Cancer is the underlying cause of approximately one in four deaths in the United States, with metastasis responsible for more than 90% of cancer related deaths. Despite decades of research, predicting where cancer will spread remains one of the biggest challenges in oncology.
Our research seeks to understand how circulating tumor cells (CTCs) move through the bloodstream, interact with blood cells and blood vessels, and ultimately establish new tumors in distant organs. By combining high fidelity blood flow simulation, multiscale modeling, and AI, we are developing computational tools to predict metastatic spread and evaluate new strategies to prevent it.
This work has the potential to improve cancer staging, help identify the primary tumor in patients with metastatic disease of unknown origin, and accelerate the development of next generation therapies and nanotechnologies that target cancer cells while they are still circulating in the bloodstream.
Our work on cancer cell transport builds on our long standing expertise in fluid structure interactions, high performance computing, and multiscale simulation. We developed HARVEY, a massively parallel blood flow solver that models blood moving through patient specific vessels, and combine it with Adaptive Physics Refinement (APR) to capture the complex interactions between circulating tumor cells (CTCs), red blood cells, white blood cells, and platelets.
A key challenge is connecting events that occur across vastly different length scales, from the deformation of individual cells measured in microns to blood flow throughout the entire vascular system. APR increases the level of physical detail only where it is needed, allowing us to capture critical adhesion and collision events while maintaining the computational efficiency needed for large scale simulations.
By combining physics based modeling, AI, and multiscale vascular simulation, we study how blood flow, cellular interactions, and vessel geometry influence CTC adhesion and extravasation, two critical steps in the metastatic process. Ultimately, our goal is to develop patient specific computational models that help predict where metastatic tumors are most likely to form.
The image below highlights one example of this work, showing the transport of a compound capsule through a constricted microchannel. These studies help us understand how cell mechanics and local blood flow influence migration and adhesion, providing insight into the earliest stages of metastasis.
The figure above illustrates APR’s “moving window” concept in action. Rather than applying the highest-resolution physics to the entire vascular domain, APR continuously tracks areas of interest—such as a circulating tumor cell approaching a vessel constriction—and refines the model only within that localized window. As the event progresses, the high-fidelity zone moves with it, ensuring critical interactions are resolved in detail while the surrounding regions are simulated at a coarser scale. This targeted refinement dramatically reduces computational cost without sacrificing accuracy in the moments and locations that matter most.