Dr. Garry Nolan
A Featured Scientist from
According to the December 2007 report of new
Essential Science IndicatorsSM from
Scientific, the work of Garry Nolan entered the
top 1% in the field of Molecular Biology &
Genetics, with 16 papers cited a total of 1,347 times.
Dr. Nolan also has Highly Cited Papers in the fields of
Clinical Medicine and Biology & Biochemistry.
Dr. Nolan received his BS degree from Cornell
University and his Ph.D. from Stanford University. He
also did postdoctoral work at MIT and Rockefeller
At present, he is an Associate Professor in the Department of Molecular
Pharmacology and the Department of Microbiology and Immunology at Stanford
University School of Medicine. He is also a member of Bio-X and the
Stanford Cancer Center, as well as the Director of the Stanford NHLBI
Proteomics Center. He is on the editorial boards for the journals Gene
Therapy & Molecular Biology, Cells to Genes, and
In the interview below, Dr. Nolan talks
with ScienceWatch.com correspondent Gary Taubes
about his highly cited work.
One of your most-cited papers of the last decade
is your 1999 Trends in Genetics paper on NF-Kappa B (Foo
SY, Nolan GP, "NF-Kappa B to the rescue—RELs, apoptosis, and
cellular transformation," 15: 229-35, June 1999). What prompted you
to write that paper and why is it so highly cited?
That paper was written at the tail end of my work in that area. It really
harkens back to much earlier research that I did in David Baltimore’s
lab at the beginning of my career. It’s just a review of the field,
which is probably why it gets cited so frequently. It says, look, this is
what NF-kappa B can do, and these are the important places where it plays a
role. It was a review that for the first time summed up a more general role
for NF-kappa B molecules in a manner that placed this signaling system in
the center of a larger web of physiologic processes. But I'd say that early
work led to our more recent research on cell profiling, which is gaining
recognition in several fields, especially related to clinical signaling
measurements, and is allowing us to look at cells in a broader context.
Okay, let’s fill in the gaps, though. What did you
do after you worked on NF-Kappa B at the Baltimore lab?
I developed an approach for delivering genes to cells that used what are
called retroviral transfer vectors. That was the beginning of my gene
transfer career and it led to many of these papers that are now highly
cited, and the essential elements of that work are embodied in a lot of
retroviral and lentiviral work being done in many labs and clinical
settings. My first company, started in the mid-1990s, was built around the
notion that the only purpose of viruses is to make more copies of
themselves. This is the selfish gene hypothesis of Richard Dawkins. That
is, viruses don’t care about the cell or the state of the cell
they’re invading. They only care about proliferating and whether the
target cell can support such replication.
My idea was to say, "Let’s reverse this process and make it to the
benefit of the virus to make the cell healthier." We could engineer an
evolutionary bottle for the virus, and make the virus understand that there
will be more copies of it, but only if it takes a sick cell and makes it
healthy. That would force the virus to basically search through
evolutionary space for incremental advances to itself to correct the
biology of the cell. And that approach has worked. The company we started,
Rigel, has been very successful and that early work has led the company to
develop a number of drugs in clinical trials for cancer and immunotherapy,
with a very successful phase II trial of an orally available drug for
And that work led to the idea of cell
We had the notion that an evolutionary bottle required a readout at the
single-cell level. If the virus is doing something to the cell to make it
healthier, we want to know what that is. So the point is that unhealthy
cells have unhealthy signaling networks. Think of it like a normal
individual versus a psychotic individual—a normal mental state versus
an abnormal state.
We use kinases and their actions as a measure of the state of the cell's
information processing system. The action of a kinase is to phosphorylate a
protein, and this process basically leaves a breadcrumb trail of cellular
information telling you what these kinases are doing. Those phosphorylation
sites on proteins are indicative of the relative functionality of
individual proteins and their capabilities. So if you measure the status of
many of these phosphorylated proteins within the cell, what you’re
really measuring is the history of these kinases and the signaling action
in the cell. But, as it turns out, you’re also measuring the future
potential of that cell.
Basically you can think of these kinases as switches; when you flow
information through the cell, the settings of these switches change, and
that tells you what environment that information can encounter and how the
cell might respond to such environments. The information will go down and
through certain conduits based on these phosphorylation settings. So you
get both the history of the cell and a prediction of the future at the same
time by measuring these phosphorylation events if you know how to interpret
When you think of it that way, you realize, "Gee, if I want to effect a
correct outcome in this cell, I better measure key indicators in the cell
before I do it." For instance, for inflammation—NF-kappa B being a
key indicator of inflammation—I should be measuring the
phosphorylation of certain proteins in the NF-kappa B signaling cascade.
