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Targeting the Biophysical Properties of the Myeloma Initiating Cell Niches: A Pharmaceutical Synergism Analysis Using Multi-Scale Agent-Based Modeling


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Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.
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Targeting the Biophysical Properties of the Myeloma
Initiating Cell Niches: A Pharmaceutical Synergism
Analysis Using Multi-Scale Agent-Based Modeling
Jing Su
, Le Zhang
*, Wen Zhang
, Dong Song Choi
, Jianguo Wen
, Beini Jiang
, Chung-Che Chang
Xiaobo Zhou
1Department of Radiology, The Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 2College of Computer and Information
Science, Southwest University, Chongqing, People’s Republic of China, 3School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York,
United States of America, 4Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, Texas, United States of America, 5Department of
Pathology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, Texas, United States of America, 6Department of Mathematical Sciences,
Michigan Technological University, Houghton, Michigan, United States of America, 7Department of Pathology, Florida Hospital, University of Central Florida, Orlando,
Florida, United States of America
Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse
and development of multiple drug resistance. We and others recently established the biophysical model that myeloma
initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then
promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the
pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-
scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-
oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and
investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used
AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel
therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results
showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1
secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance.
Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone
marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to
virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we
established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as
experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem
cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of
cancer relapse.
Citation: Su J, Zhang L, Zhang W, Choi DS, Wen J, et al. (2014) Targeting the Biophysical Properties of the Myeloma Initiating Cell Niches: A Pharmaceutical
Synergism Analysis Using Multi-Scale Agent-Based Modeling. PLoS ONE 9(1): e85059. doi:10.1371/journal.pone.0085059
Editor: Persio Dello Sbarba, Universita
`degli Studi di Firenze, Italy
Received July 8, 2013; Accepted November 21, 2013; Published January 27, 2014
Copyright: ß2014 Su et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors thank the funding resource: NIH/NLM 5R01LM010185-04. One of the authors, Dr. Le Zhang, is currently supported by the Natural Science
Foundation of China under Grant No. 61372138. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* Email: (LZ); (CC); (XZ)
Multiple myeloma (MM) and other tumors have a small
population of tumor initiating (stem) cells that retain key stem cell
properties including self-renewal and tumorigenesis [1–13].
Recent reports [3,4] showed that a small population of CD138-
negative B cells with ‘‘side population’’ characteristics present in
myeloma. These cells have clonogenic potential in vitro and, when
engrafted into immunodeficienct/nonobese diabetes (SCID/
NOD) mice, can initiate de novo myeloma lesions of bulk of
CD138+cells in both primary and secondary transplant assays.
Additionally, these myeloma initiating cells (MICs) have shown
higher resistance to chemotherapeutic agents and thus are more
likely to survive despite therapies [1–10]. These findings have led
to the hypothesis that MICs survive chemo- and radio- therapies,
regenerate the bulk of tumors, and thus cause the disease relapse.
This idea is consistent with the clinical observation that disease
relapse in multiple myeloma patients is common even if patients
are treated with new therapeutic agents that can initially result in
complete clinical responses [14–16]. Understanding and control-
ling MIC drug resistance is critical to the development of new
therapies for the cure of myeloma.
Our group pioneered the research of the roles of biophysical
properties in blood cancers and established the mechanism of the
MIC-stroma positive feedback loop [17,18]. Previous studies on
the interactions between BMSCs and myeloma cells, especially
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MICs, have predominantly focused on biochemical communica-
tions such as the stimuli of growth factors, cytokines and
chemotactic via paracrine signaling [19]. However, recent studies
in solid tumors have indicated that a critical stage of the malignant
transformation journey of cancer cells involves marked alterations
in the biomechanical phenotype of the cell and its surrounding
microenvironment [20,21]. Indeed, it has been suggested that
targeting the microenvironments (the ‘‘niches’’) of the tumor stem
cell could result in a reduction of the tumor burden [22–24]. Bone
marrow stromal cells (BMSCs), one of the major cellular
components in the MIC niches, are in close contact with MICs,
and the biomechanical properties of BMSCs, besides chemical
communications, also influence the local microenvironment of
MICs and hence MIC fates. We have recently demonstrated that
Myeloma-associated BMSCs (MBMSCs) from patients are much
‘‘stiffer’’ (higher Young’s modulus level) and more contractile than
Normal BMSCs (NBMSCs). Hydrogels are widely used to mimic
the in vivo cellular microenvironments [25,26], so we have utilized
hydrogels of various stiffness levels to investigate the impact of such
biophysical property on MIC-driven myeloma development. We
have shown that stiffer hydrogels support colony formation and
adherence of MICs better than softer hydrogels, suggesting that
myeloma BMSCs provide myeloma cell-friendly microenviron-
ments for MICs via exerting biomechanical forces [17,18]. We
also have demonstrated that MICs over-secrete SDF1 than mature
myeloma cells and that treatment of CXCR4 inhibitor,
AMD3100, leads to decreased adherence of MICs to MBMSCs,
undermined colony formation potential of MICs, and better in
vitro and in vivo drug efficacy of Bortezomib(BZM). These
discoveries were also consistent with other reports [27].
With the perspective that the biophysical properties of MIC
niches in bone marrow may be a promising drug candidate, there
is an urgent need to develop mathematical models and tools to
estimate drug effects on MICs within the context of their niches
during when screening and evaluating drug candidates. Currently
pharmaco-industries use the killing efficiency of the bulk tumor as
the major in vitro drug screening criterion. Such practices
overlook the importance of MICs cell microenvironments
contributing to the common failure of translating promising drug
candidates discovered in screening into clinical usage. However,
mathematical models that involve the cancer initiating (stem) cells
and their niches are still rare, except for few examples [28–30]
including our recent work [31].
Modeling the dynamics of the MIC-derived myeloma lineage is
crucial for associating the abnormality at cellular level with the
features of the myeloma pathogenesis and pathophysiology at
tissue level. We recently developed a myeloma lineage model [31]
to systematically analyze the myeloma lineage development and
experimentally monitor the major sub-populations in the lineage.
Briefly, cells involved in the myeloma lineage were classified as
myeloma initiating cells (MICs), myeloma progenitor cells (PCs),
and matured myeloma cells (MMs). These three sub-populations
were experimentally distinguishable by dual-staining for the
expression of plasma cell surface markers CD138 and for side
population (SP) using Hoechst staining, each showed unique
features related with lineage sub-populations. MICs, recognized by
SP staining and negative CD138 staining (SP/CD1382), showed
unlimited renewal capability, potential to initiate and fully re-
establish the whole myeloma lineage, and enhanced drug
resistance. PCs, defined as the non-SP and negative CD138
expression (non-SP/CD1382) population, were only able to
passage for limited rounds before beginning to express CD138 and
losing the key PC character. The CD138+sub-population, which
was of 90% to 95% of the total myeloma population, was classified
as the MMs and exhibit very limited proliferation capability. In
this work we used similar definitions except further distinguishing
terminal MMs (TMMs) that could no longer proliferate from those
that still could divide (MMs).
To comprehensively illustrate the interaction between the
myeloma initiating cells and their niches and to develop effective
drug treatment, the following key questions need special atten-
tions: (1) How do BMSCs, MICs, myeloma progenitor cells (PC),
matured myeloma cells (MMs) and terminal MMs (TMMs)
communicate in MIC niches? (2) How do the biomechanical
phenotypes of BMSC, in terms of cell stiffness and contractibility,
modulate MIC’s growth and fates? (3) How do MIC growth and
differentiation drive the development of myeloma? (4) How do the
molecular level intracellular features of MICs and their stromal
counterpart, BMSCs, contribute to the tissue level 3D cancer
growth via the intercellular cell-to-cell interactions? And (5) How
does the interaction between tumor cells and their niches change
the drug treatment effect?
