Computational tool differentiates between data from cancer cells and normal cells
2021
The University of Texas MD Anderson Cancer Center, Houston, USA
In an effort to address a major challenge when analysing large single-cell RNA-sequencing datasets, researchers have developed a new computational technique to accurately differentiate between data from cancer cells and the various normal cells found within tumor samples.
The new tool, dubbed CopyKAT (copy number karyotyping of aneuploid tumors), allows to more easily examine the complex data obtained from large single-cell RNA-sequencing experiments, which deliver gene expression data from many thousands of individual cells.
CopyKAT uses that gene expression data to look for aneuploidy, or the presence of abnormal chromosome numbers, which is common in most cancers. The tool also helps identify distinct subpopulations, or clones, within the cancer cells.
By applying this tool to several datasets, the authors showed that with about 99% accuracy, the tool could unambiguously identify tumor cells versus the other immune or stromal cells present in a mixed sample.
The team first benchmarked its tool by comparing results to whole-genome sequencing data, which showed high accuracy in predicting copy number changes. In three additional datasets from pancreatic cancer, triple-negative breast cancer and anaplastic thyroid cancer, the researchers showed that CopyKAT was accurate in distinguishing between tumor cells and normal cells in mixed samples.
Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
Nicholas E. Navin
Added on: 02-11-2021
[1] https://www.nature.com/articles/s41587-020-00795-2[2] https://www.technologynetworks.com/cancer-research/news/computational-tool-differentiates-between-data-from-cancer-cells-and-normal-cells-344666
