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基于模型的决策规则可降低慢性髓性白血病停止酪氨酸激酶抑制剂治疗后分子复发的风险。

Model-based decision rules reduce the risk of molecular relapse after cessation of tyrosine kinase inhibitor therapy in chronic myeloid leukemia.

机构信息

Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, Leipzig, Germany.

出版信息

Blood. 2013 Jan 10;121(2):378-84. doi: 10.1182/blood-2012-07-441956. Epub 2012 Nov 21.

DOI:10.1182/blood-2012-07-441956
PMID:23175686
Abstract

Molecular response to imatinib (IM) in chronic myeloid leukemia (CML) is associated with a biphasic but heterogeneous decline of BCR-ABL transcript levels. We analyzed this interindividual heterogeneity and provide a predictive mathematical model to prognosticate the long-term response and the individual risk of molecular relapse on treatment cessation. The parameters of the model were determined using 7-year follow-up data from a randomized clinical trial and validated by an independent dataset. Our model predicts that a subset of patients (14%) achieve complete leukemia eradication within less than 15 years and could therefore benefit from discontinuation of treatment. Furthermore, the model prognosticates that 31% of the patients will remain in deep molecular remission (MR(5.0)) after treatment cessation after a fixed period of 2 years in MR(5.0), whereas 69% are expected to relapse. As a major result, we propose a predictor that allows to assess the patient-specific risk of molecular relapse on treatment discontinuation and to identify patients for whom cessation of therapy would be an appropriate option. Application of the suggested rule for deciding about the time point of treatment cessation is predicted to result in a significant reduction in rate of molecular relapse.

摘要

慢性髓性白血病 (CML) 患者对伊马替尼 (IM) 的分子反应与 BCR-ABL 转录本水平的双峰但异质性下降相关。我们分析了这种个体间的异质性,并提供了一个预测性的数学模型,以预测治疗停止后的长期反应和分子复发的个体风险。该模型的参数是使用来自随机临床试验的 7 年随访数据确定的,并通过独立数据集进行了验证。我们的模型预测,一小部分患者(14%)在不到 15 年内实现完全白血病清除,因此可以从治疗中断中受益。此外,该模型预测,在达到深度分子缓解 (MR(5.0)) 后 2 年的固定时间内,31%的患者在治疗停止后仍将处于 MR(5.0),而 69%的患者预计会复发。作为一个主要结果,我们提出了一个预测因子,可以评估患者在治疗停止时发生分子复发的个体风险,并识别出适合停止治疗的患者。应用建议的规则来决定治疗停止的时间点,预计将显著降低分子复发的发生率。

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