To address these questions, in this study we developed a 3D
multi-scale agent-based model (ABM) using Markov Chain Monte
Carlo approaches [32] to study the role of tumor–stroma
interactions in multiple myeloma tumor progress. The system
was classified into three levels: the intracellular, the intercellular,
and the tissue level. Their relations were conceptually defined as
‘‘interfaces’’ among these levels. The agent-based model integrates
events of multiple spatial and temporal scales. (1) Spatial scales:
Intracellular signaling pathways were encapsulated into each cell
type to determine the BMSC intercellular biomechanical pheno-
type (cell stiffness) or tumor cell behaviors (migration or
proliferation). Cancer cells competed for the best location in 3D
extracellular matrix to migrate or proliferate regarding to the
change of BMSC cell’s stiffness and sensitivity of chemoattractant
(SDF1). In turn, chemoattractant cues at the tissue level triggered
intracellular signaling pathways inside cells via the agent interfaces
(receptors), and the resultant feedbacks were the changes of either
cells’ properties (change of cell stiffness or sensitivity to outside
stimuli) or behaviors (secretion of chemoattractant, proliferation,
differentiation, apoptosis, or migration). (2) Temporal scales: The
model covered minute-to-hour-level signaling dynamics; day-level
cell division, apoptosis, and local migration; week-level drug
responses; and month-level tumor growth.
The spatial characteristics of the 3D myeloma cell distributions
was described by the local cell metrics [33] we previously
developed and the corresponding parameters estimated using the
3D cell co-culture system levitated by magnetic field and nano-
shuttles [34].
Model development
We established an agent-based multi-scale model to simulate the
development of myeloma in various bone marrow microenviron-
ments in three-dimensional space, and validated the model with
experimental data. The central hypotheses are: 1) MICs drive the
development of multiple myeloma; and 2) the SDF1-centric bio-
physical and chemical positive feedback loop boosts MIC growth
and colonogenesis, and protects MICs from drug treatments. As
shown in Figure 1, myeloma development was simulated at
intracellular, intercellular, and tissue levels. In this model we
included five types of cells which were represented by five types of
agents of encapsulated intracellular signaling events and interfaces
through which these agents communicated with their microenvi-
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(1) Bone marrow stromal cell (BMSC) agent. This agent modeled
the 3D reticular network formed by BMSCs. BMSCs changed
its biophysical stiffness by cell contraction in response to SDF-
1 hints in microenvironment and thus altered the three-
dimensional distribution of bone marrow stiffness;
(2) Myeloma initiating cell (MIC) agent: This agent represented
myeloma stem cells enriched by side population staining
[4,35]. MIC sensed the stiffness of its microenvironment and
accordingly modulated its proliferation, differentiation, apop-
tosis, and drug resistance;
(3) Progenitor cell (PC) agent: This agent simulated the expansion
of tumor tissue from MICs with given passenger limits. This
type of cells was lack of clear surface markers;
(4) Mature multiple myeloma cell (MM) agent: The mature
multiple myeloma cells which presented clear surface markers
(for example CD138) while still proliferating; and
(5) Terminal myeloma cell (TMM) agent: modeling terminal
MM cells which can no longer proliferate.
Please note that although mathematically PC, MM, and TMM
can be lumped into one agent, each of them have unique
biological behaviors (such as apoptosis rates) and can be
experimentally measured by flow cytometry. Therefore we kept
them distinguished for better understanding the simulation results
for future experimental validations, and for clinical applications.
At the intercellular level the communication among agents were
simulated by the biomechanical model representing the MIC-
BMSC positive feedback loops and the myeloma lineage model
illustrating the dynamics of different myeloma populations.
Interactions between BMSC and MIC were: (a) MICs secreted
SDF1 into the neighborhood extracellular matrix. The diffusion of
SDF1 in the tissue defined the biochemical microenvironment in
bone marrow. The BMSCs contracted according the paracrine
SDF1 in their neighborhood and altered the biophysical properties
of the bone marrow. MICs sensed and preferably attached to
stiffer BMSCs. When attached, stiffer BMSCs boosted prolifera-
tion and self-renewal of MICs, and thus promoted the expansion
of both MIC and myeloma, and drove sustainable multiple
myeloma cancer growth. Stiffer BMSCs also protected MICs from
the treatment of cytotoxic drugs.
At the tissue level, the SDF1 profile defined by MIC paracrine
and diffusion and the drug concentration defined by the
administration of chemotherapeutics determined the biochemical
microenvironment in bone marrow, the tissue stiffness defined by
BMSC contraction determined the biophysical microenvironment,
and myeloma proliferation and migration determined the cellular
distribution in bone marrow.
Multiple myeloma cells at different stage of differentiation as
defined above were initially seeded at the center of the bone
marrow, 100 cells for each cell type, to mimic the initiation stage
during myeloma spreading to new locations or after implantation
into the bone marrow of animal models. Bortezomib (BZM) and
AMD 3100 were used as the representatives of cytotoxic drugs and
inhibitors against MIC-BMSC interactions, respectively. After
200 hours of tumor seeding these two drugs were delivered
intravenously into the simulated extracellular matrix at combina-
tions of various doses to eliminate multiple myeloma cancer cells.
The chemotherapy was scheduled that each cycle included a 400-
hour treatment period followed by 500 hours rest (about 2 week
treatment followed by 3 week rest), which was typical in clinics. In
our experiments usually the animals were sacrificed and the
outcome was examined 200 to 400 hours after the first treatment
period or one month after tumor xenografting, so the simulation
results of the first 600 to 1,000 hours (about one month) were
shown. We only simulated the tumor responses to the first
treatment cycle because multiple myeloma quickly develops drug
resistance and therefore elimination of all MICs in the first
treatment cycle is crucial. Parameters were directly determined or
indirectly inferred based on our previous experimental results
Figure 1. A sketch of ABM model.
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[17,18,36], or set according to the best of our knowledge of
multiple myeloma, and were summarized in Table S1 and Table
S2 in Supporting Information.
To reduce the computational cost the diffusion of the two drugs
in bone marrow was assumed to be instantly. This assumption is
valid since the myeloma-enriched regions in bone marrow are well
vascularized and the diffusion of small molecule drugs is
significantly faster comparing with either typical cell behaviors
such as migration, proliferation, and apoptosis, or comparing with
the diffusion of SDF-1.
Twelve dose levels (including level 0) for each drug and their full
combinations were examined under both myeloma-associated
BMSC (MBMSC) and normal BMSC (NBMSC) microenviron-
ments, each condition simulated for 20 times. Time resolution
(time step) was 2 hours. Totally 288 conditions was explored by
5,760 simulations using parallel computation on a Dell
850 server of 48 CPU cores.
Examples of model simulation results of MIC-driven myeloma
growth in bone marrow were visualized and compared with in
vitro 3D levitated culture image in Figure 2. The tumor growth
at different stage (100 hr vs. 500 hr post-initiating) as well as the
associated variations of biophysical properties in bone marrow and
activities of MICs were shown in Figure 2 A through D.As
contrast, tumor and stiffness distributions under BZM treatment
(dose of the 10
relative level) between 200 and 300 hr were
demonstrated in Figure 2 E and F. Quiescent MICs were
highlighted in blue and proliferating MICs in red. The details of
biophysical stiffness in bone marrow were visualized by the
sections in Figure 2 G. Stiffness was labeled in color, from blue to
red with respect to the increase of stiffness. Since in vivo 3D
imaging of BMSC contraction is yet unavailable, we co-cultured
myeloma with BMSCs in vitro using the 3D levitation system to
mimic the bone marrow microenvironment, and 4-day co-cultured
tumor tissue was stained and 3D imaged by confocal microscopy
(details see Methods section) and shown in Figure 2 H. BMSCs
(white arrows) and myeloma cells (green arrows) were recognized
by cell shapes according to F-actin staining (red) as well as nuclei
staining (blue). The primed MBMSCs (yellow arrows) were
recognized by the formation of stress fibers. The size of the
experimentally cultured tumor at day 4 was about 2666492 mm,
which was very close to the diameter of the infiltration frontier
(about 460 mm) of the simulated results at time point 100 hr
(Figure 2 A).
Details of the model can be found in the Methods section.
Typical movies as Movie S1,S2, and S3 can be found in
Supporting Information, and the corresponding simulation
conditions in the caption of Figure 2.
Model validation
The simulation results of the agent-based model of myeloma
growth under different biophysical microenvironments were
compared with experimental results [18,36] to validate the model.
