As the director of molecular pathology at Baylor College of
Medicine, Daniel H. Farkas, PhD, HCLD, has his hands full setting
up a molecular pathology service at Methodist Hospital—which
is part of the Texas Medical Center and which, until now, lacked such
He’s also setting his sights beyond the molecular. In choosing
the capital equipment for his new laboratory, Dr. Farkas wants to
look at more than just DNA and RNA.
Dr. Farkas plans to look at proteins.
Why not? Proteomics has become the ambition of many a company
and research lab. There’s big money to be made, and plenty of diseases
in need of targeted therapies, better detection methods, and prognostic
Nonetheless: Is Dr. Farkas crazy?
(Of course he isn’t. But hold that thought for just a minute.)
Some of the biggest names in proteomics entreat caution when it
comes to their field. Take Emanuel Petricoin, PhD, co-director of
the FDA/NCI Clinical Proteomics Program. Dr. Petricoin, along with
fellow co-director and NCI chief of pathology Lance Liotta, MD,
PhD, heads what is arguably one of the most successful clinical
proteomics endeavors, having identified, in ongoing work, proteomic
patterns characteristic of ovarian cancer with 100 percent sensitivity.
ovarian cancer with time to spare," July 2002.)
"When you talk about clinical applications of proteomics right
now, where the rubber hits the road, there just isn’t that much
there," says Dr. Petricoin, who is also senior investigator at FDA’s
Center for Biologics Evaluation and Research.
And while there’s no shortage of new proteomics technologies popping
up in the marketplace—"There’s a whole cottage industry around
protein array technology," Dr. Petricoin observes—most lack
the solid, peer-reviewed data that’s needed to assess their worth.
"We don’t know how these types of technology truly work, beyond
what is claimed in company press releases," Dr. Petricoin says.
"It just hasn’t made it past that propagandizing point yet."
Proteomics technology is expensive and not as sensitive as it
needs to be, says Michael Shi, MD, senior director of genomics at
Sequenom, a genetics discovery company based in San Diego. Work
such as that done by Drs. Liotta and Petricoin is mighty impressive,
says Dr. Shi. "But it’s not easy."
"Don’t overestimate what proteomics can do at this point," agrees
Tim Veenstra, PhD, director of the Biomedical Proteomics Program
at NCI-Frederick (Md.).
But—and here’s what pathologists and laboratory directors need to keep
in mind—no one in proteomics plans to cry "uncle" anytime soon, and probably
not ever. No one, least of all laboratorians, should ignore proteomics.
Now is the time for pathologists and
laboratory directors to start thinking, hard, about proteomics,
suggests Ruedi Aebersold, PhD, professor and co-founder of the Institute
for Systems Biology, Seattle. "It is important to make a mental
adjustment away from the idea that we’re going to measure just a
particular molecule to the idea that there will be diagnostic patterns—constellations
of molecules—that we’ll use for detailed diagnoses," he says.
Proteomics will mean more than "just buying a more sensitive machine
that measures the same thing we’ve measured in the past," he continues.
It won’t be a matter of moving from an ELISA to mass spectrometry
to measure PSA. "The issue is that a completely different type of
analysis or result will emerge, which is probably much harder to
grasp," he says. "It will be harder to differentiate between ’yes’
and ’no,’ because the answers won’t be black and white." Get ready,
in other words, to interpret—and explain to clinicians—many,
many, many shades of gray.
Rest assured, you’ve got plenty of time to prepare for this eventuality.
Frankly, even getting people to agree on a standard definition of
"proteomics" is a challenge, and the relative newness of the field
means people are bandying about words like "proteomically" and "bioinformatician,"
then asking, "Is that even a word?"
"’Proteomics’ is a broad statement," says Tim Fleming, president
and general manager of Vivascience AG, a subsidiary of Sartorius
AG and a maker of, among other things, protein purification kits.
"It’s a buzzword now, and a lot of companies use it for almost anything,
just to get people’s attention."
Likewise, any number of researchers are jumping on the proteomics
bandwagon without rhyme or reason. "Too many times, too many investigators
have totally altered their research plan because of the ’wonders
of proteomics,’" says Dr. Veenstra, whose lab is involved in two
major initiatives: the ovarian cancer studies spearheaded by Drs.
Liotta and Petricoin, and assessing protein expression differences
between various cell types and between similar cells treated with
There are wonders, to be sure. There are also wonders of a different
sort, chiefly, how to move proteomics into clinical practice. Most
agree it will require making a molehill out of a mountain. How do
you go about finding, characterizing, measuring, and monitoring
the most important targets out of the god-knows-how-many proteins
that fill a human being?
