| Dec
1, 2005 |
| By:
Steve
Casey |
| Pharmaceutical
Discovery |
|
With this final column for Microarrays on the Spot, I felt it would be
important to look back over the year and highlight the tremendous
scientific and medical advancements that have been made in microarrays and
pharmacogenomics.
Without question, in this author's opinion, the ability to measure
genetic response to stimuli thousands of genes at a time is one of the
greatest tools in the arsenal to improve human health. This has led to
greater understanding of biological response to therapeutics and mechanism
of action, giving researchers answers in days what has previously taken
entire careers to understand. We owe our gratitude to those pioneers who
helped make this possible, including Pat Brown, David Botstein, Mark
Schena and others.
As one indicator of the prevalence of this technology, a PubMed query
on the keyword "microarrays" yielded more than 3,600
publications in 2005 alone. Without question this is a technology of the
future in use today.
Steve Casey
|
Steve McPhail, President and CEO of Expression Analysis kicked off the
Microarrays on the Spot inaugural column with insights on harnessing the
potential of this technology to improve human health. With the final
guidance on pharmacogenomic data submissions released by the FDA in March
of this year, pharmaceutical companies now have a roadmap on how to use
this data to support INDs and NDAs. This guidance is intended to
facilitate scientific progress in the field of pharmacogenomics and its
use of pharmacogenomic data in drug development. The promise of
pharmacogenomics lies in its potential to assist in identifying
inter-individual variability in drug response (both effectiveness and
toxicity). It is hoped that this information will move us a step closer to
personalized medicine by maximizing effectiveness and minimizing risk.
During the course of the year, we learned about specific applications
of genomic technologies from Dr. Joseph Monforte of Althea Technologies.
Dr. Monforte discussed the importance of validating whole genomic profiles
in order to achieve the most reliable biomarker for application to drug
discovery and diagnostic applications. He pointed to current applications
of microarray analysis based biomarkers in such critical therapeutic areas
such as breast[1], kidney[2], prostate[3] and childhood cancers[4].
Figure 1. Experimental Design of
Proficiency Testing for Microarray Laboratories
|
Although this technology may prove to be a major advancement in the
diagnosis and treatment of disease, the research community's opinions
differ on such critical issues as oligo lengths, analysis methods and
protocols. Andrew I. Brooks, Associate Professor of Environmental Medicine
and Genetics, Rutgers University provided us with a look inside a
roundtable discussion sponsored by the Association of Biomolecular
Resource Facilities (ABRF). The participants, both academic and industry,
answered and discussed a set of open questions pertaining to microarrays
in general. Some of the major points panelists agreed on unanimously were:
- The source of genetic information and its annotation is something
that needs to be corroborated across different technologies in order
to accurately compare performance across platforms.
- The establishment of gene expression standards will be of paramount
importance for any cross platform comparisons.
- Array manufacturers need to work together to provide an information
resource describing probe set methodology and sequences.
- Normalizing sample starting material and hybridization cocktail
sensitivity will allow for more efficient comparative analysis across
microarray platforms.
Assessing industry standardization, Dr. Laura Reid, Director of
Research and Development at Expression Analysis, provided a comprehensive
look inside "Proficiency Testing," a standardization methodology
she has developed, and its value in monitoring the performance of
microarray laboratories. She cited several benefits that could be achieved
for participants in such a program, including: proof of competency for
funding agencies; performance monitoring (not just snapshots, but over
time); technician training; reagent validation and adherence to regulatory
guidance. As the year progressed, Dr. Reid's proficiency testing program
has been gaining world-wide visibility and has been enhanced to include
human reference RNA standards developed by the MicroArray Quality Control
(MAQC) project headed up by Dr. Leming Shi of the FDA. (http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/index.htm)
Figure 1 gives a brief outline of this program's structure.
To add to the complexity of this technology, researchers also must
contend with different methods of analyzing microarray data. With some
platforms consisting of millions of data points, and experimental designs
depending on replications for statistical power, deciding which approach
to take to understand, incorporate and mine data can be a great challenge.
