Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy
- University of California, Irvine, United States
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 (TKI) 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 to 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.
Data availability
Modelling code and parameter set data are available in a Github repository. Patient data is unavailable publicly as it could be used to potentially identify the patients. Deidentified raw patient transcript data will be made available to qualified researchers (academic or industry) upon request to Dr. Van Etten at vanetten@hs.uci.edu.
Article and author information
Author details
Jonathan Rodriguez
Funding
National Institutes of Health (1U54CA217378-01A1)
- Arthur D Lander
- John Lowengrub
- Richard A Van Etten
National Institutes of Health (P30CA062203)
- Arthur D Lander
- John Lowengrub
- Richard A Van Etten
National Institutes of Health (R01 CA090576)
- Richard A Van Etten
National Science Foundation (DMS-1763272)
- Arthur D Lander
- John Lowengrub
National Science Foundation (DMS-1936833)
- John Lowengrub
National Science Foundation (DMS-1953410)
- John Lowengrub
National Science Foundation (GRFP 16-588)
- Abdon Iniguez
Simons Foundation (594598QN)
- Arthur D Lander
- John Lowengrub
National Institute of General Medical Sciences (GM136624)
- Jonathan Rodriguez
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocol (AUP-19-159) of the University of California, Irvine.
Reviewing Editor
- Stacey D Finley, University of Southern California, United States
Version history
- Received: October 12, 2022
- Preprint posted: October 14, 2022 (view preprint)
- Accepted: April 26, 2023
- Accepted Manuscript published: April 28, 2023 (version 1)
- Accepted Manuscript updated: May 3, 2023 (version 2)
- Accepted Manuscript updated: May 3, 2023 (version 3)
- Accepted Manuscript updated: May 4, 2023 (version 4)
- Version of Record published: May 25, 2023 (version 5)
Copyright
© 2023, Rodriguez et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy
eLife 12:e84149.
https://doi.org/10.7554/eLife.84149
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