Detailed Gene Scan Might Help Guide Leukemia Treatment

Study found different mutations predicted prognosis in AML patients

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Wednesday, March 14, 2012

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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.
In a second study in the same journal, researchers from Washington University School of Medicine in St. Louis used DNA sequencing technology to map a series of mutations that transformed myelodysplastic syndrome into leukemia in seven patients.
Myelodysplastic syndrome occurs when blood cells produced in the bone marrow don't fully develop and immature cells crowd out healthy ones. Some patients can be treated with blood transfusions, but about one-third progress to acute myeloid leukemia, according to background information in the study.
In the study, researchers sequenced tumor cells from patients who had myelodysplastic syndrome, and then again later on, when disease had progressed to leukemia. They found that cancer cells evolved over time, gaining new mutations but also retaining certain mutations that likely first turned healthy cells into cancerous cells.
Drugs that target those original mutations that are present throughout the cancer would probably be most effective, the researchers said.
Researchers said to think of cancer as a tree.
"To kill a tree, you have to pull out the roots," senior study author Dr. Timothy Graubert said in a university news release. "If you only cut off a limb, it will just grow back. We're saying that to be effective, targeted cancer drugs probably need to attack mutations at the root of this disease."

SOURCES: Ross Levine, M.D., associate member, Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York City; Lucy Godley, M.D., Ph.D, associate professor, medicine, University of Chicago; March 15, 2012, New England Journal of Medicine

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