Modern microarrays can genotype hundreds
of thousands of SNPs at a single pass, allowing whole-genome studies of
disease genetics and drug response. As costs fall, diagnostic microarrays
are migrating from the lab to the clinic, classifying diseases more
precisely than ever before and opening the door to personalized medicine.
| Sep
1, 2005 |
| By:
Rob
Lipshutz |
| Pharmaceutical
Discovery |
|
Starting in the 1990's, a genome sequencing revolution began that
propelled research into the modern genetic era. Inexpensive, reliable, and
automated DNA sequencing methods allowed scientists to sequence the
complete genomes of organisms ranging from lowly bacteria and viruses to
higher plants, animals and humans. In the wake of this flood of
information, we are now faced with the far more daunting task of
determining how knowledge of billions of nucleotide bases can be put to
practical use to improve human health and treat disease.
Figure 1. Rapid detection of
differentially expressed genes or genetic mutations such as
amplifications, deletions or LOH, may allow for earlier cancer
diagnosis, assessment of cancer predisposing markers,
characterization of tumors for tailored chemotherapies, and enable
new insights into the molecular basis of cancer.
|
It was the invention of high-density microarray technology by Stephen P.A.
Fodor and colleagues (1-3) that allowed scientists to sift through these
enormous genome sequences and identify important biological information.
Microarrays opened up an entire new world to researchers, giving them the
ability to analyze expression from thousands or even tens of thousands of
different genes simultaneously. The first commercial microarray, produced
by Affymetrix in 1994, accommodated 16,000 probes. Its most recent human
expression array now accommodates over 1.3 million probes, able to measure
expression for all known coding DNA in the human genome—nearly 50,000
transcripts—with 11 fold redundancy.
And now, scientists can use microarrays to genotype hundreds of
thousands of single nucleotide polymorphisms (SNPs), enabling previously
unaffordable or impractical whole-genome association studies of disease
genetics or drug response (4,5). Previously, researchers were limited by
the ability to scan only a few hundred markers; the most recent generation
of SNP microarrays is capable of genotyping over 500,000 SNP markers.
These new high-density microarrays for disease mapping studies have
already allowed the pinpointing of genes linked to sudden infant death
syndrome (6), neonatal diabetes (7,8), bipolar disorder (9), age-related
macular degeneration (10) and other inherited diseases (11-13).
For over a decade, microarrays have revolutionized basic scientific
research and have redefined our view of the genome and its complexity.
Now, arrays have found their way from the laboratory bench to the clinic,
where they promise the same kind of revolution in patient care.
Information from whole-genome expression and sequence variation studies is
being applied to disease diagnosis and classification, promising earlier
disease detection and tailored therapy for certain populations. With
microarrays, researchers are able to identify the genetic differences
between diseased and healthy states, and to document changes throughout
the course of disease or treatment. The result is a more effective, more
personalized and more cost-efficient approach to healthcare.
Detecting Disease through Gene Expression
Profiling
Figure 2. High-density microarray
technology leverages photolithographic manufacturing techniques
from the semiconductor industry to package genetic information
onto individual wafers. This high information capacity enables
high performance, akin to higher powered microprocessors yielding
faster computer performance.
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Genome-wide expression profiling of diseases like breast cancer, leukemia,
and prostate cancer reveal similar tumor types have distinct molecular
differences (14). 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 may
require different treatments.
In oncology alone, hundreds of gene
expression disease studies are offering new hope for diagnosing,
classifying and treating disease, including both common and rare cancers.
Studies on medulloblastoma (15), prostate cancer (16), breast cancer
(17,18), lung cancer (19), colon cancer (20), renal cell carcinoma (21),
ovarian cancer (22), and lymphoma (23,24) are just a few examples of
cancers in which gene expression classification systems have been
developed. By developing molecular tests, researchers hope to create
early-detection methods that will allow for better treatment and more
successful long-term outcomes.
Leukemia
Leukemias are among the cancers most
studied with microarray analysis. In 1999, Golub et al. were one of
the first groups to use gene expression profiling to distinguish acute
myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) (25). They
showed that different cancers have different sets of genes expressed, and
that those gene expression profiles could be used to classify the diseases
into their respective clinical subtypes; more importantly, they showed
that expression profiles could also be used to define entirely new
subtypes of leukemia. Subsequent studies have been repeated in which
microarrays were used to discriminate between seven basic subtypes of ALL
(26).
