Skip to main content
Premium Trial:

Request an Annual Quote

H. Lee Moffit s William Kerr on Studying Blood Cancers Using Kinomics

Premium
William Kerr
Associate professor, Department of Interdisciplinary Oncology
H. Lee Moffitt Comprehensive Cancer Center

At A Glance

Name: William Kerr

Position: Associate professor, Department of Interdisciplinary Oncology, H. Lee Moffitt Comprehensive Cancer Center, University of South Florida, since 2000.

Background: Assistant professor, Department of Molecular and cellular Engineering, University of Pennsylvania School of Medicine, 1996-2000.

Research group leader, Cancer Gene Therapy, SyStemix, 1993-1995.

Postdoc, Department of Genetics, Stanford University, 1987-1993.

PhD in molecular and cellular biology, University of Alabama at Birmingham, 1987.


William Kerr is scheduled to give a talk on the kinomic comparison of normal and malignant hematopoietic cells at next month's OncoProteomics World Congress in South San Francisco. ProteoMonitor spoke with Kerr to find out more about his research background, and about kinomics.

What is your research background?

I've done a mixture of using genetics to study immune and stem cell questions. Because I started in finding genes that were differentially regulated and tried to understand what they were doing in cells, the kinome technology that we've gotten into in the last two years has been a natural extension to that. So rather than finding genes that are differentially expressed, we're now using the technology to find kinases' differential activities. We're asking, 'Are there differential activities, rather than differential expression, that determine the differentiation of blood cells?' And the stuff I'm going to be talking about at the OncoProteomics meeting is, are there differential activities of kinases that are contributing to leukemogenesis, or cancer in the blood cell system?

So again, it all started with me applying genetics to blood cell differentiation and blood cell cancers, and now we're trying to focus down on what are the key players? There's been a drift over the past few years. People are starting to realize that transcriptome analysis — microarrays — has limitations. They just tell you who's in the room. If you walk into a party or a big conference or something, and you wanted to know who are the key players at that conference, you couldn't tell that by looking at an alphabetical listing of all the people at the meeting. You'd have to go inside the meeting and see who's actively doing something, and interacting with others.

When we're trying to figure out what's going on in the cell — in this case a tumor cell — we're now using kinomics to find the genes that are active — in this case kinases. And we want to find out if they are inappropriately active, when we compare them to the normal counterpart of that blood cell tumor.

How is kinomics linked to genomics and proteomics?

Well, in a way, it's a merger of the two. Kinases are genes. You could do an array experiment and say, 'What kinases are expressed in a tumor cell?' Because kinases can be molecular targets for therapeutics, right? Some of the latest and hottest rationally designed drugs, like Gleevec for instance, are targeting kinases.

So you could do genomics and figure out that BCR-ABL or ABL is overexpressed, or some other kinase is overexpressed, but that still wouldn't tell you if that's really critical in a functional way to the growth and survival of that tumor cell.

Kinomics asks, 'Who is actually active in that cell?' We make a lysate of the cells, put them on an array, which is a peptide array with almost 1,200 different peptide substrates on there, and these substrates are specific for most of the known kinases in the cell, and we ask, 'Who's actually getting phosphorylated on this array?' And that allows us to link back to that kinase in the gene.

So in a sense, it's sort of functional proteomics. Rather than just finding out what proteins are there, the kinomics is a way of asking, 'What proteins are doing something?' and in this case, the class of proteins is kinases.

When did you get into doing kinomics?

Well, I was in Holland visiting some other immunologists I collaborate with, and there was a new fellow there named Maikel Peppelenbosch, and he's my collaborator in all these studies. Maikel was developing at that time arrays with peptides on them, and I immediately realized the value that this technology would have to the study of blood cell differentiation and also blood cell cancer analysis.

One of the fortes of my experience is the ability to identify and purify rare cell populations like stem cells, and I said if we could put purified cells — lysates of those cells — on these arrays, from the tumor cell, but also from normal cells, we could then differentially compare the active kinases in those two cell types and say, 'Does the blood tumor cell have kinases that have greater activity than in the normal counterpart?' And then that gives us a clue as to what kinases are active in the tumor cell that are maybe less active, or not active at all, in the normal cell.

Why did you choose to study kinase activity, as opposed to just studying all the proteins?

I think it's fair to say that most of the lead therapeutics that are being worked on right now in cancer, in terms of rationally designed new therapies are, in fact, kinases. The problem is right now, everybody just sort of picks out their favorite kinase.

What Maikel and I are doing is we're saying, we're not going to be biased by our background. We're taking a broad, unbiased approach. We're saying, 'Let's take our kinomic array strategy to tell us which kinases are active.' And that leads us very rapidly to say, 'Now we have a target.'

That's really what proteomics is about — it's trying to find what protein is important in a biological process, in this case a cancer process. And if we find that, then we have a target. Then we can move to rational drug design to try to make inhibitors for that kinase, and very rapidly get to a potential therapeutic — not in a time scale of a decade, but a time scale of a few years.

So it's faster than traditional proteomics?

It should be. And we already have found kinases that are differentially active in, for instance, multiple myeloma. It's early studies, but so far in the bioinformatics analysis that we've carried out on the kinome analysis, just certain kinases are preferentially active in multiple myleoma. And I went on PubMed, and there's no evidence in the literature that these kinases are even implicated in multiple myeloma at all.

Basically, now we know this handful of kinases is more active in myeloma than in the normal counterpart of the myeloma cell, which is called the plasma cell. In fact, in some cases, there are already known inhibitors for these kinases.

