Ozturk Kivilcim, Carter Hannah
Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Bioinformatics Program, University of California San Diego, La Jolla, CA, USA.
Methods Mol Biol. 2019;1907:51-72. doi: 10.1007/978-1-4939-8967-6_4.
Human cancers often harbor large numbers of somatic mutations. However, only a small proportion of these mutations are expected to contribute to tumor growth and progression. Therefore, determining causal driver mutations and the genes they target is becoming an important challenge in cancer genomics. Here we describe an approach for mapping somatic mutations onto 3D structures of human proteins in complex to identify "driver interfaces." Our strategy relies on identifying protein-interaction interfaces that are unexpectedly biased toward nonsynonymous mutations, which suggests that these interfaces are subject to positive selection during tumorigenesis, implicating the interacting proteins as candidate drivers.
人类癌症通常含有大量体细胞突变。然而,预计这些突变中只有一小部分会促进肿瘤生长和进展。因此,确定因果驱动突变及其靶向的基因正成为癌症基因组学中的一项重要挑战。在此,我们描述了一种将体细胞突变映射到复杂人类蛋白质三维结构上以识别“驱动界面”的方法。我们的策略依赖于识别那些意外偏向非同义突变的蛋白质相互作用界面,这表明这些界面在肿瘤发生过程中受到正选择,意味着相互作用的蛋白质是候选驱动因素。