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从DNA测序数据推断出染色体选择参数和癌症的错误分离率

Zijin Xiang¹, Zhihan Liu¹, Khanh N Dinh²

¹Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY, USA.

Scientific reports

|July 31, 2024

概括

这项研究引入了一种新的算法,用于使用DNA测序数据分析癌症形积分. 该方法有效地区分了复制数异常率与选择压力,改善了对癌症演变的理解.

科学领域:

基因组学就是基因组学.
癌症生物学癌症生物学
计算生物学计算生物学

背景情况:

积体,或异常的染色体数量,在癌症中很常见,并与患者的不良结果有关.
在DNA测序数据中分析积体需要将拷贝数异常 (CNA) 率与型的选择效应分开.

研究的目的:

开发一个参数推断算法,用于分析癌症中的形积分,使用批量和单细胞DNA测序数据.
为了区分CNA发生率和对癌细胞类型起作用的选择系数.

主要方法:

利用近似贝叶斯计算 (ABC) 与染色体不稳定模拟算法CINner相结合.
采用了三个统计组:基于复制数量的测量 (A),分谱尖端统计 (B) 和分谱平衡指数 (C).

主要成果:

该算法成功地从地面真相数据中恢复了CNA概率和选择参数,即使在小数据集中也表现良好.
A和C组的统计数据有效地确定了CNA概率,而只有A组准确地估计了选择参数.
由于相对于细胞数的CNA事件数量较低,研究人员发现,分化端统计 (B组) 对于染色体水平分析是不准确的.

结论:

开发的推断框架有助于分析各种癌症类型并预测癌症的克隆动态.
它使得在癌症进化过程中选择和中性漂移之间有区别.
随着大量和单细胞DNA测序数据变得越来越普遍,这种方法是有价值的.

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