Genes pointing the way in lymphoma prognosis
T&S completion compared against T&S collection relative to start of surgery
The diagnosis of diffuse large-B-cell lymphoma, the most common
type of lymphoma in adults, might soon become more informative for
patients and clinicians.
Researchers have found gene expression patterns in these tumors
that can be used to identify different subsets of the disease and
predict patient outcome.
Until recently, physicians have relied on the clinically based
international prognostic index to predict which patients with diffuse
large-B-cell lymphoma will be among the 40 percent or so who survive
"This large subgroup is thought to contain several variants that
one hadn't been able to subclassify consistently by histopathology
or immunological phenotyping," says H. Konrad Müller-Hermelink,
MD, head of the Department of Pathology, University of Würzburg,
But the work of Dr. Müller-Hermelink and his colleagues, published
in the June 20 issue of the New England Journal of Medicine,
has the potential to change that. "By analyzing gene expression
data, three different types of lymphoma can be unequivocally defined,"
he says. "This is not a subjective interpretation of any pathologist,
but rather a definite biological difference."
Furthermore, "it was found that a very limited number of genes could give
specific information on the prognosis of patients," Dr. Müller-Hermelink says.
Building on an earlier study of 40 patients, which identified
two subgroups of diffuse large-B-cell lymphoma, the authors used
DNA microarrays and hierarchical clustering to analyze the expression
of more than 7,000 genes in 240 patients.
"We found the two subgroups that we had previously identified
as well as a new subgroup that was only evident with this larger
number of tumor biopsies," says Louis Staudt, MD, PhD, section chief,
Metabolism Branch, Center for Cancer Research, National Cancer Institute,
The three gene-expression subgroups include the germinal-center
B-cell-like lymphoma, which expresses high levels of genes characteristic
of germinal-center B-cell-like lymphoma and normal germinal-center
B cells; activated B-cell-like lymphoma, which expresses genes characteristic
of mitogenically activated blood B cells; and the new subgroup,
type 3 diffuse large-B-cell lymphoma, which has a heterogeneous
gene expression that suggests it includes more than one subtype
"We believe that the large subgroups we discovered will most likely
have a bearing on many of the therapies [that are used] because
they are like different diseases," says Dr. Staudt. "They are as
different from one another as breast cancer is from lung cancer.
They differ by the expression of thousands of genes."
Germinal-center B-cell-like large-cell lymphoma had the best prognosis,
with a 60 percent five-year patient survival rate after anthracycline-based
chemotherapy. It exhibited two oncogenic events not seen in the
other subgroups: t(14;18) translocation of the bcl-2 gene
and amplification of the c-rel locus on chromosome 2p. Patients
in the type 3 subgroup had a survival rate of 39 percent; those
in the activated B-cell-like subgroup had the poorest prognosis,
with a survival rate of 35 percent.
These outcomes are partly due to the differences in the biology of the tumors,
says study coauthor Wing Chan, MD, professor of pathology, University of Nebraska
Medical Center, Omaha. "But grouping patients into these three subgroups does
not tell you everything. There are still some patients in each of the subgroups
that survive very well and some that have poor survival."
Gene expression signatures
In their most recent study, the authors also identified individual
genes whose expression helps to determine survival. "The predictors
we found are useful for each one of the patients, regardless of
which subgroup they are in," says Dr. Chan.
A preliminary analysis of 7,399 DNA microarray features identified
670 that were significantly associated with a good or bad outcome.
Using hierarchical clustering, the genes associated with outcome
were combined into four gene-expression signatures. Because genes
within the same signature are known to perform similar functions,
16 genes that were highly variable in expression were selected for
use in an outcome predictor model.
"The important point is that we grouped the genes into functional
categories that identified different biological features of the
tumors that influenced outcome," Dr. Staudt says.
The No. 1 category was proliferation. "Genes expressed in highly
proliferating cells identified patients who responded poorly to
chemotherapy," he adds.
As with the three original subgroups, the cell of tumor origin
was found to predict survival. Genes characteristic of normal germinal-center
B cells correlated with favorable patient outcomes.
The last two categories of predictive genes suggest that immune
response to the tumor is important for a curative outcome after
chemotherapy. Genes that reflected the infiltration of the lymph
node with host immune cells and a stroma cell reaction to the tumor
predicted a favorable patient outcome.
A second immune response-related gene-expression signature reflects
the major-histocompatibility-complex class II proteins, which stimulate
immune cells by presenting them with antigens. Patients who lost
expression of these genes had a poor outcome. "Our hypothesis is
that those tumors are becoming invisible to the immune system,"
Dr. Staudt says.
In an evaluation of individual genes associated with outcome that
were not included in these four signatures, one, BMP6, was
found to increase the predictive power of the model. Its expression,
which was included in the final outcome predictor, was associated
with a poor outcome.
