Introduction
Roughly three in four patients with advanced breast cancer develop
incurable bone metastases (Siegel, Miller, & Jemal, 2018). Once bone
metastasis occurs, the lesions are overwhelmingly osteolytic, putting
patients at great risk of suffering skeletal related events (SREs),
including severe bone pain and fracture (Coleman, 2005). The mechanical
environment in the skeleton is well-known to control bone tissue
homeostasis, with increased loading preventing or reversing osteoporotic
bone loss and associated fracture risk (von Stengel, Kemmler, Kalender,
Engelke, & Lauber, 2007). Similarly, mechanical signals are emerging as
a critical factor in bone metastasis and tumor-induced bone disease
(TIBD) (Beaton et al., 2009; Sheill, Guinan, Peat, & Hussey, 2018). A
few promising reports showed that in patients with existing bone
metastases, physical therapy resulted in multiple positive physical
outcomes (e.g. increased muscle strength) without SREs (Cormie et
al., 2013; Galvao et al., 2018), although changes to bone mass and
architecture were not explicitly evaluated. In preclinical models,
increased loading was protective against TIBD, such as tibial
compression (Fan et al., 2020; Lynch et al., 2013), low intensity
vibrations (Pagnotti et al., 2016), and ankle loading (Yang et al.,
2019). But, the impacts of loading on tumor cell function may be
dose-dependent, where high damage-causing forces may reverse the
protective effects of loading against TIBD (Fan et al., 2020). Clearly,
more work is needed to clarify the impacts of anabolic loading on TIBD
before successful translation to the clinic.
When forces are applied to the skeleton during physical activity, the
deforming tissue pressurizes the interstitial fluid, resulting in fluid
flow from high to low pressure. These interdependent physical signals
are then translated into intracellular biochemical signals, thereby
stimulating bone formation when increased (Robling & Turner, 2009;
Thompson, Rubin, & Rubin, 2012). Thus, elucidating the mechanisms of
how each of these mechanical signals is translated into stimuli in
cancer cells within a bone microenvironment is needed to understand how
anabolic loading is anti-tumorigenic. To this end, tissue
engineering-based approaches have been an indispensable tool.
Loading-induced interstitial fluid flow is widely recognized as an
anabolic signal for stimulating osteogenesis and bone formation (McCoy
& O’Brien, 2010), and can be generated in scaffolds via
directly-applied perfusion or compression of a hydrated scaffold.
Recently, these approaches have been leveraged to help uncover how
skeletal mechanical signals impact cancer cells in bone. Dynamic
compression applied to breast cancer cells in a mineral-containing
polymeric scaffold downregulated expression of osteolytic genes (Lynch
et al., 2013). In contrast, dynamic compression applied to Ewing Sarcoma
cells in a hydrogel increased their drug resistance (Marturano-Kruik et
al., 2018; Santoro, Lamhamedi-Cherradi, Menegaz, Ludwig, & Mikos,
2015), underscoring that cancer type and microenvironment are important
factors in bone cancer mechanobiology. Mechanical signals also regulate
tumor cell interactions with resident bone cells. For example,
mesenchymal stem cells (MSCs) increased their osteopontin production
upon exposure to breast cancer-derived soluble factors during
compression-induced osteogenic differentiation (Lynch et al., 2016),
indicating that tumor cells may stimulate bone cells to secrete proteins
that promote tumor cell adhesion. Further, mechanically loaded,
paclitaxel-releasing MSCs inhibited the growth of multiple myeloma cells
in the Rotary Cell Culture System (Bonomi et al., 2017; Ferrarini et
al., 2013).