The simulated MIC populations as well as the total tumor sizes
under the NMBSC and MBMSC (Blue vs. red lines in Figure 3 A
and B) after 4 weeks growth were consistent with experimental
results [18] (Blue vs. red bars in Figure 3 A and B). Tumor also
showed consistent BZM dose responses after 48 hr treatment in
simulation and in experiments (blue vs. red lines, Figure 3 C).
Data shown in Figure 3 were the mean values of the 20
simulations for every 20 hours (every 10 time steps).
Effects of MBMSCs in disease development
We first tuned the model to recapture experimental observa-
tions and explored the roles of myeloma-associated MBMSCs in
disease development using the NBMSCs as controls. The
simulation results of tumor growth and the comparison with
experimental data were summarized in Figure 4 and Figure 3.
The cell number of each tumor cell type during the first 600 hours
(25 days) were shown for the NBMSC (Figure 4 A) or the
MBMSC (Figure 4 B) case, and the MIC populations (Figure 3
A) as well as the total tumor sizes (Figure 3 B) were compared
with our previously published experimental results. Tumor grew
about 2.6 folds faster under MBMSC context, while the MIC
population was 6.8 folds larger. Simulation results were consistent
with experiments. The myeloma population in MBMSC was
dominated by ‘‘younger’’ tumor cells such as progenitors
comparing with the normal counterpart, which was dominated
by terminal myeloma cells. Data shown in Figure 4 were also the
mean values of the 20 simulations for every 20 hours (every 10
time steps).
Effects of MBMSCs on chemotherapy outcomes using
To explore the drug resistance to chemotherapies and the risk of
tumor relapse in MBMSC and NBMSC microenvironments, we
simulated the first BZM treatment cycle at 11 dose levels covering
two magnitudes. BZM was delivered 200 hours after tumor
initiation and the treatments lasted for 400 hours. The dynamic of
total tumor cells as well as MICs during the first 1000 hours were
shown in Figure 5. After drug delivery, the tumor populations
quickly dropped for both MBMSC and NBMSC (red and blue
lines in Figure 5 a, respectively) cases. The drug efficacy, in terms
of the elimination of myeloma population, was similar in both
cases, and during the first treatment cycle the tumor cell number
decreased to a very low level that were clinically not detectable.
However, drug efficacy on MICs, a small portion of the tumor
population, was significantly different. Medium dose of BZM
treatment (the sixth level among all eleven) completely killed all the
MICs in NBMSC-dominated bone marrow (Figure 5 b blue
line), whereas a few MIC cells survived the chemotherapy in the
MBMSC case (Figure 5 b red line) due to the drug resistance
boosted by myeloma-associated stroma. The MIC-free tumor
population kept degenerating till the cure of the disease, while in
contrast the survived MICs re-initiate the myeloma and caused
MIC-driven tumor relapse (Figure 5 a blue vs. red lines, time 600
to 1000 hr).
Two typical simulations were shown in Figure 5, and the rest
were discussed in the following sections.
Inhibition of MIC-BMSC communications by AMD 3100
We then examined if the inhibition of MIC-BMSC communi-
cations could deprive MICs from the protection of their stroma
and re-sensitize the tumor in the MBMSC context to chemother-
apy. AMD 3100, a competitive inhibitor of SDF-1, was delivered
at 11 levels crossing two magnitudes, either alone or with BZM, to
myeloma growing with either myeloma-associated or normal
BMSC stroma. The effects of medium AMD dose were visualized
by two simulations in Figure 6 and the rest summarized in
Figure 7. AMD-treated and AMD-free cases were denoted in
solid and dashed lines, and the myeloma-associated and normal
BMSC niches in red and blue colors, respectively. Consistent with
Figure 3 (A) and (B) and Figure 5 (A) and (B), MBMSC niches
promoted tumor growth (Figure 6 a red vs. blue dashed lines),
MIC self-renewal (Figure 6 b red vs. blue dashed lines), and drug
resistance (Figure 6 c red vs. blue dashed lines) driven by MICs
(Figure 6 d red vs. blue dashed lines). Introduction of niche
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inhibitor at moderate level alleviated all these effects (Figure 6 A
through Dred vs. blue solid lines).
Combined drug effects on both cases
To evaluate if interrupting the MIC-BMSC communications
has clinical potentials for multiple myeloma, we tested the
combinatorial effects of the two representative drugs, AMD3100
and BZM. For each drug, 12 doses (0 as control and 11 levels from
0.16to 106in geometric sequence relative to the original dose)
were selected and the full combinations of the two drugs were
explored for drug efficacies. The default doses for each, denoted as
16, was defined as the minimum doses that, for BZM, eliminated
all MICs in NBMSC case and, for AMD3100, deprived MBMSC
support. Each combinatorial condition was simulated for 20 times
at 400 time points for 5 cell types and SDF-1 concentration
distributions, and totally 13,824,000 data sets were generated. The
means of replicates were used for further analysis, and the
synergistic effect maps were further smoothed with a two
dimensional Gaussian kernel (h= 2) for robust visualization.
Loewe combination index [37,38] (for an introduction and a
guide of interpretation of Loewe combination index please refer to
Figure S1 in the Supporting Information), defined according
to the 50% MIC population reduction by the end of the two week
chemotherapy (E50 at time point 600 hr), was utilized to evaluate
the synergism of AMD3100 and BZM while treating myeloma in
either NBMSC or MBMSC niche. We selected the total myeloma
death rate as well as MIC death rate as the indicator of drug
efficacy because as demonstrated in Figure 5 (first and second
rows, respectively). MIC played essential role in post-treatment
disease relapse [4]. The SDF-1/CXCR4 inhibitor showed on
moderate effects on the efficacy of BZM when treating multiple
myeloma developed in both normal and myeloma-associated bone
marrow stromal cell microenvironments in terms of both the
shrinkage of total tumor size (red contour lines in Figure 7 a and
b, respectively) and the decrease of MIC populations (red contour
lines in Figure 7 c and d, respectively). In contrast, dramatic
synergistic effects of the two drugs were observed if the total
elimination of MIC (E100) was used as the criterion for Loewe
combination index in MBMSC cases (green contour lines in
Figure 7 d) but not in NBMSC cases (green contour lines in
Figure 7 c).
This work previewed the druggability of the biophysical features
of cancer stem cell niches, and cast new light on the strategies to
overcome the drug resistance and relapse of multiple myeloma.
The central hypothesis of this work, that the positive feedback
loop between MIC and MBMSC via SDF-1 paracrine and the
increase of MBMSC niche stiffness promotes myeloma develop-
ment and is responsible for drug resistance and cancer relapse, has
been successfully realized in this multi-scale agent-based model.
Figure 2 showed that MBMSCs close to MICs were primed and
activated by SDF-1 secreted by MICs provided a stiffer
microenvironment; such pro-ontogenetic niches in turn boosted
MICs in terms of proliferation and drug resistance.
It was the first time the dynamics of the bone marrow stiffness
during the development of myeloma was simulated and visualized
in three-dimensional space at multiple scales. While the in vivo
assay technologies of the bone marrow biophysical properties are
yet not available, this model provides insights into the mutual
communications between cellular and mechanical information.
The simulations (Figure 2 and Supporting Information
Movie S1 and S2) suggested that both the infiltration process of
myeloma as well as the distribution of bone marrow stiffness were
highly dynamic and the two types of distributions showed
temporal and spatial lags. In vivo monitoring the changes of bone
marrow biophysical properties can be a valuable method to
directly estimate drug efficacy of myeloma-associated BMSC
Our simulation results also emphasized the crucial role of MICs
in disease relapse, which was illustrated in Figure 5. The
elimination of cancer stem cell is the essential goal of chemother-
apies for multiple myeloma; otherwise the survived MICs will
drive the re-initiation of the cancer and the relapse of the disease
[4]. However, due to the drug resistance of MICs and the
protection of MIC niches, it is challenge for traditional chemo-
therapies to target and purge all MICs [4,39]. Targeting the MIC
niches thus becomes an intriguing strategy.
Besides that the model is conceptually consistent with our
knowledge of multiple myeloma, we also confirmed that the
simulations reproduced the key experimental findings of MIC/
MBMSC interactions [17,18,36]. Although parameters of this
piloting model were roughly and bona fide determined from
literatures, our experiments, and the best of our biological
knowledge (Table S1 and Table S2 in Supporting Informa-
tion), the model is capable to re-capture our experimental
observations (Figure 2 H and Figure 3).
