Predicting neuroblastoma using developmental signals and a logic-based model

Kasemeier-Kulesa, Jennifer C.; Schnell, Santiago; Woolley, Thomas E.; Spengler, Jennifer; Morrison, Jason A.; McKinney, Mary Cathleen; Pushel, Irina; Wolfe, Lauren A.; Kulesa, Paul M.

doi:10.1016/j.bpc.2018.04.004

Cited by 10 publications

(4 citation statements)

References 52 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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“…Kasemeier-Kulesa and al . (49) have developed a molecular network model of developmental genes and signaling pathways with a 6 gene inputs logic model, using the discrete Boolean logic, and based on 4 cell states (differentiation, proliferation, angiogenesis, apoptosis). The model was able to predict the stage of the human neuroblastoma SHSY5Y and then the outcome of 77 early stage patients.…”

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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“…Established neuroblastoma cell lines have also been used for validating the mechanistic models used for the prediction of neuroblastoma progression based on the analysis of molecular networks [ 99 ]. Due to their neuronal features, neuroblastoma cell lines are also used as models in neuroscience, e.g., in studies on Alzheimer’s disease [ 100 ], Parkinson’s disease [ 101 ], and in virology research [ 102 , 103 ].…”

Section: In Vitro Models Of Neuroblastomamentioning

confidence: 99%

Preclinical Models of Neuroblastoma—Current Status and Perspectives

Krawczyk

Kitlińska

2023

Cancers

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Preclinical in vitro and in vivo models remain indispensable tools in cancer research. These classic models, including two- and three-dimensional cell culture techniques and animal models, are crucial for basic and translational studies. However, each model has its own limitations and typically does not fully recapitulate the course of the human disease. Therefore, there is an urgent need for the development of novel, advanced systems that can allow for efficient evaluation of the mechanisms underlying cancer development and progression, more accurately reflect the disease pathophysiology and complexity, and effectively inform therapeutic decisions for patients. Preclinical models are especially important for rare cancers, such as neuroblastoma, where the availability of patient-derived specimens that could be used for potential therapy evaluation and screening is limited. Neuroblastoma modeling is further complicated by the disease heterogeneity. In this review, we present the current status of preclinical models for neuroblastoma research, discuss their development and characteristics emphasizing strengths and limitations, and describe the necessity of the development of novel, more advanced and clinically relevant approaches.

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Predicting neuroblastoma using developmental signals and a logic-based model

Cited by 10 publications

References 52 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

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

Preclinical Models of Neuroblastoma—Current Status and Perspectives

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