Discussion
Here, we report estimates of the internal mechanical field from multiphysics simulations of a bone scaffold undergoing combinations of high and low compression and perfusion. Simulations were conducted in advance of multi-modal experiments with bone metastatic breast cancer cells to ensure that mechanical stimuli occurring internally were anabolic. Our results show that mechanical stimuli throughout the scaffold were within the anabolic range of bone cells in all loading configurations, and local distributions were homogenously distributed throughout.
Overall, the wall shear stresses within the scaffold during loading were found to be in the physiological and anabolic range. We reported peak median wall shear stresses in the range of ~5 – 25 mPa across all of our loading configurations, which is in line with values reported to stimulate osteogenesis in tissue-engineered scaffolds (Fernandez-Yague et al., 2015; Stops, Heraty, Browne, O’Brien, & McHugh, 2010). Some studies reported significant osteoblastic cell loss at higher WSS ranges (~1 – 50 mPa) (Jaasma & O’Brien, 2008; Partap, Plunkett, Kelly, & O’Brien, 2010; Plunkett, Partap, & O’Brien, 2010), though how breast cancer cells will adhere to our bone scaffold at the higher WSS values is unknown. In vivo, breast cancer cells have been shown to localize to osteogenic niches at the endosteal surface (Wang et al., 2015), a site that can experience very high shear stresses according to simulations, particularly under applied loading (as high as 5 Pa) (Birmingham et al., 2015; Coughlin & Niebur, 2012). Even so, at our higher magnitude loading configurations, we and other should take care to investigate this. When breast cancer cells were seeded in the same scaffold modeled here and underwent 10% dynamic compression, the breast cancer cells’ expression of genes that controlled downstream remodeling (Runx2) was altered with no apparent loss of cellularity (based on imaging) (Lynch et al., 2013). Here, our modeling results of this experiment would indicate that these cells would have experienced peak median WSS of ~10 mPa. Ewing Sarcoma cells in a compressed hydrogel experienced peak fluid velocities ~4 – 6 um/sec (Marturano-Kruik et al., 2018), which is 3 orders of magnitude different compared to our peak of 2 mm/sec. This highlights that differences in tumor cell response may occur across different cancer types and microenvironments.
Though our WSS values are in the range for osteogenesis and bone formation by osteoblastic cells, we utilized steady flow, which is not physiological. However, in vitro, whether steady or dynamic flow is more beneficial remains an open question. In 2D studies that directly compared steady versus dynamic flow, no differences were observed (Case et al., 2011; Kreke, Sharp, Lee, & Goldstein, 2008). In contrast, one study demonstrated oscillatory flow stimulated greater Ca2+ flickers in osteoblasts (Roy, Das, Mishra, Maiti, & Chakraborty, 2014). Similarly, using a microfluidic approach, oscillatory fluid flow was more stimulatory to osteocytes (Middleton, Al-Dujaili, Mei, Gunther, & You, 2017). In 3D, most studies utilize steady flow to stimulate osteogenesis and bone formation (Bancroft et al., 2002; Cartmell, Porter, Garcia, & Guldberg, 2003; Sikavitsas, Bancroft, Holtorf, Jansen, & Mikos, 2003; Sikavitsas et al., 2005; Zhao, Chella, & Ma, 2007), but overall, studies that utilized either steady or dynamic (oscillatory, pulsatile) have reported osteogenesis. Few studies have compared steady and dynamic flow side to side in 3D, and the results are mixed. In favor of dynamic flow, intermittent flow caused greater stimulation of osteoblasts than steady flow (Jaasma & O’Brien, 2008). Pulsatile flow appeared best for bone protein formation relative to steady flow (Sharp, Lee, & Goldstein, 2009), and pulsatile flow more strongly upregulated osteoblast production of cyclooxygenase-2 while oscillatory flow more strongly upregulated prostaglandin E2 (Jaasma & O’Brien, 2008), both important osteogenic signaling factors. Even if applying steady flow, rest periods of static or low flow are recommended to overcome cellular desensitization (Robling, Burr, & Turner, 2000), with similarly mixed results in vitro (Batra et al., 2005; Jaasma & O’Brien, 2008; Kreke, Huckle, & Goldstein, 2005; Kreke et al., 2008; Partap et al., 2010; Plunkett et al., 2010; Vance, Galley, Liu, & Donahue, 2005).
