While microarray technology is broadening its applications in genomics and proteomics, the technique remains a vibrant contributor in the area of gene expression. Much of the overall developmental effort is directed at making microarrays more high throughput in both genome coverage and in sample numbers.
Victor Levenson, Ph.D., associate professor at Rush University Medical Centre, will review available platforms for microarray-based methylation detection and discuss their advantages and limitations for biomarker development at Select Biosciences’ “Microarray World Congress” to be held this month.
Dr. Levenson will also discuss correlative biomarkers for early diagnosis, prediction of outcome, and response to drugs. “There are different technologies for tumor detection based on tumor-specific mutations, but it is rather difficult to find a rare mutation, especially if we do not know what genes are mutated,” he says. “Instead, we examine tumor-specific abnormalities in DNA methylation.”
Reaching the tumor tissue to extract its DNA can be difficult. “But tumor DNA actually floats in blood, which is a treasure trove for biomarker development,” Dr. Levenson adds. “How tumor DNA makes its way into blood is only partially understood, but we can use it to develop markers for tumor detection. Importantly, the whole genome of the tumor is in blood, so multiple genes can be analyzed in the same sample for an extremely accurate result. That is why we look for tumor-specific patterns of methylation rather than a change in methylation of a single gene.”
The need to determine specific patterns requires analysis of multiple genes in DNA from the same sample, and that brings us to microarrays and how they work, notes Dr. Levenson. “We use microarrays to screen for differences in a multitude of genes, and select the most informative. Once that is done, only informative genes will be necessary to detect the disease, and microarrays will no longer be needed for a clinical test. Once we select the most informative genes—those that give us the best diagnosis—we will test 5–10 genes per disease by a simple PCR.”
To select these informative genes, however, microarrays are essential. “The Illumina GoldenGate platform can test more than 800 genes, but depends on bisulfite modification, so each analysis requires a lot of DNA,” Dr. Levenson continues.
“An alternative platform developed by Roche NimbleGen can analyze up to 28,000 promoters in each sample, but detection of methylation is based on immunoprecipitation, so the starting amount of DNA again creates the bottleneck. In our lab, we use a custom-made microarray with 56 genes for the proof-of-principle studies. Once this phase is over, biomarker production runs will employ a 12,000 gene microarray. Upstream processing involves digestion of isolated DNA with a methylation-sensitive restriction enzyme, so the whole test requires only 0.3–0.5 ng of DNA.”
There are two major ways to prepare a microarray—you can spot synthetic oligos on a special support (usually glass slides) or you can synthesize the oligo directly on the support. “These spotted microarrays allow directional attachment of oligos,” notes Dr. Levenson. “For example, arrays produced for us by Microarrays have oligos with linkers at the 5-prime end, so the oligo is fixed to the glass support in one predetermined orientation.” Not all spotted arrays are like that—if unmodified DNA is simply placed on support it will attach randomly, which might reduce its ability to hybridize.
Spotted arrays can be made by contact or noncontact spotting. The first depends on a physical touch (e.g., when needle-based instrument deposits oligos using very thin needles); the noncontact method applies electrical discharge or inkjet-type technology. Density of spots is limited in both cases—by physical thickness of a needle for contact printers or by the size of the aperture for inkjets. This means that the spot diameter can’t be smaller than a certain limit, so their number on each slide is also limited.
To increase spot density in Affymetrix arrays oligos are synthesized directly on solid support by photolithography, so that literally millions of oligos are positioned on each mictoarray. This comes at a cost—accuracy of synthesis is good for short oligos but insufficient for longer molecules. “Despite these limitations, only Affymetrix makes tiling microarrays that cover the whole genome with very small gaps between two adjacent probes,” says Dr. Levenson. “For our work, however, these arrays are far too complicated, so spotted arrays are the most appropriate.”
Biomarkers for Colon Cancer
Characterizing DNA methylation patterns is a hot topic, says Sungwhan An, Ph.D., president and CEO at Genomictree, who will also be presenting at the meeting. “We established a method for methyl DNA isolation assay (MeDIA) using the binding property of methyl-DNA binding polypeptide to methyl DNA.
“We also identified promising methylation targets for early detection and prediction of disease recurrence of colon cancer using MeDIA-coupled CpG microarray. Basically, we isolated methylated DNA specifically from a pool of fragmented genomic DNA with methyl DNA-binding domain and performed hybridization after labeling.”
Dr. An says that his company does a lot of private sector work in the microarray space, and the identification of methylation biomarkers is of great interest.
“Eventually, our goal is to identify biomarkers for colon cancer—certain methylation biomarkers for early detection and stratification,” says Dr. An. “We generated a recombinant histidine-tagged minimum size of methyl-DNA binding domain and used nickel magnetic particles for methyled DNA enrichment and adopted freeze-drying for preserving protein. This provides us better conditions for experimental replication and specificity and shelf life.
“We have a nice study design comparing genome-wide methylation patterns in a primary tumor and paired nontumor tissues of patients with colon cancers, half of them developed disease-recurrence and two super-normal tissues of healthy individuals over common reference DNA as a role of internal control.
“Methylation patterns were discerning enough to distinguish a primary tumor from nontumor tissues, which was closely grouped to super-normal tissues by unsupervised hierarchical clustering. We wanted to see if the stratification was prominent enough, and we found that this method of stratification worked on a small scale.”
Dr. An says that using methylation to study DNA is helping scientists make great inroads in cancer research. “RNA gives good feedback, but DNA is more stable for methylation.”