Quantitative prediction of long-term molecular response in TKI-treated CML – Lessons from an imatinib versus dasatinib comparison

Glauche, Ingmar; Kühn, Matthias; Baldow, Christoph; Schulze, Philipp; Rothe, Tino; Liebscher, Hendrik; Roy, Amit; Wang, Xiaoning; Roeder, Ingo

doi:10.1038/s41598-018-29923-4

Cited by 21 publications

(29 citation statements)

References 41 publications

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“…Previous studies have shown that early intervention may be considered when patients have suboptimal cytogenetic (27) Non-hematologic AEs responses on first-line imatinib [15][16][17][18][19][20]. Quintas-Cardama et al retrospectively demonstrated that response rates and survival were most favorable when dasatinib was administered early after imatinib failure, with 72% of patients who received dasatinib after loss of major cytogenetic response (MCyR) to imatinib achieving CCyR, compared with 42% of patients who were treated after loss of both MCyR and CHR [15].…”

Section: Discussionmentioning

confidence: 99%

“…In this first prospective randomized study to explore the benefit of early switching to dasatinib, the greater response rates with dasatinib and the observation that approximately half of patients who did not achieve EMR with imatinib subsequently met treatment failure criteria and crossed over to the dasatinib arm, provide further support for using strategies that increase the probability of achieving optimal responses early on in the treatment paradigm. As MMR rates are known to improve with longer treatment duration [27], additional follow-up will help to determine if the rates continue to favor the use of dasatinib and whether this early benefit translates into a greater probability of achieving DMR and improved PFS and OS. In summary, initial findings from DASCERN provide new insight into the potential benefit of switching to dasatinib in patients failing to achieve important treatment milestones with first-line imatinib.…”

Section: Discussionmentioning

confidence: 99%

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Dasatinib vs. imatinib in patients with chronic myeloid leukemia in chronic phase (CML-CP) who have not achieved an optimal response to 3 months of imatinib therapy: the DASCERN randomized study

et al. 2020

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Early molecular response is associated with improved probability of deep molecular response and superior survival in patients with CML-CP. However,~1 in 3 patients on first-line imatinib do not achieve this threshold. The phase 2b DASCERN trial (NCT01593254) assessed the outcome of early switch to dasatinib in patients with suboptimal response to first-line imatinib. Adult patients with CML-CP were randomized (2:1) to receive 100 mg dasatinib (n = 174) or continue imatinib at ≥400 mg (n = 86). The primary endpoint was the rate of major molecular response (MMR) at 12 months, which was 29% (dasatinib) and 13% (imatinib; P = 0.005). After ≥2 years of follow-up, 45 patients (52%) randomized to continue imatinib had crossed over to dasatinib. Considering treatment crossover, the 2-year cumulative MMR rate was 64% with dasatinib and 41% with imatinib (66% and 67%, respectively by intent-to-treat). Adverse events were consistent with the established safety profiles of both drugs. The results of this first prospective study support early monitoring of patients treated with first-line imatinib, and suggest that switching to dasatinib in cases of suboptimal response may offer clinical benefit. Further follow-up is needed to assess the long-term clinical benefit of early switching.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dasatinib vs. imatinib in patients with chronic myeloid leukemia in chronic phase (CML-CP) who have not achieved an optimal response to 3 months of imatinib therapy: the DASCERN randomized study

et al. 2020

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“…log $% (('() − +',) / +',) = ,)+123(4) = log $% 5+6 78 + '6 :8 ;. Each patient's BCR-ABL dynamics is, therefore, defined by the parameters A, B, α and β, which are estimated by a maximum likelihood (ML) approach (R version 3.4.4, package maxLik version 1.3.4) as described in (36). To obtain uniquely identifiable solutions in the ML procedure, we restrict < < >, according to the selection of patients showing a rapid initial decline < compared to a slower long-term decay >.…”

Section: Bi-exponential Parameterization Of Treatment Responsementioning

confidence: 99%

Inferring immunological control mechanisms from TKI dose alterations in CML patients

Haehnel

Baldow

Fassoni

et al. 2019

Preprint

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Recent clinical findings in chronic myeloid leukemia (CML) patients suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitors (TKI) treatment substantially depend on an individual, leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will most likely remain in a long-term treatment free remission (TFR). Here, we use a mathematical model for CML, which explicitly includes an anti-leukemic (presumably immunological) effect and apply it to a set of patients (n=60) for whom BCR-ABL/ABL time courses had been quantified before and after TKI stop. We demonstrate that such a feedback control is conceptually necessary to explain long-term remission as observed in about half of the patients. Based on simulation results we classify the patient data sets into three different groups according to their predicted immune system configuration. While one class of patients requires a complete CML eradication to achieve TFR, other patients are able to control the leukemia after treatment cessation. Among them, we identified a third class of patients, which only maintains TFR if an optimal balance between leukemia abundance and immunological activation is achieved before treatment cessation. Further, we demonstrate that the immune response classification of the patients cannot be obtained solely from BCR-ABL measurements before treatment cessation. However, our results strongly suggest that changes in the BCR-ABL dynamics arising after system perturbations, such as TKI dose reduction, holds the information to predict the individual outcome after treatment cessation.

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“…tumour aggressiveness, chemosensitivity, pharmacokinetics and -dynamics of anticancer drugs, risk factors), which are difficult to predict empirically. Hence, we sought to establish a number of disease and treatment models for haematological malignancies, such as high-grade Non-Hodgkin's lymphomas (NHL) ( [1][2][3][4]) and chronic myeloid leukaemia (CML) ( [5][6][7][8]). In addition to providing a better general understanding of the disease mechanisms and treatment effects, these models identify patient-specific parameterizations, which are essential to provide individually tailored predictions.…”

Section: Introductionmentioning

confidence: 99%

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Hoffmann

Cazemier

Baldow

et al. 2020

BMC Med Inform Decis Mak

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Background: Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. Results: In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. Conclusions: By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

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Quantitative prediction of long-term molecular response in TKI-treated CML – Lessons from an imatinib versus dasatinib comparison

Cited by 21 publications

References 41 publications

Dasatinib vs. imatinib in patients with chronic myeloid leukemia in chronic phase (CML-CP) who have not achieved an optimal response to 3 months of imatinib therapy: the DASCERN randomized study

Dasatinib vs. imatinib in patients with chronic myeloid leukemia in chronic phase (CML-CP) who have not achieved an optimal response to 3 months of imatinib therapy: the DASCERN randomized study

Inferring immunological control mechanisms from TKI dose alterations in CML patients

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

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