As immune cells learn to recognize cancer cells and kill them, cancer cells evolve to evade detection. Understanding how cancer stays one step ahead of the immune system is crucial to eventually figuring out how to beat it.
Now a bioinformatics researcher at The University of Texas Southwestern Medical Center is planning to use computational and genomic resources to figure out cancer’s evasive pathways and help the immune system stay in the game.
Bo Li was recruited in 2017 to the Departments of Bioinformatics and Immunology from Dana Farber Cancer Institute with the help of a First-Time Tenure-Track Award from CPRIT.
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As immune cells learn to recognize cancer cells and kill them, cancer cells evolve to evade detection. Understanding how cancer stays one step ahead of the immune system is crucial to eventually figuring out how to beat it.
Now a bioinformatics researcher at The University of Texas Southwestern Medical Center is planning to use computational and genomic resources to figure out cancer’s evasive pathways and help the immune system stay in the game.
Bo Li was recruited in 2017 to the Departments of Bioinformatics and Immunology from Dana Farber Cancer Institute with the help of a First-Time Tenure-Track Award from CPRIT.
Most immune therapies for cancer focus on amplifying the numbers of immune system warriors — T-cells — that are able to infiltrate behind enemy lines and invade a tumor. Li says sometimes that’s counterproductive, because some of those tumor-infiltrating T-cells aren’t cancer-specific, and when their numbers are increased, they end up attacking the host instead of the enemy. This makes autoimmunity one of the key potential side effects of cancer immune therapies.
Li hopes to be able to better identify and amplify the small fraction of tumor-infiltrating T-cells that actually recognize and kill cancer cells in order to recruit them into a specialized anti-cancer force.
Using single-cell genetic sequencing and biostatistical methods, Li is unraveling the complex interactions between cancer and the immune system. He works closely with his collaborator, immunologist Dr. Yang-Xin Fu, to study the changes in T-cells between early- and late-stage cancers in animal models.
In early stages, the T-cells can evolve to keep up with the cancer’s developing immune-evasion methods, but in later stages, the T-cells can’t evolve any more and cancer wins. Li and Dr. Fu hope to understand when and how this change happens in order to learn how to prevent T-cells from becoming exhausted — eventually using this knowledge to improve immunotherapy.
Li also hopes to be able to isolate cancer-specific T-cells in peripheral blood samples to identify cancer in very early stages. For example, if a physician sees a suspicious lesion on an MRI or CAT scan, a blood test could be used to detect cancer without necessarily needing to perform a biopsy. Li hopes to deploy this technology first for people at high risk of developing ovarian or pancreatic cancers — deadly diseases for which early detection is currently difficult or impossible, but would be essential for saving lives.
“This approach is completely different from traditional ways of diagnosing cancer,” Li says. “We are detecting immune-related changes in the blood, rather than cancer-related changes, because the immune system is capable of amplifying the signal hundreds or even thousands of times.”
Li acknowledges that having his biostatistical lab next to an immunology lab is unusual but essential for his research. “I am fortunate to have a collaborator who is excited to work with me and a university that was willing to make it happen,” he says.
He adds that CPRIT support is essential for doing the single-cell sequencing that is “absolutely essential for immunological research, but prohibitively expensive for most startup packages.”
Li studied physics at Peking University in Beijing before coming to the University of Michigan for his Ph.D. studies in bioinformatics. He joined the Dana Farber Cancer Institute as a postdoctoral fellow in 2014.
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