Selection of Best Drugs for Individual Cancer Patients
Choosing the best cancer treatments is often akin to throwing darts at a massive corkboard, hoping to hit the desired target. But researchers 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 researchers identified altered "pathways" -- multiple genes and their proteins -- that consistently escape normal regulation in tumors.
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 malignant activity, said the researchers 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 clinicians 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 researchers 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 pathways.
"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 clinicians 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 clinicians 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 scientists. The likelihood that someone will be cured by a single drug is low, and the new approach can guide clinicians 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."