Abnormal morphology biases hematocrit distribution in tumor vasculature and contributes to heterogeneity in tissue oxygenation

Abstract

Oxygen heterogeneity in solid tumors is recognized as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal vascular structure of the tumor, but the precise mechanisms linking abnormal structure and compromised oxygen transport are only partially understood. In this paper, we investigate the role that red blood cell (RBC) transport plays in establishing oxygen heterogeneity in tumor tissue. We focus on heterogeneity driven by network effects, which are challenging to observe experimentally due to the reduced fields of view typically considered. Motivated by our findings of abnormal vascular patterns linked to deviations from current RBC transport theory, we calculated average vessel lengths L¯ and diameters d¯ from tumor allografts of three cancer cell lines and observed a substantial reduction in the ratio λ=L¯/d¯ compared to physiological conditions. Mathematical modeling reveals that small values of the ratio λ (i.e., λ<6) can bias hematocrit distribution in tumor vascular networks and drive heterogeneous oxygenation of tumor tissue. Finally, we show an increase in the value of λ in tumor vascular networks following treatment with the antiangiogenic cancer agent DC101. Based on our findings, we propose λ as an effective way of monitoring the efficacy of antiangiogenic agents and as a proxy measure of perfusion and oxygenation in tumor tissue undergoing antiangiogenic treatment.

Association between erythrocyte dynamics and vessel remodelling in developmental vascular networks

“…Similar reverse partitioning was reported by simulations of cellular blood flow in microvascular networks with vessel diameters designed following Horton’s Law [ 30 ]. Our group recently identified reduced interbifurcation distance and complex branching topology as sources of haematocrit bias in the context of tumour blood flow, where a role was proposed for the resulting abnormal partitioning in establishing tumour tissue hypoxia [ 61 ].…”

Emergent cell-free layer asymmetry and biased haematocrit partition in a biomimetic vascular network of successive bifurcations

“…The second approach focuses on quantifying the split of RBC fluxes or discharge haematocrits within a flowing RBC suspension at bifurcations (e.g. [12,24,13,21,15,23]), aimed at elucidating the fundamental process of phase separation occurring in vivo which may inspire not only enhanced blood-on-achip devices, but also novel strategies for treating microcirculatory disorders arising from abnormal blood flow.…”

“…If the interbifurcation distances are short or only moderate (e.g. less than 10 vessel-diameter long as postulated by [8]), the transverse organisation of RBCs bears a memory of the transient flow from last bifurcation and the CFL symmetry may not be restored upon reaching the next bifurcation [42,43,23]. Indeed, recent RBC simulations in a microvascular network demonstrated increasingly asymmetric CFL that develop along curved vessels without apparent recovery of symmetry [22].…”

“…These experimental limitations can be potentially overcome by increasingly realistic computational simulations, which can query multiple aspects that are otherwise inconvenient or inaccessible to experiments, such as microscopic cell dynamics in a dense suspension, emergent cell orderings on the crosssectional plane and spatio-temporal RBC flow properties (e.g. evolving CFLs [22,23]).…”

GPU Acceleration of the HemeLB Code for Lattice Boltzmann Simulations in Sparse Complex Geometries

“…Since its release in 2008, it has been applied to a range of physiological flow problems such as cerebral and retinal flow, vascular remodelling, and magnetic drug targeting [31], [32], [33]. Recent development efforts include integration of finite element and immersed boundary methods [34], [35] and efficient self-coupling [9]. While HemeLB is actively developed to take advantage of exascale The goal of the present work is to augment the existing MPI-parallelization of HemeLB with a CUDA kernel to leverage one or more GPUs available to an MPI process.…”