Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai 200031, China.
Cell Rep. 2013 Aug 15;4(3):542-53. doi: 10.1016/j.celrep.2013.07.010. Epub 2013 Aug 8.
Ovarian cancer has a poor prognosis, with different outcomes for different patients. The mechanism underlying this poor prognosis and heterogeneity is not well understood. We have developed an unbiased, adaptive clustering approach to integratively analyze ovarian cancer genome-wide gene expression, DNA methylation, microRNA expression, and copy number alteration profiles. We uncovered seven previously uncategorized subtypes of ovarian cancer that differ significantly in median survival time. We then developed an algorithm to uncover molecular signatures that distinguish cancer subtypes. Surprisingly, although the good-prognosis subtypes seem to have not been functionally selected, the poor-prognosis ones clearly have been. One subtype has an epithelial-mesenchymal transition signature and a cancer hallmark network, whereas the other two subtypes are enriched for a network centered on SRC and KRAS. Our results suggest molecular signatures that are highly predictive of clinical outcomes and spotlight "driver" genes that could be targeted by subtype-specific treatments.
卵巢癌预后不良，不同患者的结局不同。这种不良预后和异质性的机制尚不清楚。我们开发了一种无偏倚、自适应的聚类方法，综合分析卵巢癌全基因组基因表达、DNA 甲基化、microRNA 表达和拷贝数改变谱。我们发现了七种以前未分类的卵巢癌亚型，它们在中位生存时间上有显著差异。然后，我们开发了一种算法来发现区分癌症亚型的分子特征。令人惊讶的是，尽管预后良好的亚型似乎没有被功能选择，预后不良的亚型则显然被选择了。一个亚型具有上皮-间充质转化特征和癌症标志网络，而另外两个亚型则富集了以 SRC 和 KRAS 为中心的网络。我们的结果表明，具有高度临床预后预测性的分子特征，并突出了可能成为亚型特异性治疗靶点的“驱动”基因。
