scholarly journals Tyrosine kinase inhibitor resistance in pediatric chronic myeloid leukemia: a case report

2020 ◽  
Vol 29 (4) ◽  
pp. 427-30
Author(s):  
Mururul Aisyi ◽  
Ayu Hutami Syarif ◽  
Nur Asih ◽  
Agus Kosasih

Pediatric chronic myeloid leukemia (CML) is a hematopoietic malignancy, treated by tyrosine kinase inhibitor (TKI). Previously, imatinib resistance in CML was treated with nilotinib as a second line. However, in Indonesia, where the options of TKIs are limited, no case has been reported. We describe TKI-resistance of a pediatric CML case in Dharmais Cancer Hospital, Jakarta. A 17-year-old boy presented with loss of complete hematologic response after 4 years of imatinib treatment. Diagnosis of relapsed CML with blast crisis was confirmed, and nilotinib was given accordingly. He achieved hematological and optimal response after 2 weeks and 3 months of treatment, respectively. However, in the 12-month evaluation, he failed to achieve major molecular response and acquired the second resistance to TKI. Since imatinib resistance marks the poor prognosis, initial optimal response of nilotinib treatment remains inconclusive to predict the final outcome.

2017 ◽  
Vol 92 (10) ◽  
pp. E602-E604 ◽  
Author(s):  
Adi J. Klil-Drori ◽  
Hui Yin ◽  
Laurent Azoulay ◽  
Michaël Harnois ◽  
Michel-Olivier Gratton ◽  
...  

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3099-3099 ◽  
Author(s):  
Ingmar Glauche ◽  
Hendrik Liebscher ◽  
Christoph Baldow ◽  
Matthias Kuhn ◽  
Philipp Schulze ◽  
...  

Abstract Predicting minimal residual disease (MRD) levels in tyrosine kinase inhibitor (TKI)-treated chronic myeloid leukemia (CML) patients is of major clinical relevance. The reason is that residual leukemic (stem) cells are the source for both, potential relapses of the leukemicclone but also for its clonal evolution and, therefore, for the occurrence of resistance. The state-of-the art method for monitoring MRD in TKI-treated CML is the quantification of BCR-ABL levels in the peripheral blood (PB) by PCR. However, the question is whether BCR-ABL levels in the PB can be used as a reliable estimate for residual leukemic cells at the level of hematopoietic stem cells in the bone marrow (BM). Moreover, once the BCR-ABL levels have been reduced to undetectable levels, information on treatment kinetics is censored by the PCR detection limit. Clearly, BCR-ABL negativity in the PB suggests very low levels of residual disease also in the BM, but whether the MRD level remains at a constant level or decreases further cannot be read from the BCR-ABL negativity itself. Thus, also the prediction of a suitable time point for treatment cessation based on residual disease levels cannot be obtained from PCR monitoring in the PB and currently remains a heuristic decision. To overcome the current lack of a suitable biomarker for residual disease levels in the BM, we propose the application of a computational approach to quantitatively describe and predict long-term BCR-ABL levels. The underlying mathematical model has previously been validated by the comparison to more than 500 long-term BCR-ABL kinetics in the PB from different clinical trials under continuous TKI-treatment [1,2,3]. Here, we present results that show how this computational approach can be used to estimate MRD levels in the BM based on the measurements in the PB. Our results demonstrate that the mathematical model can quantitatively reproduce the cumulative incidence of the loss of deep and major molecular response in a population of patients, as published by Mahon et al. [4] and Rousselot et al. [5]. Furthermore, to demonstrate how the model can be used to predict the BCR-ABL levels and to estimate the molecular relapse probability of individual patients, we compare simulation results with more than 70 individual BCR-ABL-kinetics. For this analysis we use patient data from different clinical studies (e.g. EURO-SKI: NCT01596114, STIM(s): NCT00478985, NCT01343173) where TKI-treatment had been stopped after prolonged deep molecular response periods. Specifically, we propose to combine statistical (non-linear regression) and mechanistic (agent-based) modelling techniques, which allows us to quantify the reliability of model predictions by confidence regions based on the quality (i.e. number and variance) of the clinical measurements and on the particular kinetic response characteristics of individual patients. The proposed approach has the potential to support clinical decision making because it provides quantitative, patient-specific predictions of the treatment response together with a confidence measure, which allows to judge the amount of information that is provided by the theoretical prediction. References [1] Roeder et al. (2006) Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications, Nat Med 12(10):1181-4 [2] Horn et al. (2013) Model-based decision rules reduce the risk of molecular relapse after cessation of tyrosine kinase inhibitor therapy in chronic myeloid leukemia, Blood 121(2):378-84. [3] Glauche et al. (2014) Model-Based Characterization of the Molecular Response Dynamics of Tyrosine Kinase Inhibitor (TKI)-Treated CML Patients a Comparison of Imatinib and Dasatinib First-Line Therapy, Blood 124:4562 [4] Mahon et al. (2010) Discontinuation of imatinib in patients with chronic myeloid leukaemia who have maintained complete molecular remission for at least 2 years: the prospective, multicentre Stop Imatinib (STIM) trial. Lancet Oncol 11(11):1029-35 [5] Rousselot 
et al. (2014) Loss of major molecular response as a trigger for restarting TKI therapy in patients with CP- CML who have stopped Imatinib after durable undetectable disease, JCO 32(5):424-431 Disclosures Glauche: Bristol Meyer Squib: Research Funding. von Bubnoff:Amgen: Honoraria; Novartis: Honoraria, Research Funding; BMS: Honoraria. Saussele:ARIAD: Honoraria; Novartis: Honoraria, Other: Travel grants, Research Funding; Pfizer: Honoraria, Other: Travel grants; BMS: Honoraria, Other: Travel grants, Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Novartis: Honoraria, Research Funding. Guilhot:CELEGENE: Consultancy. Mahon:NOVARTIS PHARMA: Honoraria, Research Funding; BMS: Honoraria; PFIZER: Honoraria; ARIAD: Honoraria. Roeder:Bristol-Myers Squibb: Honoraria, Research Funding.


