ConsensusDriver Improves upon Individual Algorithms for Predicting Driver Alterations in Different Cancer Types and Individual Patients
ConsensusDriver Improves upon Individual Algorithms for Predicting Driver Alterations in Different Cancer Types and Individual Patients
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DOI：
10.1158/0008-5472.can-17-1345
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发表时间：
2018-01-01
期刊：
影响因子：
11.2
通讯作者：
Nagarajan, Niranjan
中科院分区：
文献类型：
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作者：
Bertrand, Denis;Drissler, Sibyl;Nagarajan, Niranjan
现有的癌症驱动预测方法基于非常不同的假设，并且它们中的每一个都只能检测驱动基因的特定子集。在这里，我们对来自15种癌症类型的3，400多个肿瘤样本的18种驱动预测方法进行了全面评估，所有这些方法都是为了确定它们在指导精准医学工作中的适用性。我们将这些方法分为五组：对一般蛋白质（FI）或癌症特异性蛋白质（FIC）的功能影响，基于队列的复发突变分析（CBA），表达相关性突变（MEC）和使用基因相互作用网络分析（INA）的方法。驾驶员预测方法的表现差异很大，与金标准的一致性从9%到68%不等。FI方法表现出相对较差的性能（一致性< 22%），而CBA方法提供保守的结果，但需要大样本量的高灵敏度。INA方法通过整合基因组和转录组数据，FIC方法通过训练癌症特异性模型，提供了灵敏度和特异性之间的最佳权衡。由于这些方法被发现可以预测驱动基因的不同子集，我们提出了一种新的基于共识的方法，即CumbersusDriver，它可以显着提高患者亚组甚至个体患者的预测质量（灵敏度提高20%）。基于驱动程序的方法，如驱动程序驱动程序，承诺利用不同的驱动程序预测范式的优势。(C)2017年AACR。
Existing cancer driver prediction methods are based on very different assumptions and each of them can detect only a particular subset of driver genes. Here we perform a comprehensive assessment of 18 driver prediction methods on more than 3,400 tumor samples from 15 cancer types, all to determine their suitability in guiding precision medicine efforts. We categorized these methods into five groups: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC), and methods that use gene interaction network-based analysis (INA). The performance of driver prediction methods varied considerably, with concordance with a gold standard varying from 9% to 68%. FI methods showed relatively poor performance (concordance < 22%), while CBA methods provided conservative results but required large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provided the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of driver genes, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity) in patient subgroups or even individual patients. Consensus-based methods like ConsensusDriver promise to harness the strengths of different driver prediction paradigms. (C) 2017 AACR.