Predictive Modeling of Neuroblastoma Growth Dynamics in Xenograft Model After Bevacizumab Anti-VEGF Therapy

He, Yijie; Kodali, Anita; Wallace, Dorothy

doi:10.1007/s11538-018-0441-3

Cited by 11 publications

(10 citation statements)

References 48 publications

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“…The computer modelling of neuroblastoma growth has received attention in the literature, (e.g., 16,17 ), but most models investigate the problem at a single scale. On the contrary, there very little literature to compare to for multiscale modelling of neuroblastomas.…”

Section: Resultsmentioning

confidence: 99%

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Vinicius

Quintela

Kasztelnik

et al. 2022

Numer Methods Biomed Eng

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In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.

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Section: Resultsmentioning

confidence: 99%

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Vinicius

Quintela

Kasztelnik

et al. 2022

Numer Methods Biomed Eng

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“…At the time of writing, while the literature does record detailed models of the intracellular dynamics pertinent to neuroblastoma [ 42 , 43 , 44 , 45 ], mechanistic modelling at the population level is slightly behind. In one study, ordinary differential equations were used to model the pharmacokinetics and pharmacodynamics of bevacizumab, vascularisation, and neuroblastoma growth [ 46 ]. In another study [ 47 ], a semi-mechanistic approach reducing the metastatic process to two basic phenomena—growth and dissemination—was used, but the influence of drugs was neglected.…”

Section: Discussionmentioning

confidence: 99%

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Italia

Wertheim

Taschner‐Mandl

et al. 2023

Cancers

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Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents’ pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.

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“…Their model was used to optimize gemcitabine metronomic chemotherapy administration but did not intend to model metastatic dissemination. Elsewhere, He et al (48) coupled a complex vasculature model fitting the dynamic growth of the human neuroblastoma cell line IMR32 in mice and a pharmacokinetics/pharmacodynamics model of bevacizumab, an anti-VEGF agent, to determine the best dosing regimen for this treatment and predict its effectiveness. Kasemeier-Kulesa and al .…”

Section: Discussionmentioning

confidence: 99%

Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma

Benzekry

Sentis

Coze

et al. 2020

Preprint

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High Risk Neuroblastoma (HRNB) is the second most frequent solid tumor in children. Prognosis remains poor despite multimodal therapies. Mathematical models have been developed to describe metastasis, but their prognosis value has yet to be determined and none exists in neuroblastoma. We established such a model for HRNB relying on two coefficients: α (growth) and μ (dissemination). The model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels (LDH) and the meta-iodo-benzyl-guanidine (mIBG) SIOPEN score from nuclear imaging, using data from 49 metastatic patients treated according to the European HR_NBL1 protocol. The model was able to accurately describe the data for both total tumor mass (LDH, R^2>0.99) and number of visible metastasis (SIOPEN, R^2=0.96). Statistical analysis revealed significant association of LDH with overall survival (OS, p=0.0268). However, clinical variables alone were not able to generate a Cox-based model with sufficient prognosis ability (p=0.507). The parameter μ was found to be independent of the clinical variables and positively significantly associated with OS (p = 0.0175 in multivariate analysis). Critically, addition of this novel computational biomarker to the clinical data drastically improved the performances of predictive algorithms, with a concordance index in cross-validation going from 0.755 to 0.827. The resulting signature had significant prognosis ability of OS (p=0.0353). Mechanistic modeling was able to describe pathophysiological data of metastatic HRNB and outperformed the predictive value of clinical variables. The physiological substrate underlying these results has yet to be explored, and results should be confirmed in a larger cohort.

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Predictive Modeling of Neuroblastoma Growth Dynamics in Xenograft Model After Bevacizumab Anti-VEGF Therapy

Cited by 11 publications

References 48 publications

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma

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