(1) Tumor initiation. One major feature of multiple myeloma is
its frequent metastasis. Understanding the initiation and early
stage development of myeloma at new bone marrow sites is
thus critical for clinical intervention. However, it is yet
challenging to monitor such events in vivo. We established the
3D levitated co-culture system to mimic the early events of the
development of secondary myeloma and to reveal its unique
features. The similar growth trends from the simulated results
and the 3D co-culture in terms of tumor size (Figure 2 A vs.
H) suggested that the model is capable of predicting growth
trends after successful metastasis as well as the post-treatment
re-initiation of tumors. The geometric shapes of myeloma
growth are dynamic and subject to the stochastic feature of
cell migration as well as the context of cell growth, though.
(2) Pro-oncogenetic MBMSC microenvironments. As mentioned
before, accumulating evident suggest that MICs were capable
to prime MBMSCs but not NBMSCs in terms of BMSC
stiffness, which in turn promotes myeloma growth and
enhance drug resistance. The model re-captured these
features. MICs population was boosted in myeloma-associated
Figure 2. Simulation of myeloma development in three-dimensional bone marrow space. The tumor growth (A and C) associated with
the stiffness profiles (B and D) and the activities of MICs at early (100 hr, A and B) and later (500 hr, C and D) stages. (E and F) Tumor and stiffness
distributions after BZM treatment at relative level 10 between time point 200 hr and 300 hr. The tumor infiltrating frontiers were labeled by brown
isosurfaces, condensed tumor region in yellow, and quiescent MICs in blue while proliferating MICs in red. The stiffness distributions were labeled
with isosurfaces, blue denoted lower stiffness and red higher stiffness. (G) The distribution of stiffness in myeloma-associated bone marrow stiffness
400 hr after initial myeloma seeding. (H) The distribution of myeloma cells and the activation of MBMSCs after 4 days 3D levitated culture. Blue: cell
nuclei (stained with DAPI); red: F-actin fibers (stained with Alexa FluorH594 conjugated phalloidin); white arrow: MBMSCs; green arrow: myeloma
cells; yellow arrow: activated BMSCs marked by the formation of stress fiber.
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bone marrow (red lines in Figure 4 B versus Aand the red
vs. blue line in Figure 3 A) and thus drove the development
of myeloma (Figure 3 A versus Figure 4 A and the red vs.
blue line in Figure 3 B).
(3) Drug responses. The consistent dosing effects of BZM in terms
of the shrinkage of tumor sizes between the model predictions
and the experimental results (Figure 3 C) are strong evidence
that the model has the capability to predict the drug responses
of myeloma over a wide range of drug doses. AMD3100
deprived the support of niches to the expansion of MICs
(Figure 6 solid vs. dashed lines). MICs showed drug
resistance comparing with other myeloma cells [4,39], and
such drug resistance was enhanced at the presence of
MBMSCs (Figure 5 red vs. blue lines) [18].
The simulation results were consistent with our and others
experimental observations, which provided a solid basis for the
exploration of the synergistic effects of niche-specific drugs with
cytotoxic chemotherapeutics. Please notice that although the in
vitro 3D levitated culture has been known to be able to mimic in
vivo microenvironment [34], the bone marrow contexts are still
significantly different from the in vitro environments. Therefore,
the highly consistent results between experimental data and
simulation (Figure 2 A vs. H) only suggested that the simulation
results were reasonable and the model is sufficient for piloting in
silico study. In vivo assays of myeloma growth and drug responses
are still necessary for parameter estimation, model training, and
accurate predictions, which is future work and is beyond the focus
of this study.
Loewe drug combination analysis [40,41] strongly suggested
that inhibitors that block the MIC/BMSC communications are of
high clinical potentials. Traditional Loewe combination analyses
for chemotherapeutics often focus on the bulk tumor and use
isobole at effect level 50 (E50) as standard index, which is
convenient to monitor in animal models and clinical chemother-
apy evaluations using micro-CT, bioluminescence, biopsy, and
others. The elimination of the bulk tumor is also used as clinical
index. However, this standard may not accurately reflect the drug
efficacies against tumor-initiating-cell-driven tumor development.
Capability to eliminate of all tumor initiating cells should be the
key evaluation index. We thus defined the E100 isobole against
MICs in our Loewe analysis and explored drug combinations that
could successfully kill all MICs in the first treatment cycle.
Simulations of the combination effects of the cytotoxic drug (BZM)
and the niche-specific inhibitor (AMD3100) in myeloma-associat-
ed stroma, evaluated by the E100 isoboles against MICs, suggested
strong synergistic effects (green lines in Figure 7 D). In contrast,
E50 isoboles only indicated slight synergism between the two drugs
(red lines in Figure 7 A through D). The control groups of the
normal BMSC environments showed no responses to niche
inhibitors (Figure 7 A and C), which further confirmed that
AMD3100 did not directly affect MICs and the strong synergism
observed in the MBMSC cases were due to the interruption of the
MIC-BMSC communication. Such dramatic difference suggested
that for TIC-driven tumors drug effects should be estimated in
terms of the total eradication of TICs, and E100 isoboles against
Figure 3. Comparison of simulation results and experiment
observations. The simulation results of the MIC populations (A) [18]
and the total tumor sizes (B) [18] under NBMSC or MBMSC
environments as well as the dose responses in terms of total tumor
size decrease (C) [36] were compared with our published experiment
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TICs provide a more sensitive evaluation of the effectiveness of
treatments. Specifically, for the multiple myeloma treatment
evaluation and relapse risk estimation, our results highly suggested
using the percentage of MIC population in patient bone marrow
aspirate by either side population staining or surface marker
immuno-staining (e.g. CD138) and flow cytometry.
Simulations also suggested that niche-specific drugs are strongly
synergistic with tumor-specific cytotoxic drugs and re-sensitize
myeloma to the otherwise resistant drugs. Cytotoxic drugs alone,
though may efficiently kill the bulk of tumor, sometimes are not
sufficient to demolish all TICs. As shown in Figure 7 D, without
the aid of AMD3100, BZM alone cannot extinguish all MICs and
thus the disease will relapse soon after the chemotherapy
(Figure 5). Taken together, the physical properties of the
myeloma initiating cell niches is highly targetable, and the
inhibitors of the interactions between cancer initiating cells and
their stroma are promising co-drugs for traditional cytotoxic
As a piloting research, this computational model is conceptual
and some parameters were not directly estimated from experiment
data, and some in vivo/clinical data and assays such as drug
delivery and metabolism in patients may improve the accuracy of
the predictions. However, we have omitted such details due to the
following reasons: 1) To keep the focus of this work of the
preliminarily modeling of a novel system. It is the first time to
simulate the druggability of the biophysical properties, mainly the
stiffness, of the bone marrow microenvironments and the major
focus of this work is to establish a reliable model according to the
Figure 4. Profiles of simulation results. The growing trends of each tumor cell type under (A) NBMSC or (B) MBMSC environments were
Figure 5. MIC-driven myeloma relapse after cytotoxic chemotherapy cycle. (A) The growing trends of tumor cells under MBMSC (red line)
vs. NBMSC (blue) environments under drug treatments. (B) The growing trends of MICs under MBMSC (red line) vs. NBMSC (blue lines) environments
under drug treatments. Drug treatment periods were highlighted with gray shade.
