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May 12, 2010

Researchers Identify Gene-Expression Signature that Predicts Relapse Risk in T-ALL Patients

  • A gene-expression signature that may help predict the risk of relapse in patients with T-cell acute lymphoblastic leukemia (T-ALL) at the time of their diagnosis has been identified and validated, according to a research team from the University of Western Australia Centre for Child Health Research. The hope is that the five-gene classifier (5-GC) could be used to help the early identification of T-ALL patients who will require more aggressive therapy to boost their chances of complete remission.

    The study is detailed in Molecular Cancer in a paper titled “Gene-Based Outcome Prediction in Multiple Cohorts of pediatric T-cell acute lymphoblastic leukaemia: A Children’s Oncology Group Study.”

    T-ALL affects approximately 15% of newly diagnosed pediatric ALL patients, the authors report. While continuous complete clinical remission (CCR) in T-ALL patients is now approaching 80% due to the implementation of aggressive chemotherapy protocols, patients that relapse have poor prognosis, and aggressive therapy can lead to long-term side effects in those that do achieve CCR. Unfortunately, an accurate method of predicting in advance which T-ALL patients have the worst prognosis and thus would benefit from more aggressive therapy has yet to be identified, the authors state.

    To try and address this, Alex Beesley, M.D., and colleagues carried out microarray-based gene-expression analysis of bone marrow samples taken from 84 pediatric T-ALL patients treated under Children’s Oncology Group protocols. The resulting microarray data was then used to model a 5-GC classifier that could help predict which patients would have the least positive prognosis. The genes included in the 5-GC were ABTB2, IL7R, LGALS8, PLAC8, and FAM13A1. The 5-GC was subsequently validated against three independent cohorts of T-ALL patients using either quantitative RT-PCR or microarray gene data.

    “To our knowledge this is the first time gene classifiers have been developed that accurately model ALL relapse in more than two independent cohorts,” the authors claim. “Defined gene classifiers (such as the 5-GC) containing a smaller number of genes may be useful to augment existing risk stratification regimens for patients diagnosed with ALL as they can easily be adapted to qRT-PCR technology.”


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