Cancer is a disease that hijacks the body’s regulatory systems.

When a person is healthy, his or her cells interact fluidly with their immediate environment, responding to a series of biochemical cues in ways that promote the greater good of the organism. In cancer, this system is subverted.

The biochemical signals that instruct cells to divide or migrate, for instance, are often locked on, while signals that could prevent them from taking these steps may be fixed in the off position. By coupling computational modeling with experimentation, University of Virginia associate professor of chemical engineering Matthew Lazzara is attempting to unravel the cell signaling systems associated with two particularly deadly forms of cancer — brain and pancreatic. His ultimate goal is to identify the combination of factors that lead these networks to go awry and develop strategies to correct them.

“The essence of chemical engineering is to describe chemical (or in this case biochemical) systems mathematically,” Lazzara said. “We are using that engineer’s skill to devise better approaches to treat cancer by describing complex biology quantitatively with the ultimate hope of manipulating it.”

Matt Lazzara portrait in Vizlab

The promise of his approach has been widely recognized. He is the lead principal investigator of a large collaborative research grant funded last year by the National Science Foundation. His lab at UVA’s School of Engineering and Applied Science also receives funding from the American Cancer Society, National Institutes of Health, the Coulter Translational Research Partnership at UVA, and the University’s Emily Couric Clinical Cancer Center. Recently, he was named as an associate editor of Cellular and Molecular Bioengineering, joining other leaders in the field on the journal’s editorial board.

Students in Lazzara’s lab also find success and opportunities to apply their research experience in practical settings. Recently, second-year Ph.D. candidate Brooke McGirr received a one-year appointment under the Cancer Training Grant at under the UVA School of Medicine. The grant is funded by the National Cancer Institute.

Leaving Less to Chance

One of the challenges with 21st-century drug discovery is that it still relies principally on trial and error, although there is a high-throughput, highly automated version of this process at early stages. Researchers comb through libraries of molecules, pulling out those that have a desired effect for further investigation. One problem with this method is that there is no way to forecast with any certainty whether a molecule that works in the lab or in an animal model will work in a human being. How a drug’s therapeutic molecules might work together is also typically unknown.

“Researchers need a rational way to whittle down their hypotheses,” Lazzara said. “Computational models can provide that starting point.”

As part of his collaborative NSF grant, Lazzara is working to create a three-dimensional computational platform for describing interactions between receptors on the surface of cells and the signaling cascade they initiate within the cell when they are activated. In parallel, his collaborators at the University of Pittsburgh are engineering cells expressing fluorescent fusions of signaling proteins, which can be used to track signaling processes using live-cell, 3D microscopy. By training his models on this data, Lazzara is creating a computational platform capable of representing these processes in space and time. Ultimately, that platform can be interrogated to find rational approaches for targeting specific pathways more efficiently in cancer.

“The biological systems we are studying are so complicated that the mechanistic models we create are not usually deterministic, but they can guide you in the right direction experimentally,” Lazzara said. “What we learn from our experiments we feed back into the models to refine them.”

Ultimately, Lazzara is pushing for an even grander vision. By connecting mechanistic models of signaling with data-driven models that quantify relationships between many parallel signaling processes and cell decisions, he hopes to create an integrated platform for predicting how the most upstream therapeutic interventions will impact cancer cell behaviors.

Turning the Tables on Cancer

Lazzara is employing his quantitative methods in a variety of contexts to increase the susceptibility of cancer to treatment. For instance, he is using them to better understand the cell signaling that results in a phenomenon called the epithelial-mesenchymal transition, a normal cell developmental process that is hijacked in cancer to produce cells that are more resistant to therapy. By uncovering the combination of signaling events that drive this transformation, Lazzara hopes not only to interrupt them, but to reverse them, coaxing the cells to a state where they would be vulnerable to cancer-fighting agents.

“We want to rewire their signal processes so that they can be destroyed,” he said. “This could make an enormous difference almost immediately for cancer patients by improving the effectiveness of the treatments we already have.”