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relatively solid in vitro and animal experiment data. At this
preliminary stage, it may be not ready to reach a precise predictive
model for clinical treatment outcomes by including clinical data
about the bone marrow properties as well as the underlying
mechanisms (such as the diffusion processes of cytokines and the
pharmacokinetics and the pharmacodynamics of bortezomib and
AMD3100). 2) To address our major questions and reach
conclusions with a reasonably simple model as possible. We have
also simplified the detailed temporal profiles of drugs during
chemotherapies and adjuvant therapies to constant levels as such
detailed dynamic features, though important for providing optimal
dosing in clinical treatments in the future, are not our focus at this
stage. Indeed such simplification approaches are common in
preliminary models of new systems and do not change the main
conclusions of this work. 3) Furthermore, detailed molecular
mechanisms of underlying signaling pathways are still missing
before we can reach an accurate and more predictive model. The
major goal of this work is to extend our current knowledge of how
MICs modulate the physical properties of their niches to
evaluating the therapeutic potential of targeting such remodeling
process. For such purpose, the results of this work suggest using the
complete eradication of TICs as drug efficacy index for TIC-
driven cancers, emphasize the importance of targeting TIC niches,
and encourage following-up investigations of niche-specific ther-
It was the first time the three dimensional modeling of the
biophysical properties of cancer stem cell niches and the
therapeutic potential of targeting such mechanical features were
attempted at the system level. Our in silico model successfully
realized the bio-model of the MIC-BMSC chemical-physical
Figure 6. The effects of MIC-BMSC interaction inhibition on tumor growth and the outputs of cytotoxic chemotherapy. (A) The
growing trends of tumor cells under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (B) The growing trends of MIC cells
under MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (C) The responses of myeloma tumor to cytotoxic drugs under
MBMSC or NBMSC environment with or without niche inhibitor (AMD3100). (D) The responses of MICs to cytotoxic drugs under MBMSC or NBMSC
environment with or without niche inhibitor (AMD3100). Myeloma-associated or normal BMSC niches were denoted in red or green color, and the
effects of the niche inhibitors on tumor growth (A and B) or cytotoxic drug efficacy (C and D) were labeled in solid lines and their controls in dashed
lines, respectively.
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positive feedback loop, and was capable to capture the key features
of our experimental observations. Simulations suggest that the
isoboles of drug capability to completely eliminate TICs in Loewe
combination analysis can better predict treatment outcomes. Drug
synergism analysis suggested that intervening the communications
between myeloma initiating cells and their niches dramatically
enhanced the efficacy of cytotoxic drugs against myeloma
initiating cells, re-sensitized multiple myeloma to chemotherapies,
and reduced risks of cancer relapse.
We defined five types of agents in the model to represent
BMSC, MIC, PC, MM and TMM. We initialized the bone
marrow as a cylinder 3D rectangular framework, with the tumor
extracellular matrix (ECM) filled with BMSC agents distributed
Figure 7. Synergy Effects of AMD3100 and BZM on total myeloma size (A and B) or on MICs (C and D) in NBMSC (A and C) or
MBMSC niches (B and D). Red lines indicate the E50 isoboles and green lines mark the E100 isoboles used in Loewe drug combination analysis.
Cancer Niche Stiffness Is a Promising Drug Target
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evenly across the 3D ECM and the mixed MIC, PC, MM and
TMM agents at the center of the bone marrow as a sphere. Such
initiating cell ratios were according to the myeloma initiating cell
hypothesis as well as our previous publications that the MICs can
restore the whole myeloma population, including the composition
ratios of myeloma cell types. This multi-scale modeling consisted
of three scales: intracellular, intercellular and tissue scales, which
were illustrated in Figure 1,Figure 8, and described in details in
the following sections. The model was generally presented as
pseudo-code and listed in Figure S6. Detailed flowcharts of each
myeloma agents were illustrated in Figure S2 (for MIC agent),
Figure S3 (for PC agent), Figure S4 (for MM agent), and
Figure S5 (for TMM agent) in the Supporting Information.
Parameters were determined according to our previous work as
well as literature [17,18,36]. Mathematical and computational
details are elaborated in Support Material.
Stochastic Simulation of Cell Behaviors
The Markov Chain Monte Carlo approach was used to simulate
cell behaviors of each individual cell. As shown in Figure 8, cell
behaviors were simulated by probability-based rule implementa-
tion. A cell sensed the hints of its neighborhood such as stiffness,
SDF-1 level, and drug doses, processed them with imbedded
pathways, and outputted the changes of probabilities of corre-
sponding cell behaviors including cell proliferation rate, apoptosis
rate, differentiation ratio, migration rate, contraction rate, and
cytokine secretion rate. Cell decision was then determined by
rolling a dice and compared with the probability of a given cell
behavior. Cell behaviors in turn remodeled the properties of its
niches. Details of each cell behavior for each type of cell agent as
well as the corresponding rule was discussed in the following
sections as well as the Supporting Information.
Intracellular Scale
Each BMSC agent encapsulated signaling pathway functions to
determine its biomechanical phenotype switch, using an agent-
specific Hill function [42] to describe the SDF-1/CXCR4
signaling pathway which regulated the local stiffness in response
to the in-situ relative SDF1 concentration. The effects of the
SDF1/CXCR4 inhibitor AMD3100 was simulated by competing
with SDF1 for CXCR4 and thus reducing the effective SDF1
Meanwhile, the responses of tumor agents (MIC, PC, MM, and
TMM) to local ECM stiffness in terms of the possibilities of cells to
enter the proliferation, apoptosis, and migration status were also
simulated using Hill functions in similar ways. Cell decision-
making process was defined by agent rules with such probabilities
as the major inputs. The stochastic feature of the decision of an
individual cell was realized by die casting simulation.
Stem cell fate determination. Once a MIC decided to
enter cell cycle, its fates were further determined according to its
micronenvironment. A MIC either generate two MIC daughter
cells, known as self-renewal, or to two PC cells, know as
differentiation, or to one MIC and one PC, known as asymmetric
division. The probability of each fate was determined using Hill
functions, and the decision of each MIC agent was also realized by
die casting simulation as mentioned above.
Proliferation fates of PC and MM agents. The fates of
intermediate cell agents were determined by their passage ages as
well as the probabilities of proliferation which represent the effects
of cell neighborhood characters such as stiffness and cytokine
concentration, and the current cell cycle status of the cells. PC
agents were different from MM agents for their limited self-
renewal capability. When maximum renewal limit reached, a PC
agent differentiated to an MM agent. TMM agents did not
proliferate. According to the myeloma initiating cell hypothesis,
only MICs can self-renew and proliferate without limits, so defined
is the LGN, which is the maximum passage number a PC cell can
self-renew, or a MM cell can proliferate.
Intercellular Scale
Once a myeloma cell (MIC, PC, MM, or TMM) had
determined its responses to the biomechanical properties of its
microenviroment, it would proliferate, migrate, being quiescent, or
undergo death, which were described in the intercellular scale.
Migration. A non-M-phase cell would migrate if it could find
free space nearby. The migration was governed by rules
representing space availability, stroma preference, migration
speed, and stochastic effects using Hill functions and die-casting
simulation as mentioned above.
Figure 8. The stochastic simulation of cell behaviors using rule implementation, dice-rolling-based decision-making, and Markov
chain Monte Carlo approaches.
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Division of MIC, PC and MM agents. If M-phase cell
found at least one free location within searching distance, it would
divide, following the same migration rules but with a much smaller
migration distance (i.e., slower migration speed) so the de novo
daughter cells were always next to the parental cells. If no space
was available, cells would remain in M-phase and try in next
Apoptosis. The decision-making of cell apoptosis was simu-
lated using a pre-defined threshold for the apoptosis rate reflecting
cell microenvironment, especially the local drug (BZM and AMD)
concentrations and stiffness. The whole apoptosis process took
about 20 hours. RPMI 8226 myeloma cell apoptosis rates under
drug treatments were determined according to our previous study
Tissue scales
In the tissue scale of this ABM, the secretion of SDF1 from MIC
agents and the diffusion of SDF1 in the 3D ECM defined the
dynamic 3D distribution of SDF1 concentration [43–45]. SDF1
was uniformly initialized at the start with Dirichlet boundary.
