Effects of Shear Stress Gradients on Ewing Sarcoma Cells Using 3D Printed Scaffolds and Flow Perfusion

In this work, we combined three-dimensional (3D) scaffolds with flow perfusion bioreactors to evaluate the gradient effects of scaffold architecture and mechanical stimulation, respectively, on tumor cell phenotype. As cancer biologists elucidate the relevance of 3D in vitro tumor models within the drug discovery pipeline, it has become more compelling to model the tumor microenvironment and its impact on tumor cells. In particular, permeability gradients within solid tumors are inherently complex and difficult to accurately model in vitro. However, 3D printing can be used to design scaffolds with complex architecture, and flow perfusion can simulate mechanical stimulation within the tumor microenvironment. By modeling these gradients in vitro with 3D printed scaffolds and flow perfusion, we can identify potential diffusional limitations of drug delivery within a tumor. Ewing sarcoma (ES), a pediatric bone tumor, is a suitable candidate to study heterogeneous tumor response due to its demonstrated shear stress-dependent secretion of ligands important for ES tumor progression. We cultured ES cells under flow perfusion conditions on poly(propylene fumarate) scaffolds, which were fabricated with a distinct pore size gradient via extrusion-based 3D printing. Computational fluid modeling confirmed the presence of a shear stress gradient within the scaffolds and estimated the average shear stress that ES cells experience within each layer. Subsequently, we observed enhanced cell proliferation under flow perfusion within layers supporting lower permeability and increased surface area. Additionally, the effects of shear stress gradients on ES cell signaling transduction of the insulin-like growth factor-1 pathway elicited a response dependent upon the scaffold gradient orientation and the presence of flow-derived shear stress. Our results highlight how 3D printed scaffolds, in combination with flow perfusion in vitro, can effectively model aspects of solid tumor heterogeneity for future drug testing and customized patient therapies.