Several additional groups have now
reported large-scale classification studies of ALL, describing detailed
expression profiles that have validated and expanded the early results
(27,28). For instance, Torsten Haferlach's research team at the Ludwig-Maximilians-University,
Munich, Germany analyzed expression patterns to discriminate eight
clinically relevant acute leukemia subgroups (29). The group also
confirmed that signatures defined for pediatric ALL could be used to
stratify independent adult leukemia patient samples (30). Haferlach's work
is a prime example of the diagnostic value of gene expression signatures
across a broad range of leukemias and leukemia related disorders,
including ALL (30), AML (31), and acute promyelocytic leukemia (32).
Oral Cancer
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).
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David Wong's research team at UCLA has
developed a microarray-based salivary diagnostic system to identify
cancer-associated RNAs from the saliva of oral squamous cell carcinoma (OSCC)
patients (33). The research group profiled salivary mRNA and discovered
1,679 genes differentially expressed between cancer patient saliva and
normal controls. Many of the genes in the expression signature may have
little directly to do with cancer etiology, but accurately function as
indirect markers for the underlying oral carcinoma (34). The team focused
on seven of the most consistently expressed biomarkers, including
transcripts of IL8, IL1B, DUSP1, HA3, OAZ1, S100P, and SAT. In
combination, these markers proved 91 percent sensitive and 91 percent
specific for detecting cancer in the study of saliva samples from 32 OSCC
patients and 32 healthy controls. Results like these offer the promise of
a simple early-detection screen using an easy-to-obtain surrogate
diagnostic fluid, instead of a complicated surgical procedure.
Lung Cancer
Using high-density gene expression
arrays, Spira et al. analyzed human airway epithelial cells
obtained at bronchoscopy for expression patterns associated with smoking
status (35,36). They found an expression pattern consisting of 97 genes
that accurately predicted smokers from never-smokers. Remarkably, the
research demonstrates that some changes in gene expression are reversible
upon smoking cessation, while others are not. For instance, expression
levels of smoking-induced genes begin to resemble that of never-smokers
after 2 years of smoking cessation. Regardless of years quit, the team
found 13 genes that never returned to normal expression levels, including
a number of potential tumor suppressor genes (TSGs) that remain
down-regulated (e.g., TU3A and CX3CL1) and a number of possible oncogenes
that remain up-regulated (e.g., CEACAM6 and HN1). Genetic mutations
incurred through smoke exposure may explain these irreversible changes and
may also explain why some individuals remain at risk for lung cancer
despite having quit cigarette smoking for a lengthy duration.
Kidney Cancer
As part of a Phase II clinical study
of renal cell carcinoma (RCC), scientists at Wyeth Research Laboratories
profiled gene expression from peripheral blood mononuclear cells (PBMCs)
and found a specific set of expressed genes that accurately distinguished
RCC cancer patients from controls (21). The team used high-density
expression microarrays and multiclass variate analysis to predict patient
RCC status with 70% accuracy. Additionally, they found that PBMC
expression profiles could distinguish RCC patient samples from other types
of solid tumor samples (prostate and head and neck cancer). These findings
have important implications for diagnosis and future clinical
pharmacogenomic studies of antitumor therapies. RCC is usually detected by
imaging methods, but 30% of apparently nonmetastatic patients undergo
relapse after surgery and eventually die of disease (37); the need for a
simple, non-invasive monitoring method is critical. By using gene
expression analysis, researchers can monitor the disease with molecular
tools by analyzing a patient's PBMC gene expression and seeing if it
matches that of the RCC profile.
Detecting Disease through Genotype
Analysis
Clinical research studies have also
used microarrays for DNA genotype analysis to diagnose cancer by detecting
mutations that cause malignancy. Cancers often arise from genome
aberrations, like segments of allelic imbalance, which are identified by a
loss of heterozygosity (LOH) at polymorphic loci. Microarrays measure LOH
by genotyping multiple SNPs that are markers for regions harboring tumor
suppressor genes (38). Arrays can also be used to detect allelic
amplifications and deletions using a method called chromosomal copy number
(CCN) analysis (39).
By measuring anywhere from 10,000 to
500,000 SNPs, arrays detect more detailed copy number changes than
conventional comparative genomic hybridization (CGH), and offer many times
more markers than microsatellite analysis used in convention LOH studies.