In summary, intracellular signaling pathways were encapsulated
into each cell to determine either the BMSC intercellular
biomechanical phenotype (cell stiffness) or tumor cells’ (MIC, PC
and MM) behaviors (migration, differentiation, proliferation, or
apoptosis). Cancer cells competed for the best location in 3D
extracellular matrix to migrate or proliferate regarding to the
change of BMSC cell’s stiffness and cell density. In turn, chemo-
attractant cues (SDF1) at the tissue level triggered BMSC cell’s
intracellular signaling pathways by receptors. And the resultant
feedbacks were the changes of either cancer cells’ properties
(change of BMSC cells’ stiffness) or behaviors of cancer cells
(secretion of cytokines, proliferation, differentiation, apoptosis, or
migration). Thus, the 3D dynamics of bone marrow stiffness and
tumor growth were simulated at multiple temporal and spatial
Materials and Methods
Myeloma cell model
We chose RPMI 8226, one of the most widely used human
myeloma cell lines, as the myeloma cell model because it had been
shown representative of key myeloma pathogenesis features of
interest in this work. In vivo and in vitro evidence from our
[17,18,36] and other research groups [3,4,46] suggested the
clinical translational values [46] of the MICs from RPMI 8226,
which re-capture major clinical myeloma stem cell characters such
as drug resistance and initiating myeloma in bone marrow by
generating the whole myeloma population, both of which are
boosted by the positive feedback loops with their associated stroma
cells via the SDF-1/stiffness chemo-physical interactions. Specif-
ically, RPMI 8226 has been shown to be representative among
other frequently used human myeloma cell lines in terms of
Bortezomib treatment responses [47,48].
Cell isolation and culture
Myeloma-associated and normal stroma cells were isolated and
expanded from myeloma patients similarly as previously described
[18], depending if they were diagnosed of bone marrow involved
myeloma or not. Briefly, after lysis of red blood cells (RBC Lysis
Solution, QIAGEN), patient bone marrow aspirate was cultured in
medium (Invitrogen) in 5% CO
atmosphere at 37uC for one week and unattached cells discarded.
Attached MBMSC was expended under same culture conditions.
Usage of these samples has been approved by the Institutional
Review Board of The Methodist Hospital Research Institute
(TMHRI). RPMI8226 multiple myeloma cell line was purchased
from ATCC and cultured in RPMI 1640 medium (Mediatech)
supplemented with 8% fetal bovine serum (Invitrogen) 100 units/
ml penicillinand 100 mg/ml streptomycin (Life Technologies) at
37uC in 5% CO2. This study was granted for Consent waiver by
Levitated 3D co-culture
Both MBMSC and RPMI8226 were treated with Nanoshut-
-PL (n3D Biosciences) overnight per manufacturer’s instruc-
tion, MBMSC detached by slight trypsin treatment, mixed at 1:20
to 1:200 ratios (MBMSC:RPMI8226), transferred to CostarH6
well Ultra Low Attachment plate (Corning) to reach about 400
cells per well, and immediately levitated using the 6-well Bio-
Magnetic Drive (n3D Biosciences) and cultured in
RPMI 1640 medium (Mediatech) as described before.
Staining and imaging
At day 4 the assembled MIC/MBMSC co-cultured tissue was
collected, rinsed in PBS, fixed with 3.7% paraformaldehyde
(Fisher) for 10 min, blocked with 1% bovine serum albumin
(Fisher), and stained with Alexa FluorH594 phalloidin (Invitrogen)
using 1:40 dilution for 30 min, followed by counter staining using
300 nM DAPI (Invitrogen) for 5 min, rinsed with DI water, and
mounted with ProLongHGold Antifade Reagent (Invitrogen) for
imaging. The process was under the protection of the Magnetic
Drive to avoid lost of cells. The Nikon A1 Confocal Imaging
System was used to image samples for DAPI and Alexa FluorH594
signals with optical slicing distance of 8 mM and the 3D images
were reconstructed using NIS-Elements Microscope Imaging
Software (Nikon).
Supporting Information
Figure S1 The Loewe drug combination analysis.
Figure S2 The flowchart of the MIC agent.
Figure S3 The flowchart of the PC agent.
Figure S5 The flowchart of the TMM agent.
Figure S6 The pseudocode of the multi-scale agent-
based model.
Movie S1 Simulation of myeloma growth in three-
dimensional bone marrow space. Simulation conditions
and details see Figure 2.
Movie S2 Simulation of stiffness profiles associated
with myeloma growth in three-dimensional bone mar-
row space. Simulation conditions and details see Figure 2.
Movie S3 Spatial stiffness profile in three dimensional
bone marrow space 400 hr after initiation. Simulation
conditions and details see Figure 2.
Cancer Niche Stiffness Is a Promising Drug Target
PLOS ONE | 12 January 2014 | Volume 9 | Issue 1 | e85059
Figure S4 The flowchart of the MM agent.
The authors also acknowledge the Texas Advanced Computing Center
(TACC) at The University of Texas at Austin for providing HPC resources
that have contributed to the research results reported within this paper.
Author Contributions
Conceived and designed the experiments: JS LZ CC XZ. Performed the
experiments: JS WZ DC JW BJ. Analyzed the data: JS. Contributed
reagents/materials/analysis tools: DC JW. Wrote the paper: JS LZ CC
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PLOS ONE | 13 January 2014 | Volume 9 | Issue 1 | e85059
... After optimization, we selected WNT5A antagonist, CSF1R inhibitor PLX3397 (PLX) [20], EGFR inhibitor, anti-IL2 mAb [22] as the representatives of agents, respectively, in HMSM to predict the therapeutic effects in silico. The number of each cell type in HMSM was recorded every 2 hours [25,52,65]. The drug effects were represented as the fold changes in the number of tumor and immune cells following treatments. ...
... Although a number of mathematical approaches have been introduced to model the tumor growth and drug resistance in recent years, most of the well-defined 3D agent-based models not only neglect the stage-structured immune response during the tumor initialization and development, but also did not simulate the dynamics of intracellular pathways in the cell-cell communications [25,65]. Solovyev et al. was the first to put forward the concept "hybrid model", which combined ODE model and agent-based model to mimic signal transduction processes at the intracellular scale, stochastic cell behaviors at the intercellular scale, and the dynamic distribution of growth factors at the tissue scale [84]. ...
... Detailed flowcharts of each agent were illustrated in the S1 Text. Individual cell behaviors were simulated by probability-based rule implementation [52,65]. A cell senses the hints in its neighborhood such as local cytokines and drugs and adjusts itself with the embedded signaling pathways, and outputs the corresponding changes on its cell behaviors, including proliferation, survival, differentiation, migration, and cytokine secretion rate. ...
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Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.
... In many ways, MM-CSCs are still conceptual, so there has been controversy in defining and characterizing them [20]. Alternative terminologies might be used for describing those cells, such as MM stemness side population [7], MM clonogenic cells [21], or MM cancer-initiating cells [22]. Although MM-CSCs have not yet been properly defined, there are still several ways to detect them, such as via Hoechst staining [20] and an ALDEFLUOR assay [18], both of which were used in our study. ...
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Drug-resistance is a major problem preventing a cure in patients with multiple myeloma (MM). Previously, we demonstrated that activated-leukocyte-cell-adhesion-molecule (ALCAM) is a prognostic factor in MM and inhibits EGF/EGFR-initiated MM clonogenicity. In this study, we further showed that the ALCAM-EGF/EGFR axis regulated the MM side population (SP)-mediated drug-resistance. ALCAM-knockdown MM cells displayed an enhanced ratio of SP cells in the presence of bone marrow stromal cells (BMSCs) or with the supplement of recombinant EGF. SP MM cells were resistant to chemotherapeutics melphalan or bortezomib. Drug treatment stimulated SP-genesis. Mechanistically, EGFR, primed with EGF, activated the hedgehog pathway and promoted the SP ratio; meanwhile, ALCAM inhibited EGFR downstream pro-MM cell signaling. Further, SP MM cells exhibited an increased number of mitochondria compared to the main population. Interference of the mitochondria function strongly inhibited SP-genesis. Animal studies showed that combination therapy with both an anti-MM agent and EGFR inhibitor gefitinib achieved prolonged MM-bearing mice survival. Hence, our work identifies ALCAM as a novel negative regulator of MM drug-resistance, and EGFR inhibitors may be used to improve MM therapeutic efficacy.
... The existence of CSCs is the fundamental cause of the failure of traditional treatment and tumor recurrence. Considering the important role of CSC [26], we aimed to explore the effect of CDDP/CQ-PLA NPs on CSC. ...
