超过2500个癌症基因组的乘客突变:整体分子功能影响和后果
Sushant Kumar1, Jonathan Warrell1, Shantao Li1
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Cell
|February 22, 2020
概括
癌症突变不仅仅是"司机"或"乘客". 一项新的研究揭示了中等影响的突变,
科学领域:
- 基因组学
- 癌症生物学
- 计算生物学
背景情况:
- 传统的癌症模型将突变分为具有影响力的"驱动者"或无关紧要的"乘客".
- 这种二分论观点可能过分简化了瘤的复杂突变.
研究的目的:
- 调查驾驶员与乘客分歧之外的中等影响突变的存在.
- 探索突变影响,亚克隆架构和突变特征之间的关系.
- 评估综合乘客突变对癌症表型的预测能力.
主要方法:
- 利用ICGC/TCGA全基因组癌症分析 (PCAWG) 项目的综合变异数据集.
- 分析了突变影响,亚克隆结构 (早期与晚期突变) 和突变特征.
- 从复杂特征研究中调整了一个添加效应模型来评估突变的集体影响.
主要成果:
- 确定了第三组中等影响的乘客突变, 挑战严格的司机-乘客二分法.
- 证明了分子影响,亚克隆结构和独特的突变特征之间的相关性.
- 在预测癌症表型方面,综合乘客突变,包括潜在的弱驱动因素,提供了显著的附加变异 (约12%).
- 在缺乏已知的驱动因子的样本中估计了潜在的弱驱动因子突变的频率.
结论:
- 癌症的突变格局比简单的司机乘客模式更加细致.
- 似乎影响较小的突变的综合效应可以大大促进癌症的发展和进展.
- 这种框架提高了我们预测癌症表型的能力, 并通过考虑所有突变的集体影响来确定潜在的治疗点.
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