University of Trieste, Trieste, Italy.
The Institute of Cancer Research, London, UK.
BMC Bioinformatics. 2020 Nov 17;21(1):531. doi: 10.1186/s12859-020-03863-1.
The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolution methods are used to determine the composition of cancer subpopulations in the biopsy sample, a fundamental step to determine clonal expansions and their evolutionary trajectories.
In a recent work we have developed a new model-based approach to carry out subclonal deconvolution from the site frequency spectrum of somatic mutations. This new method integrates, for the first time, an explicit model for neutral evolutionary forces that participate in clonal expansions; in that work we have also shown that our method improves largely over competing data-driven methods. In this Software paper we present mobster, an open source R package built around our new deconvolution approach, which provides several functions to plot data and fit models, assess their confidence and compute further evolutionary analyses that relate to subclonal deconvolution.
We present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing neutral and positive selection in cancer. We showcase the analysis of two datasets, one simulated and one from a breast cancer patient, and overview all package functionalities.
从癌症组织的全基因组测序中获取大规模的全基因组测序谱，正在推动进化理论在癌症中的应用。从一个大块活检中，使用亚克隆反卷积方法来确定活检样本中癌症亚群的组成，这是确定克隆扩张及其进化轨迹的基本步骤。
在最近的一项工作中，我们开发了一种新的基于模型的方法，从体细胞突变的位点频率谱中进行亚克隆反卷积。这种新方法首次整合了参与克隆扩张的中性进化力量的显式模型；在这项工作中，我们还表明，我们的方法大大优于竞争的数据驱动方法。在本软件论文中，我们介绍了 mobster，这是一个围绕我们新的反卷积方法构建的开源 R 包，它提供了几个函数来绘制数据和拟合模型，评估它们的置信度，并计算与亚克隆反卷积相关的进一步进化分析。
我们提出了用于从批量测序中进行肿瘤亚克隆反卷积的 mobster 包，这是第一个将机器学习和群体遗传学集成在一起的方法，可以明确地对癌症中同时存在的中性和阳性选择进行建模。我们展示了对两个数据集的分析，一个是模拟的，一个是来自乳腺癌患者的，概述了所有的包功能。
