Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

中文翻译：

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通过结合大量 DNA 和单细胞 RNA 测序数据来研究肿瘤亚克隆特异性表达的贝叶斯框架

遗传和基因表达异质性是许多肿瘤的重要标志，它允许癌症进化并对治疗产生耐药性。目前，研究这种异质性最常用的数据类型是大体积肿瘤/正常全基因组或全外显子组测序（WGS、WES）；和单细胞 RNA 测序 (scRNA-seq)。然而，目前缺乏通过整合从同一肿瘤收集的基因组和单细胞转录组数据来将基因组肿瘤亚克隆性与转录组异质性联系起来的工具。为了解决这一差距，我们开发了 scBayes，这是一种贝叶斯概率框架，它使用从大量 DNA 测序数据推断出的肿瘤亚克隆结构，通过单细胞基因表达 (scRNA-seq) 测量来确定细胞的亚克隆身份。将代表相同基因定义的肿瘤亚克隆的细胞分组在一起，可以比较不同亚克隆之间的基因表达，或研究同一亚克隆内随时间（即进展、治疗反应或复发）或空间（即在多个转移性肿瘤）内的基因表达变化。部位和器官）。我们使用模拟数据集、计算机合成数据集以及从癌症样本生成的生物数据集来广泛表征和验证我们方法的性能，并展示对现有方法的改进。我们通过将其应用于已发布的数据集并概括研究结果，以及在我们自己的数据集中对癌症亚克隆表达行为得出新的见解，来展示我们的方法的有效性和实用性。我们进一步表明，我们的方法适用于广泛的单细胞测序技术，包括单细胞 DNA 测序以及 Smart-seq 和 10x Genomics scRNA-seq 协议。