"Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy"
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, exhibits variable responses to TKI treatment, predicts key factors of refractory response to TKI treatment, and predicts potential combination therapy efficacy. Recent experiments reveal that interactions and competition between different cellular compartments and between normal and BCR-ABL1-expressing cells form a threshold that determines whether the malignant cells can expand and cause leukemia. To capture these experimental dynamics, we found it necessary to incorporate additional biological factors through the introduction of new cell types and interactions. We applied an adapted model selection scheme to explore the unknown cell-cell interaction space and find subsets of models consistent with experimental dynamics. We analyzed common motifs across experimentally consistent models and identified interactions as targets for experimental design to further narrow the valid models.