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Author: Katarzyna Krukiewicz Published at: 11.10.2021 Last update: 11.10.2021

Enhancing the treatment of acute myeloid leukemia

Event-free and overall survival remain poor for patients with acute myeloid leukemia. Chemoresistant clones contributing to relapse arise from minimal residual disease (MRD) or newly acquired mutations. However, the dynamics of clones comprising MRD is poorly understood. A team led by Prof. Marek Kimmel developed a predictive stochastic model, based on a multitype age-dependent Markov branching process, to describe how random events in MRD contribute to the heterogeneity in treatment response. They employed training and validation sets of patients who underwent whole-genome sequencing and for whom mutant clone frequencies at diagnosis and relapse were available. As a conclusion, given bone marrow genome at diagnosis and MRD at or past remission, the model can predict time to relapse and help guide treatment decisions to mitigate relapse.

The results have been just published:

Dinh KN, Jaksik R, Corey SJ, Kimmel M. Predicting time to relapse in acute myeloid leukemia through stochastic modeling of minimal residual disease based on clonality data. Computational and Systems Oncology. 2021 Sep;1(3):e1026.

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