Computational/experimental evaluation of liver metastasis post hepatic injury: interactions with macrophages and transitional ECM

Abstract

The complex interactions between subclinical changes to hepatic extracellular matrix (ECM) in response to injury and tumor-associated macrophage microenvironmental cues facilitating metastatic cell seeding remain poorly understood. This study implements a combined computational modeling and experimental approach to evaluate tumor growth following hepatic injury, focusing on ECM remodeling and interactions with local macrophages. Experiments were performed to determine ECM density and macrophage-associated cytokine levels. Effects of ECM remodeling along with macrophage polarization on tumor growth were evaluated via computational modeling. For primary or metastatic cells in co-culture with macrophages, TNF-α levels were 5× higher with M1 vs. M2 macrophages. Metastatic cell co-culture exhibited 10× higher TNF-α induction than with primary tumor cells. Although TGFβ1 induction was similar between both co-cultures, levels were slightly higher with primary cells in the presence of M1. Simulated metastatic tumors exhibited decreased growth compared to primary tumors, due to high local M1-induced cytotoxicity, even in a highly vascularized microenvironment. Experimental analysis combined with computational modeling may provide insight into interactions between ECM remodeling, macrophage polarization, and liver tumor growth.

The liver matrisome – looking beyond collagens

“…115 Given the novel recognition that the hepatic matrisome responds much more dynamically to liver injury than previously appreciated, these changes may increase the fertility of the hepatic "soil" for metastases. Some studies have been performed to computationally predict these possibilities, 116,117 but a deeper understanding of this potential interaction is needed.…”

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

“…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, 2010 , 2017 , 2018 ; Holzhutter 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, 2017 , 2019 ; Meyer 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 ; Ng and 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 ; Osborne et al, 2010 ; Rejniak and McCawley, 2010 ; Vineis et al, 2010 ; Andasari et al, 2011 ; Chaplain, 2011 ; Deisboeck et al, 2011 ; Frieboes et al, 2011 ; Michor et al, 2011 ; Rejniak and Anderson, 2011 ; Swanson et al, 2011 )] including metastatic conditions ( Campbell et al, 2008 ; Haeno et al, 2012 ; …”

Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

“…Integrating additional biological details could refine the results, improve quantitative matching with targeted experiments, and potentially lead to further insights. For example, incorporating additional key cell types which are known to modulate metastatic inception and progression (e.g., fibroblasts, hepatic endothelial cells, and immune cells) would improve our simulation of cell-cell and cell-matrix interactions, similarly to recent modeling efforts that explored the role of M1 macrophage-induced cytotoxicty in well-vascularized tissues such as liver [42]. Immune and fibroblast cell agents recently developed for SARS-CoV-2 research [84] could readily be adapted to the agent-based model.…”

“…Others have modeled the role of tissue mechanics on (mostly primary) tumor growth [31][32][33][34], including some excellent work on the role of mechanosensing in individual tumor cell proliferation [35,36], although these did not focus specifically on primary or metastatic tumor growth in liver tissues. While there is less history in specific modeling of liver metastases, some have modeled the growth of metastases in the liver [37][38][39], while others have simulated the impact of ECM remodeling and macrophages in liver metastases [40][41][42]. There are also good examples of simulating nanotherapy treatments [43][44][45] and theranostics [46,47] of liver metastases.…”