Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea.
Hum Genet. 2013 Jan;132(1):15-27. doi: 10.1007/s00439-012-1218-7. Epub 2012 Aug 21.
Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.
癌症基因组的大规模测序揭示了许多新的突变和肿瘤间异质性。因此，根据潜在的有害影响对变体进行优先级排序已变得至关重要。我们构建了疾病基因网络，并提出了一种贝叶斯集成方法，该方法整合了多种来源来预测错义变体的功能效应。我们分析了 12039 个人类蛋白的 23336 个错义疾病突变和 36232 个中性多态性。结果表明，在敏感性和特异性方面，预测准确性都得到了成功的提高，我们通过将其应用于从结直肠和乳腺癌细胞系获得的体细胞突变，证明了该方法的实用性。候选基因与预测的有害突变以及已知的癌症基因在与致癌相关的许多 KEGG 途径中显著富集，支持癌症在途径水平上的遗传同质性。乳腺癌特异性网络提高了对乳腺癌突变的预测准确性。本研究提供了一个有害突变和候选癌症基因的排序列表，并表明可能发生在重要途径中的影响癌症的突变，并且应该在与表型相关的网络或途径上进行解释。疾病基因网络可能有助于预测新型疾病特异性突变的功能效应。