Few studies have studied dynamic versus steady or static mechanical signals on tumor cells. Static flow in 2D resulted in apoptosis of cancer cells across multiple lines (Hep3B hepatocarcinoma cells, MG63 osteosarcoma cells, SCC25 oral squamous cells and A549 carcinomic alveolar basal epithelial cells) while oscillatory flow did not (Lien et al., 2013). Pulsed magnetic forces applied to TCC-S leukemic cells with a magnetic bead increased tumor cell death both in vitro and in vivo, a response that enhances when combined with an anti-cancer therapy (Ogiue-Ikeda, Sato, & Ueno, 2003; Yamaguchi, Sato, Sekino, & Ueno, 2006). In a novel microfluidic device, when mechanically-flow osteocytes were adjacent to breast cancer cells, breast cancer extravasation was significantly reduced with mechanically-stimulated osteocytes compared to static osteocytes, though the breast cancer cells remained under static conditions (Mei et al., 2019). When considering the effects of strain, 2D stretching is typically applied acutely and held steady for a period of time with a variety of results across multiple cell types (Gao & Carson, 2016; Manome, Saeki, Yoshinaga, Watanabe, & Mizuno, 2003; McKenzie, Svec, Williams, & Howe, 2020; Panzetta, Fusco, & Netti, 2019; Riching et al., 2014). One study reported that 2D cyclic compression of breast cancer cells plated underneath an agarose gel by a platen regulated necrosis vs apoptosis, and the mode of death was sensitive to loading frequency, peak applied compressive displacement, and duration of loading bout (Takao, Taya, & Chiew, 2019). Similarly, in 3D, dynamic compression of a hydrogel with Ewing Sarcoma cells altered drug sensitivity compared to static controls, and the response was sensitive to peak strain magnitude (Marturano-Kruik et al., 2018). As mentioned previously, breast cancer cells in our scaffold altered their gene expression under dynamic compression relative to static controls, though no change in viability was observed (Lynch et al., 2013). Overall, these results emphasize that future work is needed to study the impacts of steady versus dynamic mechanical signals experimentally.
One of our goals is to delineate the individual roles of matrix deformation and fluid flow on tumor cell behavior, which is challenging as they are coupled together in the body. Our approach to achieving this goal is to correlate the estimated internal mechanical signals with biological outputs following applied perfusion and compression experimentation. By comparing the effects of perfusion alone to configurations that include deformation and interstitial fluid flow (i.e. compression alone and compression + perfusion), we expect to be able to isolate their respective effects. The asymmetry among the various mechanical signals will be a challenge in interpreting results. For example, compression alone, which best represents the in vivo mechanical environment by causing deformations and interstitial fluid flow, exhibits a phase lag between peak strains and peak wall shear stresses that may have biological implications. For our particular approach, a crucial consideration is that we use steady rather than dynamic perfusion. As shown by our computational results, this results in compression-induced flow and applied perfusion acting in concert (i.e. larger velocities) in the upper scaffold region during part of the loading cycle, leading to greater WSSs in that region. Conversely, in the latter half of the loading cycle, compression-induced flow and applied perfusion act in concert at the lower portion of the scaffold. Dynamic perfusion should be incorporated in future experiments to better reflect in vivo physiology, but mechanical signal asymmetries will still remain. Some approaches for dealing with the asymmetries could be to incorporate live imaging during loading to sense Ca2+ signaling (a known intracellular flow signal (Chen et al., 2000)), and/or intracellular strain signals (i.e. AP-1 (Ramani-Mohan et al., 2018)). Another approach could be to apply larger magnitudes of loading to help augment the signal-to-noise ratio in various strain- and flow-response pathways.
In summary, we have generated multiphysics models of our multimodal loading experiments to estimate interior scaffold strains and interstitial fluid velocities and wall shear stresses during loading experiments with breast cancer cells in a bone microenvironment. Our long term goal is to study bone metastatic breast cancer cell mechanobiology, and to understand how anabolic mechanical loading confers anti-tumorigenic effects to breast cancer cells (Fan et al., 2020; Lynch et al., 2013; Pagnotti et al., 2016). We confirmed that our imposed mechanical signals are within the range known to stimulate an anabolic response in bone cells, thus our experiments will reflect conditions during anabolic loading in preclinical models of bone metastasis.
Acknowledgements : We are grateful for the funding support provided for this research by NSF CBET 1605060. We highlight support from The Massachusetts Green High Performance Computing Center (MGHPCC).
Conflict of Interest: The authors have no conflicts to declare.
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