程的容易鉴定。为了克服这一局限性，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不同的小鼠群体，用于转移的定量分析。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之间高度保守，并且这些网络预测了无远处转移的生存。此外，这些结果表明，转移性疾病的易感性是细胞自主的雌激素受体阳性肿瘤和有丝分裂纺锤体检查点。相反，非肿瘤遗传学和途径活性相关的间质生物学是雌激素受体阴性肿瘤转移扩散率的重要调节因素。这些结果表明，跨物种网络分析的应用可能提供一种可靠的方法来识别与人类癌症进展相关的关键生物程序。
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.