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NIH Grant Will Help Answer Questions about Cancer Growth

Amy Brock165X235

Why do some cells proliferate into full-blown tumors, while others lie dormant for years, never fully developing into cancer? These are questions that Assistant Professor Amy Brock hopes to answer with support from her laboratory's first National Institutes of Health (NIH) R01 grant.

Brock will lead a research team from UT Austin and the Institute for Systems Biology in Seattle that will analyze individual cells within a tumor population to learn more about what prompts cancer growth.

The project, titled “The Functional Role of Heterogeneity in Cell Populations,” will be supported by a five-year $3 million grant from the National Cancer Institute of the NIH.

“We have capabilities to see what’s happening at the single-cell level,” says Brock, “When we look at these cells, we may find that average cell behavior does not reflect what the real population looks like. For example, we could do a population study where all the cells are green and make an assumption that every cell in that population is also green. But in fact, some of the cells could be blue and yellow, and we wouldn’t know unless we looked at each individual cell. That’s essentially the type of work we’re doing with this project, except instead of colors, we’re interested in looking at the different cells within a tumor to find out what genes are expressed, which proteins are interacting with each other, and how these properties initiate cancer growth.”

Cancer grows when multiple cells share signals and interact with each other, not simply from one cancer cell proliferating on its own. By looking at the single-cell level, Brock and her team will be able to determine if there are different sub-populations of cells within a tumor, and if those different heterogenous sets of cells initiate growth at different rates.

The team will use an automated imaging system to collect data from hundreds of tumor initiating cell populations in parallel. Researchers will then build computational models to identify how different cell populations contribute to the initial take off of a tumor population. They hope to gain insights into the mechanisms that expand cancer growth and the processes that prevent cancer cells from ever growing beyond just a few abnormal cells.