Emergence of spatial structure in the tumor
microenvironment due to the Warburg effect
Carlos Carmona-Fontainea,1
, Vanni Buccia
, Leila Akkarib
, Maxime Deforeta
, Johanna A. Joyceb
, and Joao B. Xaviera,1
a
Computational Biology Program, b
Cancer Biology and Genetics Program, Memorial Sloan–Kettering Cancer Center, New York, NY 10065
Edited by Robert H. Austin, Princeton University, Princeton, NJ, and approved October 15, 2013 (received for review June 25, 2013)
Drastic metabolic alterations, such as the Warburg effect, are
found in most if not all types of malignant tumors. Emerging
evidence shows that cancer cells benefit from these alterations,
but little is known about how they affect noncancerous stromal
cells within the tumor microenvironment. Here we show that
cancer cells are better adapted to metabolic changes in the microenvironment,
leading to the emergence of spatial structure. A clear
example of tumor spatial structure is the localization of tumorassociated
macrophages (TAMs), one of the most common stromal
cell types found in tumors. TAMs are enriched in well-perfused
areas, such as perivascular and cortical regions, where they are
known to potentiate tumor growth and invasion. However, the
mechanisms of TAM localization are not completely understood.
Computational modeling predicts that gradients—of nutrients,
gases, and metabolic by-products such as lactate—emerge due to
altered cell metabolism within poorly perfused tumors, creating
ischemic regions of the tumor microenvironment where TAMs
struggle to survive. We tested our modeling prediction in a coculture
system that mimics the tumor microenvironment. Using this
experimental approach, we showed that a combination of metabolite
gradients and differential sensitivity to lactic acid is sufficient
for the emergence of macrophage localization patterns in vitro.
This suggests that cancer metabolic changes create a microenvironment
where tumor cells thrive over other cells. Understanding
differences in tumor-stroma sensitivity to these alterations may
open therapeutic avenues against cancer.
tumor adaptation | mathematical model | image analysis
Cancer cells in tumors display pronounced metabolic alterations
(1–10). The genetic and biochemical mechanisms
behind these changes are under intensive investigation, but the
question of how metabolic changes affect noncancerous cells in
the tumor microenvironment remains largely unanswered. The
Warburg effect—or oxidative glycolysis, a process whereby cells
exhibit a high glycolytic rate even in the presence of oxygen—is
arguably the best-known metabolic alteration in cancer (1). Due
to a lower yield of glucose to ATP associated with glycolysis, the
Warburg effect was initially viewed as a detrimental aberration
(1, 5). However, it is now clear that ATP is not a limiting resource
for cell growth (4, 9) and that glycolytic alterations increase
glucose and glutamine uptake, enhance reductive power,
and favor anabolism by retaining carbon-rich macromolecules (4,
7, 9). Thus, rather than being detrimental, metabolic alterations
in tumor cells can be required to sustain the high proliferation
rate that characterizes malignant cancers (4, 7, 9). In fact, similar
metabolic changes occur in healthy processes with rapid population
growth such as pluripotent stem-cell proliferation (11),
T-cell activation (12), embryonic development (13), and wound
healing (14), suggesting that cancer cells have co-opted conserved
metabolic processes used by rapidly proliferating cells
(4, 7, 9).
Despite their beneficial effect for cell proliferation, metabolic
changes have dramatic consequences on the extracellular milieu.
Alterations in tumor metabolism were first identified by studying
how cancer cells alter their culture media (1, 5). Chaotic vascularization
can be a feature in tumors in vivo, which intensifies
the effect of cancer cells on their microenvironment and causes
damaging processes such as acidosis, hypoxia, and nutrient
deprivation (15, 16). Thus, cancer cells must balance the benefits
of an altered metabolism with its potentially toxic extracellular
consequences.
Cancer is a disease of clonal evolution where different cell
lineages compete (17, 18). Mathematical models in the literature
suggest that metabolic modifications can be advantageous for
lineages competing within tumors (16, 19–21). Nonetheless, how
stromal cells within tumors cope with these changes has been
largely neglected. Thus, it is possible that a toxic microenvironment
created by metabolic alterations may be a mechanism for
cancer cells to gain a selective advantage.
We focused our study on how tumor metabolism affects
macrophages. Tumor-associated macrophages (TAMs) are one
of the most common stromal cell types found within tumors, and
their number is directly correlated with poor patient prognosis in
the majority of cancers analyzed to date (22–26). TAMs are well
adapted to, and recruited toward, low-oxygen-tension regions
(22, 27, 28). However, TAMs in vivo are also enriched in wellperfused
regions of the tumor—such as the invasive edge and
perivascular areas—where they potentiate cancer progression
and invasion (29–31). Other tumor-associated stromal cells, live,
or even dying cancer cells are known to recruit macrophages to
the tumor (32–34). Nonetheless, why resident and recruited
macrophages do not infiltrate the tumor homogenously remains
poorly understood. An intriguing hypothesis then is that TAMs
may be precluded from poorly perfused regions because metabolic
alterations generate a toxic environment where only adapted
tumor cells can survive.
Significance
Cancer cells undergo dramatic metabolic alterations, such as
the Warburg effect where glucose is consumed independently
of oxygen, leading to high lactic acid production. Although
these alterations can give growth advantages to cancer cells,
they have a profound effect in the extracellular environment,
and thus it is not clear how they affect healthy cells. Here we
show that lactic acid accumulation can impair the survival of
tumor-associated macrophages. Using a multidisciplinary combination
of computational and experimental methods, we show
that this decreased survival can lead to spatial patterns of
macrophage localization that resemble how tumor-associated
macrophages distribute in real tumors. Spatial patterns can
potentiate tumor growth, and thus understanding how they
are formed may bring therapeutic insights.
