ゲノム情報科学研究教育機構  アブストラクト
Date November 1, 2012
Speaker Dr. Limsoon Wong, Professor, School of Computing, National University of Singapore
Title A Novel Principle for Childhood ALL Relapse Prediction
Abstract Childhood acute lymphoblastic leukemia (ALL) is the most common type of cancer in children. Contemporary management of patients with childhood ALL is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. However, a significant number of patients with good prognostic characteristics relapse, while some with poor prognostic features survive. There is thus a demand to improve relapse prediction. Current treatment of childhood ALL is a process of gradually removing leukemic cells in a patient. Thus, we hypothesize that a leukemic sample consists of a mixture of leukemic cells and normal cells, where the intensity of the leukemic genetic signature measured by gene expression profile (GEP) can be used to infer the proportion of leukemic cells in the sample. In addition, as early response is known to have a great prognostic value in childhood ALL, we further expect to perform relapse prediction by the rate of the reduction of leukemic cells during treatment. To validate our hypothesis, for the first time, we generate time-series GEPs in a leukemia study. We demonstrate that the time-series GEPs are capable of mimicking the removal of leukemic cells in patients during disease treatment. We further propose to predict relapse based on this. Our results suggest the prognostic strength of this approach is superior to that of any other prognostic factors of childhood ALL. In our study, e.g., our approach outperforms MRD by over 20% in the sensitivity and specificity of relapse prediction.
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