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Special Report:
ECEs and Biomedicine

April 2004


Printer-Friendly Version of this article (1.5MB PDF).

For more information, visit the Computational Bioinformatics and Bioimaging Laboratory.

Seeking molecular biomarkers for improved cancer outcomes

Within 15 years, researchers expect to have breast cancer diagnosis and prevention under the same level of control that heart disease now enjoys – thanks in part to contributions of engineers like Yue (Joseph) Wang.

Wang has been working on multidisciplinary cancer research teams since 1995, first in prostate cancer detection, then breast cancer detection and therapy. He currently heads a $5.5 million effort to help improve cancer treatment outcomes.

“Heart disease is still the nation’s number one killer,” Wang explained, “but we have some excellent tools to diagnose and treat it. The survival rate for heart disease is high. Cancer is a sadder story, though. Many patients with cancer can be cured, but all too often, by the time we make a diagnosis, either it’s too late, or there is no cure.” Significant progress on breast cancer has been made in the past decade, he said, and with new molecular research, progress should escalate.

Imaging and computational tools
Wang’s breast cancer research involves two fronts: improving biomedical imaging related to diagnosis and therapy and developing computational tools for the molecular analysis of the disease. His bioinformatics projects include The Molecular Analysis of Breast Cancer, Compre-hensive Analysis of Microarray Gene Expression Data, Timing of Dietary Exposure and Breast Cancer Risk, and Molecular Epidemiology and Mechanisms for Breast Carcinogenesis: Alcohol Drinking as a Paradigm.

Multi-level analysis: from full-body to genetic
“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.

Dreams of personalized medicine
Wang’s dream is a personalized medicine, in which doctor’s can precisely determine how an individual patient’s cancer will behave and target a precise treatment plan based on expected outcomes.

Molecular data is 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 new field, 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 help eliminate noise and develop 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.

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

(Continued. See Systems approach to Diabetes.)
(Go back to Beyond equipment and imaging.)

Gene analysis in the Center for Genetic Medicine at Children’s National Medical Center –

Top: CNMC Research Assistant Lindsay Mitchell checks the processing of a gene microarray imprinted with the DNA of a mouse with ALS (Lou Gehrig’s Disease).

In this study, the microarray is washed with messenger RNA (mRNA) that is labeled with fluorescent antibody. The mRNA binds to its corresponding complementary DNA (cDNA) probe on the gene array. Then the array is washed with a fluorescent antibody that binds to the mRNA. Fluorescence levels correspond to the amount of specific mRNAs present. In this case, red and white depict greater fluorescence.

The center left photo shows the fluorescence of the entire array after washing, and the following photos are at higher zoom levels. Specialized software imposes a grid over the array so appropriate genes can be mapped to the fluorescence. When sectors do not meet, Mitchell adjusts the grid manually.

The processing of each array introduces differing levels of background noise, she said, adding that Yue Wang’s expertise has helped the laboratory better isolate biological effects from the background noise as well as with data analysis tools and expertise.

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