Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.
Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.
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DOI：
10.3390/cancers13215291
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发表时间：
2021-10-21
期刊：
影响因子：
5.2
通讯作者：
Marchal K
中科院分区：
文献类型：
--
作者：
de Schaetzen van Brienen L;Miclotte G;Larmuseau M;Van den Eynden J;Marchal K
由于统计原因，癌症驱动基因的鉴定往往偏向于在一个队列中频繁改变的基因。然而，不太频繁突变的基因也可以改变癌症的特征。为了检测这种罕见突变的基因参与驱动转移性前列腺癌，我们分析了Hartwig医学基金会转移性前列腺癌队列。在此，我们开发了一种新的基于网络的方法GoNetic，它可以检测具有较低突变率的基因，作为在先前相互作用网络上连接的反复突变的基因集的成员。与最先进的基于网络的驱动识别方法相比，GoNetic保留了样本特异性突变的信息，并使用了先前相互作用网络的更多属性。当应用于Hartwig医学基金会队列时，GoNetic成功地优先考虑了转移性前列腺癌的已知驱动因素和很少突变的候选驱动因素。与其他公共数据集的综合验证进一步支持了这些新候选驱动程序的潜力。大多数已知的转移性前列腺癌的驱动基因经常发生突变。为了深入研究很少突变的驱动因素的长尾，我们对Hartwig医学基金会转移性前列腺癌数据集（HMF队列）进行了基于网络的驱动因素识别。在此，我们开发了一种基于概率寻径的遗传方法来识别反复突变的子网络。与大多数最先进的基于网络的方法相比，遗传可以利用样本特定的突变信息和潜在的先验网络的权重。当应用于HMF队列时，GoNetic成功地恢复了已知的原发性和转移性前列腺癌驱动因素，这些驱动因素在HMF队列中经常发生突变（TP53， RB1和CTNNB1）。此外，确定的子网络包含频繁突变的基因，反映了与转移性前列腺癌相关的过程，并包含很少突变的驱动候选基因。为了进一步验证这些很少突变的基因，我们使用独立队列评估了鉴定的基因在转移性样本中是否比在原始样本中更容易突变。然后我们评估了它们与肿瘤演变和患者淋巴结状态的关系。这导致了几个新的假定的转移性前列腺癌的驱动基因，其中一些可能是疾病演变的预后。
The identification of cancer driver genes is, for statistical reasons, often biased toward genes that are altered frequently in a cohort. However, genes that are less frequently mutated can also alter cancer hallmarks. To detect such rarely mutated genes involved in driving metastatic prostate cancer, we analyzed the Hartwig Medical Foundation metastatic prostate cancer cohort. Hereto, we developed GoNetic, a novel network-based method that can detect genes with a lower mutational rate as members of recurrently mutated sets of genes connected on a prior interaction network. In contrast to state-of-the-art network-based driver identification methods, GoNetic retains information on sample-specific mutations and uses more properties of the prior interaction network. When applied to the Hartwig Medical Foundation cohort, GoNetic successfully prioritized both known drivers and rarely mutated driver candidates of metastatic prostate cancer. Comprehensive validation with other public data sets further supported the driver potential of these novel candidates. Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.
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影响因子：
8.8
作者：
Khan I;Steeg PS
通讯作者：
Steeg PS
影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
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64.5
作者：
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作者：
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Campbell C
影响因子：
30.8
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
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Shendure, Jay