Jeff Chuang is on the front lines of big data

Jeff Chuang, The Jackson Laboratory © 2015 Dominick Reuter

Computational biologist Jeffrey Chuang applies the power of big data to big problems in biology — from how cancer cells evolve to how computational tools and knowledge can be broadly shared.

With both of its proverbial feet now firmly affixed in the information age, the field of biology is awash in ever-increasing amounts of information. As many scientists find themselves inundated with data, they are embracing one of the biggest challenges in modern biomedical research: How do we unearth meaning?

Indeed, as methods for generating biological data have advanced, often with unprecedented speed, they have opened some big gaps in the capabilities for analyzing such “big data.” Those gaps underscore the need for innovation not just in developing new computational and mathematical tools, but also in disseminating such approaches throughout the scientific community.

Associate Professor Jeffrey Chuang, Ph.D., is one of the trailblazers in these areas. “Jeff has a unique way of combining his training in theoretical physics, mathematics, and computer sciences to study the complex nature of human diseases, such as cancer,” says Charles Lee, Ph.D., FACMG, Scientific Director of The Jackson Laboratory for Genomic Medicine.  “He is also naturally soft spoken — but when he speaks, his colleagues immediately stop talking and pay attention to what he says.”

With a background in statistical physics and evolutionary theory, Chuang’s latest scientific pursuit centers on cancer: How do tumor cells evolve over time, and how does this influence their ability to resist treatment? “It’s a really important problem,” says Chuang, who joined JAX in 2012 after spending seven years as an assistant professor at Boston College. “There are some basic questions that we just don’t have good answers to. For example, if we could treat the same patient twice using the same drug, or if we could treat different portions of a patient’s tumor with the same drug, would we get the same response?”

Uncovering answers to these questions is not simply an academic exercise. Cancer drug resistance can emerge as a result of anti-cancer therapy or exist inherently within tumors, and it is an enormous clinical problem. The majority of cancer patients develop resistance at some point during the course of their disease. By exposing the mechanisms of how and when resistance emerges, Chuang and his colleagues have been researching strategies for circumventing it altogether.

With a grant from the NIH, Chuang and his colleagues developed a weeklong course geared toward professors at small colleges and universities. The goal was to develop pedagogical strategies that the professors were then able to apply in their classrooms.

Big numbers

Chuang has a deep background in science and mathematics, but surprisingly little formal training in biology. “I don’t think I’ve ever received credit for a biology course in my life, yet here I am now, working in it.”

He competed in regional math and science contests nearly every weekend as a high school student in Houston, Texas. His interests in the physical sciences propelled him through undergraduate studies at Harvard University and then to graduate school at MIT. As a graduate student, Chuang took up statistical physics, probing the physical properties of one of the workhorses of molecular biology — acrylamide gels, routinely used to separate different sized proteins

Chuang’s Ph.D. work had left him somewhat dissatisfied, because he longed for bigger statistical nuts to crack. Proteins are typically made up of just a few hundred amino acids — a fairly small number, statistically speaking. Meanwhile, a first working draft of the human genome had just been completed, providing the scientific world with the most substantial view yet of the vast string of genetic letters (or “nucleotides”) that comprise our genetic blueprint.  

“I remember thinking, ‘Three billion nucleotides. Now that’s a pretty big number.’”

Wooed by evolution

In 2001, Chuang began a postdoctoral fellowship at the University of California San Francisco, working with Hao Li, a former physicist who had made the jump to biology and genomics.

“He gave me a very open — and in hindsight, complicated — problem,” recalls Chuang. “He said, ‘I heard the mouse genome is sequenced now, why don’t you see what’s similar between that and the human genome.’”

Chuang went on to reveal some key insights into how mammalian genomes evolve, and how that evolution shapes the organization and function of the human genome. Those discoveries also carved an early and critical path to his most recent work at JAX, which combines the principles of evolution with tumor biology.

“In the last few years, one of the big changes in perspective for cancer has been that heterogeneity is important. But to connect that to implications for treatment, that is a very hard problem because it is an evolutionary problem.”

