Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure.
Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure.
复制标题
使用3D蛋白质结构，外显尺度发现了人类癌症中热点突变区域。
DOI：
10.1158/0008-5472.can-15-3190
复制
发表时间：
2016-07-01
期刊：
影响因子：
11.2
通讯作者：
Karchin R
中科院分区：
文献类型：
--
作者：
Tokheim C;Bhattacharya R;Niknafs N;Gygax DM;Kim R;Ryan M;Masica DL;Karchin R
体细胞错义突变对癌症病因和进展的影响通常难以解释。评估错义突变在致癌作用中的作用的一种常见方法是识别统计上非随机频率的突变基因。即使目前有大量已测序的癌症样本，这种方法仍然不足以检测驱动因素，特别是在研究较少的癌症类型中。需要替代的统计和生物信息方法。提高功效的一种方法是关注错义突变密度增加的局部区域或热点区域，而不是整个基因或蛋白质结构域。检测三维蛋白质结构中的错义突变热点区域也可能是有益的，因为单独的线性序列并不能完全描述密码子的生物学相关组织。在这里，我们提出了一种新颖且统计严格的算法，用于检测 3D 蛋白质结构中的错义突变热点区域。我们分析了癌症基因组图谱 (TCGA) 中的约 3×105 个突变，并确定了 216 个肿瘤类型特异性热点区域。除了通过实验确定的蛋白质结构外，我们还考虑高质量的结构模型，它将基因组覆盖范围从约 5,000 个基因增加到超过 15,000 个基因。我们提供了新的证据表明 3D 突变分析具有独特的优势。它能够发现比之前显示的更多基因中的热点区域，并提高对肿瘤抑制基因中热点区域的敏感性。虽然长期以来人们都知道热点区域存在于抑癌基因和癌基因中，但我们首次报告表明它们在两种类型的驱动基因中具有不同的特性。我们展示了癌症研究人员如何利用我们的结果将 3D 蛋白质结构与癌症中错义突变的生物学功能联系起来，并生成有关驱动机制的可检验假设。我们的结果包含在一个新的交互式网站中，用于可视化具有 TCGA 突变和相关热点区域的蛋白质结构。用户可以提交新的序列数据，从而促进生物学相关环境中突变的可视化。
The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three dimensional protein structure may also be beneficial, because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyze ~3×105 mutations from The Cancer Genome Atlas (TCGA) and identify 216 tumor-type-specific hotspot regions. In addition to experimentally determined protein structures we consider high-quality structural models, which increases genomic coverage from ~5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes. While hotspot regions have long been known to exist in both tumor suppressor genes and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biological functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context.