Transcript ClaseCondat
Competition and cooperation: tumoral
growth strategies
Carlos A. Condat
Silvia A. Menchón
CONICET
Fa.M.A.F., Universidad Nacional de Córdoba
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Collaborators:
P.P. Delsanto, M. Griffa, C. Guiot, Politecnico
di Torino, Italy
R. Ramos, University of Puerto Rico at
Mayagüez
T.S. Deisboeck, Harvard University
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Outline
•Cancer growth: Macroscopic and mesoscopic approaches.
•Macroscopic approach: Ontogenetic growth law
•Application to tumors
•Spheroids – Applications of the macroscopic theory
•Mesoscopic approach: Model rules
•Simulations
•Single-species model
•Interspecies competition and tumor evolution
•Conclusions
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Cancer
dynamics.
• Carcinogenic change
• Growth
• Invasion
• Metastasis
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Microscopic description
Study of individual cell
properties
Macroscopic description
Tumor development as a
single entity
effective
parameters
Mesoscopic approach
Simulation of the
behavior of cell clusters
and their interactions
In vitro
experiments
Biological models
predictions
In vivo experiments
Clinical results
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Ontogenetic growth law
The growth of all living organisms follows the same
master curve, if we suitable rescale the mass and use
a dimensionless time .
(West, Brown and Enquist, Nature, 2001)
This statement can be “proved” using two assumptions:
A: Energy is conserved.
B: The nutrient distribution networks are fractal
(circulatory system in mammals, tracheal system in insects,
xylem in trees).
Note: assumption B is not universally accepted.
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(m()/M)1/4
Universal
growth
curve
West, Brown and Enquist, Nature, 2001
Conservation of energy + fractality of distribution network
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The hype:
West, quoted in Nature:
“ If Galileo had been a biologist, he would have written
a big fat tome on the details of how different objects fall at
different rates.”
J. Niklas, on the work of West,
Brown and Enquist:
Enquist is working on a project
“as potentially important to biology
as Newton’s contributions are to physics”
In: Trends. Ecol. Evol.
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ONTOGENETIC GROWTH LAW
The growth of an organism is mediated by cell division
and fed by metabolism.
Metabolic
Energy
Maintenance
Cell reproduction
Maintenance includes cell replacement.
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Energy conservation equation:
maintenance
creation
B: energy income rate to the organism cells
: single cell metabolic rate
: energy to create a single cell
N: total cell number
This equation can be easily turned into a simple differential equation.
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mc:single cell mass
b = /
m = Nmc: organism
mass
dm mc
Bm bm
dt
To be modelled: the basal metabolic rate B(m).
B ~ m3/4 [Kleiber, 1932 (on phenomenological grounds;
West, 2001 (fractal distribution networks)].
B ~ m2/3 [other authors].
Generally accepted: B ~ mp : a power law.
There are hundreds of power laws in biology!
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Setting a = mc B0/, b=/,
dm
p
am bm
dt
Maximum body size:
a
M
b
1 / 1 p
mc B0
1 /(1 p )
[Take dm/dt = 0]
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If m0 is the mass at birth, and
m0 1 p
1 p bt ln 1
M
we obtain the universal solution:
m
M
1 p
1 e
This is the curve plotted by West et al., with p = 3/4.
e- is the proportion of energy devoted to cell reproduction.
It goes to zero as grows.
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Does cancer follow a universal growth
law?
We would like to understand the kinetics of tumor growth.
Energy is conserved, but, what is B(p)?
Conjecture:
As for living beings , B(p) ~ mp.
At first: avascular growth (p = 2/3 ?)
Later: angiogenic growth (p = 3/4 ?)
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Molecular diffusion towards a sphere:
B(m) = B0m2/3
Cell,
spheroid
Nutrient
molecules
p = 2/3 results from simple scaling between surface and volume.
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At later times, angiogenesis changes the tumor feeding patterns.
Angiogenesis
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p=3/4 ?
B(m) = B0mp
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Experimental results
(m()/M)1/4
Fit with p=3/4 by Guiot et al.
J. Theor. Biol. (2003).
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(m()/M)1/4
Fit with p=3/4 by Guiot et al.
J. Theor. Biol. (2003).
Tumors implanted
in rats and mice
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(m()/M)1/4
Fit with p=3/4 by Guiot et al.
