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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