Roadmap published for dynamic mapping of estrogen signaling in breast cancer

The first roadmap to mathematical modeling of a powerful basic "decision circuit" in breast cancer has been developed and published in Nature Reviews Cancer.

The preliminary mathematical model is the first result of a $7.5 million federal grant, awarded to scientists at the Lombardi Comprehensive Cancer Center at Georgetown University Medical Center (GUMC) and collaborators at Virginia Tech and Fox Chase Cancer Center, to develop a systems approach to understanding and treating one of the most common forms of breast cancer.

"A cell is an information processing system and cancer cells make decisions that promote their growth, so we are striving to understand how these cells make mathematically-based choices based on inputs, processing, and outputs," says lead investigator Robert Clarke, PhD, DSc, a professor of oncology at Lombardi and GUMC's newly appointed Dean for Research.

The model, which is being built in modules, is designed to understand estrogen signaling in breast cancer cells, and by extension, why some cancer cells are susceptible to endocrine therapy while others are not. The estrogen hormone drives over half of the 180,000 cases of invasive breast cancer diagnosed each year, yet endocrine therapies designed to shut down this growth pathway are not as successful as simpler, human-derived models would have predicted, Clarke says.

"We need an engineering approach to a biological problem, and this is a very novel, and promising, start," says Clarke. "No one has built a model of breast cancer cell fate decision- making like this before."

His colleagues in this endeavor are Louis M. Weiner, MD, director of the Lombardi Comprehensive Cancer Center; John J. Tyson, PhD, first author of this study, as well as a computational cell biologist and professor at Virginia Tech; and William T Baumann, PhD, an electrical and computer engineer and associate professor at Virginia Tech. Tyson and Baumann have assembled a team of graduate students and postdoctoral researchers to assist in the modeling project.

"We are providing a roadmap of how a modeler might capture, in mathematical form, the molecular events controlling cell growth, proliferation, damage responses, and programmed cell death," says Tyson. "The value of this enterprise will be measured ultimately by new insights provided by the model into the logic and functionality of estrogen-receptor signaling and by the effectiveness of the model as a tool for experimental prediction and design."

Although scientists have amassed a large body of information about the genes and proteins involved in pathways that govern cancer development and growth, and based on that, have developed some "good ideas about how they go awry in certain cancers, most of our understanding relies on intuitive reasoning about highly complex networks of biochemical interactions," the researchers say in their study. "Wouldn't it be better if we could frame a reaction network in precise mathematical terms and use computer simulation to work out the implications of how the network functions in normal cells and malfunctions in cancer cells?"