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De Novo Discovery of Mutated Driver Pathways in Cancer

机译:从头发现癌症中突变的驱动器途径

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摘要

Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in interpretation of this data is to distinguish functional driver mutations that are important for cancer development from random, passenger mutations. A common approach to identify driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb the necessary pathways. However, the current understanding of the somatic mutational process of cancer [3,5,6] places two additional constraints on the expected patterns of somatic mutations in a cancer pathway. First, an important cancer pathway should be perturbed in a large number of patients. Thus we expect that with genome-wide measurements of somatic mutations a driver pathway will exhibit high coverage, where most patients will have a mutation in some gene in the pathway. Second, since driver mutations are relatively rare and typically a single driver mutation is sufficient to perturb a pathway, a reasonable assumption is that most patients have a single driver mutation in a pathway. Thus, the genes in a driver pathway exhibit a pattern of mutually exclusive driver mutations, where driver mutations are observed in exactly one gene in the pathway in each patient. There are numerous examples of sets of mutually exclusive mutations [5,6].
机译:下一代DNA测序技术使大量癌症患者的体细胞突变的全基因组测量成为可能。解释该数据的主要挑战是将对癌症发展至关重要的功能性驱动基因突变与随机的,过客的突变区分开。识别驱动程序突变的一种常用方法是在大量癌症基因组中找到以显着频率突变的基因。通过观察到驱动程序突变靶向多种细胞信号传导和调节途径,使该方法感到困惑。因此,每个癌症患者可能表现出足以干扰必要途径的不同突变组合。但是,目前对癌症的体细胞突变过程的理解[3,5,6]对癌症途径中体细胞突变的预期模式提出了两个额外的限制。首先,重要的癌症途径应在大量患者中扰动。因此,我们期望通过全基因组的体细胞突变测量,驱动程序途径将显示出较高的覆盖率,其中大多数患者的途径中某些基因会发生突变。其次,由于驱动程序突变相对较少,并且通常单个驱动程序突变足以干扰路径,因此合理的假设是,大多数患者在路径中具有单个驱动程序突变。因此,驱动程序途径中的基因表现出相互排斥的驱动程序突变的模式,其中在每位患者的途径中,恰好在一个基因中观察到驱动程序突变。有许多互斥突变的例子[5,6]。

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  • 会议地点 Vancouver(CA);Vancouver(CA)
  • 作者单位

    Department of Computer Science, Brown University, Providence, RI ,Center for Computational Molecular Biology, Brown University, Providence, RI;

    Department of Computer Science, Brown University, Providence, RI;

    Department of Computer Science, Brown University, Providence, RI ,Center for Computational Molecular Biology, Brown University, Providence, RI;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物工程学(生物技术);
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