Department of Electrical Engineering, Columbia University, New York, NY, United States of America.
Department of Systems Biology, Columbia University, New York, NY, United States of America.
PLoS One. 2019 Jan 25;14(1):e0211213. doi: 10.1371/journal.pone.0211213. eCollection 2019.
Tumors are heterogeneous in the sense that they consist of multiple subpopulations of cells, referred to as subclones, each of which is characterized by a distinct profile of genomic variations such as somatic mutations. Inferring the underlying clonal landscape has become an important topic in that it can help in understanding cancer development and progression, and thereby help in improving treatment. We describe a novel state-space model, based on the feature allocation framework and an efficient sequential Monte Carlo (SMC) algorithm, using the somatic mutation data obtained from tumor samples to estimate the number of subclones, as well as their characterization. Our approach, by design, is capable of handling any number of mutations. Via extensive simulations, our method exhibits high accuracy, in most cases, and compares favorably with existing methods. Moreover, we demonstrated the validity of our method through analyzing real tumor samples from patients from multiple cancer types (breast, prostate, and lung). Our results reveal driver mutation events specific to cancer types, and indicate clonal expansion by manual phylogenetic analysis. MATLAB code and datasets are available to download at: https://github.com/moyanre/tumor_clones.
肿瘤在某种意义上是异质的，因为它们由多个细胞亚群组成，这些亚群被称为亚克隆，每个亚克隆都具有独特的基因组变异特征，如体细胞突变。推断潜在的克隆景观已成为一个重要的研究课题，因为它有助于了解癌症的发展和进展，从而有助于改善治疗效果。我们描述了一种新的状态空间模型，该模型基于特征分配框架和高效的序列蒙特卡罗（SMC）算法，使用从肿瘤样本中获得的体细胞突变数据来估计亚克隆的数量及其特征。我们的方法可以设计处理任意数量的突变。通过广泛的模拟，我们的方法在大多数情况下具有很高的准确性，并与现有方法相比具有优势。此外，我们通过分析来自多种癌症类型（乳腺、前列腺和肺）患者的真实肿瘤样本验证了我们方法的有效性。我们的结果揭示了特定于癌症类型的驱动突变事件，并通过手动系统发育分析表明了克隆扩张。MATLAB 代码和数据集可在以下网址下载：https://github.com/moyanre/tumor_clones。
