Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. .
BMC Genomics. 2012 Jun 18;13 Suppl 4(Suppl 4):S9. doi: 10.1186/1471-2164-13-S4-S9.
Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.
We mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the "mutational landscapes" of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain "peaks" were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.
Using our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, the method shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.
目前正在进行大规模肿瘤测序项目，以鉴定驱动肿瘤发生和发展的遗传突变。大多数研究采用基于基因的方法来鉴定驱动突变，突出了在大量肿瘤样本中发生突变的基因，认为这些基因可能包含驱动突变。然而，这种基于基因的方法通常不考虑突变在基因内的位置或提供突变位置的功能背景。在这里，我们引入了一种将突变映射到发生突变的不同蛋白质结构域的新方法，而不仅仅是单个基因，从而提供了突变如何导致疾病的功能背景。此外，聚合来自含有特定蛋白质结构域的所有基因的突变，能够识别在基因水平上罕见但在指定结构域中频繁发生的突变。这些高度突变的结构域可能揭示了癌症发生所必需的蛋白质功能的破坏。
我们将 100 个结肠腺癌肿瘤样本的蛋白质编码区中的体细胞突变映射到发生突变的基因和蛋白质结构域，并构建地形图来描绘基因和结构域突变频率的“突变景观”。我们发现，在 APC、TP53 和 KRAS 等先前已知在结肠癌患者中发生体细胞突变的一些基因中，存在显著的突变频率。此外，我们还发现了这些基因中特定结构域内以及突变频率较低的基因中包含的其他结构域内的显著突变频率。这些结构域“峰”富含与癌症发展相关的重要功能，包括激酶活性、DNA 结合和修复以及信号转导。
使用我们的方法创建结肠癌突变的结构域景观，我们能够鉴定出具有高潜力驱动癌症发展的体细胞突变。有趣的是，大多数涉及的基因的突变频率都较低。因此，该方法在当前大规模肿瘤测序项目中具有识别罕见驱动突变的良好潜力。此外，将突变映射到特定结构域为理解突变如何导致疾病提供了必要的功能背景，并可能揭示新的或更精细的基因和结构域药物开发靶点。
