ECE: Electrical & Computer Engineering
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Reverse engineering a network to fight breast cancer

Did you know?

  • There were 209,060 new cases of breast cancer in 2010.
  • There were 40,230 deaths from breast cancer in 2010.
  • Breast cancer is the most common non-skin cancer in the United States.
  • 70 percent of the 209,000 newly diagnosed cases of invasive breast cancer in the United States will be classified as ER+.
  • Approximately 30 percent of all ER+ early-stage breast cancers eventually recur.
A graphic showing ERα signaling in breast cancer

ERα signaling in breast cancer. Estrogen receptor signaling networks are very complex and little understood. There can be millions of interactions in the network of a single cell.

Armed with computer algorithms and data-mining techniques, Jason Xuan is diving into massive amounts of protein and gene data to fight breast cancer. The associate professor, who is posted at the Advanced Research Institute in Arlington, is seeking the key to breast cancer endocrine therapy resistance.

He has been awarded a $2.56 million grant for this work from the National Institutes of Health’s (NIH) National Cancer Institute. He and Yue (Joseph) Wang, the Grant A. Dove Professor of Electrical and Computer Engineering, are working with colleagues from the Georgetown University Medical Center on the project.

The hormone estrogen and proteins called estrogen receptors play an important role in breast cancer. For cells that contain estrogen receptors, called ER+, estrogen is considered to be fuel for breast cancer, according to Xuan, who is also associate director of Virginia Tech’s Computational Bioinformatics and Bioimaging Laboratory.

Estrogen can pass through the cell membrane. When estrogen enters a cell that is ER+, it triggers a series of biochemical pathways, referred to as the estrogen receptor signaling network, that causes cell growth and other behaviors, he explains.

Endocrine therapies (commonly called hormone therapies) for breast cancer, such as Tamoxifen, block the direct effect of estrogen and slow down the replication of breast cancer cells, Xuan says. “Only 50 percent of all ER+ tumors are responsive at first to antiestrogens such as Tamoxifen and many initially responsive tumors eventually become resistant to endocrine treatment, leading to tumor recurrence and death,” he says.

Often, the cell finds a way around the block and the cancer becomes resistant to endocrine therapy. “When it comes back, then we have no cure for patients,” says Xuan. “We have not yet found a drug to battle the recurrence. That’s very troubling.”

According to Xuan, “Evidence has begun to accumulate in our studies and others that ER-signaling can contribute, at least in part, to endocrine resistance. In this project, we hypothesize that new insights into ER-signaling can be discovered to circumvent endocrine-resistant tumor growth. We want to reverse endocrine therapy resistance.”

Estrogen receptor signaling networks are very complex and little understood. The problem is huge: there can be millions of interactions in the network of a single cell. To discover these interactions, Xuan’s team is developing new machine learning algorithms, or data mining techniques, to analyze the protein–protein interactions and gene expression data. From measurements of behavior, they are trying to reverse engineer the estrogen receptor signaling network and to build models of the network.

Xuan’s goal is to first understand the network, then understand how it becomes resistant, so that eventually researchers can find targets for drugs that can reverse the resistance. “We will discover new knowledge of ER-signaling and ultimately use this information to identify new therapeutic targets for drug discovery,” he says.

Xuan’s project is part of Virginia Tech and Georgetown University Medical Center’s Center for Cancer Systems Biology that focuses on treating breast cancer. Started in 2010, the center seeks to develop more advanced and better-targeted treatments for the disease. The center’s specific focus is on the role of the estrogen receptor in breast cells.

Scientists at Georgetown are focusing on biology, examining cell cultures, mammary tumors in animals, and patient breast tumors to determine estrogen receptor-positive molecular signaling systems. Virginia Tech researchers are providing bioinformatics analysis of the Georgetown data and building mathematical models of the molecular control systems. William Baumann, ECE associate professor, and John Tyson, professor of biological sciences, are also involved in the center, and are tackling models from the molecular level.