With an M.D., an M.P.H., an M.S. in biostatistics, and a dual Doctor of Science in biostatistics and epidemiology, Yen-Tsung Huang has the intellectual horsepower to take on the complex study of how environmental and genetic factors interact to cause diseases like lung and liver cancer across human populations.
Because the environmental risk factors for these diseases are well established, Huang and other epidemiologists have begun to turn their attention to the role of genetics in determining who gets the disease. But instead of studying the genetic and environmental effects separately, Huang wants to look at how genes interact with environmental factors — as well as how genes interact with each other — to cause cancer.
“It is well documented that smoking causes cancer, but it is puzzling that some 85 percent of smokers do not get lung cancer and that 15 percent of those who do get lung cancer are nonsmokers. This means that smoking is not the only risk factor,” Huang says, noting that tobacco companies have long used these statistics to resist government regulation. “So genomics has become very important, especially how genomes interact with smoking.”
This is where biostatistics comes into play, he says. Scientists need sophisticated statistical methods to understand how multiple genetic markers and environmental factors interact to cause disease. “There are tens of millions of polymorphisms in the human genome. How do we analyze this vast amount of data?” Huang asks. While scientists have the means to study the interaction of a single genetic marker at a time, analyzing complex combinations of data — what scientists call high-dimensional data — is a more challenging task.
“There is no well-established statistical methodology for looking at high-dimensional genomic data, so this is a very important and active research area in statistics right now,” says Huang, who was a postdoctoral fellow at the Harvard School of Public Health before joining Brown’s faculty. He also earned his two master’s degrees and his dual Doctor of Science degree at Harvard, where he worked with Drs. David Christiani and Xihong Lin.
Huang earned his M.D. at National Taiwan University’s medical school in 2003, and he has continued to study the risk factors for liver cancer with his Taiwanese mentors, Drs. Chien-Jen Chen and Hwai-I Yang. “We know hepatitis b and c interact to cause liver cancer; now we plan to look at the virus-host interaction,” he says.
The payoff of this type of research is potentially big. “We could use this information to come up with personalized medical treatment based on genetic profile,” Huang says. “Right now cancer treatment is standardized, but if we knew that an individual’s genetic profile puts that person at special risk, we might treat that person’s early-stage cancer more aggressively than we would otherwise.”
“Yen represents the cutting edge of current approaches in epidemiology,” says Stephen Buka, chair of Brown’s Department of Epidemiology. “Advances in the understanding of major diseases require cross-disciplinary work at a level that we have not seen before. Yen has the combination of training that makes him exactly the right person to integrate work being done across disciplines at Brown — and he has the personality to make such collaborative work successful.”
Huang says he immediately felt at home on campus and in Providence when he visited here with his wife, Chien-ling Lin, a molecular biologist who will begin work as a postdoctoral associate at Brown this fall. He finds his colleagues friendly and supportive, he likes Brown’s modest size, and he thinks Providence is “a cozy town.”
In addition, he is looking forward to the opportunity to work with Brown students. “Some scientists try to avoid teaching and want to put 100 percent of their effort into research. But I don't agree with that,” he says. “Educating the younger generation is also a very important part of science,” he says.