癌症驱动基因和突变的综合特征
Matthew H Bailey1, Collin Tokheim2, Eduard Porta-Pardo3
1Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA.
Cell
|April 7, 2018
概括
这项研究使用26种工具对9,423种瘤进行了全面的癌症驱动基因和突变分类. 发现了299个驱动基因和3400多个突变,
科学领域:
- 基因组学
- 计算生物学
- 癌症学
背景情况:
- 识别癌症分子驱动因素对于推进精确瘤学至关重要.
- 现有的驾驶员识别算法众多,但很少在大型数据集上进行组合或优化.
研究的目的:
- 进行全面的泛癌和泛软件分析以对癌症驱动基因和突变进行分类.
- 系统地结合和优化多个计算工具来识别驾驶员.
主要方法:
- 对所有33个癌症基因组图谱 (TCGA) 项目的9423个瘤外体进行分析.
- 使用26种不同的计算工具进行驱动基因和突变分类.
- 使用基于序列和结构的分析进行突变特征和实验验证.
主要成果:
- 确定了299个驱动基因,对解剖部位和癌症/细胞类型有影响.
- 发现了3400多种假定错误的驱动突变,
- 实验验证证了60% - 85%的预测突变是可能的驱动因素.
- 发现微卫星不稳定 (MSI) 瘤与高PD-1/PD-L1表达之间的关联.
- 确定57%的分析瘤具有潜在的临床可行的事件.
结论:
- 这项研究代表了迄今为止最广泛的癌症基因和突变发现.
- 这些发现为未来的癌症研究和临床应用提供了基础资源和蓝图.
- 这种全面的驱动因素和可操作的突变将加速精确瘤学工作.
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