New computational technique accurately differentiates between normal and cancer cells

Scientists from the University of Texas MD Anderson Cancer Center have created a new computational method to precisely differentiate between data from a wide range of normal cells and cancer cells found inside tumor samples.

New computational technique accurately differentiates between normal and cancer cells
Nicholas Navin, Ph.D. Image Credit: University of Texas M. D. Anderson Cancer Center.

The team is aiming to deal with a significant challenge when studying large datasets of single-cell RNA-sequencing. The study was recently published in the Nature Biotechnology journal.

Called CopyKAT (short for copy number karyotyping of aneuploid tumors), the new tool enables scientists to analyze the complex data acquired from large single-cell RNA-sequencing experiments more easily. Such experiments provide gene expression information from many thousands of individual cells.

According to Nicholas Navin, PhD, the senior author of the study and associate professor of Genetics and Bioinformatics & Computational Biology, that gene expression data is used by CopyKAT to search for aneuploidy, or the existence of atypical chromosome numbers, which are very common in several cancers. The new tool also allows scientists to detect distinct clones, or subpopulations, inside the cancer cells.

We developed CopyKAT as a tool to infer genetic information from the transcriptome data. By applying this tool to several datasets, we showed that we could unambiguously identify, with about 99% accuracy, tumor cells versus the other immune or stromal cells present in a mixed tumor sample. We could then go one step further to discover the subclones present and understand their genetic differences.”

Nicholas Navin, PhD, Associate Professor of Genetics and Bioinformatics & Computational Biology, University of Texas MD Anderson Cancer Center

Traditionally, tumors have been analyzed as a combination of all cells present, most of which are non-cancerous cells. In the recent past, the introduction of single-cell RNA sequencing has allowed scientists to examine tumors in a relatively higher resolution, enabling them to analyze the gene expression of all individual cells to create an image of the tumor landscape, such as the surrounding microenvironment.

But without a reliable computational technique, it is not easy to differentiate between normal cells and cancer cells, explained Navin.

Ruli Gao, PhD, a former postdoctoral fellow and currently an assistant professor of Cardiovascular Sciences at Houston Methodist Research Institute, created the CopyKAT algorithms, which enhance the older methods by increasing precision and adapting to the latest generation of single-cell RNA-sequencing data.

The researchers initially benchmarked the new tool by comparing outcomes to whole-genome sequencing information, which revealed high precision in estimating the copy number differences.

In three extra datasets obtained from anaplastic thyroid cancer, triple-negative breast cancer, and pancreatic cancer, the team demonstrated that CopyKAT was able to precisely differentiate between normal cells and tumor cells in mixed samples.

Such studies were possible, thanks to teamwork with Stephen Y. Lai, MD., PhD, professor of Head and Neck Surgery, and with Stacy Moulder, MD, professor of Breast Medical Oncology, and the Breast Cancer Moon Shot®, part of MD Anderson’s Moon Shots Program®—a joint effort to quickly develop scientific findings into meaningful clinical advancements that save the lives of patients.

While studying these samples, the team also demonstrated that the new tool can effectively detect subpopulations of cancer cells inside the tumor based on copy number variations, as demonstrated by experiments in triple-negative breast cancers.

By using CopyKAT, we were able to identify rare subpopulations within triple-negative breast cancers that have unique genetic alterations not widely reported, including those with potential therapeutic implications. We hope this tool will be useful to the research community to make the most of their single-cell RNA-sequencing data and to drive new discoveries in cancer.”

Ruli Gao, PhD, Assistant Professor of Cardiovascular Sciences, Houston Methodist Research Institute

The new tool can now be easily accessed by scientists. The authors observed that the tool is not relevant for studying all types of cancers. For instance, aneuploidy is relatively uncommon in hematologic and pediatric cancers.

Journal reference:

Gao, R., et al. (2021) Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature Biotechnology.


The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoLifeSciences.
Post a new comment
You might also like...
AI Program Reveals Cellular Neighborhoods in Cancer Microenvironments