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Rodriguez J, Iniguez A, Jena N, Tata P, Liu ZY, Lander AD, Lowengrub J, Van Etten RA. Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy. eLife 2023; 12:e84149. [PMID: 37115622 PMCID: PMC10212564 DOI: 10.7554/elife.84149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 04/26/2023] [Indexed: 04/29/2023] Open
Abstract
Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.
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MESH Headings
- Mice
- Animals
- Tyrosine Kinase Inhibitors
- Protein Kinase Inhibitors/pharmacology
- Protein Kinase Inhibitors/therapeutic use
- Drug Resistance, Neoplasm
- Myelopoiesis
- Fusion Proteins, bcr-abl/genetics
- Fusion Proteins, bcr-abl/pharmacology
- Mice, Transgenic
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
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Affiliation(s)
- Jonathan Rodriguez
- Graduate Program in Mathematical, Computational and Systems Biology, University of California, IrvineIrvineUnited States
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
| | - Abdon Iniguez
- Graduate Program in Mathematical, Computational and Systems Biology, University of California, IrvineIrvineUnited States
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
| | - Nilamani Jena
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Prasanthi Tata
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Zhong-Ying Liu
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Arthur D Lander
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Department of Developmental and Cell Biology, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
- Department of Biomedical Engineering, University of California, IrvineIrvineUnited States
| | - John Lowengrub
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
- Department of Biomedical Engineering, University of California, IrvineIrvineUnited States
- Department of Mathematics, University of California, IrvineIrvineUnited States
| | - Richard A Van Etten
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Department of Medicine, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
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Hoffmann H, Thiede C, Glauche I, Bornhaeuser M, Roeder I. Differential response to cytotoxic therapy explains treatment dynamics of acute myeloid leukaemia patients: insights from a mathematical modelling approach. J R Soc Interface 2020; 17:20200091. [PMID: 32900301 DOI: 10.1098/rsif.2020.0091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Disease response and durability of remission are very heterogeneous in patients with acute myeloid leukaemia (AML). There is increasing evidence that the individual risk of early relapse can be predicted based on the initial treatment response. However, it is unclear how such a correlation is linked to functional aspects of AML progression and treatment. We suggest a mathematical model in which leukaemia-initiating cells and normal/healthy haematopoietic stem and progenitor cells reversibly change between an active state characterized by proliferation and chemosensitivity and a quiescent state, in which the cells do not divide, but are also insensitive to chemotherapy. Applying this model to 275 molecular time courses of nucleophosmin 1-mutated patients, we conclude that the differential chemosensitivity of the leukaemia-initiating cells together with the cells' intrinsic proliferative capacity is sufficient to reproduce both, early relapse as well as long-lasting remission. We can, furthermore, show that the model parameters associated with individual chemosensitivity and proliferative advantage of the leukaemic cells are closely linked to the patients' time to relapse, while a reliable prediction based on early response only is not possible based on the currently available data. Although we demonstrate with our approach, that the complete response data is sufficient to quantify the aggressiveness of the disease, further investigations are necessary to study how an intensive early sampling strategy may prospectively improve risk assessment and help to optimize individual treatments.
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Affiliation(s)
- H Hoffmann
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - C Thiede
- Medical Clinic and Polyclinic I, University Hospital Dresden Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - I Glauche
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - M Bornhaeuser
- Medical Clinic and Polyclinic I, University Hospital Dresden Carl Gustav Carus, TU Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - I Roeder
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
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