Investigating the Influence of Red Blood Cell Interactions on Large-Scale Cancer Cell Transport: Bridging the Gap through Advances in Computational Techniques

Abstract

Metastatic cancer, the leading cause of cancer mortality, involves the complex process of circulating tumor cells (CTCs) spreading through the bloodstream and forming secondary tumors far away from the primary mass, which often travel a distance many thousand times its size. The interactions between CTCs and their neighboring red blood cells (RBCs), as well as the local hemodynamics in vessels, play a crucial role in determining these cells' fate; however, the mechanisms guiding their transit are still unclear. Predicting secondary tumor sites remains challenging due to the intricate dynamics of CTC migration. Thus, there remains a need to understand the interplay between the fluid dynamics, intercellular interactions, and vessel topology which can determine the fate of the CTC and subsequent likelihood of cancer progression.Investigating CTC transport has involved a range of \textit{in vivo} and \textit{in vitro} studies to unravel the intricate mechanisms that dictate cellular outcomes. However, the process of tracking an individual CTC's trajectory through the massive vascular system is still not possible today \textit{in vivo}. The integration of \textit{in silico} models has proven instrumental, complementing traditional experimental approaches. In this work, we utilize our advanced fluid dynamics solver HARVEY to perform high-fidelity hemodynamic simulations to capture CTC dissemination. We outline several key contributions, including the addition of new physics interactions models and software optimizations, to enable these simulations to better capture biological phenomena and run to completion within a reasonable timeframe.Numerical optimizations for \textit{in silico} models are still necessary: the drastic difference in length scales of CTC size versus distance traveled hinders current simulation models. To accurately capture intercellular dynamics, interactions must be modeled with sub-micrometer precision; meanwhile, the characteristic length scale of CTC traversal through the blood stream can be on the order of hundreds of millimeters, over 5 orders of magnitude larger. Numerically modeling hundreds of millions of individual cells at a sub-micron resolution over this timescale would require the entirety of multiple leadership-class supercomputers over the course of several weeks, if not months. Therefore, there still exists a disparity between these two ranges that needs to be addressed to make simulations of CTC transport with the presence of neighboring RBCs tractable.We also address one of the pillars of the inherent variability in cell transport: the fate of a single CTC can exhibit significant variations due to its interactions with neighboring RBCs in the context of an \textit{in vivo} experiment,. A single simulation of a CTC may not encompass the range of outcomes, necessitating the consideration of many simulations with different RBC distributions. Multiplying the number of simulations required to capture this variability by the computational workload of a single simulation results in a computationally intractable workload, making it essential to optimize the number of simulations required for proper results. The number of potential cell configurations is vast, which makes it essential to identify representative configurations that encompass the full range of possible outcomes while optimizing computational feasibility.This dissertation explores the influence of several hemodynamic and geometric parameters, microvasculature interactions, and the impact of RBCs on CTC movement, including the presence of RBC aggregation, RBC volume fraction, microvessel size, and shear rate. Furthermore, it discusses the enhancement of adaptive physics refinement methods to model cellular transport phenomena and highlights the capabilities of fluid-structure interaction models in capturing the dynamics of CTCs and RBCs across the system-scale. The dissertation concludes by discussing the development of a novel framework to account for the range of outcomes in CTC transport due to the variability in neighboring RBCs; it addresses the importance of generating representative configurations using quantitative metrics such as the Jaccard index applied to sets of sphere and RBC data sets. By integrating these advances, we further reduce the gap towards biologically accurate computational models of cancer cell transport, which holds promise for improving our understanding of cancer metastasis and developing effective strategies for cancer treatment.