Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular Response

Abstract Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase 1,2 . ABL1 -selective tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML 3–7 . The ultimate goal of CML treatment is likely to be TKI-free maintenance of deep molecular response (DMR), which is defined by quantitative measurement of BCR-ABL1 transcripts on the international scale (IS) 8 , and durable DMR is a prerequisite to reach this goal 9 . Thus, an algorithm to enable the early prediction of DMR achievement on TKI therapy is quite valuable. Here, we show that our mathematical framework based on a clinical trial dataset 10 can accurately predict the response to frontline nilotinib. We found that our simple dynamical model can predict nilotinib response by using two common laboratory findings (observation values): IS 11,12 and white blood cell (WBC) count. Furthermore, our proposed method identified patients who failed to achieve DMR with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs in clinical practice. Significance Statement Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. The goal of this treatment is the sequential achievement of deep molecular response (DMR). Tyrosine kinase inhibitors (TKIs) are effective in the reduction because they inhibit CML cell proliferation. However, because of individual differences in the TKI efficacy, some patients are unable to achieve DMR, so that early prediction of DMR reachability is necessary for personalized medicine. By combining time series analysis and mathematical modeling, we developed a mathematical method that accurately predicts patients who do not achieve DMR in the early stages of TKI administration. Our prediction method gives a basis of effective personalized treatments for CML patients.