It is perhaps the ultimate wheat-from-the-chaff exercise. "I find,
as somebody who runs a mass spectrometry facility, that people come
to me and expect, essentially, miracles," says Dr. Veenstra. "They
want me to take their cell lines, tell them every protein that’s
in it, and then compare it to another cell line and tell me which
proteins are not in that cell line." It’s impossible to know, he
says. "I don’t know if it’s a case of it’s not there, or I just
didn’t detect it. You’re looking at, potentially, a hundred thousand
species and trying to identify 2,000 of them."
So why bother?
"Proteins are the business end of the cell,"
says Dr. Petricoin.
"It’s the protein that does the dirty work," agrees Dr. Farkas,
who is also president-elect of the Association for Molecular Pathology.
"It’s becoming clear that protein actions and levels and how they
interact are highly diagnostic of what’s going on in a patient’s
clinical and subclinical disease."
The trick will be to identify the right suite of protein markers,
he says. "The genetic level is a clue, and sometimes a darn good
clue, but it is, after all, just a genotype with potentially limited
penetrance. The protein, in fact, causes the phenotype." That distinction,
he says, is the future of clinical proteomics.
That’s why Dr. Farkas is contemplating proteomics even as he sets
up his molecular pathology service at Methodist Hospital. "It’s
time for pathologists to start thinking about proteomics," he says.
Lucky for him, many others are not only thinking about proteomics,
but are doing the heavy lifting needed to bring proteomics into
everyday use. What they’re encountering are heartening successes
that march lockstep with daunting challenges.
To get a sense of the difficulty of proteomics, compare it to
"Genetics is straightforward," says Dr. Shi. "You have a genome,
you have the gene, it’s embedded in every cell, so whatever you
want to do, you know where you are. You can trust that you have
a human genome map. It’s easier to navigate."
Adds Dr. Veenstra: "I don’t want to say it was easy to do the
human genome, but you’re really dealing with a single, large-molecule
DNA, which is not that heterogeneous. You basically have four bases
to deal with, and you’re interested in doing one thing—you
want to sequence it." Proteomics, on the other hand, "is a whole
new nut to crack. You need to get the entire amino acid sequence
of the protein as well as all the associated modifications." In
gene expression transfertomics, there may be only two to three log
orders of difference—that is, a hundred- to a thousandfold—between
the most-expressed gene product and the least-expressed gene product.
In proteomics, the difference could be as much as a billionfold.
That huge dynamic range makes it tough for researchers, who end
up seeing only a small percentage of the proteome, says Dr. Petricoin.
High-abundance proteins may be apparent on a 2D electrophoresis
gel using various stains, but low-abundance proteins are elusive.
"You’re only getting the low-hanging fruit. The low-abundance proteins
you never even see. But the way nature sets this up, it’s usually
the low-abundance proteins that are the more interesting drug targets."
Proteomics lacks a direct amplifying technology; there are no
PCR-like methods for proteins. "You can’t amplify the low-abundance
proteins even if you wanted to," says Dr. Petricoin. "So sensitivity
is a huge issue in proteomics."
There are sheer numbers to consider, too. While the genome may
contain only 30,000 to 50,000 genes, there might be millions of
different proteins or different isoforms for modified proteins.
One gene product could have a protein that is clipped eight different
ways. Each of those eight clippings are localized to different sites
on the cell and perhaps modified five different ways by glycosylation.
Those glycosylations, in turn, may be phosphorylated two to three
different ways. "So one gene form could have hundreds of protein
isoforms, each of which could be important biologically," Dr. Petricoin
says. "It’s mind-boggling, the complexity of proteomics."
Posttranslational modification—especially phosphorylation,
on which cell signaling events are based—adds to the muddle.
In many cases, says Dr. Veenstra, two different cell types may exhibit
similar protein expression levels, but the protein in one will be
phosphorylated. "That changes activity 180 degrees from the same
protein in the other cell type," he explains. "And we have to be
able to try to monitor that."
It can be done with mass spectrometry. "If we noticed that a protein’s
mass was 80 daltons higher than we predicted it to be, that would
tell us it’s phosphorylated, because phosphorylation has a specific
mass associated with it," Dr. Veenstra says. "But I’m making it
sound a lot easier than it actually is. Monitoring subtle variations
in phosphorylation is still very difficult to do."
The real need, says Dr. Aebersold, is to measure overall changes
in a protein’s phosphorylation profile, because rarely does phosphorylation
consist of only one event. "And right now there is no technology
that can measure that," he says.
