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Fulbright for Cancer Research

Cancer research is fairly common in engineering departments around the country, but still a rarity among industrial and systems engineers. For Professor Kevin Leder, though, studying cancer with the tools of industrial engineering makes perfect sense.

Leder’s interest in cancer research began when he was a post-doc at Memorial Sloan Kettering Cancer Center in New York City, where he learned that tumors are typically not genetically homogenous. “They’re like a wedding cake,” he says. “There’s a foundation of pre-cancerous cells, out of which come further mutated cancer cells, out of which come even further mutated cells.” Unlike a wedding cake, these cells are typically mixed up together, but careful analysis of mutations can potentially reveal how long a tumor has been growing.

Leder also learned that most cancer treatments target only one type of mutated cell. “This raised questions about treatment optimization,” he explains. “If we had a model of mutation development, could we better deliver treatments for tumors at different stages? Could we even use such models to better identify – and possibly eliminate – precancerous cells?”

Inspired by the success of previous mathematical models of leukemia development, Leder began to sketch out models that lay the groundwork for better treatment. He discovered, though, “that to make a valid recommendation about treatment, lots of patient data is necessary.”

As he was researching opportunities for his 2018-19 sabbatical, Leder found Professor Arnoldo Frigessi at the University of Oslo. Besides similar interest in mathematical models of cancer, Frigessi has data on a large cohort of multiple myeloma patients.

Leder plans to work with Frigessi to establish and test models that make sense of the patient myeloma data. He also hopes to develop ways to use the models to improve diagnosis and treatment of multiple myeloma and other types of cancer. The long-term goal, according to Leder, is “to develop a strong family of modeling tools that can be used to improve personalized medicine, especially in cancer treatment.”

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