Crossing Disciplines to Fight Disease
The last decade has seen an explosion of new technology in the biological sciences. High-throughput genomics and advanced techniques for sequencing proteins and small molecules have expanded knowledge more than ever before—but they’ve come with their own problems.
“That explosion has led to massive quantities of data being collected,” says Charlotte Kuperwasser, an associate professor of developmental, molecular and chemical biology at Tufts School of Medicine. “As biologists, we are not trained in knowing what to do with all of that data.”
Since these technologies were developed by engineers, chemists and mathematicians, it’s more essential than ever that biologists look beyond the boundaries of their field to deal with the petabytes of data that have emerged.
“We are trying to understand more complex and deeper questions than we were ever able to think about before,” says Kuperwasser, who has devoted her career to researching the causes of and potential cures for breast cancer. “That’s why collaborations will be essential for the next wave of insight in understanding and treating disease.”
Kuperwasser has taken a giant leap forward in creating those collaborations as director of the Raymond and Beverly Sackler Convergence Laboratory, a new effort by researchers at Tufts and other area institutions to pool knowledge, resources and techniques in the treatment of disease. The lab, which launched earlier this year, is concentrating initially on breast cancer research, but its aims are much more ambitious. “Ultimately we’d like to branch out beyond breast cancer and grow this effort with the goal of tackling many other devastating diseases,” Kuperwasser says.
Unlike many breast cancer biologists, Kuperwasser has focused her research not only on the later stages of metastasis, but also on the early events during development of healthy breasts, looking to find those elusive clues that may forecast whether cancer will develop in the future.
“I think of cancer as a problem of tissue development and regeneration gone awry,” she says. That makes her ideally poised to take advantage of the vast amounts of biological data that have been generated in the field. One of the scientists Kuperwasser has invited to join the lab is Andrew Beck, a bioinformatician at Harvard Medical School who directs a research laboratory at Beth Israel Deaconess Medical Center in Boston.
Beck has applied computational image analysis to thousands of images obtained from breast biopsies in order to try and tease out precursors of abnormal development. “In mammographic screening, there are a lot of abnormalities that don’t turn into breast cancer,” says Beck. “We are using image analysis methods applied to tissue samples obtained from biopsies of mammographically identified breast lesions to better stratify women to determine who is at increased risk for future cancer.”
His image analysis examines several thousand data points on each slide—everything from the size of a nucleus in a cell to the amount of fat in tissues. “The goal,” says Kuperwasser, “is to see whether we can tweak his algorithm to segregate what looks like normal breast tissue into high-risk and low-risk categories for cancer.”
In addition to looking at breast tissue in a wide array of patients, Beck is working on examining breast tissue from women identified as having a mutation of the BRCA1 or BRCA2 genes. Research by Kuperwasser and others has shown that the mutation puts women at increased risk for cancer. “We want to see if we can use the computer in a data-driven way to uncover the morphologic and phenotypical changes that may not be visible to the naked eye,” Beck says.
By collaborating on these approaches, says Kuperwasser, the researchers are able to achieve a finer-grade analysis than scientists from any one discipline would have been able to achieve independently.
“The mathematical mind is liberated to a certain extent from the constraints of the biologist,” she says. “[Mathematicians] can examine a problem from a purely logical, unbiased approach,” she notes. “I am very impressed at how [Andrew Beck] is able to apply computational approaches and computer coding to use a microscope—a tool biologists have been using for centuries—to discover things we haven’t been able to see before.”
In addition to Beck, the Sackler Convergence Lab team includes researchers from institutions as close as Harvard and MIT and as far away as the University of North Carolina and Arizona State University, as well as two Tufts faculty: David Kaplan, the Stern Family Professor of Engineering and chair of biomedical engineering, and chemistry professor Joshua Kritzer. While all of them are experts in their respective fields, Kuperwasser says she also chose them with specific personality traits in mind.
“We are all open and thoughtful and not hyper-competitive with one another,” she says. Those factors are key in having the kind of freewheeling discussions that can lead to productive collaborations, she says. “As director, I am not dictating to the group what we are going to work on; it’s about what the interests of the group are and then finding synergy. It may take longer that way, but at the end of the day, we will have something we can pursue with more energy and vigor.”
Those kinds of open-ended discussions are a luxury in science. Most labs are so busy applying for external grants for specific projects in order to stay afloat that researchers can’t take the time to talk about new research possibilities.
“[Kuperwasser] and I have talked for years about collaborating,” says Kaplan, who also directs Tufts’ Bioengineering and Biotechnology Center. “But we’ve both been so busy it’s hard to find the time. This is an excuse to sit in a room and discuss some ideas.”
Kaplan, whose lab specializes in creating three-dimensional human tissue systems in vitro, has already started working to recreate human breast tissue that could be used by other collaborators in the lab in place of mouse or other animal models. “The collaboration forces people to portion out some time and have very informal and thoughtful discussions, and that’s a good thing. It’s creating opportunities that wouldn’t have existed before,” he says.
The work cuts both ways—the scientists also end up helping their colleagues in the lab deepen their own research so that it becomes more applicable to real-world problems. Kritzer’s lab, for example, focuses on synthetic chemistry, creating molecules out of tiny snippets of proteins in order to target proteins in the body that are “undruggable” by conventional FDA-approved treatments.
“Working with biological collaborators has given me the opportunity that when I find something that works well in the dish, I can put it in mice or rats or other animal models,” he says. “They provide me with interesting targets that have deep biomedical relevance that no chemist in my position may have ever been turned onto.”
The experience of collaborating with Kuperwasser and others has also turned Kritzer onto an entirely new application for his molecules: using them diagnostically in identifying breast cancer risk in tissues. “As a chemist, I am so focused on chemicals going in and changing a system, I never thought about my molecules being used just as a sensor,” says Kritzer. “It’s a really new idea to me.”
By taking time to explore with one another and push each other to new insights, the researchers in the Sackler Convergence Lab represent the best aspects of collaboration, where the resulting breakthroughs are much more than the sum of their parts. Kuperwasser hopes that within the next five to 10 years, the researchers will crystallize an understanding of exactly what happens in healthy breast tissue to turn it malignant. “I’d like to be able to define in a specific and concrete manner what takes place in a human breast that poises it to develop cancer,” she says.
As a result of early successes in the lab, Kuperwasser envisions expanding into an institute that would bring these innovative techniques to bear on the diagnostics and treatments of a number of cancers and other diseases—setting an example for the future of biological research, where looking outside the discipline to collaborate in analyzing vast amounts of data would not be an anomaly, but the norm.
Michael Blanding is a Boston-based freelance writer.