Strategic Treatment Interruptions During Imatinib Treatment of Chronic Myelogenous Leukemia

Paquin, Dana C.; Kim, Peter; Lee, Peter P.; Levy, Doron

doi:10.1007/s11538-010-9553-0

Cited by 17 publications

(23 citation statements)

References 21 publications

(64 reference statements)

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“…14 Many additional models have been suggested in the last decade. Recent articles include work by Michor and coworkers, 15,16 Dingli and coworkers, 17 Roeder and coworkers, 18 Levy and coworkers, 19,20 and many others (reviewed in Whichard et al 9 and Roeder and d'Inverno, 21 and in supplemental Section 1, available on the Blood Web site; see the Supplemental Materials link at the top of the online article). Importantly, such models can generate useful new insights by quantitatively interrelating datasets that seem offhand to have little connection with each other, let alone to have systematic numerical correlations.…”

Section: Cell Population Dynamicsmentioning

confidence: 99%

“…Latency times do, however, enter, directly or indirectly, into many clinically relevant predictions, 12 especially for the possibility of relapse after discontinuation or interruption of kinase inhibitor treatment subsequent to complete molecular remission. 20 Most quantitative CML models imply that, if T has a large mean and/or SD, relapses can still occur long after the treatment has stopped. And some models further predict that a large latency time SD is associated with a higher probability of developing TKI resistance (reviewed in Katouli and Komarova 68 ; the mathematical rationale for this association is outlined in supplemental Section 1.4).…”

Section: Importance For CML Modelsmentioning

confidence: 99%

“…20,46 Reconciling such differences among the models credibly calls for comparisons using common datasets, which can only be done after each model is credibly calibrated.…”

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confidence: 99%

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Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

Radivoyevitch

Hlatky

Landaw

et al. 2012

Blood

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Mathematical models of chronic myeloid leukemia (CML) cell population dynamics are being developed to improve CML understanding and treatment. We review such models in light of relevant findings from radiobiology, emphasizing 3 points. First, the CML models almost all assert that the latency time, from CML initiation to diagnosis, is at most ϳ 10 years. Meanwhile, current radiobiologic estimates, based on Japanese atomic bomb survivor data, indicate a substantially higher maximum, suggesting longer-term relapses and extra resistance mutations. Second, different CML models assume different numbers, between 400 and 10 6 , of normal HSCs. Radiobiologic estimates favor values > 10 6 for the number of normal cells (often assumed to be the HSCs) that are at risk for a CML-initiating BCR-ABL translocation. Moreover, there is some evidence for an HSC dead-band hypothesis, consistent with HSC numbers being very different across different healthy adults. Third, radiobiologists have found that sporadic (background, agedriven) chromosome translocation incidence increases with age during adulthood. BCR-ABL translocation incidence increasing with age would provide a hitherto underanalyzed contribution to observed background adult-onset CML incidence acceleration with age, and would cast some doubt on stage-number inferences from multistage carcinogenesis models in general. (Blood. 2012; 119(19):4363-4371) IntroductionChronic myeloid leukemia (CML) is characterized by Ph ϩ cells, that is, cells having a Philadelphia (BCR-ABL) chromosome translocation. 1,2 Treatment with the tyrosine kinase inhibitor (TKI) imatinib mesylate ("imatinib"), which suppresses bcr-abl oncoprotein action, 3 improves patient prognosis dramatically. 4 However, in some cases this treatment fails, a problem mitigated but not fully solved by the use of more recently developed TKI. 5,6 Moreover, many patients may need to continue TKI treatment indefinitely to avoid relapse. 7 CML is one of the best understood cancers; it has a simpler etiology than most cancers 8 and its time course is comparatively easy to monitor in the clinic. 9,10 Consequently, despite being much less prevalent than major solid tumors, CML has often been regarded as a kind of "model organism" for quantitative modeling of human carcinogenesis. 11,12 CML cell population dynamicsIn this review, we emphasize how radiobiologic studies impact CML models grounded in understanding underlying cell population dynamics. These models track CML time evolution by differential equations and/or stochastic formalisms. Such biologically based quantitative models are more ambitious, more comprehensive, and as yet less definitive than models often used in statistical analyses, which emphasize correlations analyzed by adjusting parameters in functions chosen mainly for mathematical convenience.After work by Rubinow and Lebowitz 13 on hematopoiesis, biologically based, mathematical CML models were pioneered by Clarkson and coworkers. 14 Many additional models have been suggested in the last decade. Recent a...