Author contributions: C.C.-F., J.A.J., and J.B.X. designed research; C.C.-F., V.B., L.A., and
M.D. performed research; C.C.-F., V.B., L.A., M.D., J.A.J., and J.B.X. contributed new reagents/
analytic tools; C.C.-F., V.B., L.A., M.D., and J.B.X. analyzed data; and C.C.-F., J.A.J., and J.B.X.
wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1
To whom correspondence may be addressed. E-mail: carmonac@mskcc.org or xavierj@
mskcc.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1311939110/-/DCSupplemental.
19402–19407 | PNAS | November 26, 2013 | vol. 110 | no. 48 www.pnas.org/cgi/doi/10.1073/pnas.1311939110
Here we show that metabolically altered microenvironments
can indeed provide cancer cells with a selective advantage. In
particular, these cancer cells are more resistant than macrophages
to high levels of lactic acid produced by their glycolytic
metabolism. We combine computational modeling with a custommade
cell culture system that allows the emergence of spatially
graded microenvironments ranging from well-perfused to ischemic
regions. With this approach we show that differential sensitivity to
lactic acid between cancer cells and macrophages is sufficient to
generate localization patterns that resemble in vivo observations.
Results
Glycolytic Cancer Cells Are Adapted to Lactic Acid. We first investigated
the impact of the metabolic activity of cancer and
stromal cells on their surrounding microenvironment. We used
primary bone marrow-derived macrophages (BMDMs) as our
model for stromal cells and MTLn3 cells, an aggressive metastatic
breast adenocarcinoma line widely used in tumor-stromal
studies (35), as a model cancer cell line. When grown for 24 h,
MTLn3 cells, but not macrophages, significantly increased lactate
levels and decreased glucose levels in the culture media even
in the presence of oxygen, evidencing enhanced oxidative glycolysis
[Fig. 1A; note that these trends are maintained when
metabolites levels are normalized by total biomass (SI Appendix,
Fig. S1A)]. In addition, MTLn3 cancer cells showed higher glutamine
consumption, as has been reported for other cancer cells
(36), whereas other measured metabolites were not substantially
changed (SI Appendix, Fig. S1A). We confirmed these observations
by measuring glucose consumption and lactate production
in a panel of cancerous and noncancerous cells. All tested cancer
cells showed a similar behavior to MTLn3, whereas low passage,
nontransformed, mouse embryonic fibroblasts (MEFs) behaved
similarly to macrophages (SI Appendix, Fig. S1B). These data
confirm that at least a panel of cancer cells, but not stromal cells,
display typical metabolic alterations such as the Warburg effect.
Next, we examined how MTLn3 cells and macrophages adapt
to nutrient depletion. We examined the role of starvation by
culturing cells for 24 or 48 h under a range of nutrient compositions
and measuring cell viability. Most viability methods rely
on measuring activity of metabolic enzymes at a population level,
which may be altered by nutrient limitation and produce experimental
biases. We circumvented this limitation by adding a
fluorescent dye that is incorporated only by cells with compromised
cell membranes. This allowed us to measure viability at
the single-cell level using microscopy and image analysis (SI
Appendix). Starvation by the withdrawal of glucose, glutamine, or
serum did not have a significantly different effect on the survival
of either cell type (SI Appendix, Fig. S2A). Likewise, hypoxia, i.e.,
oxygen starvation alone or combined with different nutrient
deprivations, did not induce notable differential changes in the
viability of the cells (SI Appendix, Fig. S2A). The observation that
macrophages remain viable under oxygen deprivation is consistent
with reports of macrophages being recruited to anoxic and
necrotic regions of tumors (28) and the observation that macrophages
survive and adapt well to hypoxia (27). In summary,
nutrient deprivation and hypoxia appear to affect MTLn3 cells
and macrophages equivalently.
Next we investigated the effect of lactate on cell viability,
because its secretion levels are remarkably different among tested
cancerous and noncancerous cell lines. Adding lactic acid to the
growth media showed that, although extremely high levels of
lactic acid are lethal for both MTLn3 and macrophages, MTLn3
cells survive better than macrophages at intermediate levels
(∼25–30 mM; Fig. 1B). These levels of lactic acid are close to the
ones produced by MTLn3 cells (Fig. 1A) and to those reported in
human tumors (37, 38). Control experiments carried out using
sodium lactate instead of lactic acid did not show a significant
difference in survival, suggesting that the detrimental effect of
lactic acid to macrophages is through media acidification (SI
Appendix, Fig. S2B). Additional experiments with our cell panel
using lactic acid confirmed that cancerous cell lines tend to be
more resistance to lactic acid than macrophages and MEFs (SI
Appendix, Fig. S2C). Furthermore, MTLn3 cells are also more
resistant than macrophages to acetic acid, supporting the role of
pH in cell viability (SI Appendix, Fig. S2D).
To evaluate whether this differential effect still occurs when
the two cell types share the same microenvironment, we cocultured
MTLn3 and macrophages at a range of lactic acid concentrations
and measured the relative contribution of each cell
type to the final population after 24 h. Lactic acid specifically
reduced the proportion of macrophages in the coculture population
(Fig. 1C), confirming that MTLn3 cells are fitter when
lactic acid accumulates. This effect can be reverted by the addition
of bicarbonate to the media, further confirming the role of
pH (SI Appendix, Fig. S3 C and D).
Model Reveals That Restricted Perfusion Generates Metabolic
Gradients and Spatial Structure in Cell Populations. TAMs in vivo
are enriched in well-perfused regions of large tumors such as in
perivascular regions and at the invasive edge (Fig. 2A and SI
Appendix, Fig. S4). However, in small tumors TAMs are usually
dispersed throughout the tumor mass (30) (SI Appendix, Fig. S4).