These tell us, say, whether a cell is being continuously and
inappropriately activated and here’s a phosphorylation site
that’s a measure of that inappropriate activity. Now I want to find a
drug or, in my case, a virus, that turns that off. If one of my viruses (or
drugs) makes a cell healthier, we want to know, what was the biochemical
event that changed for the better? That led us to the idea that since
signaling is very important for cellular biology, how can we measure
signaling at a single-cell level?
You can see the confluence of ideas here. If I can measure one of these
effects, maybe two will be even more informative? What about three, five,
six, 11, 15, or just as many as we can measure? Maybe we can get an angle
on what this cell is doing. You’ve seen these signaling diagrams just
filled with phosphoepitope tags. People might want to measure an estimated
40,000 of them in a cell. You can imagine them being like the blinking
lights on Sulu’s console in Star Trek. If a bunch are
flashing red, you know something is wrong. What we have shown is that you
don't need to measure them all—that's information overload—but
that a subset of those proteins, when linked together in groups of networks
and subnetworks revealed through simulation, are incredibly informative as
to the posture of a cell's current thinking and future capabilities.
So we wanted to measure these events in such groups. We went back to the
flow cytometer, developed dye and laser systems that enable us to
interrogate the algorithmic processes of cells at a single-cell level, to
ask them questions that reveal how information is passed through these
signaling conduits. Then we built up a profile of that information, and it
turns out that certain profiles say, "I will respond to some
drug"—chemotherapy, for instance—or, "I won’t respond."
And if I know that information from interrogating cells in a
patient’s blood, if I know that ahead of time, I know whether it's
worth giving that drug or not.
We’ve been developing that system into clinical diagnostics.
We’ve been proving to people that what we’re measuring is
actually right; proving that if you give me 50 patients and I’m blind
to their outcome, I can take their blood and basically say who will or will
not respond to a particular drug. I started another company recently to
really scale that up.
Can you describe in a little more detail how exactly go
about interrogating these cells? How does this system work?
Basically what we do is take cells and stimulate them with cytokines or
antibodies or drugs. Then we wait a certain amount of time and essentially
freeze the signaling network. We add a fixative—paraformaldehyde.
Very simple. When you’re dead and they want to fix your
body—embalm it—they pump you full of formaldehyde and that
basically cross-links all the proteins and prevents decomposition. So what
we’re doing here is similar. We’re basically freeze-framing the
cell, which then allows us to process it further. We can then poke holes in
the cell, allowing us to get antibodies into it, and these are specific for
particular phosphoepitopes on proteins. Each of those antibodies is tagged
with a little fluorescent agent. We have between 11 or 15 of them. Each is
Then we go to this machine called a fluorescence-activated cell sorter,
which was invented by Len and Lee Herzenberg. I studied with them when I
was a graduate student at Stanford. That machine essentially takes those
cells—say, a million of them—lines them all up in a row, and
spits them through a nozzle at a rate of about 50,000 cells per second. As
the cells come out in that stream, they pass a series of lasers, and each
laser activates these flourophores, excites them; they emit different
colors, depending upon the amount of antibody activity in each cell. In
other words, each cell will light up like a Christmas tree in a different
way. A photodetector measures the number of photons coming off at each
wavelength, and that information is digitized. That tells us how much of
the target protein is in each cell.
Then we take that information and process it, learning to recognize what a
healthy cell looks like, and what a cell looks like when it's, say, sitting
in the middle of a tumor, or how a tumor cell differs from a normal cell.
So not only can we measure what’s happening in the middle of a tumor,
but we can measure how the tumor is instructing the immune system to do the
wrong thing, and mechanistically how it's doing that. And we can determine
whether a given tumor will react appropriately to a drug, or resist the
And this method of interrogating cells was what you
described in the 2005 Science paper (Sachs K, et
al., "Causal protein-signaling networks derived from
multiparameter single-cell data," 308: 523-9, 22 April 2005) and
the 2004 Cell paper (Irish JM, et al., "Single cell
profiling of potentiated phospho-protein networks in cancer cells,"
118: 217-28, 23 July 2004)?
Right. There were important methodological and immunologic papers prior to
those, but the proof of the clinical and signaling biology reconstruction
concepts were really in the Science and Cell papers.
Was there an element of serendipity to this
Well, the mathematics we’re using is 200 or 300 years old but
it’s only recently that it’s been readily usable for this kind
of problem with the advent of certain kinds of computer systems. It just
turned out that one of my graduate students, Omar Perez (who developed many
of the initial concepts around phospho-flow in my lab), was standing at a
conference next to Karen Sachs, who was also a graduate student studying
with Doug Lauffenberger, an MIT biostatistician. I think they each had a
poster and they got to talking and that’s how we all ended up
collaborating and making this work. That was certainly serendipitous.
A similar thing happened with our Cell paper. Again one of my
graduate students was standing next to a researcher from Norway who had
once applied to my lab for a postdoc. Now he was a professor and they got
to talking and they kind of cooked up a project to measure phosphorylation
in cancer cells. At the time, we didn’t understand that it would lead
to this idea of patient stratification or the interrogation of single
More recently, another student in the group, Peter Krutzik, developed ways
to use the approach for drug screening in an extremely high throughput
manner. His work has been featured as cover articles in both Nature
Methods and Nature Chemical Biology in the past year alone.
