Dynamics of chronic myeloid leukemia response to dasatinib, nilotinib, and high-dose imatinib

Abstract

Treatment with the tyrosine kinase inhibitor imatinib is the standard of care for newly diagnosed patients with chronic myeloid leukemia. In recent years, several second-generation inhibitors - such as dasatinib and nilotinib - have become available: these promise to overcome some of the mutations associated with acquired resistance to imatinib. Despite eliciting similar clinical responses, the molecular effects of these agents on different subpopulations of leukemic cells remain incompletely understood. Furthermore, the consequences of using high-dose imatinib therapy have not been investigated in detail. Here we utilized clinical data from patients treated with dasatinib, nilotinib, or high-dose imatinib, together with a statistical data analysis and mathematical modeling approach, to investigate the molecular treatment response of leukemic cells to these agents. We found that these drugs elicit very similar responses if administered front-line. However, patients display significantly different kinetics when treated second-line, both in terms of differences between front-line and second-line treatment for the same drug, and among agents when used as second-line. We then utilized a mathematical framework describing the behavior of four differentiation levels of leukemic cells during therapy to predict the treatment response kinetics for the different cohorts of patients. The dynamics of BCR-ABL1 clearance observed in our study suggest that the use of standard or high-dose imatinib or a second-generation tyrosine kinase inhibitor such as nilotinib or dasatinib elicits similar responses when administered as front-line therapy for patients with chronic myeloid leukemia in chronic phase.

Figure 1.

The high-dose imatinib cohort. The figure shows data from four representative patients from the high-dose imatinib cohort, displaying the ratio of BCR-ABL1 to ABL1 plus one over time measured in months (broken line) as well as the exponential model (solid line).

Figure 2.

The nilotinib cohort. The figure shows data from four representative patients from the nilotinib cohort, displaying the ratio of BCR-ABL1 to ABL1 plus one over time measured in months (broken line) as well as the exponential model (solid line).

Figure 3.

The dasatinib cohort. The figure shows data from four representative patients from the dasatinib cohort, displaying the ratio of BCR-ABL1 to ABL1 plus one over time measured in months (broken line) as well as the exponential model (solid line).

Figure 4.

A mathematical framework accurately predicts the dynamics of treatment responses of cohorts of patients treated with dasatinib, nilotinib and high-dose imatinib separately. The panels display the median (orange circles) and quartiles of the dasatinib, nilotinib and high-dose imatinib response data together with the results of the mathematical framework (blue curves, see Online Supplementary Table S1). (A) Median plots and results of the mathematical framework for the dasatinib response data. (B) Median plots and results of the mathematical framework for the nilotinib response data. (C) Median plots and results of the mathematical framework for the high-dose imatinib response data. Based on the model presented in the Online Supplement, the mathematical model prediction is given by y₃/(2x₃+ y₃). Here x₃ and y₃ denote the abundance of normal and leukemic terminally differentiated cells. Parameter values are d₀ = 0.0005, d₃ = 1, r_x = 0.008, r_y = 0.01, p_x = 1.5×10⁻⁵, p_y = 1.9×10⁻6, a_x = 0.35, b_x = 5.5, c_x = 100, a_y = 2a_x, b_y = 1.5*b_x, c_y = c_x, c′_y = c_y, r′_y = r_y/15. For the dasatinib cohort, d₁ = 0.0053, d₂ = 0.0394, a′_y = a_y/200, b′_y = b_y/300; for the nilotinib cohort d₁ = 0.0028, d₂ = 0.0442, a′_y = a_y/400, b′_y = b_y/600; for the high-dose imatinib cohort, d₁ = 0.0035, d₂ = 0.055, a′_y = a_y/400, b′_y = b_y/600. Apart from the dimension-less parameters, all values are given in units per day. Note that these parameter choices represent only one example that can recapitulate the dynamics of the treatment response seen in the clinic; other choices are possible.