The differential sensitivity shown in our experiments suggests
that macrophages may not survive in conditions of high lactic
acid concentration, which is more likely to occur in larger, poorly
perfused, tumors (16). We therefore asked whether the accumulation
of lactic acid in poorly perfused regions is sufficient to
explain the localization patterns of macrophages. Computational
models have shown that glycolysis would lead to lactate gradients
due to increased secretion and to steep drops in pH (16, 19), but
the effect of these gradients on stromal cells remains unexplored.
Thus, we created a computational model where a mixed population
of tumor cells and macrophages coexist in a confined
space representing tissue adjacent to a blood capillary or at the
tumor edge next to normal tissue (Fig. 2B). In this model, cells
consume resources (e.g., glucose and oxygen, denoted as “R” in
Fig. 2B) at rate q and secrete metabolic waste products (such as
lactic acid, denoted as “W” in Fig. 2B) at rate k. Resources
CD68
DAPI (total cell number)
MTLn3
0
5
10
15
20
25
30
Concentration(mM)
21% O2
1% O2
Extracellular Lactate
Macrophages proportion in co-cultures
%
[Lactic Acid] 0 mM 20 mM
*
0
5
10
15
20
25
30
35
0 5 7.5 10 15 20 25
[Lactic Acid], mM
150 m
%Viablecells
[Lactic Acid], mM
0 10 20 30 40
20
40
60
80
100
00 00
*
MTLn3
A B
C
Fig. 1. Cancer cells are adapted to toxic environments produced by the
Warburg effect. (A) MTLn3 breast cancer cells but not macrophages (ΜΦ)
display the Warburg effect. (B) Macrophages are more sensitive to lactic acid
that lowers media pH. (C) Boxplots showing the effect of lactic acid on in
cocultures. The red plus sign (+) denotes outliers. (Right) Representative
examples. Error bars in A and B represent SD from the mean obtained in at
least three triplicated experiments. Data points are always obtained from
independent visual fields. *P < 0.05.
Carmona-Fontaine et al. PNAS | November 26, 2013 | vol. 110 | no. 48 | 19403
CELLBIOLOGY
diffuse into the system from one side (with diffusivity DR).
Similarly, waste is cleared via the same side with diffusivity DW.
In our experiments, cancer cells and macrophages are equally
resistant to starvation and hypoxia (SI Appendix, Fig. S2A), but
macrophages are more sensitive to lactic acid levels (Fig. 1 B and
C). Because lactic acid production increases in low oxygen
(Fig. 1A), hypoxia could lead to macrophage death indirectly due
to lactic acid accumulation. Thus, in our model the levels of
waste (lactic acid) are a function of resource (oxygen) depletion
(SI Appendix, Table S1).
Solving the model analytically gives the expected decreasing
curve for R and increasing levels of W (Fig. 2C). Using this
model, and incorporating biophysically relevant parameters (SI
Appendix, Table S2), we simulated the evolution of a cell coculture
using an agent-based computational approach. We adopted
an established off-lattice agent-based modeling (ABM) framework
used to model other complex multicell systems such as
bacterial biofilms (39, 40). Similar approaches have been used to
model tumor growth (20, 41). In this ABM framework, cells are
modeled as discrete agents with rules that mimic the behavior
of cells (growth, division, death, etc.), whereas gradients of metabolites
are modeled using continuum partial differential equations
(SI Appendix) (39). Under these conditions, the simulated cancer
cells and macrophages coexist in well-perfused regions, but only
cancer cells are able to survive in ischemic regions (Fig. 2D,
“Tumor”; SI Appendix, Movie S1). In contrast, when we conduct
the simulations making oxygen 10 times more diffusive to increase
perfusion, the spatial structure is lost (Fig. 2D, “Control”;
SI Appendix, Movie S1). Thus, spatial structure can be an emergent
property if cells have different sensitivities to microenvironmental
conditions when perfusion is poor. Importantly, in our
simulations, spatial heterogeneities are not externally imposed
but are the result of diffusion and reaction processes. Similar
models have been used to explain the formation of necrotic cores
(42) or the evolution of more aggressive tumor clones (16, 19, 20).
Here, our model shows that self-generated gradients can play an
important role in the spatial distribution of cancer cells and
tumor-associated macrophages resembling in vivo observations.
Experimental Validation with a Tissue Mimetic System. Computational
model predictions can be compared with in vivo data, such
as imaging (43, 44), but models are difficult to test experimentally.
In vivo manipulations are technically challenging and in
vitro models often neglect important features such as spatial
structure. To circumvent these limitations, we adapted a tissuemimetic
culture system (45) to mimic the tumor microenvironment
in vitro. This setup allows coculture of different cell types
and the spontaneous formation of gradients while still permitting
direct cell imaging (Fig. 3A and SI Appendix). Briefly, cells are
cultured in a small volume (in the ∼50-μL internal chamber) that
is connected to a larger volume (the ∼2-mL external chamber)
through a small slit or opening (∼0.3 mm wide). The external
chamber thus constitutes a bulk source of nutrients and provides
a sink of waste products, creating a directional gradient of any
diffusible substances consumed or produced by cells in the internal
chamber. The self-generated gradients in this simple setup
ensure that cells proximal to the slit will be well perfused,
whereas cells distal to the slit will be progressively more ischemic
(Fig. 3A). The cell population cultured in the interior chamber is
imaged using a programmable motorized microscope stage and
tiled microscopy to build large-scale mosaics of adjacent pictures,
producing images that combine high resolution with a wide
field of view. Thus, we can investigate cell populations at multiple
scales from the single-cell to multicellular level simultaneously
(Fig. 3 A and B; SI Appendix, Fig. S5A).
