CopyKAT enabled distinction between cancer and normal cells and analysis of tumor clonal structure.
Major Finding: CopyKAT enabled distinction between cancer and normal cells and analysis of tumor clonal structure.
Concept: Using integrative Bayesian segmentation, the tool computed copy-number profiles, detecting aneuploidy.
Impact: This method has potential to increase the information gain from rich single-cell transcriptomic data.
Single-cell transcriptomics has become an indispensable tool in cancer research; however, distinguishing cancer cells from the nonmalignant cells with which they coexist in tumors can be a challenge. Methods that exploit the fact that the copy-number profile of cancer cells is often indicative of aneuploidy whereas normal cells are typically diploid have historically proven useful, but they are not suited to the high-throughput, sparse-coverage experiments of today. To address this problem, Gao and colleagues developed a technique called copy-number karyotyping of aneuploid tumors (CopyKAT), a computational approach implementing integrative Bayesian segmentation to enable determination of copy-number profiles at moderate (5 Mb) genomic resolution from 3′ single-cell RNA-sequencing (scRNA-seq) experiments conducted using contemporary protocols. As a proof of concept, CopyKAT was applied to 3′ scRNA-seq data from tens of thousands of cells representing several tumors of multiple types, including pancreatic ductal adenocarcinoma, anaplastic thyroid cancer, and triple-negative breast cancer (TNBC). When applied to these 3′ scRNA-seq datasets, CopyKAT was able to distinguish cancer cells from normal cells with an average of 98% accuracy. Further proof-of-concept analyses showed that CopyKAT was also effective in processing first-generation scRNA-seq (SMART-seq2) data along with 5′ scRNA-seq data from invasive ductal carcinoma and glioblastoma multiforme. Application of CopyKAT to tumors from three individuals with TNBC revealed that the technique was capable of resolving clonal subpopulations of tumor cells based on their expression levels of cancer-associated genes and gene signatures. In summary, CopyKAT is a novel method by which current state-of-the-art single-cell transcriptomic analyses can be enhanced, facilitating accurate sorting of malignant from normal cells and enabling determination of the clonal structure of tumor cells.
Gao R, Bai S, Henderson YC, Lin Y, Schalck A, Yan Y, et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat Biotechnol 2021 Jan 18 [Epub ahead of print].
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