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New Strategy Guides Selection of Best Drugs for Individual Cancer Patients

New Strategy Guides Selection of Best Drugs for Individual  Cancer Patients
New Strategy Guides Selection of Best Drugs for Individual  Cancer Patients


Duke Health News Duke Health News

DURHAM, N.C. -- Choosing the best cancer treatments is often
akin to throwing darts at a massive corkboard, hoping to hit
the desired target. But scientists have now developed a novel
method for selecting the most effective anti-cancer drugs based
on the patient's unique tumor activity.

The new approach scans the tumor for evidence of widespread
genetic changes that drive the tumor's growth and survival.
Rather than simply identifying defective genes, the scientists
identified altered "pathways" -- multiple genes and their
proteins -- that consistently escape normal regulation in

Cell signaling pathways are a complex hierarchy of genes,
and the proteins they produce, that act upon one another in a
tag-team relay to ultimately drive a cell's cancerous activity,
said the scientists from the Duke Institute for Genome Sciences
and Policy and the Duke Comprehensive Cancer Center.

Identifying which pathways are deregulated in each type of
tumor -- and to what degree -- provides a critical tool for
enabling physicians to choose the right drugs for each patient,
said Joseph Nevins, Ph.D., the senior author of the study,
published Nov. 6, 2005, as an Advance Online Publication of the
journal Nature.

"Targeting drugs to deregulated pathways provides a means to
avoid giving ineffective drugs to the majority of patients,"
said Nevins. "Instead of prescribing a drug that inhibits the
SRC pathway at a tumor that has no SRC deregulation, we can
select the right drug for that tumor type." SRC is one of five
pathways often deregulated in cancer cells.

The Nevins team developed their strategy by first
distinguishing normal cells from cells with genomic
"signatures" indicative of cancer. They created artificial
cancer conditions by introducing a series of cancer-causing
genes, called "oncogenes," into otherwise normal cells. By
comparing gene expression patterns in normal cells versus cells
harboring oncogenes, they demonstrated that each cellular
signaling pathway is associated with a unique pattern of gene
expression, its so called signature.

Moreover, the gene expression signatures could be used to
actually predict which cells carried the oncogenes and their
associated deregulated pathways.

Having validated the approach in cells and then in mice, the
Duke team assessed its ability in human tumors, as well. Their
first success was distinguishing two types of lung cancer from
each other: adenocarcinoma, which originates in the periphery
of the lung, and squamous cell carcinoma, which forms in the
central chest area. They found the overwhelming majority of
adenocarcinomas were deregulated for the oncogene Ras, while
only a tiny minority of squamous cell carcinomas exhibited Ras
deregulation. Hence, deregulation of the Ras pathway is an
important signature of adenocarcinomas but not of squamous cell
carcinoma, said Nevins.

The Duke scientists then applied the approach to a series of
breast cancer cell lines. They predicted which pathways were
likely to be deregulated, then treated the cancer cells with
drugs that targeted components of the cancer-causing pathways.
Indeed, the pathways predicted to be most highly deregulated
were also the most sensitive to drugs that targeted these

"Until now, there have been very few opportunities to guide
the use of therapeutic drugs that target specific cellular
components," said Nevins, director of the Center for Applied
Genomics and Technology at the Duke Institute for Genome
Sciences and Policy.

"But now, we've developed tools to measure the activity of
critical pathways, groupings of related genes, and proteins
that are activated or silenced in a given tumor, and we can
potentially use this information to best utilize the large
array of existing drugs."

The ultimate goal of their approach is to provide
individualized treatment plans to each patient based on the
unique pathway signatures of their tumor, said Mike West,
Ph.D., professor of statistics and decision sciences at Duke
and a lead author of the study. Pre-defining a tumor's
characteristics will arm physicians with the information needed
to make effective treatment decisions, he said. If the Ras and
Myc pathways are activated in a tumor, for example, then
physicians could choose drugs that target only Myc and Ras. If
the SRC and E2F3 pathways are highly active, then drugs can be
selected that target these pathways.

Because tumors arise from multiple defective genes and their
malfunctioning proteins, their treatments must target multiple
genes and their pathways, said the researchers. The likelihood
that someone will be cured by a single drug is low, and the new
approach can guide physicians as to which combination of drugs
will most likely produce the best outcome, they said.

"We believe this approach provides a path to identifying not
only what combination of drugs might be most effective, but
also an approach to selecting the right group of patients for
the combination of drugs" said Andrea Bild, the lead author of
the study.

"We can gain even more powerful insights by looking at
patterns of multiple deregulated pathways in any given tumor,"
added Nevins. "It's really the combinations of pathways that
reveal both important biology and subgroups of patients with
quite distinct clinical outcomes."

Nevins said the next step in the research is to validate the
new method in samples from cancer patients who have been
treated with one of the pathway-specific drugs to determine if
the pathway predictors are able to select those patients most
likely to respond to the drug. A positive result would then
form the basis for a clinical study that would evaluate the
effectiveness of the pathway prediction to guide the most
effective use of therapeutics.

"If we treat patients with drug A whose pathway A is
deregulated, do we see a better response?" said Nevins. "We
need a clinical study to assess whether we can enrich patient
outcomes, but we are encouraged that this could be an approach
to the ultimate goal of personalizing the selection of the best
drugs for the individual cancer patient."

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