J. Theor. Biol. (2003).
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Multicellular Tumor Spheroids
MTS: spherical aggregates of proliferating, quiescent, and
necrotic cells
•In vitro models for the study of cancer cell biology.
•They can be grown under strictly controlled conditions.
•Spheroid-forming ability is inherent to solid tumor cells.
•Typically, they grow to diameters of up to 1.6 mm.
•A necrotized core appears when the diameter is ~ 0.8 mm.
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Multicellular
Tumor
spheroid
http://www.vet.purdue.edu/cristal/dicspheroid.jpg
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Do MSTs grow as live beings?
•Verify whether or not they grow according to West’s law.
•If so, MST’s can be used as test banks for growth theories:
Use large groups of similar specimens, varying the
environmental conditions.
•Feeding is purely diffusive p = 2/3 (?)
•p = ¾ would suggest that West’s ideas are incorrect.
Unfortunately, both power
laws yield similar-quality fits!
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The model is defined by,
dm
am p bm
dt
There is a delay in the onset of nutrient absorption,
which depends on the cell and the matrix.
We replace a by,
a1 t a 1 et / T
T: effective accommodation time
We applied these ideas to various experimental situations.
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Experiment I: Restrict feeding
(Freyer and Sutherland, Cancer Research, 1986)
The nutrient content of the medium is restricted. We model
this by introducing a feeding restriction parameter f.
f = 0 for a well-fed spheroid.
dm
1 f a1 t m p bm
dt
Asymptotic spheroid mass:
m M 1 f
1 /(1 p )
m decreases as the nutrient is decreased.
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Appl. Phys.
Lett., 2004
mt
y t 1
M
1 p
Time variation of an undernourished spheroid mass
[data: Freyer and Sutherland, 1986].
Solid curves: model fits (p=2/3). y-intercept: m0 = 2×10-6 g.
Final masses m, starting from lowest curve:
4.4 mg, 3.7 mg, 1.95 mg, and 3.56×10-5 g.
Accommodation time: T = 10 h.
Excellent fit, except for the very starved spheroid (f = 0.8).
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Experiment II: Increase matrix rigidity
(Helmlinger et al., Nature Biotechnology, 1997)
Because of the increase in mechanical stress,
growth is inhibited by increasing gel concentration.
Cells may be compacted, and the density changes.
We use the spheroid volume as the variable of interest.
m
vt
t
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Defining,
vt
z t 1
V
1 p
the energy conservation equation is,
dz
d ln g
1 f
1 e t / T
z 1
dt
dt
g
with:
g t t
R
1 p
V: volume under conditions of nutrient saturation.
R: final cell concentration
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We must specify (t)
Note: (i) Nutrient availability and growth are closely
related.
(ii) An increase in stress is a result of an increase in volume.
(iii) An increase in stress effectively hampers feeding.
Ansatz:
t R 0 R e t / T
0 : initial density.
Asymptotic volume:
v V 1 f
1 /(1 p )
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Appl. Phys.
Lett., 2004
v(t )
z (t ) 1
V
1 p
Variation of spheroid volume under different mechanical stress conditions
[Helmlinger et al., 1997]. Solid curves are model fits.
p= 2/3
3
Final volumes (in cm ) and accommodation times are, starting from the lowest curve:
(6×10-4, 30 h), (3.8×10-5, 100 h), (2.65×10-5, T = 110 h),
(4.88 ×10-6, 120 h).
T increases, and final cell density (R) increases by a factor of up to 3.
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Experiment III: periodic feeding (proposed)
Consider a periodic feeding protocol. Then,
dm
dt
a c sin t m p
bm
After a transient , the live cell mass oscillates,
following a hysteretic cycle.
m(t ) Asin t C(sin t )
Transient length: tT = 1/b(1-p)
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tT = 0.1
tT= 1
tT = 0.1
tT = 10
Hysteresis plots
m vs sin(t)
Maximum remanence:
tT = 1
This behavior is peculiar to “non-linear, non-classical” systems
(CAC,TSD, 2005).
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Mesoscopic approach
•Instead of analyzing cancer as a whole, we propose
a model for the behavior of groups of cells,
based on single-cell properties.
•Define the growth rules.
•Perform simulations for tumors containing one or two
cancer cell species.
First, we state the model rules.
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Growth rules
•Feeding: cancer cells absorb free nutrient
(concentration p) at a rate
' [1 exp( p)].
This is transformed into bound nutrient.
