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Virginia Tech, Georgetown team building computer models to prevent breast cancer regrowth

October 4, 2016

A team of researchers from Virginia Tech and Georgetown are building a mathematical model to design targeted therapies for breast cancer patients. The team will use data from experiments with a cancer cell line grown in the laboratory (left) to build an initial mathematical model that serves as a virtual cancer system (middle). The researchers plan to use the model to test different therapies. Their ultimate goal is optimized, targeted therapies (right).

computer modeling, breast cancer recurrence prevention
A team of researchers from Virginia Tech and Georgetown are building a mathematical model to design targeted therapies for breast cancer patients. The team will use data from experiments with a cancer cell line grown in the laboratory (left) to build an initial mathematical model that serves as a virtual cancer system (middle). The researchers plan to use the model to test different therapies. Their ultimate goal is optimized, targeted therapies (right).

Virginia Tech researchers are using computer models to develop a virtual cancer cell for designing and testing therapies to help breast cancer patients who have already suffered through nausea, hair loss, and a myriad side effects during treatment avoid a recurrence of the disease.

Funded by a $2 million grant from the National Institutes of Health, the project is led by William Baumann, associate professor in the Bradley Department of Electrical and Computer Engineering in the College of Engineering at Virginia Tech, in collaboration with John Tyson, University Distinguished Professor in the Department of Biological Sciences in the College of Science at Virginia Tech, and Ayesha Shajahan-Haq, research assistant professor in the Lombardi Comprehensive Cancer Center at Georgetown University Medical Center.

For the past five years, Baumann and his team have been using experimental data on cancer cell lines grown in a Georgetown lab as the basis for a mathematical model that will accurately portray the molecular signaling networks that control growth, survival, proliferation, and programmed cell death in normal and cancerous breast epithelial cells.

Traditional cancer therapies mow down healthy and sick cells alike, leading to a weakened immune system and other debilitating side effects. New cancer therapies target proteins specific to cancer cells, reducing the side effects. However, cancer cells can develop resistance to targeted therapy. Resistant cancers eventually grow back with even more virulence.

“Successful completion of this work will provide insights into scheduling therapies to make them more effective,” said Baumann. “We hope to use the models to optimize a combination of therapies that will minimize resistance.”

The National Institutes of Health grant will support the researchers as they craft experimentally derived mathematical models to optimize the order and timing of multiple targeted therapies. Their objective is to kill cancer cells while reducing toxicity to normal cells and avoiding the onset of resistance. Specifically, the team will be looking into therapy for a common type of breast cancer that is estrogen­receptor (ER) positive, which means that the growth and spread of these cancer cells is supported by the hormone estrogen.

To treat ER­-positive breast cancer, health care providers usually remove the tumor surgically, apply radiation or chemotherapy, and prescribe a long­term anti­estrogen therapy to kill any breast cancer cells remaining in the body. Initially, this strategy is successful and may lead to a complete cure. But, as with other targeted therapies, the stress of treatment too often causes the remaining cancer cells to become resistant.

Cancerous cells that originally depended on the estrogen receptor for survival switch their reliance to growth factor receptors — another type of protein­molecule — and start growing again.

But the molecular changes in these cells, which occur in the first months of treatment and ultimately lead to resistance, can be reversed if they are caught early. The team will focus on those early changes by investigating the effects of repeated strikes of targeted therapies punctuated by rest intervals.

“The sequence and timing will be designed to maximize cancer cell death, protect healthy cells, and return the remaining cancer cells to their original, sensitive state,” said Baumann. “We may not be able to kill all the cancer cells, but we hope to stop the remaining cells from growing to the point where they threaten life.”

The project is an outgrowth of a Virginia Tech/Georgetown Lombardi Comprehensive Cancer Center collaboration, led by Robert Clarke, dean for research and professor of oncology at Georgetown, with major contributions from Yue (Joseph) Wang, the Grant A. Dove Professor of Electrical and Computer Engineering at the Virginia Tech Research Center in Arlington, Virginia.

Written by Kelly Izlar.

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