A range of cellular, architectural, and physical cues in the tumor microenvironment influence the intrinsic and acquired resistance mechanisms that lead to treatment failure. Strategies that leverage photodynamic therapy (PDT), a photochemistry-based biophysical treatment modality, to regionally target and prime stubborn tumor populations may be essential to realizing durable improvements in cancer management while minimizing toxicity from traditional agents. Capturing these attributes in rationally-designed combinations has shown promise by synergistically reducing tumor area in 3D models, and durably controlling tumor burden in vivo. Among the areas that remain understudied is the influence of mechanical forces, such as hydrodynamic shear stress, on resistance, and the development of 3D tumor models and in vivo models that account for physical stress. To evaluate and optimize PDT regimens, and PDT-based combinations, designed to overcome resistance to conventional therapies due to physical stress, a multi-faceted approach is needed. Here the impact of hydrodynamic stress is evaluated in bioengineered 3D tumor models in the context of ovarian cancer. The potential value of using biologically inspired in vitro models to guide customized, rationally-designed PDT-based combination regimens will be presented.
In ovarian cancer patients, the build up of fluid in the peritoneal cavity leads to the production of protein and cell rich asciites. Physiological movement establishes ascitic currents in the peritoneal cavity. The ascitic currents represent external flow which plays an important role in disseminating and modulating the biology of the ovarian cancer. Furthermore, the interstitial flow build-up inside tumor nodules establishes outward fluidic streams. The fluidic internal and external streams play an important role in drug delivery, which is also affected by permeability, an important physical property of the tumor. Permeability defines the flow dynamics over and through the tumor nodule, influencing therapy. The permeability of the tumor also affects the magnitude and distribution of fluidic shear stress experienced by the nodule. We propose to use experimental optical observations and mathematical descriptions of flow and mass transport for estimation of (i) the flow pattern around and through 3D porous cancer nodule surrogate, and (ii) the surrogate permeability. The permeability is estimated using an optimization technique in which the permeability value is iteratively modified to minimize the difference between the numerical solution of the mathematical model and the optical measurements. This algorithm is robust to discrepancy between the mathematical model and the experimental measurements. In this presentation, we show the feasibility of using particle image velocimetry (PIV) and confocal microscopy for estimating the permeability of a tumor surrogate by the optimization technique. Results suggest that the developed optimization toolbox can be used to estimate the tumor permeability in live 3D models of ovarian cancer.
Previous studies have demonstrated that flow-induced shear stress induces a motile and aggressive tumor phenotype in a microfluidic model of 3D ovarian cancer. However, the magnitude and distribution of the hydrodynamic forces that influence this biological modulation on the 3D cancer nodules are not known. We have developed a series of numerical and experimental tools to identify these forces within a 3D microchannel. In this work, we used particle image velocimetry (PIV) to find the velocity profile using fluorescent micro-spheres as surrogates and nano-particles as tracers, from which hydrodynamic forces can be derived. The fluid velocity is obtained by imaging the trajectory of a range of florescence nano-particles (500–800 μm) via confocal microscopy. Imaging was done at different horizontal planes and with a 50 μm bead as the surrogate. For an inlet current rate of 2 μl/s, the maximum velocity at the center of the channel was 51 μm/s. The velocity profile around the sphere was symmetric which is expected since the flow is dominated by viscous forces as opposed to inertial forces. The confocal PIV was successfully employed in finding the velocity profile in a microchannel with a nodule surrogate; therefore, it seems feasible to use PIV to investigate the hydrodynamic forces around 3D biological models.
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