Personalized medicine to improve the outcome for breast cancer patients

Oct 12 2005

As cancer remains a leading cause of death in the United States, early detection and treatment are critical keys to improving the survival rate. Yet, an individual's response to treatment varies considerably, even among cancers of the same histological type. Given these variables, individualized patient assessment becomes a very challenging procedure.

Virginia Tech's Yue (Joseph) Wang, who currently leads a $5.5 million research effort to improve the outcome for breast cancer patients, dreams of a more personalized medicine in which doctors can precisely determine how a patient's cancer will behave. Then, based on the expected outcomes the physician can target a precise treatment plan.

Researchers are now studying disease at molecular levels and need the analytical skills of engineers to aid in both discovery and understanding of biological systems, Wang, a member of the Bradley Department of Electrical and Computer Engineering, said.

"Personalized medicine requires a quantitative-plus-molecular equation, in which intelligent computing tools can play a major role. However, many difficulties need to be overcome before a molecular signature-based computer-aided diagnosis can be developed. Yet, prognosis and monitoring therapy are all among our future tasks," he said.

"We are working with physicians to analyze cancer data from all levels: the entire body, the cellular, the molecular, and the genetic," he said. "We are seeking to understand how disease starts, how it progresses, and which biomarkers can be used for therapeutic purposes," he explained "Not all molecules in the body are responsible for a disease; only a certain subset are. If we can accurately identify the responsible molecules and determine appropriate biomarkers, we can develop rational treatments."

He stressed that, since cancer progression is a process of acquisition of multiple and alternative mutations, molecular imaging must be able to image multiple biomarkers.

In studying any single disease, thousands of genes and proteins that interact with each other are studied and tested. Proteins, the basic building blocks of cells, are also involved in cellular function and control. A single cell can contain one billion molecules capable of interacting with each other. These numbers produce "vast amounts of data that need to be interpreted and analyzed so that the components involved with diseases can be isolated and identified," Wang explains.

This data processing and manipulation typically falls under the computational bioinformatics field, where a number of computational engineers and computer scientists are now working.

Another, newer field, called systems biology or systems biomedicine, is emerging. It requires modeling and systems engineering skills based on a solid mathematical and theoretical background, Wang said. The completion of the human genome project, in which every gene in the human body was identified and mapped, has provided a foundation for the field. A frequently used metaphor is that the genome project provided a location map, but the roads and traffic patterns remain unknown.

Molecular data are typically obtained from gene microarrays, which are silicon chips imprinted with DNA and its thousands of genes. The microarrays get 'washed' with a solution carrying fluorescent messenger RNA from the biopsied tissue sample of a cancer patient. The RNA molecules then attach to their corresponding DNA genes. The more RNA segments that attach to a gene, the more that gene will glow or fluoresce, which is called gene expression. The expression can then be measured and analyzed.

Wang's team is also working with similar technology involving protein microarrays to study cancer at an even more precise level. The area of study, called proteomics, is expected to help researchers better study the function and control of the molecules involved.

Both technologies yield "vast amounts of data," Wang explained. His team is developing tools that create analysis algorithms so that the true biological effects can be studied. They are also developing, optimizing, and validating neural network classifiers so that cancer can be more accurately classified and therapy can be personally tailored for optimal response.

"This is important with diseases that are caused not by a single factor, but by multiple factors. Cancer, for example, can be caused by genetic predisposition, with contributing factors, such as diet, environment, and alcohol consumption. Type 2 diabetes requires a systems approach, as it is caused almost entirely by multiple social factors, including diet and lack of exercise."

Wang, based at Virginia Tech's Northern Virginia campus, also serves as an adjunct professor of radiology at Johns Hopkins Medical Institutions. He works with teams that include biologists and physicians from Georgetown University, Johns Hopkins Medical Institutions, the National Institutes of Health, and the Children's National Medical Center. He is a member of the Virginia Tech – Wake Forest University School of Biomedical Engineering and Science.

In February, Wang was inducted into the College of Fellows of the American Institute for Medical and Biological Engineering (AIMBE) for his contributions to biomedical informatics. AIMBE Fellows represent only two percent of the researchers active in medical and biological engineering.

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