Modelling the transport of fluid through heterogeneous, whole tumours in silico

Abstract

Cancers exhibit spatially heterogeneous, unique vascular architectures across individual samples, cell-lines and patients. This inherently disorganised collection of leaky blood vessels contribute significantly to suboptimal treatment efficacy. Preclinical tools are urgently required which incorporate the inherent variability and heterogeneity of tumours to optimise and engineer anti-cancer therapies. In this study, we present a novel computational framework which incorporates whole, realistic tumours extracted ex vivo to efficiently simulate vascular blood flow and interstitial fluid transport in silico for validation against in vivo biomedical imaging. Our model couples Poiseuille and Darcy descriptions of vascular and interstitial flow, respectively, and incorporates spatially heterogeneous blood vessel lumen and interstitial permeabilities to generate accurate predictions of tumour fluid dynamics. Our platform enables highly-controlled experiments to be performed which provide insight into how tumour vascular heterogeneity contributes to tumour fluid transport. We detail the application of our framework to an orthotopic murine glioma (GL261) and a human colorectal carcinoma (LS147T), and perform sensitivity analysis to gain an understanding of the key biological mechanisms which determine tumour fluid transport. Finally we mimic vascular normalization by modifying parameters, such as vascular and interstitial permeabilities, and show that incorporating realistic vasculatures is key to modelling the contrasting fluid dynamic response between tumour samples. Contrary to literature, we show that reducing tumour interstitial fluid pressure is not essential to increase interstitial perfusion and that therapies should seek to develop an interstitial fluid pressure gradient. We also hypothesise that stabilising vessel diameters and permeabilities are not key responses following vascular normalization and that therapy may alter interstitial hydraulic conductivity. Consequently, we suggest that normalizing the interstitial microenvironment may provide a more effective means to increase interstitial perfusion within tumours.

Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging

“…Our method provides a comprehensive characterization of the vasculature whereby each vessel is characterized by its length, diameter, and connectivity. Simulations based on the characterized vasculature provided here may, for example, involve modelling blood flow through vasculature [ 7 , 48 , 49 ] or flow of endothelial cells through vasculature during organ regeneration. However, for future studies involving modelling fluid flow in a vascular network extracted using our methods, researchers should be aware of the imaging limitations discussed in this study.…”

“…Quantifying the changes in lung morphology is highly desirable to further the understanding of pulmonary disease and develop disease treatments. For quantitative measurements of aberrant morphology, X-ray computed tomography (CT) has been used in the study of pulmonary vascular disease [2][3][4][5], vasculature quantification and fluid simulations in tumors [6][7][8][9], and tissue engineering methods such as de-and recellularization of whole organs including the lungs [10]. X-ray computed tomography is an imaging technique that provides high-resolution 3D images with resolutions ranging from several millimeters down to several micrometers via micro-computed tomography (μCT).…”

Macro-scale models for fluid flow in tumour tissues: impact of microstructure properties

“…Multiscale models have further been employed to investigate the coupling between tumor growth, angiogenesis, vascular remodelling and fluid transport [35] and the impact of collagen microstructure on interstitial fluid flow [60]. Imaging data have been integrated to both continuum and discrete models to quantify the effect of the heterogeneity on the fluid transport [62,54].…”

Numerical Investigation on the Anti-Angiogenic Therapy-Induced Normalization in Solid Tumors

“…Other studies [43,44] followed up the effect of anti-angiogenic therapy on therapeutic agent transfer into the tumor site by developing governing partial differential equations. Sweeney et al [45] mimicked the vascular normalization by modifying the parameters of interstitial and intravascular flow in a tumor with a real image-based capillary network. Wu et al [46] and Moath and Xiao [47] studied the normalization by pruning the microvascular network produced by mathematical modeling.…”