By Duke Medicine News and Communications
Researchers in the Duke Institute for Genome Sciences &
Policy have developed a model for predicting risk of recurrence
in early stage colon cancer patients, and have used the model
to also predict sensitivity to chemotherapy and targeted
therapy regimens.
"These findings have important implications for
individualizing therapy," said Katherine Garman, M.D., a
gastroenterology fellow at Duke and lead investigator on the
study. "By examining gene expression in early-stage colon
cancer tumors, we have found certain patterns that seem to put
some patients at higher risk for recurrence. By identifying
these patients up front, we may be able to treat them in a
targeted and proactive manner to prevent this recurrence and
help them live longer and healthier lives."
The findings are due to appear in the online edition of the
Proceedings of the National Academy of Sciences, between
November 24 and November 26, 2008. The study was funded by the
Emilene Brown Cancer Research Fund and the National Institutes
of Health.
The researchers studied gene expression data from 52 samples
of early stage colon cancer tumors, looking for patterns. Then
they correlated the gene expression patterns with patient
progress reports to track the recurrence of cancer. The
predictive power of the correlations was subsequently tested in
two independent data sets from 55 and 73 tumors,
respectively.
"In our small dataset, we were able to predict which tumors
were at risk for recurring, with 90 percent accuracy," Garman
said.
In collaboration with colon cancer specialist David Hsu,
M.D., the researchers then took their study one very
significant step further, using the data garnered about gene
expression and prognosis to examine response to several
different types of therapy.
"Importantly, we found that the traditional chemotherapy
given to patients with colon cancer varies considerably in its
ability to treat tumors with a high likelihood of cancer
recurrence," Garman said. "Using the gene-expression data to
guide us, we then identified several other drugs and tested
those drugs in our samples. The drugs chosen were novel
targeted therapies and anti-inflammatory agents that go after
certain cancer cell pathways and had been previously shown to
alter colon cancer biology."
"Two of the drugs we tested seemed to cause significant
changes in tumor biology in a laboratory dish, effectively
making a high-recurrence-risk tumor into a low-recurrence-risk
tumor by altering the genetic makeup," Garman said. "These
therapies would need to be tested further in a clinical
trial."
Conventional methods of characterizing tumors currently rely
on pathological information such as tumor size, lymph node
involvement and degree of metastasis, Garman said. Doctors use
these kinds of clinical data to determine whether an early
stage colon cancer patient receives chemotherapy after surgery,
and if so, what type.
"Integration of genomic and genetic markers will
revolutionize the way we care for patients," Garman said.
"This is a perfect example of how science can change the way
cancer care is practiced," said Anil Potti, M.D., a researcher
in the Duke Institute for Genome Sciences & Policy and
senior investigator on this study. "We hope that advances such
as this will individualize the treatment plans for patients
with colon cancer and improve survival."
About 150,000 people are diagnosed with colorectal cancer
each year in the United States and almost 50,000 are expected
to die of the disease in 2008. Up to 30 percent of patients
diagnosed with early stage colon cancer can go on to experience
recurrences despite initial cure with surgery and chemotherapy
when indicated.
Other researchers involved in this study include Elena
Edelman, Chaitanya Acharya, Shivani Sud, William Barry, Anna
Mae Diehl, Dawn Provenzale, Geoffrey Ginsburg, Joseph Nevins,
and Sayan Mukherjee of the Duke Institute for Genome Sciences
& Policy; Marian Grade and Thomas Ried of the National
Cancer Institute; and Jochen Gaedcke of Georg-August-University
Gottingen, in Germany.