Jana Gevertz was drawn to medicine, but she didn’t want to be a doctor.
“I was a math major, but wanted to do something that could have a direct societal impact,” she says.
Then it happened. As an undergrad at Rutgers University, her father was diagnosed with stage IV lymphoma.
“That sealed the deal for me,” says the Princeton PhD of her decision to begin exploring cancer from a mathematical perspective.
Over ten years later, her dad is essentially cancer-free, but his colleague—similarly diagnosed at the same time—died within 18 months. Which begs one of the questions Gevertz’s work is trying to answer: what makes cancer patients respond so differently to the same treatments?
Gevertz and her collaborators are working to write a set of equations that describes the growth and treatment of melanoma tumors in mice. These mice are treated using genetically engineered viruses designed to selectively kill cancer cells while also delivering immune-boosting molecules to the tumor site. The experiments are all done in the lab of her collaborator, Dr. Chae-Ok Yun at Hanyang University in Seoul, South Korea.
“She treats the melanoma tumors with different types of genetically engineered viruses, and we get data that describe how the size of the tumor changes over time during and after these treatments,” says Gevertz.
Using that data and what is known about the biological mechanisms behind it, Gevertz and collaborators work to translate this knowledge into a mathematical representation that can be tweaked outside of the lab in an effort to boost the efficacy of these treatments in clinical trials.
“It is too expensive and too time consuming to keep experimenting different scenarios (dose of drugs, ordering of drugs, etc.) on mice,” she explains. “Once we have mathematical equations we can trust, all it costs is time.” This efficacy is especially valuable when testing these drugs in human clinical trials.
Gevertz will spend this academic year focused exclusively on her research. With support from the Gitenstein-Hart Sabbatical Prize—of which she is the second recipient—she will continue unraveling the complexities of tumor response to cancer therapeutics. Finely tuned enough, these models could aid in personalizing cancer treatments to the individual.
“We miss so much by not understanding the underlying complexity of these diseases,” she says. “If we can understand the data from an individual patient, we can figure out the best way to deliver personalized treatment that will work for them.”
—Emily W. Dodd ’03