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Section: Cell Population Dynamicsmentioning

confidence: 99%

Section: Importance For CML Modelsmentioning

confidence: 99%

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Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

Radivoyevitch

Hlatky

Landaw

et al. 2012

Blood

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“…In [12], Paquin et al used the delay differential equations model presented in [6] to study the effectiveness of strategic treatment interruptions as a potential clinical approach to enhancing the effects of imatinib treatment for CML. The authors demonstrated that treatment programs that involve strategic treatment interruptions in which imatinib treatment is temporarily stopped in order to leverage the antileukemia immune response may prevent leukemia from relapsing and may prevent remission for significantly longer than continuous imatinib treatment, and, in many cases, strategic treatment interruptions may completely eliminate leukemic cells from the body.…”

Section: Introductionmentioning

confidence: 99%

An analysis of strategic treatment interruptions during imatinib treatment of chronic myelogenous leukemia with imatinib-resistant mutations

Paquin

Sacco

Shamshoian

2015

Mathematical Biosciences

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Chronic myelogenous leukemia (CML) is a cancer of the white blood cells that results from increased and uncontrolled growth of myeloid cells in the bone marrow and the accumulation of these cells in the blood. The most common form of treatment for CML is imatinib, a tyrosine kinase inhibitor. Although imatinib is an effective treatment for CML and most patients treated with imatinib do attain some form of remission, imatinib does not completely eradicate all leukemia cells, and if treatment is stopped, all patients eventually relapse (Cortes, 2005). In Kim (2008), the authors developed a mathematical model for the dynamics of CML under imatinib treatment that incorporates the anti-leukemia immune response, and in Paquin (2011), the authors used this mathematical model to study strategic treatment interruptions as a potential therapeutic strategy for CML patients. Although the authors presented the results of several numerical simulations in Paquin (2011), the studies in that work did not include the possibility of imatinib-resistant mutations or an initial population of imatinib-resistant leukemia cells. As resistance is a significant consideration in any drug treatment, it is important to study the efficacy of the strategic treatment interruption plan in the presence of imatinib resistance. In this work, we modify the delay differential equations model of Kim (2008), Paquin (2011) to include the possibility of imatinib resistance, and we analyze strategic treatment interruptions as a potential therapeutic tool in the case of patients with imatinib-resistance leukemia cells.

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“…Several models for the description of the origin and evolution of CML on a cellular level have been proposed, see for example, and the literature therein. In more general terms, models for the proliferation of metastases are given in , and, with respect to the signaling between cells, which is considered a crucial part when it comes to understanding tumors, in .…”

Section: Introductionmentioning

confidence: 99%

Bifurcation patterns in generalized models for the dynamics of normal and leukemic stem cells with signaling

Flå

Rupp

Woywod

2014

Math Methods in App Sciences

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The dynamics of normal and leukemic stem cell clones competing for resources in the same bone marrow niche can be modeled in a systems biology approach. We derive three related models describing the time evolution of normal and chronic myelogenous leukemia (CML) stem cells. The approach is based on the idea that stem cell proliferation is regulated by feedback mechanisms. We assume here that the growth of stem cell clones can be approximated by Tsallis functions, which depend on population numbers to ensure the communication between different stem cell strains. Of particular interest is the influence of medication, for example, by the CML drug imatinib, on the stem cell dynamics, which can be simulated by a variation of the parameterization of the differential equation systems. The basic 2D model represents the contest between cycling normal and wild‐type CML stem cells. In addition, extension of the basic model (i) by coupled reservoirs of quiescent stem cells and (ii) by a third stem cell species, which could be interpreted as an imatinib‐resistant mutant, is investigated. We analyze the global dynamics of the corresponding 2D, 3D, and 4D equation systems by analytic means and provide a complete map of the bifurcation landscape that occurs for changing values of the respective signaling strengths and growth or death parameters. This includes a complete classification of equilibria and their linear and non‐linear Lyapunov stability. Copyright © 2014 John Wiley & Sons, Ltd.

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Strategic Treatment Interruptions During Imatinib Treatment of Chronic Myelogenous Leukemia

Cited by 17 publications

References 21 publications

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

An analysis of strategic treatment interruptions during imatinib treatment of chronic myelogenous leukemia with imatinib-resistant mutations

Bifurcation patterns in generalized models for the dynamics of normal and leukemic stem cells with signaling

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