MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach

Cited by 5 publications

(4 citation statements)

References 46 publications

(58 reference statements)

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“…Furthermore, we have now chosen to model the effect of cytokines and chemokines implicitly (e.g., by basing an interaction between two cells on the distance between them), but it would be an interesting addition to model humoral factors explicitly [e.g., using hybrid models ( 80 )], for example when wanting to zoom in more on androgen dependence and the path to castration-resistant disease. Adding to this, we have chosen to model mutations generally; all mutations confer a proliferative advantage and increased mutation chance to its host.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Passier,

van Genderen,

Zaalberg

et al. 2023

Cancer Research Communications

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Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients’ clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

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Section: Discussionmentioning

confidence: 99%

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Passier,

van Genderen,

Zaalberg

et al. 2023

Cancer Research Communications

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show abstract

“…by basing an interaction between two cells on the distance between them), but it would be an interesting addition to model humoral factors explicitly (e.g. using hybrid models (93)), for example when wanting to zoom in more on androgen dependence and the path to castration resistant disease. However, this would also increase the number of model parameters and the computational costs.…”

Section: Discussionmentioning

confidence: 99%

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

Passier

Genderen

Zaalberg

et al. 2023

Preprint

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Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients' clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

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“…Using lightweight neural networks, Axenie et al 34 extracted the mechanistic relations governing phenotypic staging and tumor volume development. 90 Very recently, Cesaro and her colleagues 91 demonstrated their multi-level TME simulator that couples mechanistic agent-based models with PDE-based solvers in two dimensions. An innovative feature of their paper was the data-driven strategy they adopted to inform the agent-based model using bulk gene expression data from The Cancer Genome Atlas database.…”

Section: Agent-based Modeling In Cancer Biomedicinementioning

confidence: 99%

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

Cogno,

Axenie,

Bauer

et al. 2024

Cancer Biology & Therapy

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Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor – host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models’ validation and provide an ambitious outlook toward rigorous and reliable calibration.

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MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach

Cited by 5 publications

References 46 publications

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

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

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