Figure 2. Correction of Systematic
Biases
|
Several of our columnists supplied us
with their viewpoints, solutions and alternatives to handling these data
sets. Joel Parker, Research Biostatistician from The Constella Group, Inc.
looked at a public dataset from a different perspective, and subsequently
came to an alternative conclusion, challenging the notion that data sets
generated from differing platforms could not be compared directly. By
removing interference from artifacts by methods such as SVD5 (Figure 2),
datasets can provide comparable results. It's just a matter of how results
are analyzed.
Dr. Wendell Jones, Senior
Statistician at Expression Analysis, discussed differences in performance
of microarray probes, where differential expression could potentially be
masked. Because each probe in a probeset has a unique sequence and
distinct hybridization characteristics, not all probes in the same
probeset hybridize equally well to their intended target. This may be due
to probe sequence errors or other characteristics such as probe affinity.
To address this issue a proprietary method, REDI, has been designed to
assist researchers in obtaining truer gene expression representation by
removing the effects of these malperforming probes; thereby allowing the
probe set to better reflect and more easily detect differential
expression.
And to round out the statistical look
at microarrays, Dr. Gary Fogel, Vice President of Natural Selection Inc.,
discussed the use of Computational Intelligence in recognizing patterns
from within microarray data. Addressing the problems associated with
cancer classification, Dr. Fogel uses Computational Intelligence (the
accumulation of artificial neural networks, evolutionary computation,
fuzzy logic and their combination) to reduce potential biomarkers into
sets that retain high predictive accuracy at cell-type classification.
This type of computational intelligence will play an increasingly
significant role in the areas of gene expression and gene network
reconstruction in the near future Dr. Fogel concluded.
And most recently, Dr. Tom Goralski,
Laboratory Director at Expression Analysis, provided a look inside his
methods for developing and incorporating validation activities necessary
to operate a federally compliant microarray lab. Tom built the first Good
Laboratory Practices (GLP) compliant microarray processing laboratory in
the world; one of the first steps necessary in moving gene expression
microarray data to the clinic. His experience in detailed compliance
activities such as Reagent Validation, Equipment/Instrument Validation,
Processing Standard Operating Procedures and Analyst Training, Sample
Tracking and Chain of Custody, and Process Validation gave many of our
readers who operate their own microarray labs food for thought as they
pursue regulatory compliance activities.
What does the future hold for this
technology? By July 2005, 22 submissions using pharmacogenomics data had
been provided to the FDA.
2006 should be an exciting year and
with that, I thank you all for your readership, contributions and
comments, and wish you all continued success in your research and a
prosperous New Year.
Please take the time to visit the
websites of our authors and see in more detail the advancements being made
in microarray and pharmacogenomic applications.
www.altheatech.com
www.natural-selections.com
www.constellagroup.com
www.expressionanalysis.com
www.maqc.org
www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/index.htm
EOHSI-BRTC.com
Steve Casey is the founder and
COO for Expression Analysis Inc., Durham, North Carolina, USA, a provider
of regulatory compliant genomic processing services. He can be reached at:
casey@expressionanalysis.com.
References
1. L. van't Veer, et.al. (2002) Gene
Expression Profiling Predicts Clinical Outcome of Breast Cancer. Nature
415:530-536.
2. J. Vasselli, et.al. (2003)
Predicting Survival in Patients with Metastic Kidney Cancer Using
Gene-Expression Profiling in the Primary Tumor. PNAS 100:6958-6863.
3. J. Best, et.al. Molecular
Differentiation of High- and Moderate-Grade Human Prostate Cancer by cDNA
Microarray Analysis. Diagn. Mol. Pathol. 12:63-70.
4. J. Khan, et.al. (2001)
Classification and Diagnostic Prediction of Cancers Using Gene Expression
Profiling and Artificial Neural Networks. Nature 7:673-679.
5. Nielsen et al (2002)
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