Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Karolak, Aleksandra; Markov, Dmitry A.; McCawley, Lisa J.; Rejniak, Katarzyna A.

doi:10.1098/rsif.2017.0703

Cited by 114 publications

(88 citation statements)

References 181 publications

(219 reference statements)

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“…The realization of such a tool requires a level of integration of highly heterogeneous biological data in multiple space and timescales, as illustrated in Figure 1, making it a complex problem to address. However, current developments in computational, mathematical and systems oncology [1,4,9,16,22,34,38,46,53,56,60] show great potential to develop predictive, personalised clinical cancer practice, integrating mathematical and computational approaches with traditional bench and clinical experiments. Moreover, the availability of vast amount of patient-specific data with the help of technical and computational advances can further help in developing such in silico framework/tool.…”

Section: Making Personalised Medicine a Realitymentioning

confidence: 99%

Hybrid data-based modelling in oncology: successes, challenges and hopes

Stéphanou

Ballet²,

Powathil³

2020

Math. Model. Nat. Phenom.

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In this review we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges than need to be addressed to allow for a better integration of the model parts and of the data into the models. And Finally, the Hopes with a focus towards making personalised medicine a reality.Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05.As of today, models are massively used in the domain of cancer to pursue four main goals: to improve diagnosis, to improve therapy, to identify and develop new drugs and to bring new knowledge on the development of the disease. Those goals contribute to bring the models much closer to the clinic. Models are developed in the

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Section: Making Personalised Medicine a Realitymentioning

confidence: 99%

Hybrid data-based modelling in oncology: successes, challenges and hopes

Stéphanou

Ballet²,

Powathil³

2020

Math. Model. Nat. Phenom.

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“…In order to investigate the emergence of the observed 3D-neighbourhood structure in 24 h old ICM organoids, we use a 3D agent-based model. Agent-based models provide a technique to represent a wet-lab experiment under idealised conditions (30). The model is given as a set of di erential equations, describing mechanical cell-cell interactions, such as adhesion and repulsion forces, stochastic cell fate decision (omitting a detailed description of the signalling pathway dynamics), cell growth, and cell division involving cell fate heredity.…”

Section: Introductionmentioning

confidence: 99%

Cell Fate Clusters in ICM Organoids Arise from Cell Fate Heredity & Division – a Modelling Approach

Liebisch

Drusko

Mathew

et al. 2019

Preprint

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During the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first cell fate decision, cells become either part of an outer trophectoderm or part of the inner cell mass. Subsequently, the inner cell mass segregates into an embryonic or an extraembryonic lineage giving rise to the epiblast and the primitive endoderm, respectively. Inner cell mass organoids represent an experimental model system for preimplantation development, mimicking the second cell fate decision taking place in in vivo mouse embryos. In a previous study, the spatial pattern of the di erent cell lineage types was investigated. The study revealed that cells of the same fate tended to cluster stronger than expected for purely random cell fate decisions. In order to investigate the emergence of the cell lineage type clustering behaviour, we developed an agent-based model. Hereby, cells are mechanically interacting with direct neighbours, and exert adhesion and repulsion forces. The model was applied to compare two current assumptions of how inner cell mass neighbourhood structures are generated. We tested how di erent assumptions regarding cell fate switches a ect the observed neighbourhood relationships. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. The model further shows that the observed neighbourhood structures can emerge due to cell fate heredity during cell division and allows the inference for a time point for the cell fate decision. STATEMENT OF SIGNIFICANCECell fate decisions in early embryogenesis have been considered random events, which also cause a random distribution of cells of di erent cell fates. Using an agent-based mathematical model, fitted to data derived from ICM organoids, we show that a random distribution occurs only for a short time interval, as cell fate heredity and cell division quickly lead to spatial cell fate clustering. Our results speak against neighbourhood interactions determining an individual cell fate. Instead our approach indicates four consecutive phases of early development: 1) co-expression of cell fate markers, 2) cell fate decision, 3) cell division and local cell fate clustering, and 4) phase separation, whereby only the phases 1-3 occur in ICM organoids during the first 24h of growth. NANOG and GATA6 are described as the first markers for Epi and PrE segregation, respectively. Expression levels of NANOG and GATA6 undergo progressive changes during the morula stage and the early blastocyst (9, 10). In early blastocysts (E3.0), all ICM cells seem to co-express NANOG and GATA6 (7,11,12). Subsequently, NANOG and GATA6 are gradually up-or down-regulated during the 32-cell stage. Thereby, both transcription factors repress each other locally (10, 13-17), leading to a mutually exclusive transcription factor expression in late blastocysts (64 cells) (18). Once a cell-fate is determined it is only possible to switch the fate by an external m...

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“…Cells respond on mechanical stress passively and actively, hence an understanding of growth and division processes is not possible without properly taking into account the mechanical components underlying these processes. Mathematical models are being established as an additional cornerstone to provide information to clinicians entering in their decisions [2,3]. In particular effects based on nonlinear dynamics need mathematical modeling.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models

Neitsch

Johann

et al. 2018

Preprint

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Mathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. The focus in this paper is to study the regeneration of liver after drug-induced depletion of hepatocytes, in which surviving dividing and migrating hepatocytes must squeeze through a blood vessel network to fill the emerged lesions. Here, the cells' response to mechanical stress might significantly impact on the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined quantitative understanding of the cell-biomechanical impact on the closure of drug-induced lesions in liver. Our model represents each cell individually, constructed as a physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow and divide, and infer the nature of their mechanical elements and their parameters from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models. This effect is expected to be even more present in chronic liver disease, where tissue stiffens and excess collagen narrows pores for cells to squeeze through.

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Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Cited by 114 publications

References 181 publications

Hybrid data-based modelling in oncology: successes, challenges and hopes

Hybrid data-based modelling in oncology: successes, challenges and hopes

Cell Fate Clusters in ICM Organoids Arise from Cell Fate Heredity & Division – a Modelling Approach

Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models

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