Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer 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 with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which 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 that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.

大多数癌症基因组数据都是从由癌症亚群和正常细胞的混合物组成的大量样本中生成的。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群并推断它们的进化历史。然而，当前的方法完全是数据驱动的，并且与进化理论无关。我们证明，如果不考虑进化，分析中就会出现系统错误，并且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的新方法，称为 MOBSTER，它将机器学习与理论群体遗传学相结合。使用来自不同队列的 2,606 个样本的公共全基因组测序数据、新数据和综合验证，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加稳健和准确。这种方法最大限度地减少了非进化方法的混杂因素，从而可以更准确地恢复人类癌症的进化史。