1
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
Collapse
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
| |
Collapse
|
2
|
Narendra G, Raju B, Verma H, Silakari O. Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis. Med Oncol 2021; 38:123. [PMID: 34491453 DOI: 10.1007/s12032-021-01569-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/20/2021] [Indexed: 12/27/2022]
Abstract
Cyclophosphamide (CP), an important alkylating agent which is used in the treatment therapy for chronic myeloid leukemia (CML). However, acquired drug resistance owing to the inactivation of its active metabolite aldophosphamide via tumoral-overexpressing aldehyde dehydrogenase (ALDH1A1) is one of the major issues with the CP therapy. However, the underlying mechanism of ALDH1A1 overexpression in cancer cells remains poorly defined. Therefore, the current study focused on analyzing the ALDH1A1-overexpressing microarray data for CP resistance and CP-sensitive CML cell lines. In this study, the microarray dataset was obtained from Gene Expression Omnibus GEO. The GEO2R tool was used to identify Differentially Expressing Genes (DEGs). Further, protein-protein interaction (PPI) network of DEGs were constructed using STRING database. Finally, Hub gene-miRNA-TFs interaction were constructed using miRNet tool. A total of 749 DEGs including 387 upregulated and 225 downregulated genes were identified from this pool of microarray data. The construction of DEGs network resulted in identification of three genes including ZEB2, EZH2, and MUC1 were found to be majorly responsible for ALDH1A1 overexpression. miRNA analysis identified that, hsa-mir-16-5p and hsa-mir-26a-5p as hub miRNA which are commonly interacting with maximum target genes. Additionally, drug-gene interaction analysis was performed to identify drugs which are responsible for ALDH1A1 expression. The entire study may provide a deeper understanding about ALDH1A1 regulatory genes responsible for its overexpression in CP resistance cancer. This understanding may be further explore for developing possible co-therapy to avoid the ALDH1A1-mediated CP resistance.
Collapse
Affiliation(s)
- Gera Narendra
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Baddipadige Raju
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Himanshu Verma
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Om Silakari
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
| |
Collapse
|