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使用单细胞测序和进化约束推断癌症中的活跃突变过程.

Gryte Satas1,2, Matthew A Myers1,2, Andrew McPherson1,2

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超低覆盖的单细胞全基因组测序 (scWGS) 可以区分活跃的癌症与历史的突变过程. 这种方法揭示了与瘤进化和治疗耐药性相关的动态突变模式.

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科学领域:

  • 基因组学就是基因组学.
  • 癌症生物学 癌症生物学
  • 进化生物学 进化生物学

背景情况:

  • 癌症的自然史是由正在进行的突变发生,创造遗传多样性的形状.
  • 从历史突变过程中区分活跃的对于理解瘤进化,呈现和治疗耐药性至关重要.
  • 大量测序通常只捕获历史的突变特征,限制了对动态过程的洞察力.

研究的目的:

  • 调查超低覆盖单细胞全基因组测序 (scWGS) 是否可以区分癌症中历史和活跃的突变过程.
  • 开发一种方法,在稀疏的scWGS数据中对单核酸变异 (SNV) 进行可靠的分析.
  • 揭示各种癌症类型的突变发生的时间和空间模式.

主要方法:

  • 引入了Articull,这是一种通过利用进化约束来识别和删除scWGS数据中的SNV文物的新方法.
  • 应用Articull分析了胰腺管腺癌 (PDAC),三阴性乳腺癌 (TNBC) 和高度血清性卵巢癌 (HGSOC) 的scWGS数据.
  • 检查了治疗和未治疗癌症模型中的突变模式,以确定活跃的突变特征.

主要成果:

  • 证明scWGS数据尽管稀少,但包含了关于动态突变过程的有价值信息.
  • 在PDAC中观察到不匹配修复缺陷 (MMRd) 的时间增加.
  • 鉴定了治疗诱导的突变发生和APOBEC3无活化在西斯普拉丁治疗的TNBC异种移植中.
  • 揭示了不同的APOBEC3突变发生模式和HGSOC的晚期瘤全方位激活.
  • 检测到克隆特异性的SBS17活动增加与HGSOC.中复发相关.

结论:

  • 超低覆盖scWGS是研究癌症活跃突变过程的强大工具.
  • 这种方法可以提供关于正在进行的克隆进化和治疗耐药性的机制的见解.
  • 这些发现突显了scWGS在多种癌症中剖析动态变异原体景观的潜力.