Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2 , to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2 .

中文翻译：

---

在没有拷贝中性假设的情况下表征体细胞突变的肿瘤内异质性

同一患者的大量样本本质上是异质的，包括不同的癌细胞亚群（亚克隆）。肿瘤亚克隆中的细胞具有独特的突变基因型特征。通过估计肿瘤中存在的基因型、细胞比例和亚克隆数量来解决肿瘤异质性有助于了解癌症进展和治疗。我们提出了一种新颖的方法，ChaClone2，有效地去卷积观察到的变异等位基因部分（VAF），并考虑突变位点拷贝数畸变的可能影响。我们的方法描述了特征分配模型的状态空间公式，将同一患者样本中观察到的 VAF 解卷积为三个矩阵：突变基因的亚克隆总数和变异拷贝数，以及每个样本中亚克隆的比例。我们描述了一种有效的顺序蒙特卡罗 (SMC) 算法来估计这些矩阵。广泛的模拟表明，与解决类似问题的其他最先进的方法相比，ChaClone2 产生了更好的准确性，并且它为大型数据集提供了可扩展性。还，ChaClone2 的特点是，只要有新测序的基因组位置的新突变数据可用，就可以改进模型参数估计。MATLAB 代码和数据集可从以下网址下载：https://github.com/moyanre/method2 .