Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes.
Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes.
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DOI：
10.1038/s41587-020-00795-2
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发表时间：
2021-05
影响因子：
46.9
通讯作者：
Navin NE
中科院分区：
文献类型：
--
作者：
Gao R;Bai S;Henderson YC;Lin Y;Schalck A;Yan Y;Kumar T;Hu M;Sei E;Davis A;Wang F;Shaitelman SF;Wang JR;Chen K;Moulder S;Lai SY;Navin NE
单细胞转录组学分析广泛用于研究人类肿瘤。然而，区分肿瘤微环境中的正常细胞类型和恶性细胞以及解析肿瘤内的克隆亚结构仍然具有挑战性。为了解决这些挑战，我们开发了一种称为CopyKAT（非整倍体肿瘤的拷贝数核型分析）的综合贝叶斯分割方法，以从高通量scRNA-seq数据的读取深度中以5 Mb的平均基因组分辨率估计基因组拷贝数谱。我们应用CopyKAT分析了来自21种肿瘤的46，501个单细胞，包括三阴性乳腺癌，胰腺导管腺癌，甲状腺未分化癌，浸润性导管癌和胶质母细胞瘤，以准确（98%）区分癌细胞和正常细胞类型。在三个乳腺肿瘤中，CopyKAT解决了克隆亚群，这些亚群在癌症基因（如KRAS）的表达和EMT、DNA修复、凋亡和缺氧等特征方面存在差异。这些数据表明，CopyKAT可以帮助分析各种实体人类肿瘤中的scRNA-seq数据。
Single-cell transcriptomic analysis is widely used to study human tumors. However it remains challenging to distinguish normal cell types in the tumor microenvironment from malignant cells and to resolve clonal substructure within the tumor. To address these challenges, we developed an integrative Bayesian segmentation approach called CopyKAT (Copynumber Karyotyping of Aneuploid Tumors) to estimate genomic copy number profiles at an average genomic resolution of 5Mb from read depth in high-throughput scRNA-seq data. We applied CopyKAT to analyze 46,501 single cells from 21 tumors, including triple-negative breast cancer, pancreatic ductal adenocarcinomas, anaplastic thyroid cancer, invasive ductal carcinoma and glioblastoma to accurately (98%) distinguish cancer cells from normal cell types. In three breast tumors, CopyKAT resolved clonal subpopulations that differed in the expression of cancer genes such as KRAS and signatures including EMT, DNA repair, apoptosis and hypoxia. These data show that CopyKAT can aid the analysis of scRNA-seq data in a variety of solid human tumors.
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影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
影响因子：
16.6
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Gao R;Kim C;Sei E;Foukakis T;Crosetto N;Chan LK;Srinivasan M;Zhang H;Meric-Bernstam F;Navin N
通讯作者：
Navin N
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64.5
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Kim C;Gao R;Sei E;Brandt R;Hartman J;Hatschek T;Crosetto N;Foukakis T;Navin NE
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Navin NE
影响因子：
30.8
作者：
Gao, Ruli;Davis, Alexander;McDonald, Thomas O.;Sei, Emi;Shi, Xiuqing;Wang, Yong;Tsai, Pei-Ching;Casasent, Anna;Waters, Jill;Zhang, Hong;Meric-Bernstam, Funda;Michor, Franziska;Navin, Nicholas E.
通讯作者：
Navin, Nicholas E.
影响因子：
4.6
作者：
AuBuchon, J. P.;de Wildt-Eggen, J.;Dumont, L. J.
通讯作者：
Dumont, L. J.