By Jenifer Goodwin
HealthDay Reporter
WEDNESDAY, March 14 (HealthDay News) -- By analyzing gene mutations in patients with acute myeloid leukemia, researchers were able to more accurately predict which ones had the best chances of going into remission, and which ones would respond well to standard treatments or needed more aggressive treatment.
Doctors from Memorial Sloan-Kettering Cancer Center in New York City analyzed 18 genes from about 500 patients with acute myeloid leukemia (AML). AML is a cancer of the bone marrow, or the soft tissue that forms blood cells.
The patients had previously taken part in a clinical trial for a chemotherapy drug, daunorubicin, and researchers knew how everyone had fared in that study.
In the new analysis, the scientists used the latest gene-sequencing technology to determine what mutations were present in the cancer cells of the patients, and whether the presence of those mutations predicted how well people did.
They found that certain combinations of mutations were associated with both better or worse chances of survival, and that those genetic predictors could be used to determine whether patients would respond to the standard dose of daunorubicin or whether they should receive a higher, more aggressive dose of the drug.
Currently, some cancer hospitals already do a limited genetic analysis in leukemia patients to look for three mutations that are associated with a low or high risk of relapse, experts explained.
But about 60 percent of people fall into the intermediate category, said senior study author Dr. Ross Levine, an associate member in the Human Oncology and Pathogenesis Program at Sloan-Kettering. That leaves oncologists with a lot of uncertainty about how aggressively to treat those patients and what to tell them about their prognosis.
"If you know patients have a high chance of cure, you would pursue a standard therapeutic route," Levine said. "If you have a patient with a low chance of cure, you might consider more aggressive or investigational therapies."
Using the information from the more extensive analysis, about half of the patients who were in the intermediate risk could be put into a low- or high-risk category, Levine said.
"What we found was by studying the DNA of patients with leukemia and classifying all 500 patients, you could identify a set of mutations, which allows us to more accurately separate those at high risk of relapse, at intermediate risk of relapse and at low risk of relapse," Levine said. "Specifically, risk stratification with more extensive mutational profiling better predicts outcome than current classification schema."
The study is published online March 14 in the New England Journal of Medicine.
Dr. Lucy Godley, an associate professor of medicine at University of Chicago, who wrote an accompanying editorial, said the strengths of the study include the large number of patients, and that Levine and his colleagues used tissue samples that were already available from a prior study. That speeded up how quickly they could do their analysis and get answers that may benefit patients, she said.
Clinical trials take years to recruit patients, get approvals and follow patients long enough to determine how they did. With this study, the researchers already had all that information.
"What the Levine group did was to take samples from a completed study and ask a question with a modern, molecular eye," Godley said. "They found patients with certain mutations did better if they got a higher dose of the drug. What it implies is that the future of medicine is to molecularly fingerprint cancer patients, which we do a very little bit of right now."
As gene sequencing has become faster and less expensive, researchers are building up vast amounts of information about various mutations that are present in cancers, Levine said.
"The challenge we're all facing, both as cancer providers and cancer patients, is to try to understand how do we use these new, very innovative technologies which allow us to very rapidly categorize the different genes and mutations in the genes that are in cancers, to use that knowledge in the near term to actually help out patients," he said.
Next steps for the researchers include seeing if the findings hold up in other groups of patients, and then determining how to make the mutational analysis available to patients outside a research setting.
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