Department of Computational Biology, University of Lausanne (UNIL), 1011 Lausanne, Vaud, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
Department of Computational Biology, University of Lausanne (UNIL), 1011 Lausanne, Vaud, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Institute for Experimental Cancer Research (ISREC), Ecole Polytechnique Federale Lausanne (EPFL), 1015 Lausanne, Vaud, Switzerland.
Cancer Cell. 2017 Aug 14;32(2):155-168.e6. doi: 10.1016/j.ccell.2017.06.010. Epub 2017 Jul 27.
Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response.
癌症是通过分子改变的出现和选择而进化的。癌症基因组分析揭示了特定事件或多或少可能被共同选择，这表明一个事件的选择取决于其他事件。然而，这些进化依赖性的性质及其影响仍不清楚。在这里，我们设计了 SELECT，这是一种从改变模式中系统地识别进化依赖性的算法方法。通过分析来自多种肿瘤类型的 6456 个基因组，我们构建了与细胞途径、转录读数和治疗反应相关的致癌依赖性图谱。最后，对癌症进化的建模表明，只有在条件选择下，改变的依赖性才会出现。这些结果为设计预测癌症进展和治疗反应的策略提供了框架。
