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DOI: 10.1126/science.opms.p1300072

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Clinical Aspirations of Microarrays


Although most microarray applications are currently research-use-only, this technology appears poised to move to the clinic for genomics-based applications. In fact, some products can already be used in medical diagnostics and many more are in development. For example, microarrays can be customized to detect small, specific genetic changes that indicate a particular disease. In the future, this technology will likely remain a useful tool for both research and clinical applications.

By Mike May

Inclusion of companies in this article does not indicate endorsement by either AAAS or Science, nor is it meant to imply that their products or services are superior to those of other companies.

In today's translational genomics research, says Seth Crosby, alliance director of the Genome Technology Access Center at Washington University School of Medicine in St. Louis, "The biggest challenge is interpretation." Available technology makes it easy enough to collect information from someone's genome. The tricky part comes in interpreting the clinical relevance of that information. "Then, one can say a variation in a particular gene is known to have such and such impact on the patient's health or treatment options," Crosby explains.

As an example, Crosby describes a clinically certified next generation sequencing panel of 45 oncology genes offered by Genomics and Pathology Services, Washington University's clinical genomics laboratory. This panel is actively being used to profile tumors and guide the treatment of cancer patients. "We had to look at hundreds of papers," Crosby says, "to build a clinical-grade database of authoritative interpretations for each clinically relevant mutation found in these genes." He adds, "That took hundreds of Ph.D. and M.D. hours, reading through papers to identify the pertinent information."

Crosby notes that, over time, clinicians might come to understand which changes in the genome impact a patient's health and which are harmless. "Once the lists of relevant and irrelevant genes are narrowed down, and we have a sense of which polymorphisms are important, these could be used to create a very cheap array that would help detect diseases," he says. Beyond being economical, microarrays also deliver manageable amounts of data. As Crosby explains, "Much of the genome is invariant." So with microarrays, he says, "We collect only the data we need."

Developing Diagnostics

In some cases, clinicians can link specific chromosomal defects with particular diseases, and microarrays bring new capabilities to this karyotyping, or counting and assessing the appearance of chromosomes. Down syndrome is one of the best-known examples, in which the person has an extra copy of chromosome 21. Although additions or deletions of entire chromosomes, and even defects in parts of them, can be seen under a microscope, microarrays reveal fine-detail changes in chromosomes. "Using microarrays as tools in cytogenetics is really accelerating," says Andy Last, executive vice president of the genetic analysis business unit at Affymetrix in Santa Clara, California. When experts are asked in which areas microarrays are being used the most, many mention copy-number variation—the addition or deletion of specific regions of DNA, particularly those with clinical consequences.

"There are literally hundreds of syndromes [that have] chromosomal rearrangements associated with a particular phenotype," says James Clough, vice president, clinical and genomic solutions at Oxford Gene Technology (Oxfordshire, United Kingdom). "Depending on the population being tested, traditional karyotyping under a microscope provides a diagnosis about 5–8 percent of the time, and a microarray provides an 18–25 percent diagnostic yield. The resolution is far higher with an array." Still, he adds, "The challenge is determining if a small aberration is pathogenic or nonpathogenic, or a variance of unknown significance."

To help researchers make such distinctions, Oxford Gene Technology supplies a range of microarrays, such as the CytoSure ISCA Arrays, which look for genetic defects involved with known syndromes, such as Prader Willi and Williams-Beuren syndromes.

PerkinElmer (Waltham, Massachusetts) has also developed assays for detecting disease-related structural changes in chromosomes. For example, the company provides genomic testing of oncology samples with its OncoChip. "This is a high-density whole-genome array, targeting more than 1,800 cancer genes and clinically relevant balanced translocations," says Christopher Williams, market segment leader at PerkinElmer. The array can reveal copy-number variations as low as 10 kilobases in the targeted regions.

Additionally, the CytoScan HD Cytogenetics Solution, from Affymetrix, offers probes for both copy-number variation and single nucleotide polymorphisms (SNP). The probes for copy-number variation can reveal chromosomal changes that could cause clinical concerns, and the SNP probes help researchers assess more detail on the exact variation—specifically the changes in individual nucleotides. "This is the highest density microarray on the market," says Last. "It's being used in the classical sense of cytogenetics, [to detect] inherited disorders, and increasingly in oncology, especially [for] hematological malignancies." The array can reveal copy-number variations in chromosomal segments as small as 25,000 nucleotides. For the moment, this is a research-use-only tool, but Affymetrix is running clinical trials to test the use of this platform as an in vitro diagnostic.

