New Technology
Application of Technology to Lymphoma
Classification
Correlation between gene expression
and immunophenotype
Gene Expression of Transform Lymphomas
Treatment Modalities for Groups
with Genetic Risk
GREG BERK, MD: Welcome
to Conversations with the Experts. My name is Dr. Gregory Berk. I'm on
the faculty at New York Presbyterian Hospital, Cornell Medical School.
I am pleased to have Dr. Ronald Levy with us who is Professor of Medicine
and Chairman of the Division of Oncology at Stanford University. Welcome
Dr. Levy.
RONALD LEVY, MD: Thank you very much.
GREG BERK, MD: Dr. Levy,
today we are going to talk about some of the recent findings regarding
gene expression in the various types of lymphomas, specifically diffuse
large cell lymphomas and B cell lymphomas.
I'd like to ask you if you can
describe some of the new technology that was recently reported on.
RONALD LEVY, MD: Yes, I would
be happy to. The new technology is based on the work of the Genome Project,
Human Genome Project whereby toward the end of this year we will probably
know of the human genes and have clones for parts—at least parts of all
of these genes.
At the current time, there may be half of them in hand.
One with this technology can collect those genes as pieces of DNA and put
them down as spots on a small grid where maybe 10,000 at a time can be
put as independent spots. Then one can take sources of cells from various
places—in this case, lymphoma cells—and ask which of the genes are over
or underexpressed compared to some other specimens—say a normal lymphocyte.
Then you can compare different types of lymphoma to each
other to develop a taxonomy, a way of distinguishing them from each other
by their pattern of gene expression.
That's the basic technology.
GREG BERK, MD: Do you
think, Dr. Levy, that there may be some ultimate applications for this
in lymphoma classification.
RONALD LEVY, MD: It's already clear that in a small
study that's already been done and published. It was clear to begin with
that one can use the pattern of gene expression to tell the difference
between chronic lymphocytic leukemia, follicular lymphoma, and diffuse
large B cell lymphoma. So those things that the pathologist can already
distinguish, are clearly distinguishable by the gene expression profiling.
But it goes further than that.
One can subdivide some of these diseases. The pathologists
do not currently have any way of subdividing by looking at their patterns
of gene expression. For instance, in the study that was done, diffuse large
B cell lymphoma was able to be subdivided into two major groups based on
the pattern of gene expression. Those groups seem to be important from
a prognostic and biologic point of view.
GREG BERK, MD: Was there
any correlation between these groups as distinguished by their gene expression
pattern and the immunophenotype?
RONALD LEVY, MD: Yes. Immunophenotype is an abbreviated
way of looking a gene expression. For instance, when we test for BCL-2
expression with an antibody staining, or BCL-6, or CD-10, these are very
informative for subdividing large cell lymphomas.
When we look by gene expression we're looking at 10,000
different genes at the same time, including the ones that I just mentioned.
What was discovered is that many of the informative ones are genes that
don't yet have names. They are genes that have been cloned from human cells.
We don't know what the protein is. We don't know what the function of the
protein is. We don't have any antibodies yet against those proteins. But
eventually this will lead us to get better antibodies, better reagents
that will make the critical distinctions for us by the usual antibody staining
technology.
GREG BERK, MD: There are
already gene subclasses that have prognostic differences than other gene
subclasses.
RONALD LEVY, MD: Yes. [With] the first pass at the
data, it was possible to subdivide diffuse large B cell lymphoma into two
major groups. One which has a gene expression pattern more like the
germinal center in normal B cell, and one which has a different group that
has the gene expression pattern more like a peripheral B lymphocyte that's
been activated by stimuli of various kinds.
We have the germinal center type and the peripheral activated
B cell type. Not only are these different in relation to normal cells that
they look like, but they're also different in their outcome after treatment
with standard chemotherapy. There is clear ability to make prognostic distinctions
between patients as to which type of gene expression pattern their diffuse
large B cell lymphoma has.
GREG BERK, MD: That's very
interesting. Have they looked at any of the low-grade lymphomas in this
regard?
