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Mutations Identified Could Improve Prognosis for Patients with Rare Leukemia

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Researchers at Cold Spring Harbor Laboratory have identified mutations that may lead to improved therapy for patients with mast cell leukemia, a form of acute myeloid leukemia that has a poor prognosis. Their research, published online in Leukemia in December, used exome sequencing and array comparative genomic hybridization to identify two previously unknown mutations in the genome of a patient with MCL. They found mutation in the genes KIT and MS4A2 that could predict response to currently available drugs. The patient in the study had been pre-screened for a typical KIT mutation called D816, which confers resistance to the drug imatinib — sold commercially as Gleevec. Because this mutation was not found, imatinib was added to the patient's therapy regimen. Unfortunately, the patient carried another KIT mutation, V654, which also affects imatinib response. "If the clinician had been aware of this mutation, this drug would not have been administered, sparing the patient the side effects of that drug," says Mona Spector, a researcher in the Tita Monti Cancer Research Laboratory at CSHL.

Spector and her colleagues are also convinced that the other mutation identified in the study, MS4A2, encodes for the beta chain of the IgE receptor — which signals to spleen tyrosine kinase, or Syk — could lead to aberrant Syk activation.

"As there are Syk inhibitors that have already shown efficacy in clinical trials in lymphoma, we are hopeful that our study will spark future studies to examine all the components of the Syk signaling pathway and, in particular, in leukemia and lymphoma cases which might then reveal candidate patients who may respond to therapeutic Syk inhibition," Spector says.

One of the primary challenges the CSHL team faced was study design. As anxious as she and her colleagues were to get the data, Spector says they spent months working out the experimental conditions. "We made certain to have Institutional Review Board approval to obtain matched normal and tumor sample from the same patient to enable us to narrow down the single nucleotide variants to cancer-specific ones," she says. "Another challenge — which is being faced by most scientists using the latest deep-sequencing platforms — is that an enormous amount of data is generated, which is challenging to deal with both from an IT and a computational analysis viewpoint."

Spector says she hopes that future research will uncover more mutations in this pathway, and will lead to better treatment options for patients with MCL.