Agilent (Santa Clara, California) also makes microarrays that detect copy-number variations, such as its CGH (comparative genomic hybridization) plus SNP microarrays. "These can be customized from more than 28 million probes in our library, and users can even upload their own probe sequences," says Patricia Barco, product manager for cytogenetics at Agilent. Within an exon, for example, these microarrays can detect copy-number variations in DNA segments as small as 100 base pairs. "These can be used in prenatal and postnatal research and cancer," Barco says. The most popular format, according to Anniek De Witte, product manager, clinical software at Agilent, includes 180,000 probes divided into four samples. "This is the best balance of the number of samples and resolution," she says. In addition, the Agilent CytoGenomics 2.5 software compares the results from these arrays with external and internal databases to distinguish between common variations and ones that could cause clinical problems.

Creating Custom Tools

To apply microarrays to clinical problems, physicians need approved tools. One FDA-cleared diagnostic tool, the Pathwork Tissue of Origin test from Pathwork Diagnostics in Redwood City, California, uses an Affymetrix microarray to determine the tissue type in which a patient's cancer started, such as breast or colon. Raji Pillai, senior director, product development and clinical affairs at Pathwork Diagnostic says, "This test uses formalin-fixed, paraffin-embedded [FFPE] tissue from a patient's tumor and 2,000 transcript markers to provide a readout of a tumor's gene-expression profile." Using several thousand different tumor specimens and proprietary computational algorithms, researchers at Pathwork Diagnostics have identified a set of 2,000 genes that can be used to distinguish 15 kinds of tumors.

When a pathologist receives a cancer sample that is difficult to identify visually, they can send it to Pathwork Diagnostics. "It takes four to five days to report out a result that's interpreted by a pathologist in our lab," says David Craford, the company's chief commercial officer. A company pathologist reviews the results to ensure the most accurate interpretation of this diagnostic.

In the future, the company hopes to develop microarray tests that determine a tumor's tissue of origin and also distinguish between tumor subtypes. Such advanced tests might even "provide information on the [patient's] predicted response to a particular therapy," Craford says.

In addition to being used for studying an individual's genetic profile, microarrays can be used to explore genetic variations across different populations and cultures. For example, Jennifer Stone, market development manager at Illumina in San Diego, California, says, "We developed our Infinium HumanCore BeadChip family of microarrays to provide a solution for population-level or biobank studies." Such research involves tens to hundreds of thousands of samples. "These genetic studies are on a scale above and beyond what's historically been done," says Stone. Because these microarrays accommodate a large number of samples, they provide an opportunity for researchers to perform robust statistical analyses which can reveal differences in the distribution of genetic variation between normal and diseased populations.

The HumanCore microarrays provide a standard set of over 300,000 SNP probes, which covers the entire genome and includes additional probes specifically focused on "variants that exist in the population and lead to the loss of function of genes," explains Stone. These new microarrays can also be customized, so researchers can study variants found from their own experiments or from public databases.

To explore genetic variations across entire populations, researchers need a family of flexible microarrays. Thus the second member of the HumanCore family, the Infinium HumanCoreExome BeadChip, includes the standard set of over 300,000 SNP probes plus 240,000 exome-focused markers. With this combination of markers, a scientist can compare single nucleotide variations between samples and potentially determine how they impact a protein's production, as indicated by the exome-based markers.

As companies begin to create increasingly customized tools, the concept of what makes a microarray has begun to evolve. Traditionally, microarrays consisted only of nucleotides attached to a solid surface, but variations on this theme also exist. For example, Life Technologies (Carlsbad, California) developed its TaqMan OpenArray Real-Time PCR plates, which include 3,072 wells. "The arrays can be formatted from our inventory of eight million TaqMan assays," says Jami Elliott, market development manager at Life Technologies. The real-time PCR assays, which use TaqMan probes (so named because these assays rely on the Taq polymerase), can be used to measure gene expression, identify biomarkers, and more.

