从单细胞癌症基因组的时间序列建模推断的克隆适应性
Sohrab Salehi1, Farhia Kabeer1,2, Nicholas Ceglia3
1Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada.
Nature
|June 24, 2021
概括
这项研究揭示了拷贝数的变化如何影响癌症的演变. 在三阴性乳腺癌中,TP53突变和化疗改变了克隆健康动态,影响了治疗耐药性.
科学领域:
- 基因组学
- 癌症生物学
- 进化生物学
背景情况:
- 由于时间序列单细胞采样和时间统计模型的局限性,在癌症中定义基因组适应性景观,特别是涉及拷贝数变化 (CNA) 的景观具有挑战性.
- 了解克隆动态对于开发有效的癌症疗法至关重要.
研究的目的:
- 研究TP53突变和西斯普拉丁化疗对乳腺癌中CNA定义的克隆健康动态的影响.
- 从时间序列单细胞基因组数据推断出新的时间统计模型.
主要方法:
- 从多年时间序列的单细胞乳腺上皮和三阴性乳腺癌 (TNBC) 患者衍生的异种移植 (PDX) 中生成了42,000个基因组.
- 应用了一种新的赖特-费舍尔种群遗传模型,从基于CNA的基因型推断出克隆适应性.
- 在TNBC PDX模型中进行了评估克隆竞争动态和耐药性演变的实验.
主要成果:
- 发现TP53突变改变了健康状况,将健康状况分布在与不同CNA相关的大量克隆中.
- 在TP53突变的TNBC PDX模型中,推断的适应性系数准确地预测了实验性克隆竞争动态.
- 西斯普拉丁治疗导致了先前健身不佳的克隆的出现,并消除了高健身的克隆,这表明了健身格局的逆转.
- 药物戒断逆转了选择压力,表明与治疗抗性相关的健康成本.
结论:
- 这项研究定义了与多克隆瘤的CNA和治疗耐药性相关的克隆适应性.
- 这些发现为了解基因突变和化疗等选择性压力下的癌症演变提供了框架.
- 开发的模型和实验方法为研究瘤异质性和预测治疗反应提供了新的工具.
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