Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan.
Division of Molecular Therapy, Advanced Clinical Research Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
NPJ Syst Biol Appl. 2022 Oct 13;8(1):39. doi: 10.1038/s41540-022-00248-3.
Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal 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.
慢性髓性白血病(CML)是一种由 BCR-ABL1 酪氨酸激酶引起的骨髓增殖性疾病。尽管包括尼洛替尼在内的 ABL1 特异性酪氨酸激酶抑制剂(TKI)显著改善了 CML 患者的预后,但 TKI 的疗效取决于个体患者。在这项工作中,我们发现可以使用我们的简单动力学模型的估计参数来对具有不同尼洛替尼反应的患者进行分类,该模型具有两个常见的实验室发现。此外,我们提出的方法根据在尼洛替尼治疗期间仅在三个初始时间点收集的数据,以高精度识别未达到治疗目标的患者。由于我们的模型依赖于 TKI 反应的一般特性,因此我们的框架将适用于接受一线尼洛替尼或其他 TKI 治疗的 CML 患者。