Excerpts from March, 2001 CAP Today Feature Story
Arraying the data
by Eric Skjei
Although tissue microarrays have great potential to sharply reduce the
time and cost involved in conducting what would otherwise be lengthy and
expensive research, there is a catch: They pose great challenges in terms
of capturing, organizing, updating, exchanging, and analyzing the data they
"You could make the case that the information-management requirements
of the data-numbers, text, and images-from a single tissue microarray over
time rival those of a small pathology department," says Bruce Friedman,
MD, professor of pathology and director of clinical support information
systems, University of Michigan Health System, Ann Arbor.
"You can then obtain a section of the new block and stain for, say, tumor
antigens," explains Dr. Friedman. "And what you have is also a
high-throughput array, such that any testing you perform is 500 times faster
and more efficient."
"This is an exciting technology," says Jules Berman, MD, PhD, "one that
involves using slides containing up to a thousand specimens and
performing large research studies at once and in miniature." Dr. Berman is
program director for pathology informatics in the National Cancer Institute's
Cancer Diagnosis Program.
What does this dramatic increase in productivity mean? Among other
possibilities, it means identifying possible cancer biomarkers and checking
their validity in many samples, across many cases, very quickly. "If you're
looking for a tumor marker," says Dr. Friedman, "and you're using an
automated system so you don't have to laboriously inspect every core
manually but can scan for specific staining patterns, a microarray allows
you to, in effect, run hundreds of experiments simultaneously."
It also means pathologists have a way to build on gene array research. The
two technologies, gene arrays and tissue microarrays, complement one
another. "Once genes of interest are identified in a gene array study," says
Dr. Berman, "their value as biomarkers can be tested using tissue
Dr. Berman offers the following hypothetical example. A molecular biologist
may perform gene array studies on several prostate cancers for which
history has been obtained that allows the researcher to categorize each
tumor as clinically indolent or clinically aggressive. Analysis of the gene
array expression patterns may identify a few genes that have expression
patterns that are different in the two clinically distinct types of prostate
cancers. Once candidate genes are identified, the researcher may test
their biologic relevance in a tissue microarray containing large numbers of
samples of prostate cancers with well-characterized clinical information. By
examining the expression of candidate genes in a tissue microarray, the
researcher can validate or invalidate the hypothesis that the gene's
expression is a marker of the tumor's clinical behavior. In this scenario, the
gene array is used as a discovery, or hypothesis-generating, tool, and the
tissue microarray is used to test the hypotheses generated by the gene
"One of the planned projects for the NCI's Cooperative Prostate Cancer
Tissue Resource will be to prepare prostate cancer tissue microarrays that
can be used in just these kinds of studies," says Dr. Berman.
A need for standards
Researchers working with tissue microarrays operate more or less
independently, using a variety of different formats, procedures, and
data-collection and organization strategies to complete their studies. And
they do not typically make special attempts to share their results, other
than in a traditional manner. "They go forward, do their research, come up
with some sort of observation based on the slide and the data and,
hopefully, get published," says Dr. Berman. "Their publications contain only
summary data. Their actual measurements are never shared."
Achieving agreement on simple standards will greatly enhance the value of
this technology for collaborative research projects that would otherwise
stop at the traditional research boundaries. "What we want to be able to
do," says Dr. Berman, "is exchange the data, share it, and merge it into
large datasets, to distribute multiple copies of one tissue microarray to
multiple labs supporting a wide range of studies, and then access all the
data from different labs." (See "Collaborating over the Web," page 62.) The
standards needed are, in many ways, basic. "We have to agree on very
simple things, like how we're going to identify a file as a tissue microarray
file, the name of its creator, the date it was created or modified, where the
clinical data starts and stops and how it is organized, where the image
data starts and stops, how to identify specimens uniquely while
maintaining patient confidentiality-things like that," Dr. Berman says.
A parallel might be drawn, Dr. Berman suggests, with the informal process
that shaped the standards underlying a much earlier and simpler
technology, that of writing a traditional letter. At some point when this
medium was becoming established as a prevalent means of
communication, many simple conventions became generally accepted:
Writers would put the date in the upper left or right corner, then, a bit lower,
the name of the person to whom the letter was addressed, followed by the
address of the recipient, a salutation, the body of the letter, and so on, to
the close. The evolution of these formatting conventions made the
technology of the letter more easily and widely understood and thus more
useful. But in the case of tissue microarrays, the challenge is
understandably more complex.
A significant payoff
The expectation, over the long run, is that work with tissue microarrays will
help improve clinical practice, most likely in cancer treatment first.
"Ultimately what we're going to do is look at the tumor, look at the DNA,
and then direct the therapy based on the genetic constituents of that
tumor, which define its biologic behavior," says Dr. Friedman.
"Let's say you have something you think is going to be an important marker
for prognosis that can predict response to therapy for a given cancer," says
Dr. Berman. To investigate that hypothesis might take a decade because it
involves collecting tissues from numerous patients who are at different
stages of the disease-patients with precancers, patients in early stages,
patients with indolent cancer forms, patients who died in less than five
years, patients who resisted therapy, and so on.
The study process also would be extremely costly. To collect those
tissues, conduct studies on them using the markers of interest, and
determine their validity could cost millions of dollars and take years to
complete. "We need to have methods that will accelerate progress toward
developing diagnostic and prognostic markers that can be used in clinical
laboratories," says Dr. Berman. "Tissue microarrays have enormous
potential benefit in this area."
"If a researcher can work with a tissue microarray that has been designed
so that it has large numbers of samples of, say, breast cancers, collected
as different histologic types, with different grades of the cancer, different
clinical types, different prognostic types, all on one slide, he or she can
conduct an otherwise lengthy and costly study at once with a small
amount of reagent,"adds Dr. Berman.
"You've got the entire experiment on a slide, and you can start to see
whether or not your new markers have the kind of expression patterns that
are useful," he says. "You can make large numbers of observations very
quickly on a tissue microarray, and that is one of the key benefits of this
Predictions are that tissue microarrays will cost several thousand dollars,
but they have the potential to save hundreds of thousands of dollars.
"Collecting tissue, obtaining consent as appropriate, data acquisition,
quality control, and so on-all of that is done up front, so researchers will be
paying for the finished slides and associated data," says Dr. Berman. "If
they can acquire a well-designed tissue microarray slide for $3,000, it
might be a tremendous windfall for them."