This high-density combined approach provides researchers with a rapid way
to locate often difficult to detect tumor genes responsible for cancers of
the breast (39-43), bladder (44,45), prostate (46,47), bone (48), mouth
(49) and lung (40,50,51), adding diagnostic and prognostic value in the
clinical management of cancer and precancerous conditions.
Lung & Breast Cancer
Dr. Mathew Meyerson and Dr. Bill
Sellers of the Dana Farber Research Institute are using SNP genotyping
microarrays to detect cancer mutations that may allow earlier diagnosis,
assessment of cancer predisposing markers and characterization of tumors
for tailored chemotherapies.
In May of 2004, Meyerson's group
reported using microarrays capable of genotyping 10,000 SNPs (GeneChip
Mapping 10K Array), to simultaneously detect cancer-specific DNA copy
number changes and LOH, two major causes of neoplastic growth. In a study
of 7 lung cancer cells (40), the team used the 10K arrays to detect twice
the LOH regions as microsatellite analysis, identifying the already-known
deletions, as well as 14 previously unknown LOH regions on 9 different
chromosomes. In a second study of 18 lung and breast cancer cell lines,
Meyerson's research team found copy number amplifications (encompassing
known proto-oncogenes) and deletions (encompassing TSGs), and
distinguished LOH caused by a DNA deletion coupled with a gene mutation
(50).
Skin Cancer
Sellers—in collaboration with
Meyerson—is characterizing cancer-specific genetic anomalies with new
100,000-SNP microarrays, and in the July 2005 issue of Nature, the
group reported a critical gene, MITF, that was amplified in 20
percent of metastatic melanoma patients studied and was also functionally
required to transform human melanocytes (52).
Oral Cancer
In another study, David Wong of UCLA
has used a 10,000-SNP microarray to identify four regions of the genome
associated with mouth cancer. The study highlights the need to track both
chromosomal copy number changes along with loss of heterozygosity. For
example, on chromosome 3, the team used copy number analysis to find DNA
amplification and deletion mutations shared by the premalignant and
malignant cells. But finding the mutations that converted those
premalignant cells into malignant cancers required looking for a different
type of mutation—LOH. When the team analyzed the 10K data for LOH, they
found two regions of chromosome 3 that had mutated in the malignant cells.
Developmental Disease
In addition to cancer, gene deletions
and amplifications are also a major cause of developmental defects, like
Down's syndrome. In December 2004 researchers from the Friedrich-Alexander
University, Max-Delbruck Center and Affymetrix reported using the
10,000-SNP microarray to study chromosomal amplifications and deletions
that cause a variety of mental retardation syndromes (53). They were able
to detect aberrations as small as 700KB.
Using Genetic Information to Improve Drug
Success
By helping understand genetics of
disease and patient, microarrays are helping researchers predict which
drugs will work for which patient with which disease. There are over 40
examples of array profiling used in clinical trials to classify disease
markers and allow predictions of drug efficacy and clinical trial
outcomes.
Expression Analysis
A recent Phase III clinical trial by
Novartis Pharmaceuticals used expression profiles to predict the success
or failure of Gleevec treatment on chronic myelogenous leukemia (54). They
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 for the
Millenium Pharmaceutical drug Velcade, researchers used arrays to collect
pharmacogenomic data from myeloma patients treated with the drug (55). 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.
Using microarrays to identify
predictive markers of disease may ultimately provide more tailored,
effective and safer courses of treatment and help avoid the over 100,000
annual fatalities from adverse drug reactions in the U.S. alone (56).
Genotype Analysis
While much progress has already been
made using gene expression analysis, studies to identify genetic
variations associated with drug response, efficacy and toxicity may become
one of the most promising applications for whole genome DNA analysis.
Microarrays able to genotype more than 100,000 SNPs distributed across the
genome now allow researchers to readily genotype large populations of
responders vs. non-responders to a given drug for phenotypes including
efficacy and toxicity. With these kinds of genetic studies, scientists
hope to elucidate the genes contributing to variable drug response.
In late-stage clinical trials for
example, microarray genotype analysis could be used to stratify patient
populations to eliminate poor or toxic responders from key Phase III
trials. Such stratification would help ensure maximum effectiveness
through clearer statistical differentiation between drug and placebo,
while also reducing size and cost of trials and improving the odds of drug
approval. In addition, once a drug is on the market, patient
stratification could be used to accelerate drug expansion into new
indications through faster, smaller, more definitive Phase IV trials, or
to establish medical superiority of a late-to-market drug relative to
entrenched competitors in an important class of patients. Genome-wide
genotype information will also fuel future research. By better
understanding genetic mechanisms of drug response in patients, researchers
will have made significant progress on finding the next generation drug.
