College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA.
Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
Bioinformatics. 2020 Mar 1;36(6):1712-1717. doi: 10.1093/bioinformatics/btz851.
Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.
We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.
An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.
Supplementary data are available at Bioinformatics online.
癌症驱动基因的功能在不同的组织和器官中有很大的差异。区分每个癌症类型的乘客基因、癌基因（OGs）和肿瘤抑制基因（TSGs）对于理解肿瘤生物学和确定临床可操作的靶点至关重要。尽管有许多计算工具可用于预测潜在的癌症驱动基因，但用于 OG 和 TSG 上下文感知分类的资源有限。
我们表明，蛋白质编码突变的体细胞选择的方向和幅度对于乘客基因、OG 和 TSG 有显著的不同。基于这些模式，我们开发了一种新的方法（肿瘤中选择的基因），以特定于癌症类型的方式发现 OG 和 TSG。通过严格的交叉验证评估，肿瘤中选择的基因显示出很高的准确性（92%）。其在 10172 个肿瘤外显子中的应用发现了具有高组织特异性的已知和新的癌症驱动基因。在 13 个在多种癌症类型中共享的 OG 中，我们发现了在不同癌症中选择性参与的功能域，这表明疾病机制存在差异。
GUST 算法的 R 实现可在 https://github.com/liliulab/gust 获得。一个带有预计算结果的数据库可在 https://liliulab.shinyapps.io/gust 获得。
补充数据可在生物信息学在线获得。
