National Research Council Canada, Montreal, QC H4P 2R2, Canada; Department of Anatomy and Cell Biology, McGill University, Montreal, QC H3A 2B2, Canada; Center for Bioinformatics, McGill University, Montreal, QC H3G 0B1, Canada.
Cell Rep. 2013 Oct 17;5(1):216-23. doi: 10.1016/j.celrep.2013.08.028. Epub 2013 Sep 26.
Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations), making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival) onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.
单个癌细胞携带数量惊人的独特基因组改变（例如，拷贝数变异和突变），这使得发现控制肿瘤发生的基因组驱动机制具有挑战性。在这里，我们对代表两种亚型（腔细胞型和基底细胞型）的几种乳腺癌细胞系进行了外显子组测序。我们将这些测序数据和功能 RNAi 筛选数据（用于鉴定对细胞增殖和存活至关重要的基因）整合到一个人类信号网络中。鉴定出两个潜在代表肿瘤发生核心信号机制的亚型特异性网络。在这两个网络中，我们发现不同的细胞系中基因受到不同的影响；也就是说，在某些细胞系中，一个基因通过 RNAi 筛选被鉴定出来，而在其他细胞系中，它则是基因组改变的。有趣的是，我们发现高连接网络基因可用于根据基因组改变将乳腺癌肿瘤正确分类为不同亚型。此外，这些网络还可以有效地预测亚型特异性药物靶点，这些靶点已经过实验验证。