If eight million choices aren't enough, Joshua Trotta, director, business development genetic analysis at Life Technologies, says, "We can custom design one."

Ongoing Data Dilemmas

Rather than making microarrays, Expression Analysis, a Quintiles company in Durham, North Carolina, uses arrays to conduct a wide range of studies for customers, such as gene-expression profiling. In doing so, Expression Analysis uses microarrays from several vendors, including Affymetrix, Illumina, and Fluidigm in South San Francisco, California. According to Pat Hurban, vice president of R&D at Expression Analysis, "Microarrays are very mature as a technology, but there are still a number of challenges in working with the data, especially when you want to drill down into the biology." He adds, "It's one thing to provide a statistical treatment of data, but another to understand the pathways involved and translate that into biology." This company uses a collection of proprietary tools in an effort to bridge that knowledge gap.

In fact, Hurban advises researchers to reassess the best technology to use as a project advances. "You must be mindful of the technical limitations of microarrays," he says. For example, he points out that microarrays provide excellent discovery tools. "It's not uncommon to identify specific genes of interest with microarrays," Hurban says. "When it comes to translational research, the question becomes: Is it advisable to continue on a microarray platform as you get closer to the clinic or transition to a more suitable and robust technology, such as [quantitative] PCR or sequencing."

In a recent project, Expression Analysis worked with a client who had what Hurban describes as "a preliminary gene-signature panel that was very useful as a diagnostic in a certain indication area." Researchers at Expression Analysis worked with patient samples from the sponsor to put that signature on microarrays. "We showed the validity of this panel," Hurban says. "Ultimately, the sponsor wanted to turn this signature into a diagnostic and became concerned with the microarray results because the precision was a bit of a challenge." Consequently, the client eventually turned to a PCR-based platform for the final diagnostic. As a result, Hurban says, "You might use a microarray to some point, and then go to another technology."

Tomorrow's Tools

The ongoing advances in sequencing technology have made more than a few experts predict the demise of microarrays. For example, Elizabeth Chao, director of translational medicine at Ambry Genetics in Aliso Viejo, California, says, "The expression arrays that I've been using for 14 years are incredible tools, but RNA sequencing is starting to replace microarrays in research and translation." She adds, "Sequencing is not replacing microarrays in the clinical setting yet, but it probably will soon."

The data generated by sequencing can be both beneficial and challenging. Sequencing provides a gigantic amount of data in a short period of time, but it can be difficult to interpret so much data. Chao is confident that interpreting sequencing data will improve rapidly. She says, "Bioinformatics has really come up, and new methods are making it possible to look at sequences across the entire genome."

To evolve with changes in technology, some companies provide services that teach researchers to use the growing amounts of data. For example, Todd Smith, senior leader, research and application at PerkinElmer, says, "We can help people as they go from microarrays to DNA sequencing." This can include analytical techniques for handling the higher volume of data. These technologies, though, will likely complement each other, according to Smith and his colleagues. "There are applications where microarrays work best, and others where sequencing works best," says Williams. "There are areas where sequencing won't work well, but microarrays can." As an example, Williams says they are about to start a study that involves 160 samples that must be processed in a matter of weeks. "There's no way we could go through that with sequencing and get it turned around in time to have meaningful data," he says. Moreover, Smith says microarrays are superior to sequencing when it comes to searching for structural variations in a genome.

Though some experts may have differing opinions, the general consensus predicts that microarrays will continue to benefit basic research and provide clinical tools related to genomics. In the end, microarrays will advance where they work the best.

Chinese Translation

Featured Participants



Ambry Genetics

Expression Analysis

Life Technologies

Oxford Gene Technology

Pathwork Diagnostics


Washington University School of Medicine in St. Louis

Note: Readers can find out more about the companies and organizations listed by accessing their sites on the World Wide Web (WWW). If the listed organization does not have a site on the WWW or if it is under construction, we have substituted its main telephone number. Every effort has been made to ensure the accuracy of this information. Inclusion of companies in his article does not indicate endorsement by either AAAS or Science, nor is it meant to imply that their products or services are superior to those of other companies.

Mike May is a publishing consultant for science and technology.

DOI: 10.1126/science.opms.p1300072

This article was published as a special advertising feature in the 15 February 2013 issue of Science magazine.

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