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Richard Caprioli, On Linking Proteome Patterns and Patients


At A Glance

Name: Richard Caprioli

Position: Professor of biochemistry, Vanderbilt University School of Medicine, since 1998.

Director of Mass Spectrometry Research Center, Vanderbilt University.

Background: Wrote recent biomarker article “Proteomic patterns of tumour subsets in non-small-cell lung cancer.” (Lancet. 2003 Aug 9;362(9382):433-9)

Professor of biochemistry and molecular biology, University of Texas Medical School, Houston, 1975-98.

Assistant professor of biochemistry, Purdue University, 1970-75

Post-doc with John Beynon, 1969-70.

PhD in biochemistry, Columbia University, 1969.

BS., Columbia University, 1965.


How did you get involved with biomarkers?

I’m a biochemist by training, so my interest is in molecules in cells — how they integrate, and of course along the way you have to start to identify what they are and what they do — so it becomes obvious that this information is layered. At the first layer, you see signals, and you know what their molecular weights are, but you don’t necessarily know what protein it is. But even at that level, you can use the patterns — much the way you would use fingerprints-to tell you something. The next level then becomes understanding what the nature of the protein is, its structure and function. Then the next level is how it relates to all other molecules in the cell. As you get into these very interesting threads, you find yourself walking down those roads quite easily, trying to understand what’s going on.

For the stuff I’m doing here at Vanderbilt, it actually came from getting interested in translating discoveries we make in the basic science labs to clinical problems — that’s really one of my underlying passions. So there are some really neat fantastic things we can do in the lab, but we don’t know enough about the clinical side, and then clinical people are doing some neat and fantastic things but don’t know enough about what we’re doing. So when I came here we put some collaborations together which now led us into saying ‘ok, we got a patient, what can we do for them?’ Let’s look at the proteome patterns and see what that tells us. Then the next step is, what are those proteins? Which ones are markers for the disease? And which ones can we use to forecast disease?

So is this the next step that you’re doing?

Absolutely. We did that with the lung cancers — we worked with a surgeon here, Dave Carbone, and when we put our heads together and looked at a lot of patients and looked at the molecular profiles, we were able to judge from the profiles to tell which patients were going to live a few months and which were going to live a few years. The aggressiveness of the disease shows up in the molecules. So if you measure these patterns, you can begin to see. Better yet, we were able to see which patients were at highest risk for metastases. We’d have to do a larger study, but this is predicting now which patient is going to get a secondary tumor. And of course most of these patients die of secondary tumors. So if you can predict the group at risk and treat them prophylactically, I think that’s a major step forward. So we come full circle — back to this idea of discoveries in the basic science lab being integrated with clinical health problems.

Many mass spec people aren’t as actively interested in what’s happening on the clinical side of things as you are — where does this interest come from?

I think it comes from a couple places. I’ve always been at medical schools — I got my PhD at a medical school, I was 20 years in a medical school at the University of Texas in Houston. So that was where my love of the [clinical research] environment [came from]. At the risk of sounding a little corny, I get a lot of joy out of thinking that somewhere down the road, maybe I can make a difference. So maybe it’s the wish of a missionary — maybe in another life I would be in Africa somewhere preaching!

What are you working on now aside from your lung cancer studies?

We’re working on a whole bunch of diseases now. What worked out with the lung tumors is repeating with the brain gliomas. We just finished a study of about 150 human brain tumors — these are tumors that are resected here at Vanderbilt mostly. We measured molecular patterns, and again, from the proteomic patterns that we’re finding, we can predict longevity, involvement, [and] what is the progression of the disease. So now what we want to do is look for biomarkers in the serum for brain cancer. Because as you suspect, it’s difficult to get a biopsy whenever you want it. You have to drill a hole in somebody’s skull and it’s obviously very invasive.

There are some other programs around where people are just screening serum for disease, but we took another course, because patients are multi-faceted. They have many kinds of complications. I’ve seen lung tumor patients out in the hallways smoking. They drink too. People get sick and they do all kinds of things that in the global sense affect their health. And almost everything that happens to you winds up in your plasma — proteins break down, metabolites, toxins. Your plasma has a huge number of compounds from all different kinds of causes. So we thought it probably wasn’t the best approach to just go fishing in plasma and hope that you find something. Why don’t we go into the tumors, identify the biomarkers which are important in tumor growth or homeostasis. It could be a housekeeping protein — we don’t care, as long as it’s a protein that is very important in the tumor and not in the normal tissue. Then, because of things like apoptosis, the cell’s going to break down and [the protein] is going to wind up in the blood. So why don’t we now go smart-fishing. It makes sense — we know they’re in the tumor, they’re going to have to show up in the blood at some level. The question is, will the level be high enough for us to detect? That’s the process we’re in right now.

We’ve started screening some patients’ blood from some of the lung cancer and glioma patients. But we also have a program with Carlos Artiaga in breast tumors and I think we have about 20 to 30 patients that we’re starting to study.

So you’re working on lung, brain, and breast cancer …

Those are the three we’re furthest ahead on. We also have neck cancer [research] ... colorectal cancers with Bob Koffe — all these clinicians are my colleagues on this. It is a three-legged stool — we have the clinical aspect, the basic science discovery aspect, and then we need the bioinformatics. In order to cull the data out-which protein is most important to the tumor - we’re looking at tens, hundreds of thousands of peaks. So [bioinformatics] becomes a really important leg of the stool.

Before we can understand the integration of the cell and the integration of tissues, we have to integrate as human beings — we need mathematicians, physicists, clinicians, basic scientists — it’s when these teams come together that you begin to get the leapfrogging of learning. The day of the lonely guy in the back lab is gone. Big science is here, and in this kind of context it means expert teams.

What other institutions are you collaborating with?

We’ve got collaborations with many other institutions, a lot of the pharmaceutical companies I’ve got collaborative programs with-places like Merck and Eli Lilly — are very big pharma companies. Big companies like this, although they have the resources, they’re not set up to do this kind of discovery. They’re set up in drug lines — drug development, phase trials, and whatnot. But when you go and say how are drugs arriving in tissues, how are they changing the proteome — that’s not really in any one particular product line. So for the proof of concept and early stuff, they’re happy to farm it out.

An example of this is what we found with the breast tumor model. We found that with the treatment from a drug, we can see very early changes in the proteome of a tumor, before the tumor even starts to shrink. When you treat a patient and you give them a drug, you don’t know if the drug is going to be effective. So you give it to the patients and they come back in a month and you see if the tumor has shrunk. But imagine if it hasn’t. Now the patient is compromised by the fact that the tumor is worse, and they have all the immunological and other side effects of having taken these rather toxic drugs. So that’s the problem. And patients, if they’re unlucky, start this downward spiral. Now imagine if you can measure from the first dose of this toxic drug whether we are beginning to see the changes that mean the tumor is shrinking. The tumor can’t change until the proteome changes. So if we’re clever enough to measure things early, we may be able to pick which patients are going to be susceptible to a particular drug, and which patients are not going to respond to the drug.

So you’re doing this for breast cancer?

Yes, and we’re about ready to publish a paper that shows that with one of these drugs and one of these studies we were able to see in a matter of hours changes that were predictive of the efficacy of the drug. In animals that got sub-therapeutic doses, the changes did not occur. It would take in an animal model five or six days to see any actual shrinkage of the tumor. If this [finding] holds, I think it will drastically change the way therapeutics are given. This is an entry into individualized medicine. It’s really a very exciting time in medicine.


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