Another limitation, says Dr. Aebersold, is that many samples used
for proteomics, particularly blood serum and cerebrospinal fluid,
are extremely difficult to work with. While they may be more accessible
than tissue, these samples’ protein mixtures are dominated by the
presence of a few, highly expressed proteins. The serum proteome,
for example, is marked by an overwhelming presence of albumin protein,
which effectively masks the presence of other, possibly important,
proteins. Any analytical technique for looking at large numbers
of proteins "has to deal with this rather difficult distribution,"
he says. The trick is to selectively remove these proteins. (This
is harder than that last sentence would have you believe. "It is
difficult to completely remove a protein," says Dr. Aebersold, a
man who knows from difficult. Nor can researchers tell, with certainty,
what else is being removed along with the targeted proteins.)
Jim Wittliff, PhD, MD, professor of biochemistry and molecular
biology, James Graham Brown Cancer Center, University of Louisville,
Ky., notes some other not-so-simple problems that widen the already
Grand Canyon-sized gulf between proteomics research and clinical
Proteomics "requires skilled folks to perform technically complex
assays. This is not the kind of thing where you write a set of instructions
and prepare some reagents. We’ve got to simplify this," says Dr.
Wittliff, who developed CAP’s proficiency Survey for estrogen and
Then there’s cost. "These analyses aren’t cheap right now," Dr.
Just as important is teaching physicians how to interpret proteomics
data. Dr. Wittliff would like to see gene expression profiling and
proteomic expression profiling made part of studies by cooperative
clinical trial groups. "It’s not going as quickly, in my opinion,
as it should."
Dr. Petricoin cites one final challenge, one that may be the biggest hurdle
of all. "Proteomics, unlike genomics, is completely context-dependent," he says.
"We can’t do human proteome project, because there is no such thing as the human
proteome. Every cell type in your body and in my body has a different proteome,
which changes due to environmental impacts, time of day, hormonal changes, aging.
So when you talk about clinical applications, what you really need to have are
technologies that can take a few hundred cells from a patient biopsy and analyze
quantitatively the many, many proteins in that cell simultaneously. And that
technology doesn’t exist."
Plenty of other proteomics technologies do exist,
but it’s anyone’s guess whether they’ll migrate from research to
everyday use. Some platforms are already available for clinical
labs, but "it’s akin to buying a computer with no software," says
Dr. Farkas. And until the boundaries of proteomics are better defined—until
diagnostically relevant proteins are more clearly identified—no
one will know the best way to perform proteomic analysis on a day-to-day
Each of the current proteomics technologies have different strike
zones, as Dr. Petricoin puts it. "So right now it’s not a matter
of committing to any one technology platform, but rather using many
at the same time."
Many such platforms should be adaptable to the clinical laboratory
once it’s clear which proteins are of interest. In fact, many will
need to be adapted, suggests David Hicks, director of market and
business planning for the discovery proteomics and small molecule
business of Applied Biosystems, based in Foster City, Calif. "There’s
not going to be any single, one-stop shop for all things proteomics,"
Naturally, many hope to borrow a page or two from genomics technology.
There’s even talk of putting proteins on biochips. "And rightfully
so," says Dr. Veenstra. Such chips could be used to look at specific
protein pathways repeatedly, in rapid fashion.
Among the current methods, "The protein array, I think, will gradually
become part of clinical laboratory practice. But we are talking
about at least three to five years down the road," says Haifeng
M. Wu, MD, assistant professor of pathology and director of the
clinical coagulation laboratory at Ohio State University. "We need
to identify those proteins first and then make protein or antibody
At OSU, Dr. Wu and his colleagues are using 2D electrophoresis
to identify proteins and protein patterns related to the pathogenesis,
clinical development, and clinical progress of acute myocardial
infarction. Within two years, he says, they hope not only to have
pinpointed key proteins/protein patterns, but to have transformed
current methods into more user-friendly technologies. "And then,
with these tests, we’d like to extend our study from acute MI to
other diseases, such as autoimmune diseases."
"We’re trying to be pioneers," he adds, cheerfully acknowledging
the magnitude of these tasks. "I’ve got up to 3,000 proteins on
each 2D gel, and it’s difficult to analyze the data since there’s
no established method at this time."
The Institute for Systems Biology is in the thick of developing
quantitative methods for measuring proteins. Its base technology
combines chemistry, mass spectrometry, and computer informatics;
the general strategy, says Dr. Aebersold, involves using selected
chemical reactions to introduce stable isotope-coded affinity tags
at specific sites in proteins or protein subclasses. These tags
represent signatures that can be read quantitatively by a mass spectrometer,
then analyzed and interpreted via computer.
