Subclonal reconstruction of tumors by using machine learning and population genetics.
Subclonal reconstruction of tumors by using machine learning and population genetics.
复制标题
通过使用机器学习和种群遗传学对肿瘤的亚克隆重建。
DOI：
10.1038/s41588-020-0675-5
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发表时间：
2020-09
期刊：
影响因子：
30.8
通讯作者：
Sottoriva A
中科院分区：
文献类型：
--
作者：
Caravagna G;Heide T;Williams MJ;Zapata L;Nichol D;Chkhaidze K;Cross W;Cresswell GD;Werner B;Acar A;Chesler L;Barnes CP;Sanguinetti G;Graham TA;Sottoriva A
大多数癌症基因组数据来自由癌症亚群和正常细胞混合组成的大量样本。基于机器学习的亚克隆重建方法旨在分离样本中的这些亚种群并重建其进化史。然而，目前的方法完全是数据驱动的，对进化论不可知。我们证明，如果不考虑进化，分析中会出现系统错误，而对同一肿瘤进行多次采样会加剧这种错误。我们提出了一种基于模型的肿瘤亚克隆重建（MOBSTER）的新方法，该方法将机器学习与理论群体遗传学相结合。利用来自不同队列的2,606个样本的公开全基因组测序数据、新数据和合成验证，我们表明该方法在单样本、多区域和纵向数据方面比现有技术更稳健和准确。这种方法最大限度地减少了非进化方法的混杂因素，从而更准确地恢复了人类癌症的进化历史。
The majority of cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction approaches based on machine learning aim to separate those subpopulations in a sample and reconstruct their evolutionary history. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated by multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction (MOBSTER) that combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show this method is more robust and accurate than current techniques in single sample, multi-region and longitudinal data. This approach minimizes the confounding factors of non-evolutionary methods, leading to more accurate recovery of the evolutionary history of human cancers.
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影响因子：
64.8
作者：
Greaves, Mel;Maley, Carlo C.
通讯作者：
Maley, Carlo C.
影响因子：
1.6
作者：
Kessler DA;Levine H
通讯作者：
Levine H
影响因子：
5.4
作者：
Dentro, Stefan C.;Wedge, David C.;Van Loo, Peter
通讯作者：
Van Loo, Peter
DOI：
10.1109/34.865189
发表时间：
2000-07-01
影响因子：
23.6
作者：
Biernacki, C;Celeux, G;Govaert, G
通讯作者：
Govaert, G
影响因子：
9.3
作者：
Griffith, Malachi;Miller, Christopher A.;Wilson, Richard K.
通讯作者：
Wilson, Richard K.