Gene-based Screen Sorts Out Cancer Cases, Say Stanford Researchers

When a person is diagnosed with diffuse large-B-cell lymphoma, doctors use a group of indicators called the International Prognostic Index, or IPI, which includes a person’s age, tumor stage and blood markers to decide how to treat the cancer. Those with the highest IPI scores get the most aggressive therapy. However, two people with the same score may still react differently to treatment. One thought has been that a genetic screen may fine-tune the distinction between the most aggressive and least aggressive cancers.

“It makes a big difference in your treatment decisions if you think you have a high chance of success or if you don’t,” said Ronald Levy, MD, the Robert K. and Helen K. Summy Professor, who led the study. He said if doctors know a patient isn’t likely to respond well to standard therapy, the patient may be a candidate for novel therapies undergoing clinical trials.

“New therapies are usually tested in people who have failed the standard therapy,” Levy said. “If you know in advance who won’t respond well, you can treat them more aggressively or include these patients in trials of the many promising, new targeted therapies.”

Over the past five years groups of researchers have used microarrays, which take a snapshot of which genes are active in different tissues, to find large groups of genes that predict a person’s survival. These studies have resulted in huge lists of mostly non-overlapping genes. Not only are there too many genes to be screened by most medical labs, the research groups haven’t agreed on which genes should be part of those screens.

Levy and his colleagues narrowed the list of potentially informative genes down to the 36 most likely candidates. They then analyzed how active those genes were in 66 tumor samples from people with diffuse large-B-cell lymphoma who had been treated at Stanford. The results of this work are published in the April 29 issue of the New England Journal of Medicine.

On its own, none of the genes predicted how long a patient lived after treatment. But six of the genes taken together could predict how long the 66 patients survived. Researchers then tested the predictive power of those six genes in patients who participated in two previous microarray studies. In this combined group of 298 patients the six genes once again distinguished between patients who responded well to treatment and those who did not.

Rather than using microarray technology to analyze the genes’ activity, Levy and his colleagues worked with scientists at Applied Biosystems Inc. to develop a screen using a technique called RT-PCR. This approach would be easier for medical labs, which already use RT-PCR for other disease tests. Levy said although microarrays and RT-PCR give similar information, the new test is more likely to become widely used if it is easy to incorporate into existing medical labs. “You want to introduce something that helps people do what they are doing now – but better,” he said.

He added that the group still needs to check how well the six genes discriminate between good and bad responders in additional groups of patients, especially those who are receiving therapies that weren’t available when the original patients were treated. If the test can still predict those who need the most aggressive treatment, the researchers will move forward with making the screen widely available. He said doctors would likely combine information from the IPI clinical index with results from the six-gene screen to decide how best to treat the patient.

Postdoctoral scholar Izidore Lossos, MD, was the first author on the paper. Other collaborators include Debra Czerwinski, research assistant; Ash Alizadeh, MD, PhD, postdoctoral scholar; Robert Tibshirani, PhD, professor of health research and policy; and David Botstein, PhD, who was professor of biochemistry at Stanford until last year.