We first confirmed that our system allows the spontaneous
formation of metabolite gradients. We used a glioma cell line
(C6-HRE-GFP) that expresses GFP under hypoxic conditions
HighPerfusion
(capilaryor
tumoredge)
R0
0 L
Low
Perfusion
(nocells)
R
W
W clearance (Dw)
R influx (DR)
Cancer cell
q q q q
k kk k
Capilary
In vivo
CD68 DAPI
Tumor
Stroma
0
.2
.4
.6
.8
1
2
8
6.2
4.5
2.7
1
3.810
Distance
Oxygen (R)
Lactate(W)
Normalizedconcentration, BA
L
C D
Cancer
Oxygen
Lactate
NS
***
NS
Distance ( m)
450 0540 0
11
Distance ( m)
Control Tumor
Fig. 2. Mathematical model predicts that differential sensitivity to waste
products (W), such as lactic acid, leads to spatial structure. (A) TAMs (ΜΦ) in
vivo show spatial patterns of localization, with enrichment at the invasive
edge and in perivascular areas. Representative image of macrophage
staining (CD68, green) of a pancreatic islet tumor in the RT2 model. (B)
Schematic representation of the mathematical model. (C) Concentration
profiles of resources and waste products calculated with different parameters
(indicated by the numbers over the lines) in the combined analytical
model. (D) (Upper) Agent-based simulation showing the distribution of
particles representing macrophages (green) and cancer cells (blue). (Lower)
Oxygen/lactate profiles. Note that if oxygen diffusion is increased by 10x,
gradients are shallow and no spatial structure emerges. Boxplots show the
distribution of centroids across the two axes. ***P < 0.001; NS, not significant.
10mm
Slit Cover
Lateralview
Top view
Cells
Internal
External
Cover
nt rn
xt rn l
Hif1
HRE gfp
GFP
2
4
6
8
C6-HRE-GFP
cells
100
GFPintensity(foldchange)
D
400 800 1200 1600 2000 2400
0
.2
.4
.6
.8
1
Distance ( m)
GFPintensity[A.U.]
Glass cover
PDMS cover
No cover
Am
m
oniumPotassiumG
lucoseLactateG
lutam
ateG
lutam
ine
0
2
4
6
FoldChange(Internal/External)
C
BA
Fig. 3. Experimental culture system that mimics the tumor microenvironment.
(A) Schematic representation of the culture system scanned using
tiling microscopy. (B) C6-HRE-GFP cells showing GFP (green) expression and
a cell membrane stain (CMPTX, Molecular Probes). (Upper) GFP/CMPTX ratios
(in grayscale) used for quantification. (C) Quantification of GFP signal shows
that oxygen gradients emerge in chambers with glass covers but not in
control or PDMS-covered chambers. Data were obtained from a representative
experiment. (D) Lactate accumulates in the internal chamber of the culture
system. Error bars represent SD from the mean obtained in two triplicated
experiments.
19404 | www.pnas.org/cgi/doi/10.1073/pnas.1311939110 Carmona-Fontaine et al.
(46) as a reporter for oxygen limitation. We used two experimental
controls. First, we produced a similar culture system but
without the separation between the two chambers. Under these
conditions, diffusible substances can diffuse freely to and from
the bulk above the cells, and therefore no horizontal gradients
should be formed. Second, we modified our graded assay by
separating the two chambers with a gas-permeable membrane of
olydimethylsiloxane (PDMS). In this setting, certain diffusibles
such as glucose and lactate will not permeate through the
membrane. However, oxygen and other gases can diffuse freely
(47). As expected, no GFP was detected in either of the control
settings, and cells maintained their viability, confirming that oxygen
permeates PDMS freely. In contrast, in the glass-separated
chamber, GFP levels increased significantly in a manner dependent
on the distance from the slit, showing evidence of oxygen
limitation (Fig. 3C and SI Appendix, Fig. S5B). Hence, our
system allows the spontaneous formation of oxygen gradients
due to its diffusion into the internal chamber antagonized by cell
consumption, similar to the process in actual tumors (42).
We next asked whether waste products, and lactic acid in
particular, accumulate within our assay. We measured the levels
of relevant metabolites in the internal chamber and in the external
chamber (Fig. 3D). Most measured metabolites did not
show significant changes. However, the levels of lactate in the
small internal chamber were more than fourfold higher than
those in the external chamber (Fig. 3D; note that these are average
values, and thus levels in ischemic regions are expected to
be even higher). Visual inspection of any cell culture in our
graded microenvironment chamber clearly reveals that pH gradients
are formed because phenol red in the media gains a yellow
hue in the deep interior regions, indicating low pH, while it
retains its pink color near the slit. To assess this more rigorously,
we measured spatial gradients of pH directly using BCECF,
a widely used fluorescent ratiometric pH probe. After only 2 d of
culture, clear pH gradients emerged in our graded microenvironment
chamber (SI Appendix, Fig. S6). Taken together, these
data show that the in vitro cellular system adequately models the
gradients of resources, the accumulation of waste products, and
the pH gradients that occur within tumors.
Emergence of Spatial Structure in Tumor/Macrophage Cocultures. We
cocultured MTLn3 cells and macrophages using our graded
microenvironment system. Typically, an ∼105
cell homogenous
mix of macrophages and MTLn3 cells at a 1:1 ratio was seeded.
As expected, in cocultures with no gradients both cell types
remained evenly distributed during the entire experiment (1 wk)
(Fig. 4A). In sharp contrast, when diffusion was limited by the
glass cover, spatial structure emerged in the cell population after
a similar time (Fig. 4B). In well-perfused regions close to the slit,
macrophages coexist with MTLn3 cells. However, in distal
regions, the number of macrophages drops significantly relative
to MTLn3 (Fig. 4B, P < 0.0001). Using the position of macrophages,
we calculated macrophage density as a function of the
distance from the slit (SI Appendix, Fig. S7 A and B). We found
that macrophage density drops ∼10-fold in ischemic regions (SI
Appendix, Fig. S7C). Although macrophages are affected the
most, metabolic gradients additionally affect MTLn3 cells as
their density also drops. In fact, high-cell-density clumps are
visible in the culture without cover whereas in the graded condition
there is high cell density only near the slit (Fig. 4A). We
conducted the same experiment with two alternative cancer cell
lines, H1650 or MDA-MB-231, also in coculture with macrophages.
