Gene Expression Microarrays: Industrial and Clinical Research Applications

In 1961, Francois Jacob and Jacques Monod discovered the biology of gene expression and gene regulation. Their pioneering work began to explain how a static DNA genome can adapt and respond to a constantly changing environment. But it would be another 28 years until scientists could analyze expression from more than a few genes at a time.

Figure 1 - The GeneChip Human Genome U133 Plus 2.0 array offers researchers the protein-coding content of the human genome on a single commercially available catalog microarray.

The 1989 invention of the DNA microarray by Stephen P.A. Fodor and colleagues1–3 provided researchers with the ability to analyze expression from thousands or even tens of thousands of different genes simultaneously. Up until that point, researchers picked two or three genes they believed were central to a certain disease and spent years or even entire careers studying just that handful of genes in the hope of finding some causal link to the disease. Using GeneChip® arrays (Affymetrix, Santa Clara, CA), researchers no longer need to hypothesize about which genes to focus on; instead they can look at the entire genome at one time and compare disease-state expression to a normal control (Figure 1). This objective analysis method enables scientists to discover the underlying genetics and associated biochemical pathways that are disrupted in a wide range of diseases, from cancer4–6 to multiple sclerosis.7 There are literally hundreds of examples, presented in nearly 3000 scientific publications, in which GeneChip technology has been used to extend our understanding of disease and to identify the molecular pathways that modulate it. Furthermore, the successful use of expression profiling to classify complex diseases enables researchers to identify molecular mechanisms that are more likely to be causative of disease and therefore better diagnostic indicators or possible therapeutic targets.

The same characteristics that make GeneChip expression arrays so ideally suited for basic disease research—high data capacity, reproducibility, and accuracy—have allowed the technology to revolutionize drug discovery and clinical research and development. High-data-capacity GeneChip microarrays have been adopted by pharmaceutical companies for disease-pathway target validation, compound profiling, and toxicology studies. Additionally, the arrays are currently being used in dozens of clinical trials to stratify disease, predict patient outcome, and generate information that can be used to make better therapeutic choices. From scientific obscurity 15 years ago, the now widespread use of GeneChip expression arrays is reviewed, from the laboratory bench to the industrial and clinical arenas, demonstrating the highly scaleable nature of GeneChip technology and its ability to provide more in-depth analysis of the genome.

Data capacity

Figure 2 - Multiple GeneChip arrays are simultaneously synthesized on a single 5-in. wafer. One wafer can be cut into thousands of separate arrays.

Researchers use GeneChip arrays in all areas of disease research because of their ability to measure massively parallel gene expression. GeneChip microarrays are a classic Silicon Valley innovation, integrating semiconductor fabrication techniques with random-access combinatorial chemistry (Figure 2). This results in a scaleable photolithographic manufacturing process capable of producing GeneChip arrays with millions of probes on a single glass chip about the size of a dime.

As in semiconductor fabrication, photolithography has enabled Affymetrix to increase the amount of information it can fit on each successive generation of arrays. Every time the company reduces the size of microarray features by two, it gains a fourfold increase in data capacity. The first commercial microarray, produced by Affymetrix in 1994, accommodated 16,000 probes. Its most recent human expression array accommodates over 1.3 million probes, able to measure expression for all known coding DNA in the human genome (nearly 60,000 transcripts) with 11-fold redundancy.

Industrialized research

Commonly used to identify genes and pathways responsible for disease, GeneChip expression arrays are well suited to validate those disease pathways and screen potential drug compounds for treatment efficacy and toxicity.

Disease pathway target validation

Figure 3 - A computer readout from a scanned microarray shows the genes that are detected by a single GeneChip probe array. When scientists zoom in, they can see the different levels of fluorescence coming from the individual probe locations. Some probes detect intense gene expression (bright white and red features) and some do not (dim blue and black features).

Once a disease pathway is identified, researchers need to know that disrupting the pathway will actually affect the disease etiology. Using whole-genome expression profiling (Figure 3), scientists can understand a wide range of effects, desirable and undesirable, that result from disrupting a pathway and are then able to better evaluate potential targets for drug design. Modern technologies, such as small interfering RNA, are now being used to rapidly and specifically inhibit gene function, speeding up the exploratory process of validating useful drug targets. However, being able to affect many different genes quickly requires an equally efficient way to measure the downstream effects generated by those changes.8 Additionally, custom resequencing microarrays can be used to pinpoint disease-causing mutations or to measure the genetic variability of a target gene in clinical populations. The information generated from GeneChip microarrays gives researchers a more complete understanding of how a gene functions within a cell and adds significant value to the biological models used to validate gene targets.

Compound screening: Mechanism of action

Following disease pathway identification and validation, whole-genome microarray analysis can be used to characterize lead compounds for selectivity and specificity, and to identify molecules that disrupt expression of intended disease genes. While existing technologies are well suited to measuring the anticipated action of a development compound, these methods do not typically identify any additional or unexpected effects. Whole-genome expression analysis provides a complete and unbiased measure of both on- and off-target effects for each compound tested. Clearly, on-target effects are desired; however, off-target changes in expression may help treat different diseases, operating through a different mechanism. For example, despite their development to treat hypertension and depression, the respective blockbuster successes of Viagra® (Pfizer Pharmaceuticals, New York, NY) for erectile dysfunction and Wellbutrin® (GlaxoSmithKline, London, U.K.) for smoking cessation are prime examples of exploiting off-target drug action to serve other therapeutic markets. By developing large databases of information on the global activity of each member of a compound library, microarray expression analysis allows companies to ultimately create “smarter” compound libraries, with recorded and known effects for each member compound.

