Emergence of heterogeneity in acute leukemias

Stiehl, Thomas; Lutz, Christoph; Marciniak–Czochra, Anna

doi:10.1186/s13062-016-0154-1

Cited by 33 publications

(43 citation statements)

References 65 publications

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“…In the stable hematopoietic steady state the numbers of stem cells and mature cells are taken to be approximately 10 4 and 4 • 10 10 , respectively, which are compromises between reported values [26,27,25,62,63,20]. In the final stage of full blown cancer, the number of hematopoietic cells is vanishing and the cancer cells will approach a stable steady state with a higher amount of cells than in the healthy steady state.…”

Section: Model Validationmentioning

confidence: 99%

Bridging blood cancers and inflammation: The reduced Cancitis model

Ottesen

Pedersen

Sajid

et al. 2019

Journal of Theoretical Biology

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A novel mechanism-based model-the Cancitis model-describing the interaction of blood cancer and the inflammatory system is proposed, analyzed and validated. The immune response is divided into two components, one where the elimination rate of malignant stem cells is independent of the level of the blood cancer and one where the elimination rate depends on the level of the blood cancer. A dimensional analysis shows that the full 6-dimensional system of nonlinear ordinary differential equations may be reduced to a 2-dimensional system-the reduced Cancitis model-using Fenichel theory. The original 18 parameters appear in the reduced model in 8 groups of parameters. The reduced model is analyzed. Especially the steady states and their dependence on the exogenous inflammatory stimuli are analyzed. A semi-analytic investigation reveals the stability properties of the steady states. Finally, positivity of the system and the existence of an attracting trapping region in the positive octahedron guaranteeing global existence and uniqueness of solutions are proved. The possible topologies of the dynamical system are completely determined as having a Janus structure, where two qualitatively different topologies appear for different sets of parameters. To classify this Janus structure we propose a novel concept in blood cancer-a reproduction ratio R. It determines the topological structure depending on whether it is larger or smaller than a threshold value. Furthermore, it follows that inflammation, affected by the exogenous inflammatory stimulation, may determine the onset and development of blood cancers. The body may manage initial blood cancer as long as the self-renewal rate is not too high, but fails to manage it if an inflammation appears. Thus, inflammation may trigger and drive blood cancers. Finally, the mathematical analysis suggests novel treatment strategies and it is used to discuss the in silico effect of existing treatment, e.g. interferon-α or T-cell therapy, and the impact of malignant cells becoming resistant.

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Section: Model Validationmentioning

confidence: 99%

Bridging blood cancers and inflammation: The reduced Cancitis model

Ottesen

Pedersen

Sajid

et al. 2019

Journal of Theoretical Biology

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“…Emergence of chemoresistant clones and relapse after intensive chemotherapy or stem cell transplantation remain the chief obstacles. Identification of MRD as an independent prognostic factor, stratification of patients by their status, and early intervention with change in therapy has improved outcomes in acute lymphoblastic leukemia (16)(17)(18)(19)(20)(21)(22). A challenge has been how to define MRD in AML.…”

Section: Discussionmentioning

confidence: 99%

“…In (21), the authors modeled the emergence of clonal heterogeneity by modifying the ODE model to accommodate new leukemic clones arising from mutations. We do not include mutations in our model here because relapse more likely results from MRD than new mutations during and after chemotherapy (24,25).…”

Section: Discussionmentioning

confidence: 99%

“…We do not include mutations in our model here because relapse more likely results from MRD than new mutations during and after chemotherapy (24,25). The deterministic ODE models in (7,14,21) were used to track the evolution of different leukemic clones from diagnosis to relapse. This is an appropriate approach when the populations advance toward equilibrium.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Minimal Residual Disease in Acute Myeloid Leukemia through Stochastic Modeling of Clonality

Dinh

Jaksik

Corey

et al. 2019

Preprint

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Event-free and overall survival remains poor for acute myeloid leukemia (AML). Chemoresistant clones contributing to relapse of the disease arise from minimal residual disease (MRD) rather than resulting from newly acquired mutations during or after chemotherapy.MRD is the presence of measurable leukemic cells using non-morphologic assays. It is considered a strong predictor of relapse. The dynamics of clones comprising MRD is poorly understood and is considered influenced by a form of Darwinian selection. We propose a stochastic model based on a multitype (multi-clone) age-dependent Markov branching process to study how random events in MRD contribute to the heterogeneity in response to treatment in a cohort of six patients from The Cancer Genome Atlas database with whole genome sequencing data at two time points. Our model offers a more accurate understanding of how relapse arises and which properties allow a leukemic clone to thrive in the Darwinian competition among leukemic and normal hematopoietic clones. The model suggests a quantitative relationship between MRD and time to relapse and therefore may aid clinicians in determining when and how to implement treatment changes to postpone or prevent the time to relapse. Author summaryRelapse affects about 50% of AML patients who achieved remission after treatment, and the prognosis of relapsed AML is poor. Current evidence has shown that in many patients, mutations giving rise to relapse are already present at diagnosis and remain in small numbers in remission, defined as the minimal residual disease (MRD). We propose a mathematical model to analyze how MRD develops into relapse, and how random events in MRD may affect the patient's fate. This work may aid clinicians in predicting the range of outcomes of chemotherapy, given mutational data at diagnosis. This can help in choosing treatment strategies that reduce the risk of relapse.

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“…Cancer models based on differential equations form a vast body of literature: see, among others [9,11,12]. There exist models considering tumorigenesis in greater detail than is done here, for instance [4,7,34,36]; as concerns the interaction of cancer with the immune system, [3,9,20], modelling of therapies, [14,31] and more specifically on drug resistance to therapies, [5,15,21]. Extensive bibliographic references can be found in the review papers [2,10].…”

Section: The Modelmentioning

confidence: 99%

Efficiency of cancer treatments:in silico experiments

Piretto

Delitala

Ferraro

2020

Math. Model. Nat. Phenom.

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Despite the advances in the formulation of different therapies to fight cancer, the design of successful protocols is still a challenging problem. In order to provide some indications on the effectiveness of medical treatments, results from in silico experiments are presented based on a mathematical model comprising two cancer populations competing for resources and with different susceptibilities to the action of therapies. The focus is on the outcome of protocols in which the total dose can be administered with different time distributions. An efficiency index is proposed to quantify the effectiveness of different protocols. Simulations show that a standard dose chemotherapy is effective when the sensitive clone has a marked competitive advantage, whereas its outcome is much worse when a resistant clone emerges; obviously combinations of immune and chemotherapy work better. These results, in accord with previous finding reported in the literature, stress the importance to take into account competitive interactions among cancer clones to decide which therapeutic strategy should be adopted. However, it is not just the efficiency that changes in these different configurations of clonal composition and therapy timing. A general rule seems to emerge: when evolutionary pressures are strong, the best protocols entail and early starting of the treatment, whereas, on the contrary, when interactions among clones are weak, therapy should start later. Finally the model has been adapted to investigate the relative efficiency of different protocols, by using data reported in literature regarding experiments with breast cancer cells.

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Emergence of heterogeneity in acute leukemias

Cited by 33 publications

References 65 publications

Bridging blood cancers and inflammation: The reduced Cancitis model

Bridging blood cancers and inflammation: The reduced Cancitis model

Predicting Minimal Residual Disease in Acute Myeloid Leukemia through Stochastic Modeling of Clonality

Efficiency of cancer treatments:in silico experiments

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