The experiments showed similar emergence of spatial
structure (SI Appendix, Fig. S8A).
Our computational model predicts that lactic-acid–induced
patterns occur even when glucose, and other nutrients, are not
limiting. If this is correct, cocultures under nutrient gradients,
but with uniformly low-lactic-acid levels, should not display
spatial structure. To test this, we used a PDMS separation between
the two chambers that, because of its oxygen permeability,
will diminish lactic acid production (Fig. 1A). No spatial structure
emerged under these conditions (SI Appendix, Fig. S7C),
supporting that the hypoxic boost in lactic acid production is
required for the emergence of spatial structure. Because macrophages
are not directly affected by hypoxia (SI Appendix, Fig.
S2A) but hypoxia can lead to high levels of lactic acid that are
lethal for macrophages (Fig. 1 B and C), we conclude that metabolic
alterations of the microenvironment cause macrophage death
and the spontaneous emergence of tumor spatial structure.
Macrophage Death Due to Glycolytic Metabolism. Macrophages also
produce lactic acid, especially under hypoxia, but they do so at
a much lower level than MTLn3 cells (five- to eightfold lower
levels, Fig. 1A). Accordingly, macrophages cultured alone at
the same initial density showed little or no spatial structure (SI
Appendix, Fig. S8B). However, in theory, higher numbers of
Effect of cell migration
No gradients
0 1000 2000 3000 4000 5000
0.2
0.4
0.6
0.8
1
24hrs
48
72
88
ViabilityRatio
Distance ( m)
10
2
10
3
10
4 Mean Square Displacement
1000100
Time (mins)
MSD(m2)
Slope
=
1
Ischemic
Normal
90
270
180
90
270
0
IschemicNormal
x-axis Distance ( m)
0 1000 2000 30000 1000 2000 3000
x-axis Distance ( m)
y-axisDistance(m)
y-axisDistance(m)
BA
EC Effect of cell death
NS
NS NS
***
MTLn3
aimehcsIlamroN
IschemicNormal
5mm
Ischemic
Normal
t = 88h
Viable Non-viable
Direction of migration
MTLn3
D
Fig. 4. Emergence of spatial structure in tumor/
macrophage cocultures. (A) Cocultures with no
gradients show no spatial structure as the distribution
of macrophages (ΜΦ, labeled in green with
CD68) and cancer cells is not significantly different
(boxplots). (B) Spatial structure emerges when
cocultures are performed in the culture system that
mimics the tumor microenvironment. (C and D) Effect
of cell migration. (C) Comparison of macrophage
migration in normal versus ischemic regions.
Rose plots show that macrophages move evenly in
all directions [direction vectors are not different
from a distribution of random vectors (P > 0.5,
modified Rayleigh’s test)]. (D) Mean square displacement
(MSD) analysis shows that macrophage
movements are subdiffusive. Blue line indicates
diffusive movement (slope = 1). (E) Percentage of
nonviable macrophages increases over time and
with the distance from opening. (Right) Representative
visual fields. Macrophages are in green, and
nonviable macrophages are in red. ***P < 0.001;
NS, not significant. Data were obtained from representative
cases. Experiments were repeated at
least two times.
Carmona-Fontaine et al. PNAS | November 26, 2013 | vol. 110 | no. 48 | 19405
CELLBIOLOGY
macrophages should produce enough lactate to reproduce the
localization patterns. We therefore repeated the experiment, but
this time seeding ∼106
macrophages (10× the previous cell
density), and we were able to generate spatial structure in
macrophage monocultures (SI Appendix, Fig. S8B). Macrophage
disappearance was so extreme that virtually no macrophages
could be found in ischemic regions (SI Appendix, Fig. S8C).
These data support the conclusion that the spatial structure
observed in cocultures is not caused by cancer cells specifically,
but rather by the accumulation of metabolic waste products.
The patterns in our model can be explained by cell death (Fig.
2D). An alternative mechanism is that cells migrate from ischemic
to well-perfused regions. To test this, we performed timelapse
imaging on cocultures within the graded microenvironment
assay. We took advantage of the combination of high resolution
and the wide field of tiling microscopy to track individual macrophages
over a large area (∼5.4 × 0.65 mm). For quantitative
analyses, we defined two regions of interest: the region comprising
the first 2 mm proximal to the slit was designated as
“Normal,” and the distal 2 mm of the image was designated as
“Ischemic” (Fig. 4C). Initially the two cell types were distributed
along the entire area but, after 72 h, macrophages were practically
absent from the ischemic region (SI Appendix, Movie S2).
More than 700 trajectories were analyzed. Only small but statistically
significant differences in speed and persistence of normal
versus ischemic macrophages were found (SI Appendix, Fig.
S9 A and B). Nevertheless, there was no significant preference in
the direction of migration that could explain the spatial structure
observed in our experiments (Fig. 4C). More formally, the mean
square displacement of either macrophage group was diffusive or
subdiffusive, revealing no directional bias toward the slit (Fig.
4D). The calculated diffusivity constant for these cells was less
than 2 μm2
/min (1.7 ± 0.2 μm2
/min in the highest case), which
means that the cells would require times on the order of months
to travel the distances in the millimeter scale required to explain
the patterns. Thus, the role of cell migration in determining this
spatial structure is negligible.
