An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.
An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.
复制标题
DOI：
10.1371/journal.pcbi.1004350
复制
发表时间：
2015-09
影响因子：
4.3
通讯作者：
Michor F
中科院分区：
文献类型：
--
作者：
Foo J;Liu LL;Leder K;Riester M;Iwasa Y;Lengauer C;Michor F
癌症是一种遗传性疾病，可以通过靶向突变癌蛋白的药物成功治疗，这一传统观点推动了许多人类癌症类型的全基因组测序工作。然而，只有在癌症基因组版图中发现的一部分突变才可能为细胞提供健康优势。将这种“司机”突变与无害的“乘客”事件区分开来，对于优先确认疾病相关模型中的候选突变至关重要。我们设计了一种新的统计指数，称为搭便车指数，它反映了给定横截面癌症样本集改变频率的情况下，任何观察到的候选基因是乘客改变的概率，并将其应用于结直肠癌突变数据集。我们的方法是基于一个群体动力学模型的突变积累和选择在结直肠组织在癌症启动前以及在肿瘤形成期间。这种方法可以用来帮助确定功能验证的候选突变的优先顺序，并有助于药物发现的过程。进化动力学模型已被广泛研究，以阐明肿瘤的发生过程。研究肿瘤发生的一个关键方面是区分为癌细胞提供健康优势的“司机”突变和中性的“乘客”或“搭便车”突变。已经开发了许多统计模型来解决这一问题。然而，进化模型通过考虑组织内突变积累和选择的过程，增加了另一层复杂性。在这里，我们提出了一种结合统计学和进化思维的新方法，利用横截面突变数据来识别癌症基因组中的驱动突变。我们的方法考虑了在结直肠癌发生之前和期间突变积累和选择的过程。这项工作证明了使用进化种群动力学模型来研究肿瘤发生的驱动因素的重要性。
The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such “driver” mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery. Evolutionary dynamic models have been intensively studied to elucidate the process of tumorigenesis. One key aspect of studying tumorigenesis is to distinguish the “driver” mutations providing a fitness advantage to cancer cells against neutral “passenger” or “hitchhiking” mutations. Many statistical models to address this question have been developed. Evolutionary models, however, add another layer of complexity by taking into account the process of mutation accumulation and selection within the tissue. Here we present a novel approach combining both statistical and evolutionary thinking to identify driver mutations in cancer genomes using cross-sectional mutation data. Our method considers the process of mutation accumulation and selection before and during colorectal cancer initiation. This work demonstrates the importance of using evolutionary population dynamic models to study driver events of tumorigenesis.