By adding together the expression of these 17 genes, each patient
was assigned an outcome predictor score that ranged from -1.7 to
2.4. Each unit increase in score increased the relative risk of
death by a factor of 2.7.
Patients were ranked based on their score and divided into one
of four quartiles that were found to have five-year survival rates
of 73, 71, 34, and 15 percent, respectively.
This work found that the expression of a limited number of genes
could be combined into a predictive model that gives specific information
about the prognosis of every patient.
"The clinical utility would be, on the one hand, identifying half
the patients who have a very favorable chance of being cured by
conventional chemotherapy with a five-year survival rate of between
71 and 73 percent," says Dr. Staudt. "On the other hand, fully one
quarter of patients were in a very poor risk group that had only
a 15 percent chance of being cured by current therapy."
Based on this model, it might be possible to identify those patients who are
the best candidates for alternative treatments, such as bone marrow transplantation
or therapeutic clinical trials. "This is a method that works," says Dr. Staudt.
"Our gene-expression predictor reflects aspects of the biology of the tumor
that we can understand and perhaps target therapeutically."
International prognostic index
The international prognostic index, which commonly has been used
to predict outcome, measures such parameters as patient age, stage
of disease upon diagnosis, total tumor bulk as measured by serum
lactate dehydrogenase, performance status of patient upon diagnosis,
and whether the tumor has spread beyond the lymph nodes.
The gene-expression-based method and the international prognostic
index were found to be independent predictors of prognosis. "You
can combine both of the predictors and do an even better job of
predicting the outcome of the individual patient than using either
alone," Dr. Staudt says.
Though the gene-expression outcome predictor is not yet available
commercially, it would be easy to translate it into a clinical test,
he adds. "If this could be applied, it would be like any other blood
test you would get in the hospital." A patient would be given a
score, for example 2.7, that, based on a database of patients with
this type of lymphoma, predicts a 70 percent chance of being cured.
Several platforms could be used to measure these individual gene-expression
parameters, including a reverse transcriptase/polymerase chain reaction
assay or DNA microarray assay. Another option would be to look at
the proteins expressed by these predictor genes using an immunochemical
assay or monoclonal antibody chip.
"What is so exciting about this gene array data is that by gene
expression you have a very objective possibility to define and classify
the type of tumor not only by its histological features but also
by objective biological criteria," Dr. Müller-Hermelink says. "In
addition, these prognostic genes and signatures have been defined
so that in the future it appears to be possible to state an individual
prognostic index for every patient that has such a tumor. This will
have implications for treatment strategies and treatment of these
In an editorial accompanying the New England Journal study,
Ian Magrath, DSc, president of the International Network for Cancer
Treatment and Research, Brussels, Belgium, states that "diagnostic
precision is an essential foundation for effective therapy, since
individual diseases might be expected, in general, to require individual
Yet he also notes that DNA microarray analysis cannot identify all of the
factors that determine treatment outcome. For example, the final concentration
of active drug in tumor cells is also influenced by genes expressed by normal
tissues, circulating enzymes, and anatomical or physiological factors. Microarrays
are also not likely to be useful in identifying variations in the activity of
drug metabolism or DNA repair enzymes or factors that influence tumor volume.
The goal of this work, however, isn't just to improve clinicians'
ability to predict patient outcome.
"Finding a predictor is just the first step," says Dr. Chan. "We
want to find out why it is predictive, what is the molecular mechanism
behind that. Once we understand the mechanism, we can design therapeutics
to improve the treatment and outcome of patients." Dr. Chan and
his colleagues at the Lymphoma/Leukemia Molecular Profiling Project,
a consortium of institutions that collaborated on the New England
Journal study, are also analyzing gene expression in other types
of lymphoma, including Mantle cell and follicular lymphomas.
Another line of research involves correlating gene expression
to genomic alterations within tumors. "We have cytogenetic and genomic
data already available for many of these tumors," Dr. Müller-Hermelink
A clinical trial involving diffuse large-B-cell lymphoma patients
who have relapsed is also slated to begin later this year at the
NCI and other institutions. It will target the nuclear factor kB
signaling pathway, which has been shown to prevent tumor cell death
after chemotherapy in activated B-cell-like lymphomas.
The gene expression patterns of patient tumors will be profiled
before treatment with an inhibitor of NF-kB and chemotherapy. "We
will then use the profiles, after the fact, to find out who responded
and who didn't," says the NCI's Dr. Staudt.
"Because we're using a new therapy in a clinical trial," he adds,
"by definition we would not be able to predict with certainty which
genes would be most important for dictating response. The actual
set of genes will change with each change in therapy. We may find
a new signature."
Vida Foubister is a writer in Mamaroneck, NY.