Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.
Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.
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DOI：
10.1093/nar/gku1393
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发表时间：
2015-04-20
影响因子：
14.9
通讯作者：
Nagarajan N
中科院分区：
文献类型：
--
作者：
Bertrand D;Chng KR;Sherbaf FG;Kiesel A;Chia BK;Sia YY;Huang SK;Hoon DS;Liu ET;Hillmer A;Nagarajan N
最近的癌症组学概况项目产生的广泛和多维的数据集为揭开癌症基因组图景的复杂性提供了新的挑战和机会。特别是，为了将癌症基因组测序的全部好处转化为临床，有必要将驱动每个患者肿瘤发生的独特基因互补与大量辅助突变区分开来。我们通过提出一个数据集成框架(OncoIMPACT)来解决这一需求，该框架可以根据患者的表型影响来命名患者特定的驱动基因。广泛的计算机和体外验证帮助建立了OncoIMPACT的稳健性、比竞争方法更高的精确度以及可验证的患者和细胞系特定预测(分别为2/2和6/7真阳性和真阴性)。特别是，我们通过计算预测和实验验证了TRIM24基因可能是黑色素瘤患者的一种新的放大驱动因素。将OncoIMPACT应用于1000多个肿瘤样本，我们生成了五种不同癌症类型的患者特定驱动基因列表，以确定协同作用的模式。我们还提供了第一个证明，在实现患者分层和预测方面，计算得出的驱动程序突变签名可以总体上优于单基因和基于基因表达的签名。OncoIMPACT的源代码和可执行文件可从http://sourceforge.net/projects/oncoimpact.免费获得
Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact.
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DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
通讯作者：
--
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.7
作者：
Cerami E;Demir E;Schultz N;Taylor BS;Sander C
通讯作者：
Sander C
影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
12.3
作者：
Bashashati A;Haffari G;Ding J;Ha G;Lui K;Rosner J;Huntsman DG;Caldas C;Aparicio SA;Shah SP
通讯作者：
Shah SP