As the technology is refined, says Dr. Aebersold, "we are working
toward getting better ’depth perception,’ to see deeper down into
the low-abundance species of the proteome." Other adaptations of
the platform permit researchers to determine the composition and
changes in composition of proteins, protein-protein interactions,
how proteins direct themselves as well as DNA, and changes in phosphorylation.
Though these technologies are not quite ready for clinical use,
they’re close, Dr. Aebersold contends.
When Dr. Veenstra and his colleagues at NCI generate protein patterns
to be used as diagnostics, they now use the entire pattern. "But
it’s really a few key areas within the pattern that are diagnostic.
Ultimately, we’d like to be able to develop simpler assays that
focus just on those differences within the pattern, instead of having
to judge the whole thing."
Currently they use surface-enhanced laser desorption ionization/time
of flight, or SELDI-TOF, a mass spectrometry technique that uses
protein chips containing chemical surfaces that bind to particular
protein classes. The beauty of this approach is that it allows researchers
to work with "very, very crude samples," Dr. Veenstra says. "We
take raw serum and apply 5 microliters onto this chip surface, wash
it a couple times, put it in the mass spec, and the SELDI-TOF basically
reads the pattern."
The pattern is not pretty. "It’s not a nice pattern, like a circle
or series of figure eights," Dr. Veenstra says. "You can’t look
at it with your eye and say, ’Yes, this person has ovarian cancer.’
We have to use it in combination with that computer software, which
reads the patterns and measures them and compares them to previous
patterns we’ve measured. The patterns are that complex."
To identify individual proteins, he uses a higher-end mass spectrometer, with
higher mass accuracy and higher resolution. It also has tandem mass spectrometry
capability; tandem MS permits researchers to fragment peptides, thus determining
their sequence—and the identity of a protein.
Often lost in the shuffle is proeomics’ predecessor—genomics.
"There’s a lot of jealousy and anxiety in the genomics field about
being left behind," notes Dr. Petricoin.
Don’t bet on it. Proteomics couldn’t have gotten its start without
genomics, and it’s unlikely to supplant it. Right now, no one actually
knows what happens between the gene level and the protein level.
"A lot of people use complex bioinformatic acrobatics to look at
gene arrays to see if they can predict what’s happening at the protein
level," says Dr. Petricoin. What they’re finding is no or very little
correlation between gene expression analysis and protein expression
analysis, and even less of a connection between gene expression
analysis and cell signaling events, which occur before the genome
can sense them. Therefore, proteomics will not, by itself, revolutionize
molecular medicine, he suggests. It will add tremendously to the
study, analysis, and treatment of disease—but only in conjunction
with genomics. Using either one alone means physicians will be missing,
and misinterpreting, crucial information.
Separating genomics and proteomics, biologically or technologically,
is an artificial boundary at best, says Hicks. "Certainly many of
our customers would like to combine gene expression and protein
expression work. The bioinformatics folks we’re talking to are working
with both sets of data, and with other data sets as well."
Indeed, says Dr. Wittliff, physicians will need to incorporate
yet another layer of information—the metabolome, or metabolic
state, as determined by functionally active proteins and their impact
on energy-containing molecules, substrates, signal molecules, and
so on. "This is the greater and perhaps more clinically revelant
aspect of the picture," he says.
Dr. Wittliff, in fact, can barely contain his excitement when
he talks about proteomics. Already the language and preliminary
technologies of proteomics and genomics are creeping into everyday
practice, he notes. "We’re teaching the medical students the very
principles of gene expression profiles. They’re learning how to
read 2D gel profiles. They’re learning what mass spectrometry is,
what MALDI-TOF is." At Louisville, he and his colleagues are working
with transplantation immunologists to figure out how proteomics
information might help determine better tissue matches.
"This is moving quickly," he says.
It is and it isn’t. While the word "proteomics" has gathered steam
in recent years, the phrase "clinical proteomics" is chugging behind
a bit more slowly. "It’s almost oxymoronic," says Dr. Farkas. "I’m
certainly not practicing, and anyone who is is in a translational
And yet that hasn’t kept him from seeking out platforms flexible
enough to allow him to do proteomics work down the road. "This is
the next big thing," he says. "Of course, the next big thing could
be two, five, or 10 or more years away."
Karen Titus is CAP TODAY contributing editor and co-managing