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Michigan's Lubman Builds Glyco-Array to Test for Cancer


A looming challenge in the proteomics field is finding specific, validated biomarkers for detection of disease state, particularly for early stage cancer. A new microarray technique developed by David Lubman at the University of Michigan has made it possible to perform large-scale analysis of glycoproteins, which may be early cancer biomarkers. The glyco-microarray, as he calls it, is similar to a protein array but instead has glycoproteins spotted down. Lubman presented data from initial samples at this year's US HUPO meeting, held in March in Bethesda, Md.

When looking for disease biomarkers, whether for cancer, diabetes, or others, "usually what most people focused on is assaying the level of a protein," Lubman says. "They've been mainly using protein levels, or quantification, and that's not what we're doing. We're focusing on the sugar groups … the glycan groups on the proteins."  The microarray that his team designed focuses on differentiating between early- and late-stage cancer signatures of glycoproteins from a variety of biofluids, such as plasma, serum, and urine.

The glyco-microarray probes for specific N-linked glycans within a mixture of glycoproteins. N-linked glycans are the sugar portion of glycoproteins, and these particular kinds of additions on proteins have been shown to play an important role in encouraging proper protein folding, engaging in cell-cell interactions, and in directing and altering development and disease, Lubman says.

To create the array, Lubman follows a stepwise protocol. First, he and his group use a lectin column to extract the N-linked glycoproteins from a sample mixture. Next they separate the mixture of eluted proteins and spot them onto a coated glass microarray slide. Using fluorescently-labeled lectins specific for individual glycoproteins, they can then perform hybrid-ization and bioinformatic analysis to tease out unique patterns from individual plasma samples.

Finding targets among these patterns means using computational analysis to highlight the best glycoprotein on the array. "Certain glycoproteins are better than others," Lubman says. "If you have a hundred on an array, maybe five of those spots are really giving you the best information."

Validating these targets on an initial small scale requires another step. After determining the best glycoproteins from microarray analysis, Lubman's group uses an immunoassay to pull those proteins out of a complex mixture. Then, they probe the antibody assay with lectins, which again will bind in a specific manner with their distinct glycoprotein targets. "We come in with lectin and we probe … the glycan structure on those capturedg lycoproteins," Lubman says. "Based upon the response of those glycans to different lectins, we get a very specific response. That's what we're using for high-speed marker validation."

That response may stem from structural differences of the glycoprotein. "What we're interested in is looking at the differences between cancer and normal, and the difference between cancer and normal is not defined by the actual glycoprotein itself, it's defined by the structure of the glycan on the glycoprotein," Lubman adds. "It's the difference in response to the structure of that glycan that gives us the information about whether it's cancer or normal, or cancer or adenoma, or early cancer or late cancer."

While the research is still in discovery mode, the end goal is to bring the array to the clinic. In the realm of biomarkers, the main advantage of using glycoproteins over simply quantifying protein levels in plasma, Lubman says, is that they erase some of the gray area when it comes to determining cancer disease state among large numbers of people. "In this case the data is a lot cleaner," he says. "When somebody has cancer, the results are much clearer; the glycan changes in a very well-defined way."

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