Purpose: Poly lactic acid (PLA) combined with cisplatin chloroquine nanoparticles (CDDP/CQ-PLA NPs) and PLA combined with cisplatin nanoparticles (CDDP-PLA NPs) were prepared to investigate their inhibitory effects on the proliferation of oral squamous cell carcinoma (OSCC) Cal-27cell line. Patients and methods: We prepared CDDP/CQ-PLA NPs and CDDP-PLA NPs. Transmission electron microscope (TEM) and dynamic light scattering (DLS) were used to detect the physiological characteristics and particle size parameters of drug-loaded nanoparticles. The drug concentration and cumulative release were measured by UV and visible spectrophotometer. MTT assay was used to detect viability of Cal-27 cells. Annexin/PI staining was used to detect cell apoptosis. Biological kits were used to detect malondialdehyde (MDA) content, catalase (CAT) activity, antioxidant enzyme superoxide dismutase (SOD) activity and glutathione peroxidase (GSH PX) activity in Cal-27 cells. Western blot was used to detect apoptosis and autophagy of Cal-27 cells. Results: CDDP/CQ-PLA NPs and CDDP -PLA NPs had good drug loaded nanoparticles and drug release. CDDP/CQ-PLA NPs showed higher ROS and apoptosis rate, and lower autophagy than CDDP-PLA NPs. Conclusion: CDDP/CQ-PLA NPs reduced autophagy and enhanced ROS and apoptosis of Cal-27 cells, which shows a potential in the clinical treatment of OSCC.
... A second model, the cancer stem cell (CSC) theory, suggests that cancer originates from a small population of stem-like cells, which are responsible for populating and maintaining an entire tumor [5]. There is evidence that these stem-like cells exist in GBM, harbor the same somatic mutations as tumor cells, and can produce tumor endothelium [6][7][8]. Furthermore, studies have shown that CSCs can have an enhanced DNA repair capacity, potentially leading to a resistance to chemoradiotherapy [9,10]. With these characteristics, the CSC theory can be applied to GBM recurrence wherein the glioma stemlike cells repopulate a tumor after chemoradiotherapy treatment. ...
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Introduction: Glioblastoma (GBM) is the most common primary malignant brain tumor in humans and, even with aggressive treatment that includes surgical resection, radiation (IR), and chemotherapy administration, prognosis is poor due to tumor recurrence. There is evidence that within GBMs a small number of glioma stem-like cells (GSLCs) exist, which are thought to be therapy resistant and are thus capable of repopulating a tumor after treatment. Like most cancers, GBMs largely employ aerobic glycolysis to create ATP, a phenomenon known as the Warburg Effect. There is no consensus on the metabolic characteristics of cancer stem cells. GSLCs have been shown to rely more heavily on oxidative phosphorylation, but there is also evidence that cancer stem cells can adapt their metabolism by fluctuating between energy pathways or acquiring intermediate metabolic phenotypes. We hypothesized that the metabolism of GSLCs differs from that of differentiated GBM tumor cell lines, and that the steady state metabolism would be differentially altered following radiation treatment. Materials and methods: We evaluated the oxygen consumption rate, extracellular acidification rate, and metabolic enzyme levels of GBM cell lines and GSLCs before and after irradiation using extracellular flux assays. We also measured absolute metabolite levels in these cells via mass spectroscopy with and without radiation treatment. Results: GSLCs were found to be significantly more quiescent in comparison to adherent GBM cell lines, highlighted by lower glycolytic and maximal respiratory capacities as well as lower oxygen consumption and extracellular acidification rates. Analysis of individual metabolite concentrations revealed lower total metabolite concentrations overall but also elevated levels of metabolites in different energy pathways for GSLCs compared to GBM cell lines. Additionally, the metabolism of both GSLCs and GBM cell lines were found to be altered by IR. Conclusions: While there is not one metabolic alteration that distinguishes irradiated GSLC metabolism from that of GBM cell lines, therapies targeting more metabolically quiescent tumor cells and thus the resistant GSLC population may increase a cancer's sensitivity to radiotherapy.
... As illustrative example of works on angiogenesis, Finley et al. [52]. With the idea of a global multicellular-multiscale-integrative model in mind, modeling tumor growth/size and angiogenesis is arguably important, as they may influence the intratumor heterogeneity overtime. ...
Cancer stem cells (CSCs) control tumor occurrence and development, chemotherapy tolerance, and cancer recurrence and metastasis due to their characteristics of high proliferation, self-renewal, multidirectional differentiation, high tumorigenicity, and multiple drug resistance. Therefore, effective elimination of CSCs is the key to improving the efficacy of cancer therapy. Due to their small size, good biocompatibility, multi-drug co-delivery, and many other advantages, nanocarriers are often used for targeted drug delivery to achieve efficient tumor therapy, and they also provide the possibility of eliminating CSCs. Therefore, this paper reviews the current carrier forms, targeting strategies, and therapeutic applications of nanodrug delivery systems (NDDS) implemented as a therapeutic option for CSCs. We also discuss the challenges and future research directions of NDDS for CSCs therapy.
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Purpose This study sought to compare the efficacy of prophylactic long-acting and standard granulocyte colony-stimulating factor (G-CSF) on febrile neutropenia, early infections, and treatment delay in patients with newly diagnosed multiple myeloma receiving the therapeutic regimen of bortezomib, lenalidomide, and dexamethasone (BLD). Methods A prospective study with 68 consecutive patients with multiple myeloma was conducted in three regional hospitals. Participants were randomly treated with the BLD regimen in combination with prophylactic long-acting G-CSF (treatment group) or standard G-CSF (control group). The primary endpoints were the incidence rates of febrile neutropenia, early infection, and treatment delays. The secondary endpoint was clinical outcomes. Results Thirty-three patients were assigned to the treatment group and thirty-five patients were assigned to the control group. The incidence of febrile neutropenia was 6.1% and 17.1% in the treatment and control groups, respectively (p = 0.297). However, the rates of early infection and treatment delay were markedly lower in the treatment group than in the control group (6.1% vs. 25.7% and 9.1% vs. 31.4%; p < 0.05). Notably, all early infections occurred during the first four cycles of BLD therapy, and the most common type of infection was pneumonia. No significant difference in clinical efficacy was found between the two groups. All participants achieved at least partial remission. Conclusions Prophylactic administration of domestic long-acting G-CSF markedly reduced the rates of early infection and treatment delay as compared with standard G-CSF in patients newly diagnosed with multiple myeloma. Notably, all early infections occurred during the first four cycles of BLD therapy. As such, it seems appropriate to administer long-acting G-CSF with the aim of primary prophylaxis of early infection, particularly within the first four courses of chemotherapy in the setting of newly diagnosed multiple myeloma.
Alzheimer’s disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States. Unfortunately, current therapies are largely palliative and several potential drug candidates have failed in late-stage clinical trials. Studies suggest that microglia-mediated neuroinflammation might be responsible for the failures of various therapies. Microglia contribute to Aβ clearance in the early stage of neurodegeneration and may contribute to AD development at the late stage by releasing pro-inflammatory cytokines. However, the activation profile and phenotypic changes of microglia during the development of AD are poorly understood. To systematically understand the key role of microglia in AD progression and predict the optimal therapeutic strategy in silico, we developed a 3D multi-scale model of AD (MSMAD) by integrating multi-level experimental data, to manipulate the neurodegeneration in a simulated system. Based on our analysis, we revealed that how TREM2-related signal transduction leads to an imbalance in the activation of different microglia phenotypes, thereby promoting AD development. Our MSMAD model also provides an optimal therapeutic strategy for improving the outcome of AD treatment.
Cancer is an inherently multiscale process, wherein genetic lesions at the sub-nuclear level propagate to changes in intracellular biochemistry, cell-level behaviors, and ultimately to tissue-scale interactions that are also partially controlled by tumor cell-extrinsic aspects of the microenvironment. As computational modeling methodologies across those scales have improved, so too has our ability to embrace fully the multiscale nature of cancer in developing models to predict key aspects of tumor development, diagnosis, and response to therapy. The explosion of studies to understand the origins and effects of cell-to-cell heterogeneity in tumors has provided impetus for the development of multiscale models capable of predicting dynamics in systems where spatial heterogeneities exist. Here, we summarize recent progress and developments in this field.
