肿瘤异质性的贝叶斯特征分配模型
A Bayesian feature allocation model for tumor heterogeneity
摘要：
我们开发了一个特征分配模型，用于使用下一代测序数据推断遗传肿瘤变异。具体而言，我们基于映射到人类参考基因组的短读段记录单核苷酸变体（SNV），并通过定义为相同同源基因组上SNV的支架的潜在单倍型来表征肿瘤异质性。对于来自单个肿瘤的多个样品，假设每个样品由这些单倍型的一些样品特异性比例组成，然后我们拟合每个样品的SNV的观察到的变体等位基因分数并估计单倍型的比例。不同样本中单倍型的比例不同是肿瘤异质性的证据，因为它意味着细胞亚群的组成不同。从贝叶斯的角度来看，我们对所有相关未知量进行了先验概率模型，特别是包括表征潜在单倍型的二元指标的先验概率模型。这种先验模型被称为特征分配模型。具体来说，我们定义了一个简化版本的印度自助餐过程中，最传统的功能分配模型之一。所提出的模型允许重叠的SNV在定义潜在的单倍型，这反映了在肿瘤样品的亚克隆扩增的进化过程中的聚类。
英文摘要：
We develop a feature allocation model for inference on genetic tumor variation using next-generation sequencing data. Specifically, we record single nucleotide variants (SNVs) based on short reads mapped to human reference genome and characterize tumor heterogeneity by latent haplotypes defined as a scaffold of SNVs on the same homologous genome. For multiple samples from a single tumor, assuming that each sample is composed of some sample-specific proportions of these haplotypes, we then fit the observed variant allele fractions of SNVs for each sample and estimate the proportions of haplotypes. Varying proportions of haplotypes across samples is evidence of tumor heterogeneity since it implies varying composition of cell subpopulations. Taking a Bayesian perspective, we proceed with a prior probability model for all relevant unknown quantities, including, in particular, a prior probability model on the binary indicators that characterize the latent haplotypes. Such prior models are known as feature allocation models. Specifically, we define a simplified version of the Indian buffet process, one of the most traditional feature allocation models. The proposed model allows overlapping clustering of SNVs in defining latent haplotypes, which reflects the evolutionary process of subclonal expansion in tumor samples.
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