It is possible, then, that ischemic macrophages undergo a
metabolic collapse due to high lactic acid, low glucose, hypoxia,
etc. In fact, careful examination of the later frames in the timelapse
imaging movie shows that ischemic macrophages indeed
slow down their movements, round up, and eventually disappear
(SI Appendix, Movie S2). Cells typically round up in shape before
dying (48). Accordingly, calculation of cell circularity shows that
macrophages distal to the slit tend to be more circular than
proximal ones (SI Appendix, Fig. S9C). To examine cell death
more closely, we performed a time-lapse of a high-density macrophage
culture with propidium iodide (PI) in the media to label
nonviable cells (SI Appendix, Movie S3); PI is more adequate
for time-lapse experiments (SI Appendix). As shown in Fig.
4E, the proportion of nonviable macrophages significantly
increases over time but only for macrophages distant from the
slit. Together, these results show that cell death, not migration,
drives pattern formation because ischemic regions of the gradient
assay are more toxic for macrophages than for MTLn3 cancer cells.
Role of Macrophage Recruitment in Spatial Structure. Our measurements
show that macrophage motility does not play a role in
the emergence of spatial structure in the in vitro graded microenvironment.
In vivo, however, macrophages can be activated
and recruited to a tumor (22, 23, 32–34), and this active recruitment
is likely to have an important role in TAM spatial
structure. To test the role of TAM recruitment on spatial organization,
we conducted 2D simulations of an expanding tumor
with and without macrophage recruitment. We adapted the computational
model to simulate an expanding tumor mass surrounded
by well-irrigated stroma (SI Appendix, Fig. S10A) and simulated
several scenarios by varying (i) the presence of macrophages in
the initial tumor, (ii) the recruitment of macrophages to the
tumor, and (iii) different values for the relative sensitivity of
cancer cells and macrophages to an acidic environment. The
simulations showed that macrophage recruitment can lead to
spatial structure, but macrophage sensitivity to an ischemic environment
can greatly enhance the effect (SI Appendix, Fig. S10
and Movie S4). To test the role of macrophage recruitment
further, we conducted additional chamber experiments where
macrophages were introduced in the chamber only after 48 h.
Consistent with our model, macrophages could not colonize
deep regions within the system (SI Appendix, Fig. S11), supporting
that the ischemic environment plays a key role in spatial
patterning even when macrophages arrive in the system at later
stages of tumor development.
Discussion
Mathematical models of cancer have been developed for more
than half a century, but only recently have oncologists recognized
their value (49). Here we used a combination of mathematical
modeling and in vitro experiment to show that cell metabolism
can spontaneously create spatial heterogeneity in the extracellular
milieu when perfusion is limited. Low-grade early tumors typically
have TAMs, but these are homogenously spread throughout the
tumor, showing no evident spatial structure. Spatial structure
where TAMs are enriched at the edge of tumors is evident only
in later, possibly more ischemic, tumors (SI Appendix, Fig. S4)
(30). Our model provides a mechanistic explanation for these
observations, suggesting that microenvironmental heterogeneities
are key to establishing spatial patterns of localization of tumorassociated
macrophages.
In addition to being at the edge of large tumors, TAMs can
also be found in necrotic/anoxic regions from where they are
proposed to promote angiogenesis (28). This is consistent with
our experiments, as hypoxia per se does not kill macrophages (SI
Appendix, Fig. S2A). Hypoxia and pH levels in tumors are not
always correlated (15). Thus, we expect that TAMs in the necrotic
core could survive in regions that, despite being hypoxic,
have lower levels of lactic acid (50). Accordingly, in our experiments
we have observed that macrophages can survive in hypoxic
regions where there are fewer cancer cells.
Our model does not rule out additional mechanisms for spatial
patterning of TAMs (22, 23, 32–34). Macrophages can infiltrate
through tissues via para- or transcellular migration (51). However, it
is not clear why infiltrating macrophages do not adopt a homogeneous
distribution within tumors. Our results suggest one explanation:
the same microenvironment that is lethal for macrophages
in our culture should prevent infiltrating macrophages from colonizing
deep regions of the tumor. Thus, the effect of metabolic
alterations on the tumor microenvironment may synergize with
other known mechanisms of macrophage localization.
We investigated spatial structuring of TAMs, which typically
constitute the most prominent stromal cell population in tumors
and often promote tumor progression (23, 24, 52, 53). Clinical
evidence shows that the colocalization of carcinoma cells and
macrophages near capillaries is correlated with metastasis in
breast cancer (54). Thus, spatial structure may be a key element
in tumor-promoting activities of TAMs. For example, localized
invasion and entry of cancer cells into the bloodstream may be
more effective when macrophages are not evenly distributed.
Nonetheless, the metabolically altered microenvironments can
have effects on stromal cells other than TAMs and, thus, may be
a general mechanism for the emergence of spatial structure. For
example, human cytotoxic T lymphocytes infiltrating lactic-acid–
producing multicellular tumor spheroids have reduced cytokine
production and proliferation, and low pH induces anergy (55, 56).
The intimate link between metabolism and intracellular processes,
such as cell-signaling cascades and gene regulation, has
revitalized tumor metabolism research (2–9). However, most
studies focus on the intracellular mechanisms, and little attention
has been paid to the extracellular consequences of cancer metabolism.
We show here that tumor metabolic alterations can
have a considerable impact on their microenvironment, leading
to alterations of tumor-stromal spatial structure. If shown to be
widespread, the modulation of cell metabolism and the extracellular
19406 | www.pnas.org/cgi/doi/10.1073/pnas.1311939110 Carmona-Fontaine et al.
milieu composition may open therapeutic possibilities. At the
same time, they may force a rethinking of the microenvironmental
consequences of current therapeutic strategies. For
example, because ischemic regions can favor the emergentresistant
and aggressive clones (57, 58), therapies that target
processes such as angiogenesis can lead to more and larger
ischemic regions and potentially select for cancer lineages that
are fitter than stromal cells.