2016 ◽  
Vol 136 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Fiorenzo Santoleri ◽  
Ruggero Lasala ◽  
Elena Ranucci ◽  
Gaetano La Barba ◽  
Roberto Di Lorenzo ◽  
...  

Objective: Adherence to tyrosine kinase inhibitor treatment is a significant factor in the achievement of a good clinical response in chronic myeloid leukemia (CML). The aim of this retrospective study is to investigate 1- and 2-year medication adherence to imatinib treatment, linking adherence rates with the clinical outcome, in accordance with European LeukemiaNet Recommendations for the management of CML. We have tried to find a cutoff value for adherence in order to achieve a good clinical outcome. Methods: The method used to calculate medication adherence was the ratio between the received and the prescribed daily dose. Results: We observed the levels of mean adherence for each of the following response groups (in years 1 and 2, respectively): complete response (0.96, 0.95), MR4.5 (1.00, -), MR4 (0.93, 0.91), major molecular responses (0.96, 0.97), warning (0.91, 0.89) and failure (0.79, 0.84). Conclusion: Results show that the higher the adherence, the lower the level of BCR-ABL1. Furthermore, using cutoffs ≥0.9, outcomes were significantly improved compared to those with cutoffs <0.90. This value of adherence is in line with previous publications.


2017 ◽  
Vol 89 (12) ◽  
pp. 86-96 ◽  
Author(s):  
A G Turkina ◽  
E Yu Chelysheva ◽  
V A Shuvaev ◽  
G A Gusarova ◽  
A V Bykova ◽  
...  

Aim. To assess the results of following up patients with chronic myeloid leukemia (CML) and a deep molecular response (MR) without tyrosine kinase inhibitor (TKI) therapy. Subjects and methods. The reasons for TKI discontinuation in 70 patients with CML and a deep MR of more than 1 year’s duration were adverse events, pregnancy, and patients’ decision. Information was collected retrospectively and prospectively in 2008-2016. Results. The median follow-up after TKI therapy discontinuation was 23 months (2 to 100 months). At 6, 12 and 24 months after TKI therapy discontinuation, the cumulative incidence of major MR (MMR) loss was 28, 41 and 48%, respectively; the survival rates without TKI therapy were 69, 50, and 39%, respectively. MMR loss was noted in 28 (88%) patients at 12 months; it was not seen without TKI therapy at 2-year follow-up. Deaths due to CML progression were absent. The Sokal risk group was a reliable factor influencing MMR loss (p ≤ 0.05). The cumulative recovery rate for deep MR after resumption of TKI use was 73 and 100% at 12 and 24 months, respectively, with a median follow-up of 24 months (1 to 116 months). Deep MR recovered at a later time when the therapy was resumed more than 30 days after MMR loss. Conclusion. Safe follow-up is possible in about 50% of the patients with CML and stable deep MRs without TKI therapy. The introduction of this approach into clinical practice requires regular molecular genetic monitoring and organizational activities. Biological factors in maintaining remission after TKI discontinuation need to be separately studied.


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