•Consumption: bound nutrient q is consumed by
cancer cells at a rate
' [1 exp( q / c)].
Both rates are proportional to the concentration for low
concentrations and then saturate.
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Growth rules
•Death: A low concentration of bound nutrient leads to cell death.
•Mitosis: A high concentration of bound nutrient leads to cell
replication.
q (i )
QD
c (i )
Death
QM
QD
q (i )
c (i )
Mitosis
QM
•Migration: A cell that senses a low nutrient level in its
neighborhood tends to migrate.
p(i )
c(i)
PD
Migration
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Simulation
•Consider a piece of tissue of arbitrary shape,
which is discretized using a square or cubic grid.
•Each node point represents a volume element
that contains many cells and nutrient molecules.
•Due to the complexity of the problem, we write all
equations directly in their discrete form.
•Initially the tissue is composed only of healthy cells
(h per node)
and nutrients [concentration p(i,t)].
Scalerandi et al., 1999; CAC et al., 2001.
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Simulation
The nutrient concentration evolves according to,
NN
pi , t pi pi ' pi hi pi S i
i
NN Diffusion
Absorption
Sources
• Once the steady-state is reached, a cancer seed is placed
somewhere in the lattice.
•Cell populations are modified because of migration, reproduction,
and death. Nutrient concentrations are modified through
diffusion, absorption, and consumption.
•Discretized iteration equations embodying these rules are
written and implemented in a simulation.
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Simulation
Here we consider a square piece of tissue,with a blood
vessel running along the lower edge. There the free
nutrient concentration is a constant, P0.
The cancer seed is placed at the center of the tissue.
Initial conditions:
ci ,0 c0 i I
d i ,0 0
q i ,0 q 0 i I
(I)
Typical lattice sizes: 300300
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Single species
Latency
Growth
These are two phase
diagrams, corresponding
to different values of .
Both data sets are well fitted by a power law with exponent 1/3.
Power laws crop up everywhere!
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Morphology
Red arrows:
= 0.44
Green arrows:
= 0.22
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Coming out of latency
Method A: angiogenic development.
Mediates the transition between the spheroid and the
vascularized stages.
Method B: cell mutations and emergence of a species
having comparative advantages.
Cell mutations lead to the development of acquired
resistance to chemotherapy. Chemotherapy may induce
latency or remission, but fails when a resistant
subspecies develops.
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Two species
• We let a single-species tumor evolve up to a time tm .
• At tm some cells at a localized position mutate (i.e., some of
their defining parameters are changed) and begin to compete
for nutrients with the original population.
• If the original tumor is either latent or slowly growing, small
parameter modifications may drastically alter the tumor
evolution.
• The tumor evolution depends not only on the intrinsic properties
of the new species, but also on the location of the mutation.
• Main determinants: local nutrient availability and local
concentration of competing cells – there is intraspecies
competition and there is inter-species competition.
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Two species: restarted growth
tm=20000
Just latent
tumor has a
= 0.44
mutation
t=25000
Observe
fast growth
of species 2
t=35000
t=30000
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Two species: second latency
tm=20000
t=30000
t=45000
t=40000
Original cancer
well inside
latent region
leads to
second
latency
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Example: = 1
tm=20000
t=45000
t=25000
t=35000
Restarted
growth for
cells with
restricted
mobility
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Modeling therapy
No therapy: cancer cells, dead cells and healthy cells
Simultaneous snapshots
G.Rivera, MS Thesis, UPR, 2005
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Modeling therapy
Cancer treated with immune therapy.
Cancer, dead, and healthy cell concentrations.
Lymphocyte concentration
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Modeling therapy
No therapy
With therapy
Therapy favors reproduction of surviving cancer cells,
accelerating tissue destruction (!)
Modeling therapy can help to determine optimal therapeutic courses.
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Conclusions
• Both macroscopic and mesoscopic techniques are useful to
study tumor growth.
• Macro: Ontogenetic growth laws describe observed behavior
(starving, stress) and lead to predictions.
• Meso: Simulations reproduce morphologies. Phase diagrams are
useful to predict tumor evolution.
• Modeling of subspecies competition can be useful for therapy
design. Mutations leading to an increase in absorption rates are
particularly aggressive.
• The success of a mutation depends not only on its intrinsic
competitive advantages, but also on its location.
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Conclusions
• FUTURE WORK:
• Modeling therapies
• Relate macro and meso approaches
• Modeling metastasis
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