Methods
Extraction and differentiation of BMDMs were performed according standard
protocols (30). The graded microenvironment assay was created
from glass-bottom glass dishes (Matek) based on a tissue mimetic assay
(45). Microscopy was performed using an AxioObserver.Z1 (Zeiss), and
images were analyzed with custom-made scripts in Matlab (MathWorks). For
modeling details and complete methodology, please refer to SI Appendix.
ACKNOWLEDGMENTS. We thank Catherine Konopacki, Inna Serganova,
Grégoire Altan-Bonnet, Chris Sander, and the J.B.X. laboratory for helpful
discussions and critical reading. We thank Inna Serganova for C6-HRE-GFP
cells, Jeffrey Segall for MTLn3 cells, the Memorial Sloan–Kettering Cancer
Center (MSKCC) metabolomics core facility for aid in measuring metabolites,
and Joana Alves Vidigal for help in obtaining fresh MEFs. This work was
supported by National Cancer Institute Grant CA148967 (to J.B.X. and J.A.J.)
through the Integrative Cancer Biology Program and by the Office of the
Director, National Institutes of Health, under Award DP2OD008440 (to J.B.X.).
C.C.-F. is a Center for Cancer System Biology Independent Fellow. L.A. is supported
by a MSKCC Brain Tumor Center fellowship.
1. Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314.
2. Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev
Cancer 4(11):891–899.
3. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB (2008) The biology of cancer:
Metabolic reprogramming fuels cell growth and proliferation. Cell Metab 7(1):11–20.
4. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg
effect: The metabolic requirements of cell proliferation. Science 324(5930):1029–1033.
5. Koppenol WH, Bounds PL, Dang CV (2011) Otto Warburg’s contributions to current
concepts of cancer metabolism. Nat Rev Cancer 11(5):325–337.
6. Cairns RA, Harris IS, Mak TW (2011) Regulation of cancer cell metabolism. Nat Rev
Cancer 11(2):85–95.
7. Lunt SY, Vander Heiden MG (2011) Aerobic glycolysis: Meeting the metabolic requirements
of cell proliferation. Annu Rev Cell Dev Biol 27:441–464.
8. Wellen KE, Thompson CB (2012) A two-way street: Reciprocal regulation of metabolism
and signalling. Nat Rev Mol Cell Biol 13(4):270–276.
9. Ward PS, Thompson CB (2012) Metabolic reprogramming: A cancer hallmark even
Warburg did not anticipate. Cancer Cell 21(3):297–308.
10. San Martín A, et al. (2013) A genetically encoded FRET lactate sensor and its use to
detect the Warburg effect in single cancer cells. PLoS ONE 8(2):e57712.
11. Zhang J, Nuebel E, Daley GQ, Koehler CM, Teitell MA (2012) Metabolic regulation
in pluripotent stem cells during reprogramming and self-renewal. Cell Stem Cell
11(5):589–595.
12. Wang R, et al. (2011) The transcription factor Myc controls metabolic reprogramming
upon T lymphocyte activation. Immunity 35(6):871–882.
13. Mazurek S, Boschek CB, Hugo F, Eigenbrodt E (2005) Pyruvate kinase type M2 and its
role in tumor growth and spreading. Semin Cancer Biol 15(4):300–308.
14. Lee EY, et al. (2009) Hypoxia-enhanced wound-healing function of adipose-derived
stem cells: Increase in stem cell proliferation and up-regulation of VEGF and bFGF.
Wound Repair Regen 17(4):540–547.
15. Helmlinger G, Yuan F, Dellian M, Jain RK (1997) Interstitial pH and pO2 gradients in
solid tumors in vivo: High-resolution measurements reveal a lack of correlation. Nat
Med 3(2):177–182.
16. Gatenby RA, et al. (2007) Cellular adaptations to hypoxia and acidosis during somatic
evolution of breast cancer. Br J Cancer 97(5):646–653.
17. Nowell PC (1976) The clonal evolution of tumor cell populations. Science 194(4260):
23–28.
18. Merlo LM, Pepper JW, Reid BJ, Maley CC (2006) Cancer as an evolutionary and ecological
process. Nat Rev Cancer 6(12):924–935.
19. Gatenby RA, Gawlinski ET (1996) A reaction-diffusion model of cancer invasion.
Cancer Res 56(24):5745–5753.
20. Anderson AR, Weaver AM, Cummings PT, Quaranta V (2006) Tumor morphology and
phenotypic evolution driven by selective pressure from the microenvironment. Cell
127(5):905–915.
21. Gatenby RA, Gillies RJ (2008) A microenvironmental model of carcinogenesis. Nat Rev
Cancer 8(1):56–61.
22. Mantovani A, Allavena P, Sica A, Balkwill F (2008) Cancer-related inflammation.
Nature 454(7203):436–444.
23. Joyce JA, Pollard JW (2009) Microenvironmental regulation of metastasis. Nat Rev
Cancer 9(4):239–252.
24. Qian BZ, Pollard JW (2010) Macrophage diversity enhances tumor progression and
metastasis. Cell 141(1):39–51.
25. Bingle L, Brown NJ, Lewis CE (2002) The role of tumour-associated macrophages in tumour
progression: Implications for new anticancer therapies. J Pathol 196(3):254–265.
26. Zhang QW, et al. (2012) Prognostic significance of tumor-associated macrophages
in solid tumor: A meta-analysis of the literature. PLoS ONE 7(12):e50946.
27. Cramer T, et al. (2003) HIF-1α is essential for myeloid cell-mediated inflammation. Cell
112(5):645–657.
28. Murdoch C, Giannoudis A, Lewis CE (2004) Mechanisms regulating the recruitment
of macrophages into hypoxic areas of tumors and other ischemic tissues. Blood
104(8):2224–2234.
29. White C, et al. (2009) Copper transport into the secretory pathway is regulated by
oxygen in macrophages. J Cell Sci 122(Pt 9):1315–1321.
