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

• DOI: 10.1101/2021.02.17.431221
• Publication Date: 2021-02-18T00:41:33Z
• Authors: Yuji Okamoto; Mitsuhito Hirano; Kai Morino; Masashi K. Kajita; Shinji Nakaoka; Mayuko Tsuda; Kei-ji Sugimoto; Shigehisa Tamaki; Junichi Hisatake; Hisayuki Yokoyama; Tadahiko Igarashi; Atsushi Shinagawa; Takeaki Sugawara; Satoru Hara; Kazuhisa Fujikawa; Seiichi Shimizu; Toshiaki Yujiri; Hisashi Wakita; Kaichi Nishiwaki; Arinobu Tojo; Kazuyuki Aihara
• Keywords: 0301 basic medicine; 03 medical and health sciences; Pyrimidines; QH301-705.5; Leukemia, Myelogenous, Chronic, BCR-ABL Positive; Fusion Proteins, bcr-abl; Humans; Biology (General); Protein Kinase Inhibitors; Article; 3. Good health

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