2022, Number 3
<< Back Next >>
Rev Cubana Hematol Inmunol Hemoter 2022; 38 (3)
Temporal evolution of a leukemic line that competes with healthy hematopoiesis
Martínez HMÁ, Lumpuy OD, Rodríguez FCC, López SA
Language: Spanish
References: 11
Page: 1-16
PDF size: 615.99 Kb.
ABSTRACT
Introduction:
The stem cell cancer hypothesis has become one of the most important paradigms in biomedical research. In recent years, evidence has accumulated for the existence of stem cell-like populations in different types of cancer, especially in leukemias.
Objectives:
To show, through mathematical modeling and computational simulation, how changes in the parameters that describe proliferation rates and self-renewal properties can influence the dynamics of healthy and leukemic cell populations.
Methods:
A mathematical model was used which is an extension of the healthy hematopoiesis models. The model was solved using computational tools based on numerical methods, this allowed to carry out countless simulations with different parameters and time intervals.
Results:
By imposing certain initial conditions and mathematically solving the model, the temporal evolution of the state variables of the hematopoietic system was obtained, that is, starting from a known state of the hematopoietic system, the behavior over time of the state variables of the system was predicted. It was particularized for four clinically relevant scenarios.
Conclusions:
The analysis of the model results in different growth scenarios of leukemic cells, among which the increased proliferation of malignant cells is the most prominent. However, different scenarios are possible, such as apoptosis induction or enhanced self-renewal.
REFERENCES
Stiehl T, Marciniak-Czochra A. How to characterize stem cells? Contributions from mathematical modeling. Curr Stem Cell Rep. 2019;5(2):57-65. DOI: https://https://doi.org/10.1007/s40778-019-00155-01.
Pedersen RK, Andersen M, Stiehl T, Ottesen JT. Mathematical modelling of the hematopoietic stem cell-niche system: Clonal dominance based on stem cell fitness. J Theor Biol. 2021;518:110620. DOI: https://https://doi.org/10.1007/s40778-019-00155-02.
Chen SY, Huang YC, Liu SP, Tsai FJ, Shyu WC, Lin SZ. An overview of concepts for cancer stem cells. Cell Transplant. 2011;20(1):113-20. DOI: https://10.3727/096368910X5328373.
Stiehl T, Marciniak-Czochra A. Stem cell self-renewal in regeneration and cancer: insights from mathematical modeling. Curr Opinion Syst Biol. 2017;5:112-20. DOI: https://10.1016/J.COISB.2017.09.0064.
Stace REA, Stiehl T, Chaplain MAJ, Marciniak-Czochra A, Lorenzi T. Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy. Math Model Nat Phenom. 2020;15:14. DOI: https://doi.org/10.1051/mmnp/20190275.
Wang W, Stiehl T, Raffel S, Hoang VT, Hoffmann I, Poisa-Beiro L et al. Reduced hematopoietic stem cell frequency predicts outcome in acute myeloid leukemia. Haematologica. 2017;102(9):1567-77. DOI: https://10.3324/haematol.2016.1635846.
Lorenzi T, Marciniak-Czochra A, Stiehl T. A structured population model of clonal selection in acute leukemias with multiple maturation stages. J Math Biol. 2019;79(5):1587-621. DOI: https://10.1007/s00285-019-01404-w7.
Stiehl T, Wang W, Lutz C, Marciniak-Czochra A. Mathematical modeling provides evidence for niche competition in human AML and serves as a tool to improve risk stratification. Cancer Res. 2020;80(18):3983-92. DOI: https://10.1158/0008-5472.CAN-20-02838.
Stiehl T. Using mathematical models to improve risk-scoring in acute myeloid leukemia. Chaos. 2020;30(12):123150. DOI: https://10.1063/5.00238309.
Manta L, Saeed B, Poisa-Beiro L, Pyl P, Stiehl T, Barth-Miesala K. et al. Clonal heterogeneity in acute myeloid leukemia. Oncol Res Treat. 2017;40:50-1.
Stiehl T, Marciniak-Czochra A. Mathematical Modeling of Leukemogenesis and Cancer Stem Cell Dynamics. Math Model Nat Phenom. 2012;7(1):166-202.