Compound screening: Mechanisms of toxicity

Microarray gene expression screening not only helps to identify mechanisms of drug action, but also points to other off-target effects that may suggest the compound produces far too many side effects to be approved. For instance, if changes in gene expression match those of a known toxin, a compound can be eliminated from the screening process early in development, saving both time and money. Compound toxicity is typically not evaluated until later stages in the development pipeline and has become a major reason for the high attrition rate in drug development. In the past, the belief has been that once a compound is found to be active, it can be sufficiently modified to avoid toxic effects while retaining its specific activity.9 However, a recent review of the literature demonstrates that, generally, successfully developed drugs undergo few modifications from their initial lead form.10 Using microarrays to understand a compound’s risk profile earlier in the development process allows for more efficient and cost-effective decision making regarding compound prioritization for future drug development.

Clinical research

The benefits of gene expression analysis are not limited to the initial stages of drug development. GeneChip expression arrays can be further used in clinical research and disease classification, areas with direct impact on human health.

Classifying disease

The first step in treating patients is to correctly identify their diseases. This has typically been performed by classical clinical exams, histological data, and laboratory tests. However, genome-wide expression profiling of diseases like breast cancer, leukemia, and prostate cancer have provided for more precise disease classifications and have revealed that similar tumor types have distinct molecular differences.11 This explains why clinicians have been baffled for years when, for instance, two breast tumors looked identical, but patient response to treatment and patient outcomes were radically different. In reality, the patients had different diseases, and different diseases require different treatments. By more accurately classifying the molecular nature of the disease, clinicians would be able to choose and design more effective treatments.

Nowhere is this fundamental change more profound than in cancer research, where hundreds of microarray studies are offering new hope for diagnosing, classifying, and treating both common and rare cancers. Studies on medulloblastoma,12 prostate cancer,13 breast cancer,14,15 lung cancer,16 colon cancer,17 renal cell carcinoma,18 and diffuse large B-cell lymphoma19 are just a few examples of cancers in which established gene expression classification systems have been developed, often offering important prognostic indications for cancer outcome and recurrence, as well as patient response to treatment. Studies have thus far been retrospective, but future maturation and commercialization of the technology promises prospective uses in the clinic and direct impact on patient treatment.

In the realm of initial disease identification, gene expression patterns may provide early, noninvasive clues to the detection of deep internal organ malignancies. For example, as part of a Phase II clinical study studying renal cell carcinoma,18  Wyeth (Madison, NJ) used GeneChip expression arrays to profile gene expression from peripheral blood. They found a specific set of expressed genes that could be used to distinguish blood cells from renal cell carcinoma patients and normal volunteers with high accuracy. Use of peripheral blood gene expression as a diagnostic marker for disease has important implications for both the clinical diagnosis and future clinical pharmacogenomic studies of antitumor therapies.

Predicting drug response

The efficacy of even the most successful drugs can vary widely from individual to individual. Whole-genome expression arrays provide a way to examine the underlying genetics of responders and nonresponders without any of the assumptions or limitations used in candidate-gene approaches. For most drugs with variable responses, little is known about why they work in some patients and not in others. Microarray analysis enables scientists to explore the whole genome and to identify predictive markers of disease and drug response. This may ultimately provide more tailored, effective, and safer courses of treatment and help avoid some of the over 100,000 annual fatalities from adverse drug reactions in the U.S. alone.20

In a recent Phase III clinical trial by Novartis Pharmaceuticals (Basel, Switzerland) expression profiles were used to predict the success or failure of Glivec®/Gleevec® treatment on chronic myelogenous leukemia.21 Researchers analyzed gene expression patterns from patients prior to treatment and found a 31-gene “No Response” signature, which predicts a 200-fold higher probability of failed therapy.

Similarly, in a Phase II clinical trial conducted at the Dana Farber Cancer Research Institute (Boston, MA) for the Millennium Pharmaceuticals (Cambridge, MA) drug Velcade™ (generic name bortezomib), researchers used GeneChip arrays to collect pharmacogenomic data from myeloma patients treated with the drug.22 Demonstrating the predictive power of gene expression profiles, the scientists discovered a pattern consisting of 30 genes that correlate with response or lack of response to therapy. Clinical utility of biomarkers will be further assessed in a Phase III trial.

Standards for microarray data

As clinical researchers use genomic information and compare their array data within and between laboratories or hospitals, standardized array methodologies and data reporting criteria will be essential. Affymetrix is actively involved in a number of consortia aimed at providing guidelines and standards for microarray applications. The Microarray Gene Expression Data Society (MGED) has taken the first step by developing data reporting guidelines,23,24 enabling scientists to properly compare data from different experiments. However, guidelines will also need to address variability in data generation and interpretation. There are at least four key areas for microarray optimization and standardization: study design, variation in platform, analysis method variation, and “back-end” statistical analyses.25 By standardizing each of these areas, microarray analysis can be performed according to defined standards and protocols necessary for regulated applications.

The way ahead

To improve human health, we need to shift the paradigm from diagnosing and treating an existing disease to one in which we predict disease susceptibility, determine individual response to drugs, and focus on earlier detection, more accurate diagnosis, and therapeutic management. This is the very definition of personalized medicine, and it is where GeneChip technology holds the greatest promise. While grounded in basic laboratory research, advances in industrialization, automation, and standardization have fundamentally changed the use of GeneChip arrays, and have created new applications for this highly flexible and scaleable technology. By translating research findings into clinically relevant information, GeneChip arrays are positioned to revolutionize patient health care, as surely as Jacob and Monod’s 1961 discovery revolutionized modern biology.

References

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Mr. Dance is Senior Vice President, Product Marketing, Affymetrix, 3380 Central Expy., Santa Clara, CA 95051, U.S.A.; tel.: 408-731-5000; fax: 408-731-5441; e-mail: alan_dance@affymetrix.com.

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