Jeff Chuang, The Jackson Laboratory

JAX, PDX, and cancer

Throughout his research career, Chuang has been drawn to projects that are, as he puts it, “practical and useful.” It is not surprising, then, that when he arrived at JAX, he was inspired by the institute’s pioneering work on cancer, especially the projects that harness patient-derived xenografts (PDX). Using mice whose immune systems are engineered to tolerate human cells, JAX scientists can now take small pieces of a human tumor, implant them in the mice, and grow them over extended periods of time. As the xenografts expand, they are divided and transplanted into additional mice, thereby creating multiple copies of the original human tumor.

This PDX system offers a new path to precision medicine in cancer, making it possible to test different anti-tumor therapies in mice and then formulate predictions about which ones will be most effective for individual patients. It also opens up critical new avenues for basic research, allowing Chuang and his colleagues to study human tumors in exquisite detail — for example, by describing what happens on a cellular level when different cancer drugs flow.

“A lot of studies just look at the size of the tumor after you treat it to determine whether you have a response or not,” says Chuang. “Here, we can do replicates, we can do drug comparisons, and we can also use genomic sequencing to see what remains — that is, if the same subpopulations of cells are resisting treatment in all cases.”

JAX is one of the few places in the world with the expertise and resources to pursue these PDX projects. The experiments require skills and technologies in a range of areas, from mouse husbandry and care, to tumor engraftment and drug treatment, to large-scale sequencing and data analysis.

As a leader of several PDX studies, Chuang devotes significant energy to figuring what types of DNA sequencing data will enable his team to see what they want to see within the engrafted tumors. He likens it to glimpsing Earth from different points of view: “If you live on the planet, you might not know what was happening around you,” he explains. “But if you saw a movie of the Earth or a bunch of different snapshots, then you might figure out that the clouds move, and you might figure out that the Earth rotates. If you have a good enough camera, maybe you’d even recognize creatures, and people moving around.”

With sequencing, Chuang and his colleagues can collect multiple snapshots that help reveal the different cell populations within a tumor and how they change — over time and in response to different drugs.

Chuang is leading projects that examine different tumor types. Together with JAX President and CEO Edison Liu, Carol Bult, Karolina Palucka and other JAX scientists, he is studying a particular form of breast cancer, which lacks the three molecular markers commonly found in breast tumors. Such “triple-negative” tumors are not only missing these biological signposts, but they also fail to respond to drugs aimed at those signposts. New treatments for these tumors could have a sizeable impact: Triple-negative breast cancers represent nearly one-fifth of the total number of breast cancer cases diagnosed each year worldwide.

Chuang is also spearheading an effort in collaboration with Charles Lee’s Korea-based laboratory at the Ewha Womans University to characterize the tumors of gastric cancer patients, specifically those living in Asia. This form of cancer is relatively rare in the U.S., but is a significant problem in Korea and Asian countries. Little is known about why these differences persist. Although treatments are available, the JAX team believes promising new drug targets can be found. “There’s a lot of work yet to be done to understand gastric cancer,” says Lee.

Teach the teachers

In addition to his drive to solve problems at the laboratory bench, Chuang is also deeply passionate about training others to apply computational and mathematical approaches. He taught multiple bioinformatics courses to undergraduates while on the faculty at Boston College and also oversaw the bioinformatics components of the institution’s graduate program. Now, in his role at JAX, he is setting out to tackle one of the big conundrums of big data — that is, the large disparities that exist between major academic research organizations, which often have ready access to computational tools and know-how, and smaller colleges and universities, which often struggle to tap these resources.

With a three-year grant from the National Institutes of Health, Chuang and his colleagues will have a chance to do just that. Together with Charles Wray, Reinhard Laubenbacher, and Joel Graber, he will lead a weeklong course in big data at the end of May geared toward professors at small colleges and universities.

The course will be split between lectures and hands-on activities, and seeks to develop pedagogical strategies that the professors can apply in their classrooms.

“What we hope will come out of this is a curriculum that has really been tested — that the different partners can go back to their respective universities and try out, and then tell us what works and what doesn’t,” says Chuang. “In the end, that will help democratize our knowledge of big data, and enable more people around the country to understand and study it.”

Nicole Davis, Ph.D., is a freelance writer and communications consultant specializing in biomedicine and biotechnology. She has worked as a science communications professional for nearly a decade and earned her Ph.D. studying genetics at Harvard University.