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Brain Tumor Center @ MGH
Neurosurgery @ MGH
Research @ MGH Neurosurgery
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1. Modeling Tumor Growth:
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Figure
5: 3D-Cellular Automaton Model: Shown is
the central slice of a human GBM tumor (volume = 65 cm3)
roughly 3 month after the expected time of diagnosis. The
outermost red cell fraction corresponds to highly metabolizing
and rapidly dividing tumor cells, whereas the yellow region
consists of quiescent (alive but non-proliferative) tumor
cells. The innermost black tumor region corresponds to an
area of apoptotic and necrotic cells. (Compare with
Figure 3).
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For more details please see:
Kansal A.R., Torquato S., Harsh IV G.R., Chiocca E.A., and
Deisboeck T.S.: Simulated brain tumor growth dynamics using
a three-dimensional cellular automaton. J. Theor. Biol.
203: 367-382, 2000.
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2. Modeling Clonal Competition:
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Figure 6:
Cross-section of a tumor, showing the emergence and dominance of a more rapidly growing
secondary tumor clone. The red region corresponds to the proliferating cells of the
primary clone and the blue region depicts those of the secondary strain. Yellow and black
fractions correspond again to the quiescent and necrotic regions of either strain (compare
with Figure 5). The cross-sections are taken 2.5 mm from the tumor’s
central plane. (a) Depicts the tumor roughly 1 month after the initial mutated CA cell was
introduced in the simulation, (b) 5 months after the mutation, (c) 10 months after the
mutation and (d) 20 months after the mutation. Note that the center of mass of the tumor
shifts before being forced back by boundary conditions. |
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For
more details please see: Kansal A.R., Torquato S., Harsh
IV G.R., Chiocca E.A., and Deisboeck T.S.: Simulated brain
tumor growth dynamics using a three-dimensional cellular
automaton. J. Theor. Biol. 203: 367-382, 2000.
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3. Modeling Tumor Invasion:
"under construction"
4. Modeling Tumor Heterogeneity:
(click image for larger view)
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Figure 7: Depicted is the
3D-growth of a brain tumor over time, showing the entire tumor (top row) and its central
slice (bottom row) respectively. The different colors correspond to proposed
genetic-net-states of tumor cell populations, which in turn are linked to distinct
epigenetic traits. The purpose of this model is to specifically investigate the impact of
environmental factors on the regional genetic and epigenetic composition of malignant
brain tumors. Since such heterogeneity is thought to be essential for the emergence of
treatment resistance in these tumors, a better understanding of the underlying dynamics is
crucial for the development of novel treatment strategies. |
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5. Modeling Tumor Cell Clusters:
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| Figure 8: Shown is a
simulation using an agent-based computer model. The clusters represent dynamically
emerging tumor cell aggregates as studied in experimental settings. The hypothesis is that
in reality secondary as well as recurrent brain tumor foci can emerge from such
microscopic cell clusters. Since these structures would be below the all current imaging
thresholds, computer animations such as this one may help understand the importance of
these structures for the dynamics of the entire tumor system as well as for future
treatment strategies. The model describes the emergence of these structural elements
depending on guiding environmental factors. In this particular animation, a nutrition
source is located in the top lattice corner but is non-replenished during the run. Tumor
cells aggregate in this area, consume the nutrients and move on once the nutrient
concentration declines. |
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| Figure 9: Shown is a
different simulation using the aforementioned model. In this particular animation the
nutrient source is replenished, much like a blood vessel in the real brain. In comparison
with Figure 8, tumor cells now tend to be much less mobile, i.e. stay
attached to the growing mass. Thus the model describes two variations of spatio-temporal
tumor systems growth: migratory-invasive behavior and expansive volumetric growth. Both
patterns are likely to occur in real (brain) tumors depending on various intrinsic and
extrinsic factors. |
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For more details please see:
Mansuri Y., M. Kimura, Lobo J., and Deisboeck T.S.: Emerging
pattern in tumor systems: simulating the complex dynamics
of multicellular cluster structures by using a novel agent-based
spatio-temporal agglomeration model. J. Theor. Biol. In
press.
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