Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.

中文翻译：

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SiCloneFit：根据单细胞基因组测序数据对肿瘤克隆的种群结构、基因型和系统发育进行贝叶斯推断。

达尔文框架中体细胞突变的积累和选择导致肿瘤内异质性 (ITH)，这对癌症的诊断和临床治疗提出了重大挑战。肿瘤细胞群（克隆）的鉴定和它们进化关系的重建可以阐明这种异质性。最近开发的单细胞 DNA 测序 (SCS) 技术有望将 ITH 解决到单细胞水平。然而，SCS 数据集中的技术错误，包括由于等位基因丢失和细胞双联体导致的假阳性 (FP) 和假阴性 (FN)，使这些任务变得非常复杂。在这里，我们提出了一种非参数贝叶斯方法，将克隆群体重建为单细胞簇、每个克隆的基因型以及克隆之间的进化关系。它采用树结构的中餐厅流程作为克隆种群数量和组成的先验。克隆种群的进化由克隆系统发育和进化的有限位点模型建模，以解释潜在的突变复发和损失。我们从概率上考虑了 FP 和 FN 错误，并且通过使用 Beta-二项式分布对单元格双峰进行建模。我们开发了一种 Gibbs 采样算法，包括部分可逆跳跃和部分 Metropolis-Hastings 更新，以探索所有参数的联合后验空间。我们的方法在合成和实验数据集上的性能表明，在有限位点进化模型下联合重建肿瘤克隆和克隆系统发育可以得出更准确的推论。