University HealthCare Alliance

February 03, 2020

A Single Number Helps Stanford Data Scientists Find Most Dangerous Cancer Cells

Stanford data scientists have shown that figuring out a single number can help them find the most dangerous cancer cells.

Biomedical data scientists at the Stanford University School of Medicine have shown that the number of genes a cell uses to make RNA is a reliable indicator of how developed the cell is, a finding that could make it easier to target cancer-causing genes. 

Cells that initiate cancer are thought to be stem cells, which are hard-to-find cells that can reproduce themselves and develop, or differentiate, into more specialized tissue, such as skin or muscle — or, when they go bad, into cancer. 

“Right now, targeted therapies are focused on specific genes or molecules, the vast majority of which may not be specific to cancer stem cells,” said Aaron Newman, PhD, assistant professor of biomedical data science and a member of the Institute for Stem Cell Biology and Regenerative Medicine. “Usually these therapies don’t work for very long. But if you can identify the least-differentiated cells and then look for markers specific to them, it’s no longer a guessing game to find the genes to target.”

The study’s finding is also significant because identifying stem cells of various tissue types is an important step toward regenerating damaged or malfunctioning tissues.

What the scientists showed is that as stem cells become more differentiated and more like adult cells, they express fewer and fewer genes. Previously, other researchers had noticed this correlation and thought it might be an interesting coincidence. But Newman and his colleagues were the first to sort through thousands of single-cell genetic tests in public databases and prove this pattern was consistent and reliable.

Newman and MD-PhD student Gunsagar Gulati combined the measurement of the number of genes expressed in a cell with the measurement of the number of RNA copies created per gene as the basis for a computer algorithm, CytoTRACE, designed to determine how developmentally advanced cells are.

A paper describing the research was published online Jan. 24 in Science. Newman is the senior author. Gulati and Shaheen Sikandar, PhD, an instructor at the institute, share lead authorship.

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