Challenges of applying multicellular tumor spheroids in preclinical phase

Cancer Cell International, Journal Year: 2021, Volume and Issue: 21(1)

Published: March 4, 2021

Abstract The three-dimensional (3D) multicellular tumor spheroids (MCTs) model is becoming an essential tool in cancer research as it expresses intermediate complexity between 2D monolayer models and vivo solid tumors. MCTs closely resemble tumors many aspects, such the heterogeneous architecture, internal gradients of signaling factors, nutrients, oxygenation. have growth kinetics similar to those tumors, cells spheroid mimic physical interaction cell-to-cell cell-to-extracellular matrix interactions. These similarities provide great potential for studying biological properties a promising platform drug screening therapeutic efficacy evaluation. However, are not well adopted preclinical tools behavior up now. In this review, we addressed challenges with application discussed various efforts overcome challenges.

Language: Английский

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Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

Biomedicines, Journal Year: 2023, Volume and Issue: 11(4), P. 1058 - 1058

Published: March 30, 2023

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and vivo animal models, gold standards preclinical assessment of anticancer drug efficacy. 3D can be generated through multitude techniques, from both immortalized lines primary patient-derived tumor tissue. Among them, spheroids organoids represent most versatile promising they faithfully recapitulate complexity heterogeneity human cancers. Although their recent applications include screening programs personalized medicine, not yet established tools for studying efficacy supporting preclinical-to-clinical translation, which remains mainly based on experimentation. this review, we describe state-of-the-art evaluation agents, focusing potential contribution to replace, reduce refine experimentations, highlighting strength weakness, discussing possible perspectives overcome current challenges.

Language: Английский

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Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Frontiers in Cell and Developmental Biology, Journal Year: 2024, Volume and Issue: 12

Published: March 1, 2024

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as scaffold, providing structural support to cells well points potential attachment. Although well-studied structure, its role in many biological remains difficult investigate comprehensively due complexity variation within an organism. tandem with experiments, mathematical models helpful refining testing hypotheses, generating predictions, exploring conditions outside scope experiments. Such can be combined calibrated vivo vitro data identify critical cell-ECM interactions that drive developmental homeostatic processes, or progression diseases. this review, we focus on computational such migration including cancer metastasis, tissue morphogenesis. By highlighting predictive power these models, aim help bridge gap between experimental approaches studying provide guidance selecting appropriate model framework complement corresponding studies.

Language: Английский

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Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(41)

Published: Oct. 8, 2021

Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification heterogeneous noisy data-often with outliers, artifacts, mislabeled points-such as those from tissues, remains a challenge. The mathematical field that extracts information the shape data, topological analysis (TDA), has expanded its capability for analyzing real-world datasets in recent years by extending theory, statistics, computation. An extension to standard theory handle is multiparameter persistent homology (MPH). Here we provide an application MPH landscapes, statistical tool theoretical underpinnings. computed (noisy) agent-based model simulations immune cells infiltrating into spheroid, are shown surpass existing statistics one-parameter homology. We then apply landscapes study cell location digital histology images head neck cancer. quantify intratumoral find regulatory T have more prominent voids their patterns than macrophages. Finally, consider how TDA can integrate interrogate different types scales, e.g., locations regions differing levels oxygenation. This work highlights power quantifying, characterizing, comparing features within tumor microenvironment synthetic real datasets.

Language: Английский

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Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Cancer Biology & Therapy, Journal Year: 2024, Volume and Issue: 25(1)

Published: April 28, 2024

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial temporal scales, but also integrate information from well-established

Language: Английский

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Designing and interpreting 4D tumour spheroid experiments

Communications Biology, Journal Year: 2022, Volume and Issue: 5(1)

Published: Jan. 24, 2022

Tumour spheroid experiments are routinely used to study cancer progression and treatment. Various inconsistent experimental designs used, leading challenges in interpretation reproducibility. Using multiple designs, live-dead cell staining, real-time cycle imaging, we measure necrotic proliferation-inhibited regions over 1000 4D tumour spheroids (3D space plus status). By intentionally varying the initial size temporal sampling frequencies across lines, collect an abundance of measurements internal structure. These data difficult compare interpret. However, using objective mathematical modelling framework statistical identifiability analysis quantitatively identify design choices that produce reliable biological insight. Measurements structure provide most insight, whereas measurement frequency is less important. Our general applies grown different conditions with types.

Language: Английский

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A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling

Journal of The Royal Society Interface, Journal Year: 2022, Volume and Issue: 19(189)

Published: April 1, 2022

tumour spheroids have been used to study avascular growth and drug design for over 50 years. Tumour exhibit heterogeneity within the growing population that is thought be related spatial temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us identify position status individual cells spheroid, giving rise notion a four-dimensional (4D) spheroid. We develop first stochastic individual-based model (IBM) 4D spheroid show IBM simulation data compares well with experimental using primary human melanoma line. provides quantitative information about availability which important because it difficult measure these experimentally.

Language: Английский

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Calibrating agent-based models to tumor images using representation learning

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(4), P. e1011070 - e1011070

Published: April 21, 2023

Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore spatiotemporal evolution its microenvironment. However, these face serious drawbacks realm parameterization – ABM parameters are typically set individually based on various data literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit simple time-course (such as volume), that type loses spatial information is defining feature ABMs. images provide information, it exceedingly difficult compare simulations beyond qualitative visual comparison. Without quantitative method comparing similarity simulations, fitting not possible. Here, we present novel approach applies neural networks represent both low dimensional points, with distance between points acting measure difference two. This enables comparison where simulated experimental minimized using standard parameter-fitting algorithms. describe this two examples demonstrate application estimate for distinct Overall, robustly parameters.

Language: Английский

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Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(3), P. e1010994 - e1010994

Published: March 27, 2023

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends existing (PCF) and cross-PCF to describe relationships between points marked with combinations of discrete continuous labels. validate its use through application agent-based model (ABM) which simulates interactions macrophages tumour cells. These are influenced by positions cells macrophage phenotype, variable that ranges from anti-tumour pro-tumour. By varying parameters regulate we show ABM exhibits behaviours resemble ‘three Es cancer immunoediting’: Equilibrium, Escape, Elimination. analyse synthetic images generated ABM. generates ‘human readable’ statistical summary where different phenotypes located relative both blood vessels also define distinct ‘PCF signature’ characterises each three immunoediting, combining measurements describing applying dimension reduction techniques this signature, identify key features train support vector machine classifier distinguish simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple statistics can be combined complex generates, partition them into interpretable groups. intricate produced similar those state-of-the-art multiplex imaging distribution intensity biomarkers in biological tissue regions. Applying methods such as data would exploit variation biomarker intensities generate more detailed characterisation phenotypic heterogeneity samples.

Language: Английский

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Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art

Processes, Journal Year: 2021, Volume and Issue: 9(3), P. 447 - 447

Published: March 2, 2021

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells organoids, three-dimensional cell co-cultures, organ/body-on-chip tools. Efficient computer-aided mathematical model-based methods are required for efficient knowledge-driven characterization, optimization, routine manufacturing systems. As an alternative purely experimental-driven research, usage comprehensive models as a virtual silico representation culture, namely digital twin, can be advantageous. Digital twins include mechanistic biological system form diverse models, which describe interaction between techniques growth, metabolism, quality tissue. In this review, concepts, expectations, state art concepts will highlighted. general, DT’s applied along full process chain product life cycle. Due complexity, focus review especially on design, operation techniques.

Language: Английский

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