SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data
SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data
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DOI：
10.1101/gr.243121.118
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发表时间：
2019-11-01
期刊：
影响因子：
7
通讯作者：
Nakhleh, Luay
中科院分区：
文献类型：
--
作者：
Zafar, Hamim;Navin, Nicholas;Nakhleh, Luay
在达尔文进化论框架下，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。鉴定肿瘤细胞群体（克隆）和重建它们的进化关系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解析到单细胞水平。然而，SCS数据集中的技术错误，包括由于等位基因缺失和细胞双联体导致的假阳性（FP）和假阴性（FN），使这些任务显着复杂化。在这里，我们提出了一个非参数贝叶斯方法，重建克隆群体的单细胞，每个克隆的基因型，克隆之间的进化关系的集群。它采用了一个树状结构的中国餐馆过程作为克隆种群的数量和组成的先验。克隆种群的进化是由克隆的重复发生和有限位点的进化模型来模拟的，以考虑潜在的突变复发和损失。我们概率帐户FP和FN的错误，和细胞双峰采用β-二项分布建模。我们开发了一个Gibbs抽样算法，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生殖导致更准确的推断。我们的方法是第一个在完全贝叶斯框架中启用这种联合重建的方法，从而提供了支持其推理的措施。
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.