Winter 2013 | Article by Benjamin Morris
Everyone remembers their classes in high school math: the dreaded problem sets in algebra, calculus, and geometry brought home night after night, with seemingly never any end in sight. Students across the country routinely bemoan math as their least favorite subject, but what if they knew what they could use it for as they pursue their careers—not just designing buildings or bridges or analyzing the stock market, but in something as important as saving lives?
Over the past generation of scientific research, a new field of study has emerged that combines the study of math with the goal of fighting cancer, a field known as mathematical oncology. To tackle a problem as complex as cancer—which is nonlinear, multiscaled, and often treatment-resistant—the approaches have to be equally creative, and a subject as intrinsically creative as math is ideally suited for the task. Mathematical oncology incorporates a range of approaches drawn from across the spectrum, from statistical analyses of data sets, to building models of tumor development, to theoretical analyses describing tissue growth, mutation, and death.
Associate Professor of Mathematics and a Member of the Tulane Cancer Center
At Tulane, all of these approaches are being brought to bear on a problem that calls for all hands on deck. Zhao Kun is Assistant Professor of Mathematics, who studies the interface of tumor-host phenomena. “While there are more than 100 types of cancer, each with many subtypes, it has been hypothesized that nearly all cancers develop a common set of basic characteristics,” he wrote in an e-mail. “By focusing on these common elements, mathematical study aims to contribute to the prevention, diagnosis and treatment of this complex disease.”
Because the problem is so immense, researchers have broken cancer research down into constituent scales. Michelle Lacey is Associate Professor of Mathematics, and a member of the Tulane Cancer Center. She studies one of the smallest compounds in the body, the genome, from the perspective of a statistician, sifting through millions of data points at a time, looking for patterns and signals that can reveal differences between kinds of tissue—a critical aspect of cancer research. “Models tease out the strongest signal in the data from the noise,” she says. “We’re looking for information strongly associated with cancerous tissue as compared to control tissue. Mathematical computation is key to this process—there are aspects of cancer development that statistics can identify that traditional diagnostics alone can’t.”
Professor of Mathematics and Associate Director of the Tulane Center for Computational Sciences
Lisa Fauci, Professor of Mathematics and Associate Director of the Tulane Center for Computational Sciences, agrees. An applied mathematician by training, she studies biological fluid dynamics in organic tissue, and supervises students in mathematical oncology on a regular basis. For Fauci, math is not just a powerful tool in cancer research—it’s far more than that. It’s a source of understanding the underlying laws behind processes across fields. “The beauty of mathematics is that the governing equations and the methods for obtaining numerical solutions to problems transcend the application,” she says. “Our students learn methods that could be applied to problems in geophysics or mathematical oncology equally.”
The interdisciplinary aspect of this research is a central aspect of Tulane’s culture, say all the researchers who are involved in this work. Fauci’s students take classes in biomedical engineering at the same time as they study applied math, and Lacey, who says she is “approached all the time by people in other departments, to work on new problems,” insists that her graduate students be able to read papers in biomedical and statistics journals with equal comfort. She calls it cross-training: “Graduate students often think that they can be specialists,” Lacey says. “In biomedical research, that doesn’t suffice. You won’t be working with people exactly like you, nor should you want to. At Tulane, we’re embracing that in our training.”
For many Tulane alums in the field, that training has paid off. Two of Fauci’s students, Kasia Rejniak (PhD, 2002) and Kristin Swanson (BS, 1996), now lead groups of their own. Rejniak heads a group of mathematical oncologists at the highly-respected Moffitt Cancer Center in Tampa, and Swanson is Professor of Neurological Surgery at Northwestern’s School of Medicine. Swanson, who was a Newcomb Summer Scholar while at Tulane, lost her father to lung cancer her senior year as an undergraduate, an event that as she puts it, set her life course to mathematical oncology, and to applying its insights to prevention and care for others.
So what is the future of the field? While the war against cancer will not be won overnight—as physicians and scientists both know, this is a war waged on many fronts at once—many researchers do see promise in what they term patient-specific treatment. Cancers, just like the patients who experience them, are unique; with the genetic revolution and the emergence of personalized medicine, patients in the future may increasingly see treatments custom-tailored to their genetic data. Such developments are costly, says Lacey, and will require advances in diagnosis, imaging, and medical technology to make treatments both accessible and effective, but they are within sight.
Regardless, Tulane researchers will continue to be at the forefront of these advances, aided, Swanson says, not just by the faculty and facilities at the university, but by the nurturing environment that attracts the brightest minds from all over the world. As a pioneer in the field, she recalls the stimulating, collegial atmosphere that led to her success: “I am very thankful for Tulane’s Honor Program,” Swanson says, “and the intimate math department where I really felt at home.”
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