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Osteosarcoma (OS), the most common malignant bone tumor, is the main cause of cancer-related death in children and young adults. Despite the combination of surgery and multi-agent chemotherapy, patients with OS who develop resistance to chemotherapy or experience recurrence have a dismal prognosis. MicroRNAs (miRNAs) are a class of small noncoding RNAs that repress their targets by binding to the 3'-UTR and/or coding sequences, leading to the inhibition of gene expression. miR-221 is found to be upregulated in tumors when compared to their matched normal osteoblast tissues. We also observed significant miR-221 upregulation in the OS cell lines, MG-63, SaoS-2, and U2OS, when compared to the normal osteoblast cell line, HOb. Overexpression of miR-221 promoted OS cell invasion, migration, proliferation, and cisplatin resistance. MG-63 and SaoS-2 cells transfected with miR-221 mimics were more resistant to cisplatin. The IC50 of MG-63 cells transfected with control mimics was 1.24 μM. However, the IC50 of MG-63 cells overexpressing miR-221 increased to 7.65 μM. Similar results were found in SaoS-2 cells, where the IC50 for cisplatin increased from 3.65 μM to 8.73 μM. Thus, we report that miR-221 directly targets PPP2R2A in OS by binding to the 3'-UTR of the PPP2R2A mRNA. Restoration of PPP2R2A in miR-221-overexpressing OS cells recovers the cisplatin sensitivity of OS cells. Therefore, this study suggests a new therapeutic approach by inhibiting miR-221 for anti-chemoresistance in OS.
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Side population (SP) cells in cancers, including multiple myeloma, exhibit tumor-initiating characteristics. In the present study, we isolated SP cells from human myeloma cell lines and primary tumors to detect potential therapeutic targets specifically expressed in SP cells. We found that SP cells from myeloma cell lines (RPMI 8226, AMO1, KMS-12-BM, KMS-11) express CD138 and that non-SP cells include a CD138-negative population. Serial transplantation of SP and non-SP cells into NOD/Shi-scid IL-2γnul mice revealed that clonogenic myeloma SP cells are highly tumorigenic and possess a capacity for self-renewal. Gene expression analysis showed that SP cells from five MM cell lines (RPMI 8226, AMO1, KMS-12-BM, KMS-11, JJN3) express genes involved in the cell cycle and mitosis (e.g., , , , ), polycomb (e.g., ) and ubiquitin-proteasome (e.g., ) more strongly than do non-SP cells. Moreover, , and were also upregulated in the SPs from eight primary myeloma samples. On that basis, we used an aurora kinase inhibitor (VX-680) and a proteasome inhibitor (bortezomib) with RPMI 8226 and AMO1 cells to determine whether these agents could be used to selectively target the myeloma SP. We found that both these drugs reduced the SP fraction, though bortezomib did so more effectively than VX-680 due to its ability to reduce levels of both phospho-histone H3 (p-hist. H3) and EZH2; VX-680 reduced only p-hist. H3. This is the first report to show that certain oncogenes are specifically expressed in the myeloma SP, and that bortezomib effectively downregulates expression of their products. Our approach may be useful for screening new agents with which to target a cell population possessing strong tumor initiating potential in multiple myeloma.
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The proteasome inhibitor bortezomib (Velcade) is prescribed for the treatment of multiple myeloma. Clinically achievable concentrations of bortezomib cause less than 85% inhibition of the chymotrypsin-like activity of the proteasome, but little attention has been paid as to whether in vitro studies are representative of this level of inhibition. Patients receive bortezomib as an intravenous or subcutaneous bolus injection, resulting in maximum proteasome inhibition within one hour followed by a gradual recovery of activity. In contrast, most in vitro studies use continuous treatment so that activity never recovers. Replacing continuous treatment with 1 h-pulse treatment increases differences in sensitivity in a panel of 7 multiple myeloma cell lines from 5.3-fold to 18-fold, and reveals that the more sensitive cell lines undergo apoptosis at faster rates. Clinically achievable inhibition of active sites was sufficient to induce cytotoxicity only in one cell line. At concentrations of bortezomib that produced similar inhibition of peptidase activities a different extent of inhibition of protein degradation was observed, providing an explanation for the differential sensitivity. The amount of protein degraded per number of active proteasomes correlated with sensitivity to bortezomib. Thus, (i) in vitro studies of proteasome inhibitors should be conducted at pharmacologically achievable concentrations and duration of treatment; (ii) a similar level of inhibition of active sites results in a different extent of inhibition of protein breakdown in different cell lines, and hence a difference in sensitivity.
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Recurrence of multiple myeloma (MM) after therapy suggests the presence of tumor-initiating subpopulations. In our study, we performed flow cytometry-based Hoechst 33342 staining to evaluate the existence of a MM population with stem-like features known as side population (SP) cells. SP cells exhibit substantial heterogeneity in MM cell lines and primary MM cells; express CD138 antigen in MM cell lines; display higher mRNA expression and functional activity of ABCG2 transporter; and have a higher proliferation index compared with non-SP cells. We observed evidence for clonogenic potential of SP cells, as well as the ability of SP cells to regenerate original population. Moreover, SP cells revealed higher tumorigenicity compared with non-SP cells. Importantly, lenalidomide decreased the percentage and clonogenicity of SP cells, and also induced phosphorylation changes in Akt, GSK-3α/β, MEK1, c-Jun, p53, and p70S6K in SP cells. Adherence to bone marrow stromal cells (BMSCs) increased the percentage, viability, and proliferation potential of SP cells. Lenalidomide and thalidomide abrogated this stimulatory effect of BMSCs and significantly decreased the percentage of SP cells. Our studies demonstrate a novel mechanism of action for lenalidomide, namely targeting SP fraction, providing the framework for new therapeutic strategies targeting subpopulations of MM cells including presumptive stem cells.
Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
Human livers have maturational lineages of cells within liver acini, beginning periportally in stem cell niches, the canals of Hering, and ending in polyploid hepatocytes pericentrally and cholangiocytes in bile ducts. Hepatic stem cells (hHpSCs) in vivo are partnered with mesenchymal precursors to endothelia (angioblasts) and stellate cells, and reside in regulated microenvironments, stem cell niches, containing hyaluronans (HA). The in vivo hHpSC niche is modeled in vitro by growing hHpSC in two-dimensional (2D) cultures on plastic. We investigated effects of 3D microenvironments, mimicking the liver's stem cell niche, on these hHpSCs by embedding them in HA-based hydrogels prepared with Kubota's Medium (KM), a serum-free medium tailored for endodermal stem/progenitors. The KM-HA hydrogels mimicked the niches, matched diffusivity of culture medium, exhibited shear thinning and perfect elasticity under mechanical loading, and had predictable stiffness depending on their chemistry. KM-HA hydrogels, which supported cell attachment, survival and expansion of hHpSC colonies, induced transition of hHpSC colonies towards stable heterogeneous populations of hepatic progenitors depending on KM-HA hydrogel stiffness, as shown by both their gene and protein expression profile. These acquired phenotypes did not show morphological evidence of fibrotic responses. In conclusion, this study shows that the mechanical properties of the microenvironment can regulate differentiation in endodermal stem cell populations.
Recent research in cancer biology has suggested the hypothesis that tumors are initiated and driven by a small group of cancer stem cells (CSCs). Furthermore, cancer stem cell niches have been found to be essential in determining fates of CSCs, and several signaling pathways have been proven to play a crucial role in cellular behavior, which could be two important factors in cancer development. To better understand the progression, heterogeneity and treatment response of breast cancer, especially in the context of CSCs, we propose a mathematical model based on the cell compartment method. In this model, three compartments of cellular subpopulations are constructed: CSCs, progenitor cells (PCs), and terminal differentiated cells (TCs). Moreover, (1) the cancer stem cell niche is, considered by modeling its effect on division patterns (symmetric or asymmetric) of CSCs, and (2) the EGFR signaling pathway is integrated by modeling its role in cell proliferation, apoptosis. Our simulation results indicate that (1) a higher probability for symmetric division of CSC may result in a faster expansion of tumor population, and for a larger number of niches, the tumor grows at a slower rate, but the final tumor volume is larger; (2) higher EGFR expression correlates to tumors with larger volumes while a saturation function is observed, and (3) treatments that inhibit tyrosine kinase activity of EGFR may not only repress the tumor volume, but also decrease the CSCs percentages by shifting CSCs from symmetric divisions to asymmetric divisions. These findings suggest that therapies should be designed to effectively control or eliminate the symmetric division of CSCs and to reduce or destroy the CSC niches.