While fluid flow is clearly important for bone homeostasis, the impact
of matrix deformations is much less understood. Matrix deformations and
fluid flow occur together (Robling & Turner, 2009), thus delineating
the individual roles of each signal in cancer cell mechanobiology
studies is challenging. One approach is to apply perfusion and
compression in combination and isolation, and several recent studies
using this approach report that fluid flow- and compression-induced
signals together enhance bone anabolism (Ramani-Mohan et al., 2018;
Zhao, Mc Garrigle, Vaughan, & McNamara, 2018; Zhao, Vaughan, &
McNamara, 2015). For example, multiscale computational modeling of a
hydrogel scaffold undergoing perfusion, compression, or both predicted
that the combination of low magnitude (0.5% peak strain) compression
and pore pressure (10 kPa) would induce more osteogenic differentiation
and bone mass (Zhao et al., 2018), perhaps as a result of greater cell
deformation (Zhao et al., 2015). Further, when computational approaches
were combined with combinatorial experiments involving MSCs with an AP-1
(an intracellular strain sensor) luciferase reporter, applied
compression resulted in the greatest cellular deformation and
osteogenesis, suggesting physical strain is the main driver of bone
anabolism rather than fluid flow alone (Ramani-Mohan et al., 2018).
Thus, both mechanical signals should be considered when investigating
the role of anabolic loading on tumor cells in the skeleton. Further,
computational simulations may help shed light on their respective roles.
Here, we report on multiphysics computational simulations of multi-modal
loading bioreactor experiments in advance of the metastatic breast
cancer cellular experiments to ensure a mechanical environment in the
anabolic range. Our bioreactor delivers compression and perfusion
individually or in combination to multiple bone-mimetic 3D constructs
(Fig. 1A). We have previously determined the necessary inlet flow
velocity to engender physiological and anabolic wall shear stresses
(WSSs) in our bone-mimetic scaffold (Liu, Han, Hedrick,
Modarres-Sadeghi, & Lynch, 2018), and we also determined an osteogenic
level of dynamic compression that also impacted bone cell-tumor cell
interactions (Lynch et al., 2016). Here, we extend our previous work to
include simulations of multiple magnitudes as well as dynamic
compression, and we anticipate that the combination of applied
compression and perfusion will synergize to produce the greatest
mechanical signals (Zhao et al., 2018).
Materials and Methods
Overview of Experimental
Setup used for Simulations
Our experimental setup includes a multi-modal loading bioreactor and
bone-mimetic scaffold. With our bioreactor (Bangalore integrated System
Solutions), we can apply compression and perfusion, which are
independently controlled, either in isolation or in combination.
Mechanical stimuli is applied to breast cancer cells seeded in highly
porous scaffolds fabricated from poly(lactide-co-glycolide) (PLGA)
microspheres and hydroxyapatite (HA) scaffolds (Lynch et al., 2013;
Pathi, Kowalczewski, Tadipatri, & Fischbach, 2010). We simulated eight
experiments with dynamic compression and steady perfusion in various
combinations with a static, nonloaded control (Fig. 1B). The compression
conditions included no compression (C-), low compression (5% peak bulk
strain, C+), and high compression (10% peak bulk strain, C++) applied
at 1 Hz. The high compression value (10% peak strain) previously
inhibited expression of resorption genes in breast cancer cells within
the same PLGA-HA scaffold modeled here (Lynch et al., 2013), and
modulated their interactions with bone marrow mesenchymal stems during
osteogenic differentiation (Lynch et al., 2016). The low compression
value (5% peak strain) serves as a lower stimulus, but is within the
range of commonly used strains in experiments to stimulate osteogenic
responses (Bhatt et al., 2007).
The inlet perfusion velocity conditions included no perfusion (P-),
steady low perfusion (0.3 mL/min, P+), and steady high perfusion (0.6
mL/min, P++). Our rationale for applying steady, as opposed to dynamic,
perfusion is that no clear consensus currently exists as to which
profile is better for osteoinductive responses in bone cells in tissue
engineered constructs (McCoy & O’Brien, 2010). The low perfusion rate
(0.3 mL/min) was previously simulated via CFD, and resulted in internal
shear stresses that are in the osteogenic range and flow velocities
similar to intracanalicular flow velocities during in vivo tibial
loading (Liu et al., 2018).