Already, leading pharmaceutical,
biotech, and university researchers have embraced microarray genotyping
technology. In fact, Dr. Peter Nürnberg, Director of the Gene Mapping
Center at the Max-Delbruck Center in Berlin, used a 10,000 SNP genotyping
microarray in a linkage study to find a disease locus in just one week. In
a pharmacogenomic study at the Mayo Clinic, researchers are using
100,000-SNP genotyping arrays to investigate the genetic basis for
differential responses to antihypertensive drugs in different patients and
populations. These scientists hope to identify genes influencing drug
response and ultimately tailor antihypertensive therapy for individual
patients.
Whole genome microarrays provide
scientists with a way of examining the underlying genetics of responders
and non-responders without any of the assumptions or limitations used in a
candidate-gene approach. For most drugs with variable responses, little is
known about why they work in some patients and not in others. Microarrays–gene
expression and DNA analysis–enable scientists to explore the whole
genome and identify predictive markers of disease and drug-response, that
may ultimately provide more tailored, effective and safer courses of
treatment (56).
Beyond gene discovery efforts,
microarrays can also be used as a diagnostic tool to screen patients for
particular genetic variations that may affect drug response. For example,
the Roche AmpliChip CYP450 Test has been cleared for diagnostic use in
Europe and the United States to identify certain naturally occurring
variations in the drug metabolism genes, CYP2D6 and CYP2C19. Variations in
these drug metabolizing genes affect the rate at which an individual
metabolizes many common drugs used to treat diseases, including
cardiovascular disease, high blood pressure, depression and ADHD.
Knowledge of these variations, when considered with other contributing
factors, can help a physician select the best drug and set the right dose
for a patient sooner, as well as avoid drugs that may cause the patient to
suffer adverse reactions.
A vital step in personalized medicine:
Data integration and standardization
Microarrays offer the promise of
personalized medicine, but to get there, genetic data needs to be
integrated with existing medical records and then be made readily
accessible to the practicing physician on an every day basis.
Additionally, genetic information used by physicians needs to be generated
by standardized methods and in a standardized format such that information
from one patient can be readily compared to another.
Data Integration
Affymetrix recently teamed with IBM
to provide a solution that will integrate genomic research and patient
clinical data from disparate databases into a centrally organized format.
The infrastructure will allow clinicians to readily apply knowledge of the
human genome to the diagnosis and treatment of disease, ultimately
improving patient care, while reducing healthcare cost. The powerful
combination of standard medical information with microarray genetic data
will then be cross-referenced against the databases enabling genetic
clinical research to be translated into life saving, targeted therapies
and treatments.
The H. Lee Moffitt Cancer Centre and
Research Institute at the University of South Florida and IBM/Affymetrix
are collaborating to design innovative clinical trials that use genomic
information to tailor treatment options for Moffitt patients. "At
Moffitt, we're committed to adopting the latest technologies to help
researchers and clinicians speed cancer screening and diagnosis,"
said William Dalton, Moffitt Cancer Center. "In order to quickly
identify patients at risk and select potential clinical trial
participants, we constantly seek out new solutions."
Data Standardization
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.
The Microarray Gene Expression Data
Society (MGED) has taken the first step by developing data reporting
guidelines (57,58), 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 (59). By standardizing each of these areas,
microarray analysis can be performed according to defined standards and
protocols necessary for regulated applications.
Conclusion
It's clear that microarray technology
can provide the means to enable personalized medicine. Arrays not only
help to make sense of vast genome sequences, but allow us to apply what we
learn directly to the patient, helping to detect disease earlier and
predict which drugs will work best for each indivdual. Patients can be
diagnosed or treated based on the genetic specifics of their particular
disease, drug tolerance, or immune response, to yield an entirely
personalized approach to healthcare. While the hurdles of data integration
and standardization remain, gene-based diagnosis offers our best chance to
combat the unprecedented healthcare crisis we are currently facing. With
an increasingly older population to care for and skyrocketing costs,
microarrays offer the prospect of more efficient, cost-effective, and
personalized approaches to human health issues.
Robert Lipshutz, PhD, is
senior vice president for Molecular Diagnostics and Emerging Markets at
Affymetrix, Inc., 3380 Central Expressway Santa Clara, CA 95051. He can be
reached at rob_lipshutz@affymetrix.com
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