生物
计算生物学
基因组
否定选择
癌细胞
基因
癌症
遗传学
选择(遗传算法)
癌症的体细胞进化
计算机科学
机器学习
作者
Marco Mina,Franck Raynaud,Daniele Tavernari,Elena Battistello,Stéphanie Sungalee,Sadegh Saghafinia,Titouan Laessle,Francisco Sanchez‐Vega,Nikolaus Schultz,Elisa Oricchio,Giovanni Ciriello
出处
期刊:Cancer Cell
[Elsevier]
日期:2017-08-01
卷期号:32 (2): 155-168.e6
被引量:99
标识
DOI:10.1016/j.ccell.2017.06.010
摘要
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.
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