Date |
November 1, 2012 |
Speaker |
Dr. Limsoon Wong, Professor, School of Computing, National University of Singapore
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Title |
A Novel Principle for Childhood ALL Relapse Prediction
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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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