If you were to play devil’s advocate, what would
you tell me about why this cell profiling idea might fail to live up
to your expectations? In other words, what could go wrong?
Well, we could just be flat-out wrong and completely misinterpreting our
results. But we’ve done all the statistical tests so it’s hard
to imagine that’s the case, and the technology has now been
replicated in several other well-known labs. At one level—and this is
a national problem in biomedicine—we might not be able to get access
to the kinds of antibodies we need—that is, the specific reagents
that allow one to visualize certain molecular events. But this is more a
technical limitation and not a conceptual barrier. There might be
intellectual property patents that we don’t even know about that
could prevent us having access to the key antibodies, key reagents, etc.,
but there are workarounds for that. But those are problems that might delay
a company or clinical goals, not the academic pursuit itself and certainly
not to the degree that the technology won't move forward.
It’s hard to think that anything there could go wrong, because
it’s all gone right so far—in five or six different clinical
disease conditions we've tested and numerous others I've seen, or papers I
have reviewed and I know are in press from other groups. And we have raised
enough money to pursue this industrially, provided by some premier venture
capital groups who did considerable due diligence on the approach to the
point that they are believers as well. So if the project goes awry, it
would have to be just a failure to execute, not a failure of the clinical
Where do you think your technology will be in five
Commercially, we’ll probably have three or four diagnostics on the
market that will be able to detect rare cells in patients that are about to
become aggressively cancerous—in leukemias and lymphomas first off.
We may have perhaps one or two diagnostics to say which drugs should be
used with such patients, and these will provide a way of funneling patients
to the right drug. With a little bit of extra effort, this same technology
can be used on solid tumors, so that may be happening as well. We’ve
demonstrated that in my academic laboratory already.
In fact, people are now beginning to use these as pharmacodynamic
monitoring assays in clinical trials, and once they look at it, everyone
says to me, "Why didn’t we think of it, it’s so easy to use and
seems so obvious." But of course at the time, all I heard was that it
Why were people so skeptical? Why did they think it
No one thought it would be sensitive enough to do what we were claiming.
And they did not think it would be quantitative enough. To me, these were
technical limitations that could be proven. The key issue was getting
people to believe the conceptual advances the techniques could prove or
What was the most challenging part of this
Getting it commercialized; that was the hardest part. In this case, not
just finding the right people to work on it, but getting financial support.
I spent probably two to three years tromping around, being laughed out of
venture capital offices, being told nobody would ever make a penny on this,
that it was useless.
To me at the time it was mind-boggling that people did not see the
advantages: if you could tell who gets a benefit from a drug ahead of time
and who doesn’t, if you have a way to get value from drugs that might
otherwise be thrown away, why not do it? I was ready to throw in the towel
when a family connection got me to Kleiner Perkins and Texas Pacific Group.
They had started a couple of companies in the diagnostic area and they
looked at me and said, "This should work." They believed what I believed.
Luckily, now, many groups are moving in this area, and it's become more
than just a cottage industry driven by my lab alone, which is gratifying.
What do you mean by getting value out of drugs that
would otherwise be thrown away?
The notion is that there are tons of drugs out there that work at a 10%
rate: 10% show complete remission from cancer, for instance, and
that’s great, but another 10% have a really hard time on the drug and
for 80% it does nothing. Since we already have a drug out there—say,
standard chemotherapy—which works on 40% of the patients,
there’s an ethics of standard care that says that’s what you
have to use. Even if 10% of your patients will get much better on this
other drug, you don’t know who they are, so what benefit is it to
these patients to put them through your drug first? You have to do the 40%
drug, even if that drug is practically nuking the patient, first before you
have a reason to allow the second drug. At the point at which the patient
has failed the first drug regimen, the cancer might have advanced to a
stage that if detrimental to the patient, or the first drug regimen (by
nature of its inherent toxicity) might have compromised the health of the
patient to the point that the new drug cannot work.
So there are a lot of drugs out there, even some that have virtually no
side effects, that don’t gain approval because their success rate
isn’t better than what’s already available—even if the
already available drug is highly toxic. Ironically, the standard of care
ethics doesn't always balance the equations correctly. And the large
pharmaceutical companies don’t consider a 10% drug worth the effort
even if they were able to overcome the ethical limitations. But what if you
know in advance who the drug will work on and who it won’t? If you
could pre-select the population of patients that would respond, now you
have a market for the drug. If you can find those individuals ahead of
time, the pharmaceutical companies, the insurers, and, most importantly,
the patients can do very well.
Garry P. Nolan, Ph.D.
Stanford University School of Medicine
Stanford, CA, USA