Mathematical modeling provides evidence for niche competition in human AML and serves as a tool to improve risk stratification
Acute myeloid leukemia (AML) is a stem cell-driven malignant disease. There is evidence that leukemic stem cells (LSC) interact with stem cell niches and outcompete hematopoietic stem cells (HSC). The impact of this interaction on the clinical course of the disease remains poorly understood. We deve...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
July 10, 2020
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| In: |
Cancer research
Year: 2020, Volume: 80, Issue: 18, Pages: 3983-3992 |
| ISSN: | 1538-7445 |
| DOI: | 10.1158/0008-5472.CAN-20-0283 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1158/0008-5472.CAN-20-0283 Verlag, lizenzpflichtig, Volltext: https://cancerres.aacrjournals.org/content/80/18/3983 
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| Author Notes: | Thomas Stiehl, Wenwen Wang, Christoph Lutz, and Anna Marciniak-Czochra |
MARC
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| 520 | |a Acute myeloid leukemia (AML) is a stem cell-driven malignant disease. There is evidence that leukemic stem cells (LSC) interact with stem cell niches and outcompete hematopoietic stem cells (HSC). The impact of this interaction on the clinical course of the disease remains poorly understood. We developed and validated a mathematical model of stem cell competition in the human HSC niche. Model simulations predicted how processes in the stem cell niche affect the speed of disease progression. Combining the mathematical model with data of individual patients, we quantified the selective pressure LSCs exert on HSCs and demonstrated the model's prognostic significance. A novel model-based risk-stratification approach allowed extraction of prognostic information from counts of healthy and malignant cells at the time of diagnosis. This model's feasibility was demonstrable based on a cohort of patients with ALDH-rare AML and shows that the model-based risk stratification is an independent predictor of disease-free and overall survival. This proof-of-concept study shows how model-based interpretation of patient data can improve prognostic scoring and contribute to personalized medicine. - Significance: Combining a novel mathematical model of the human hematopoietic stem cell niche with individual patient data enables quantification of properties of leukemic stem cells and improves risk stratification in acute myeloid leukemia. | ||
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