So whereas, if I were doing a traditional mass spec, MALDI-TOF-based proteomics approach, and I found all these different proteins, and some were expressed at a higher level, then I'd have to say, 'OK, what's that protein doing in the cell, and how do I inhibit it?' In this case, with the kinome analysis, I already know what the kinase does — it phosphorylates certain proteins in the cell — and I already know there are certain inhibitors for it. So I'm already way ahead of the gang.

And the other thing to point out is that proteomics, like microarray analysis for the transcriptome, just tells you what is the relative expression level of a protein. If that protein is expressed at the same level, in this case, in a tumor cell and normal cell, like a kinase might be, your proteomic analysis would miss that it's actually more active. Because activity is very often unrelated to its expression level. If something's active, it doesn't necessarily mean that it's expressed at a higher level, so proteomic analysis would miss that.

The key to kinomics is it's a more functional assay than the traditional proteomic approach where you measure all the different proteins in the cell, and you measure their relative level.

And the way that you're measuring activity is by measuring what gets phosphorylated?

Right. We've tried to make an array that's covering all the known and major kinases in the cell. Right now we're up to almost 1,200 substrates. The company that Maikel is working with is called Pepscan. It's based in Holland.

What kind of results have you gotten from doing kinomics?

The first thing we did together is we sorted hematopoietic stem cells. Hematopoietic stem cell make all the blood cells, but they're a very rare cell in the bone marrow. Basically, one in 10,000 to one in 100,000 bone marrow cells are stem cells. So if you were to do traditional proteomics — the MALDI-TOF mass spec type approach — to ask what is the proteome of the hematopoietic stem cell, you would have to get probably 10 million or more of those cells to make enough protein to have a really efficient way of representing the whole proteome of the stem cell.

Because we have an enzymatic assay, and enzymes can do things multiple times — they amplify the signal — we found we can just sort 100,000 or so, maybe 200,000 of these rare cells, and get a really nice representative readout on an array of the different things that kinases in that stem cell can phosphorylate. And that allows us to track back to what kinases are active in the stem cell.

That's one of the things we've done, is the stem cell kinome. And then we're starting to do this now in lymphoid cancers, mainly multiple myeloma, which is a plasma cell malignancy. So we've compared the kinomes of multiple myeloma cells to plasma cells, and we've found differentially regulated kinases that have higher activity in multiple myeloma.

And we're just starting to get data in non-Hodgkin's lymphoma, namely follicular lymphoma and diffuse large B-cell lymphoma. So the progress we've made is we've been able to apply this technology to using high-speed cell sorter-purified rare cell types, like stem cells, that are not readily studied using traditional proteomics and biochemical approaches. And the other advance is we've applied it to comparing normal cells and tumor cells sorted out of actual patient samples — not tumor cell lines.

We're finding the technology is sufficiently sensitive to get a representative readout of the active kinome of those tumor cells, and we're actually finding significant differences in some of the kinases in their activities between the normal cells and the blood cell tumors.

Do you have something that could possibly serve as a drug target?

Yes, we do. We're making an invention disclosure of it to the university, and then eventually, the plan should be that the university will file a patent for it.

What kind of projects do you have planned for the future?

For the next two years, we've just started analyzing a number of myeloma and Non-Hodgkin lymphoma samples, but what we eventually want to do, and what we're hopefully going to get funding for from the National Cancer Institute, is to analyze between 50 and 100 different multiple myeloma and non-Hodgkin lymphoma samples.

Not all myeloma patients have exactly the same type of tumor. And not all follicular lymphoma or non-Hodgkin lymphoma patients have exactly the same type of disease and tumor. In other words, there are individual differences from patient to patient in how aggressive the tumor is, whether it metastasizes, whether it's sensitive to therapy or not.

So what we hope to figure out is not only what's different in a broad way between multiple myeloma and plasma cells, or non-Hodgkin's lymphoma and normal B cells, but to figure out are there specific kinome differences that track to each individual type of non-Hodgkin's lymphoma or multiple myeloma. In other words, we could maybe some day do this so we get the kinome of an individual patient, and say, 'OK, you're like every other myeloma in terms of the kinases that are active, but we found this one kinase in your tumor that's more active than what we typically see in other multiple myeloma patients, and maybe we could tailor your therapy to try to target that kinase as well.'

We hope to find different kinomes within these disease populations that may show some of the same features, but they'll have some subtle differences from other patients with the same disease, and knowing those differences may help us pick the right therapy for them, or maybe develop a new therapy for them.

Do you have large-scale patient studies that are underway?

We've already accumulated about ten different patients in the last six months for multiple myeloma, and a similar number for non-Hodgkin's lymphoma. So what we hope — and this is based on the funding we've applied for — is that we can do something like 50 to 100 over the next two to four years for each of those diseases. Currently, my work is funded by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and I am the Newman Scholar of the Leukemia and Lymphoma Society.

File Attachments
The Scan

Not Yet a Permanent One

NPR says the lack of a permanent Food and Drug Administration commissioner has "flummoxed" public health officials.

Unfair Targeting

Technology Review writes that a new report says the US has been unfairly targeting Chinese and Chinese-American individuals in economic espionage cases.

Limited Rapid Testing

The New York Times wonders why rapid tests for COVID-19 are not widely available in the US.

Genome Research Papers on IPAFinder, Structural Variant Expression Effects, Single-Cell RNA-Seq Markers

In Genome Research this week: IPAFinder method to detect intronic polyadenylation, influence of structural variants on gene expression, and more.