Bridging scales: A hybrid model to simulate vascular tumor growth and treatment response

Duswald, Tobias; Lima, Ernesto A.B.F.; Oden, J. Tinsley; Wohlmuth, Barbara

doi:10.1016/j.cma.2023.116566

Computer Methods in Applied Mechanics and Engineering

2024

DOI: 10.1016/j.cma.2023.116566

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Bridging scales: A hybrid model to simulate vascular tumor growth and treatment response

Tobias Duswald,

Ernesto A.B.F. Lima,

J. Tinsley Oden

et al.

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2024

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Cited by 4 publications

References 74 publications

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AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

Kunz,

Bosque,

Nikmaneshi

et al. 2024

Bull Math Biol

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AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

Kunz,

Bosque,

Nikmaneshi

et al. 2024

Bull Math Biol

View full text Add to dashboard Cite

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Kemkar,

Tao,

Ghosh

et al. 2024

Front. Physiol.

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Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

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Recent Treatment Strategies and Molecular Pathways in Resistance Mechanisms of Antiangiogenic Therapies in Glioblastoma

Rahman,

Ali

2024

Cancers

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Malignant gliomas present great difficulties in treatment, with little change over the past 30 years in the median survival time of 15 months. Current treatment options include surgery, radiotherapy (RT), and chemotherapy. New therapies aimed at suppressing the formation of new vasculature (antiangiogenic treatments) or destroying formed tumor vasculature (vascular disrupting agents) show promise. This study summarizes the existing knowledge regarding the processes by which glioblastoma (GBM) tumors acquire resistance to antiangiogenic treatments. The discussion encompasses the activation of redundant proangiogenic pathways, heightened tumor cell invasion and metastasis, resistance induced by hypoxia, creation of vascular mimicry channels, and regulation of the tumor immune microenvironment. Subsequently, we explore potential strategies to overcome this resistance, such as combining antiangiogenic therapies with other treatment methods, personalizing treatments for each patient, focusing on new therapeutic targets, incorporating immunotherapy, and utilizing drug delivery systems based on nanoparticles. Additionally, we would like to discuss the limitations of existing methods and potential future directions to enhance the beneficial effects of antiangiogenic treatments for patients with GBM. Therefore, this review aims to enhance the research outcome for GBM and provide a more promising opportunity by thoroughly exploring the mechanisms of resistance and investigating novel therapeutic strategies.

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Bridging scales: A hybrid model to simulate vascular tumor growth and treatment response

Cited by 4 publications

References 74 publications

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Recent Treatment Strategies and Molecular Pathways in Resistance Mechanisms of Antiangiogenic Therapies in Glioblastoma

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