A New Tool That Can Differentiate Tumor Cells Has Been Developed

University of Texas MD Anderson Cancer Center researchers have developed a new computational technique for analyzing large single cell RNA sequencing data sets. Thanks to this new technique, data of various normal cells and cancer cells in tumor samples can be differentiated more accurately.

The new tool, called CopyKAT, makes it easier to analyze complex data from large single-cell RNA sequencing experiments. Single cell RNA sequencing experiments enable gene expression data from thousands of individual cells.

Tool that separates cancer cells and normal cells

This tool, called CopyKAT, is common in many cancers. to abnormal chromosome numbers using gene expression data to look up. The tool also helps identify specific subpopulations, or clones, in cancer cells.

Nicholas Navin, co-author of the study published in Nature Biotechnology, used this tool in various datasets to identify tumor cells and stromal cells in mixed tumor samples. With 99% accuracy He said they showed what they could detect. Navin stated that later they could go one step further to discover the existence of the subclones and understand their genetic differences.

The developed CopyKAT algorithm has made a great improvement over the old techniques used in this field by increasing the accuracy rate and adjusting it for next generation single cell RNA sequencing data. CopyKAT can detect cancer cells with tumor cells in mixed samples in tests with different cancer cells. was able to successfully parse.

The authors of the study stated that they hope that this tool will be useful for new discoveries in the field of cancer, and that this tool is also completely open to the use of researchers. However, the authors believe that this tool is not suitable for studies in all types of cancer. is not applicable also expressed.