30. Gocheva V, et al. (2010) IL-4 induces cathepsin protease activity in tumor-associated
macrophages to promote cancer growth and invasion. Genes Dev 24(3):241–255.
31. Wyckoff JB, et al. (2007) Direct visualization of macrophage-assisted tumor cell intravasation
in mammary tumors. Cancer Res 67(6):2649–2656.
32. Ren G, et al. (2012) CCR2-dependent recruitment of macrophages by tumor-educated
mesenchymal stromal cells promotes tumor development and is mimicked by TNFα.
Cell Stem Cell 11(6):812–824.
33. Gil-Bernabé AM, et al. (2012) Recruitment of monocytes/macrophages by tissue factor-mediated
coagulation is essential for metastatic cell survival and premetastatic
niche establishment in mice. Blood 119(13):3164–3175.
34. Ahn GO, et al. (2010) Inhibition of Mac-1 (CD11b/CD18) enhances tumor response to
radiation by reducing myeloid cell recruitment. Proc Natl Acad Sci USA 107(18):8363–8368.
35. Goswami S, et al. (2005) Macrophages promote the invasion of breast carcinoma cells
via a colony-stimulating factor-1/epidermal growth factor paracrine loop. Cancer Res
65(12):5278–5283.
36. DeBerardinis RJ, et al. (2007) Beyond aerobic glycolysis: Transformed cells can engage
in glutamine metabolism that exceeds the requirement for protein and nucleotide
synthesis. Proc Natl Acad Sci USA 104(49):19345–19350.
37. Brizel DM, et al. (2001) Elevated tumor lactate concentrations predict for an increased
risk of metastases in head-and-neck cancer. Int J Radiat Oncol Biol Phys 51(2):349–353.
38. Walenta S, et al. (2000) High lactate levels predict likelihood of metastases, tumor
recurrence, and restricted patient survival in human cervical cancers. Cancer Res
60(4):916–921.
39. Xavier JB, Picioreanu C, van Loosdrecht MCM (2005) A framework for multidimensional
modelling of activity and structure of multispecies biofilms. Environ Microbiol
7(8):1085–1103.
40. Nadell CD, Foster KR, Xavier JB (2010) Emergence of spatial structure in cell groups
and the evolution of cooperation. PLOS Comput Biol 6(3):e1000716.
41. Peirce SM, Van Gieson EJ, Skalak TC (2004) Multicellular simulation predicts microvascular
patterning and in silico tissue assembly. FASEB J 18(6):731–733.
42. Thomlinson RH, Gray LH (1955) The histological structure of some human lung cancers
and the possible implications for radiotherapy. Br J Cancer 9(4):539–549.
43. Swanson KR, et al. (2011) Quantifying the role of angiogenesis in malignant progression
of gliomas: In silico modeling integrates imaging and histology. Cancer Res
71(24):7366–7375.
44. Hawkins-Daarud A, Rockne RC, Anderson ARA, Swanson KR (2013) Modeling tumorassociated
edema in gliomas during anti-angiogenic therapy and its impact on imageable
tumor. Front Oncol 3:66.
45. Cochran DM, Fukumura D, Ancukiewicz M, Carmeliet P, Jain RK (2006) Evolution of
oxygen and glucose concentration profiles in a tissue-mimetic culture system of
embryonic stem cells. Ann Biomed Eng 34(8):1247–1258.
46. Brader P, et al. (2007) Imaging of hypoxia-driven gene expression in an orthotopic
liver tumor model. Mol Cancer Ther 6(11):2900–2908.
47. Cox ME, Dunn B (1986) Oxygen diffusion in poly(dimethyl siloxane) using fluorescence
quenching. I. Measurement technique and analysis. J Polym Sci A Polym Chem
24:621–636.
48. Rello S, et al. (2005) Morphological criteria to distinguish cell death induced by apoptotic
and necrotic treatments. Apoptosis 10(1):201–208.
49. Rejniak KA, Anderson ARA (2012) State of the art in computational modelling of
cancer. Math Med Biol 29(1):1–2.
50. Serganova I, et al. (2011) Metabolic imaging: A link between lactate dehydrogenase
A, lactate, and tumor phenotype. Clin Cancer Res 17(19):6250–6261.
51. Ley K, Laudanna C, Cybulsky MI, Nourshargh S (2007) Getting to the site of
inflammation: The leukocyte adhesion cascade updated. Nat Rev Immunol 7(9):678–689.
52. Mantovani A, Bottazzi B, Colotta F, Sozzani S, Ruco L (1992) The origin and function
of tumor-associated macrophages. Immunol Today 13(7):265–270.
53. Hanahan D, Coussens LM (2012) Accessories to the crime: Functions of cells recruited
to the tumor microenvironment. Cancer Cell 21(3):309–322.
54. Robinson BD, et al. (2009) Tumor microenvironment of metastasis in human breast
carcinoma: A potential prognostic marker linked to hematogenous dissemination.
Clin Cancer Res 15(7):2433–2441.
55. Fischer K, et al. (2007) Inhibitory effect of tumor cell-derived lactic acid on human
T cells. Blood 109(9):3812–3819.
56. Calcinotto A, et al. (2012) Modulation of microenvironment acidity reverses anergy in
human and murine tumor-infiltrating T lymphocytes. Cancer Res 72(11):2746–2756.
57. Pàez-Ribes M, et al. (2009) Antiangiogenic therapy elicits malignant progression
of tumors to increased local invasion and distant metastasis. Cancer Cell 15(3):
220–231.
58. Ebos JML, et al. (2009) Accelerated metastasis after short-term treatment with a potent
inhibitor of tumor angiogenesis. Cancer Cell 15(3):232–239.
Carmona-Fontaine et al. PNAS | November 26, 2013 | vol. 110 | no. 48 | 19407
CELLBIOLOGY