RONALD LEVY, MD: That's going on right now. We had,
on that first study, a very small group of low-grade lymphomas. We had
five to ten CLLs, five to ten follicular lymphomas, low-grade types—not
enough of each type to subdivide within them. There is a large international
effort going on now coordinated by Lou Staudt at the National Institutes
of Health. He is collecting samples from various centers around the world
of various kinds of lymphomas where the clinical outcomes are known.
Within each kind of lymphoma he is going to attempt to
subdivide the disease and see whether one can get meaningful subdivisions
in the same way we were able to do with large cell lymphoma.
GREG BERK, MD: Dr. Levy,
I'm particularly interested in some of the transform lymphomas as well.
Patients who have a history of low-grade lymphoma or CLL who transform.
Is anything known yet about their gene expression patterns when this happens?
RONALD LEVY, MD: Not yet. That experiment—I agree
that's a really great question and we have some samples from people where
we have the original sample and the subsequently transformed sample from
the same person. We have maybe a dozen of those paired samples. Those are
now going to be compared by gene expression profiling to see what the genetic
program differences are between those clonally related transformed cells.
I think that right now we don't have all the human genes
available for this kind of study. We only have a subset. So if there is
going to be some magical gene that explains the whole story, we're unlikely
to find it this way until we have all the genes. What we're likely to find
is collections of expression patterns that go according to the change that
we're studying. Some of those patterns will be downstream events of the
primary genetic events. Some of them will be perhaps critical events in
determining the change. But the patterns will be able to be related to
the change.
For instance, if you could predict which patient with
low-grade lymphoma was destined to transform eventually, that would really
be very useful clinically. Those are the kinds of things that should come
out of this kind of work.
GREG BERK, MD: Also related
to the results of this type of work, are there any ongoing studies as of
now or planned—I'm sure they're planned, if not already started—looking
at specific treatment modalities for specific risk groups?
RONALD LEVY, MD: That's obviously what you would
like eventually to do. To pick out the poor risk group and then tailor
the therapy or make some—include them in some new trials for new agents
and new therapies, strategies of treatment. Right now there is nothing
apparent from the gene expression patterns that we see that would give
you any ideas about a different therapy you might use.
But the data is very immature at this point. It still
needs a lot of crunching of the numbers, a lot of looking at the data in
lots of different ways. For instance, if you had a new agent where
you knew the pathway by which it acted, you could then interrogate the
sample for the proteins on that pathway for whether they're being expressed
or not. Then you could make some predictions about a new agent and its
likely effect on the tumor.
GREG BERK, MD: So as far
as some of the newer therapies that are available now, such as some of
the new monoclonal antibodies, it's really too premature to tell if they
may be particularly active in one subgroup versus another.
RONALD LEVY, MD: That's totally unknown. I think
the best way to ask that question is to take samples from patients who
have been treated with let's say monoclonal antibody where you know who
responded and who didn't. Then compare them to each other. That may give
you clues about how the antibody works and it also may give you ways of
applying predictors to new patients who you're thinking of using that treatment
on.
GREG BERK, MD: Dr. Levy,
I think this is really very fascinating and really speaks to the age of
molecular oncology. We have much more targeted therapy in a lot of fields
of oncology. I think that the fact that you've been able to really isolate
these profiles at the genetic level and will subsequently be able to develop
specific targets, is truly fascinating.
RONALD LEVY, MD: I would like to put a plug in for
the Web site that anyone can go to and look at this data and actually interrogate
it yourself. The address is llmpp (leukemia lymphoma molecular profiling
project)... llmpp.nih.gov. You
go there and you can see the data. You can see the figures. You can see
the actual gene expression raw data and you can interrogate the data yourself
and plug in your own favorite gene and see how it behaves across the lymphomas.
You can then make discoveries of your own. It's freely available to everyone.
GREG BERK, MD: That's fantastic.
It really is. Well, thank you very much, Dr. Levy. I really appreciate
you joining us today and giving us your insights.
RONALD LEVY, MD: Thank you.
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