Model Prediction and Validation of an Order Mechanism Controlling the Spatiotemporal Phenotype of Early Hepatocellular Carcinoma

Hoehme, Stefan; Bertaux, François; Weens, William; Grasl‐Kraupp, Bettina; Hengstler, Jan G.; Drasdo, Dirk

doi:10.1007/s11538-017-0375-1

Cited by 26 publications

(14 citation statements)

References 55 publications

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“…Mathematical modeling and computational analysis are actively being pursued in several aspects of oncology to personalize and improve therapeutic outcomes (e.g., Ibrahim-Hashim et al, 2017). In particular, tissue structure and transport in liver (Rani et al, 2006;Hoehme et al, 2007;Campbell et al, 2008;Hoehme et al, 2010Hoehme et al, , 2017Hoehme et al, , 2018Holzhutter et al, 2012;Drasdo et al, 2014;Dutta-Moscato et al, 2014;Lettmann and Hardtke-Wolenski, 2014;Schliess et al, 2014;Siggers et al, 2014;Bethge et al, 2015;Ricken et al, 2015;Schwen et al, 2015;Nishii et al, 2016;Sluka et al, 2016;White et al, 2016;Friedman and Hao, 2017;Hudson et al, 2017Hudson et al, , 2019Meyer et al, 2017;Fu et al, 2018;Mahlbacher et al, 2018;Clendenon et al, 2019;Van Liedekerke et al, 2020) as well as pancreas (Haeno et al, 2012;Louzoun et al, 2014;Frieboes, 2017, 2018;Roy and Finley, 2017;Yamamoto et al, 2017;Chen et al, 2020;Dogra et al, 2020b) have been modeled. While numerous studies have simulated tumor growth and angiogenesis [see recent reviews and related work (Cristini et al, 2008;Edelman et al, 2010;Lowengrub et al, 2010;<...>…”

Section: Modeling Of Cancer Nanotherapy Taking Into Account the Micromentioning

confidence: 99%

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

Frieboes

Raghavan

Godin

2020

Front. Bioeng. Biotechnol.

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The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular-to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

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Section: Modeling Of Cancer Nanotherapy Taking Into Account the Micromentioning

confidence: 99%

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

Frieboes

Raghavan

Godin

2020

Front. Bioeng. Biotechnol.

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“…The combination of experimental data with computational models of tissues has proven successful in elucidating pathogenetic mechanisms using animal models 16,63,64 . However, animal models very often fail to mimic human diseases 65 , including NAFLD 66 .…”

Section: Discussionmentioning

confidence: 99%

3D spatially-resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression

Segovia-Miranda

Morales‐Navarrete

Kücken

et al. 2019

Preprint

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25Early disease diagnosis is key for the effective treatment of diseases. It relies on the 26 identification of biomarkers and morphological inspection of organs and tissues. 27Histopathological analysis of human biopsies is the gold standard to diagnose tissue 28 alterations. However, this approach has low resolution and overlooks 3D structural changes 29 that are consequence of functional alterations. Here, we applied multiphoton imaging, 3D 30 digital reconstructions and computational simulations to generate spatially-resolved 31 geometrical and functional models of human liver tissue at different stages of non-alcoholic 32 fatty liver disease (NAFLD). We identified a set of new morphometric cellular parameters 33 correlated with disease progression. Moreover, we found profound topological defects in the 34 3D bile canaliculi (BC) network. Personalized biliary fluid dynamic simulations predicted an 35 increased pericentral biliary pressure and zonated cholestasis, consistent with elevated 36 cholestatic biomarkers in patients' sera. Our spatially-resolved models of human liver tissue 37 can contribute to high-definition medicine by identifying quantitative multi-parametric cellular 38 and tissue signatures to define disease progression and provide new insights into NAFLD 39 pathophysiology. 42High definition medicine is emerging as an integrated approach to profile and restore 43 the health of an individual using a pipeline of multi-parametric analytical and therapeutic 44 technologies 1 . High-definition medicine relies on large data sets, e.g. genomics, 45 metabolomics, to characterize human health at the molecular level. It also relies on imaging, 46 image analysis and computational modelling approaches to identify structural and functional 47 abnormalities in organs and tissues associated with a disease state. Histology has been 48 classically used to characterize tissue structure and remains the method of choice to 49 describe and monitor a large variety of pathologies 2 . However, this technique has several 50 disadvantages, e.g. it is subjective (depends on the pathologist's skills), is often semi-51 quantitative and provides only two-dimensional (2D) information, i.e. does not account for 52 the three-dimensional (3D) complexity of tissues 3 . In recent years, an increasing number of 53 studies have highlighted the importance of considering 3D information for the 54 histopathological examination of tissues 4-7 . This is particularly crucial for the analysis of 3D 55 structures. The liver is a pertinent example of an organ with a complex 3D tissue 56 organization 8 . It consists of functional units, the liver lobule 9,10 , containing two intertwined 57 networks, the sinusoids for blood flow and the bile canaliculi (BC) for bile secretion and flux 9 . 58 Sinusoids and BC run antiparallel along the central vein (CV)-portal vein (PV) axis. The 59 hepatocytes are the major parenchymal cells and display a peculiar and unique type of cell 60 polarity distinct from that of simple epithelia 11 . Whereas in epit...

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“…Several ABM have simulated the immune system's involvement in maintaining homeostasis and disease conditions, such as bacterial infections [59], fungal infections [60], abnormal systemic inflammatory response [61], ulceration [62], allergens [63], ischemia [64], tuberculosis [65], sepsis [66], and wound healing [67]. For cancers, such models include tumor growth and invasion [68], as well as specific cancer types such as hepatocellular carcinoma [69], breast cancer [70], melanoma [71], colorectal [72], lung cancer [73,74], and metastasis [75]. Software packages have been developed based on the ABM framework to study the immune system; these include ImmSim [76][77][78][79], Immunogrid [80,81], Simmune [82], Cycell [83], and PhysiCell [84].…”

Section: Overview Of Computational Modeling Methodologies Including Amentioning

confidence: 99%

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

et al. 2019

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Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

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Model Prediction and Validation of an Order Mechanism Controlling the Spatiotemporal Phenotype of Early Hepatocellular Carcinoma

Cited by 26 publications

References 55 publications

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

3D spatially-resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

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