bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Dec. 8, 2023
Abstract
Individual
cells
in
a
tumour
can
be
distributed
among
Epithelial
(E)
and
Mesenchymal
(M)
cell-states,
as
characterised
by
the
levels
of
canonical
E
M
markers.
Even
after
(E-M)
subpopulations
are
isolated
then
cultured
independently,
E-M
heterogeneity
re-equilibrate
each
population
over
time,
sometimes
regaining
initial
distribution
parental
cell
population.
However,
it
remains
unclear
which
population-level
processes
give
rise
to
dynamical
changes
observed
experimentally,
including
1)
differential
growth,
2)
cell-state
switching,
3)
frequency-dependent
growth
or
state-transition
rates.
Here,
we
analyse
necessity
these
three
explaining
dynamics
distributions
PMC42-LA
HCC38
breast
cancer
cells.
We
find
that
differences
subpopulations,
with
without
any
interactions
(cooperation
suppression)
sub-populations,
insufficient
explain
dynamics.
This
insufficiency
is
ameliorated
transitions,
albeit
at
slow
rates,
both
data.
Further,
our
models
predict
treatment
TGFβ
signalling
JAK2/3
inhibitors
could
significantly
enhance
transition
rates
from
state
state,
but
does
not
prevent
transitions
M.
Finally,
devise
selection
criterion
identify
next
most
informative
time
points
for
future
experimental
data
optimally
improve
identifiability
estimated
best
fit
model
parameters.
Overall,
study
identifies
necessary
shaping
npj Systems Biology and Applications,
Journal Year:
2025,
Volume and Issue:
11(1)
Published: March 6, 2025
Phenotypic
heterogeneity
along
the
epithelial-mesenchymal
(E-M)
axis
contributes
to
cancer
metastasis
and
drug
resistance.
Recent
experimental
efforts
have
collated
detailed
time-course
data
on
emergence
dynamics
of
E-M
in
a
cell
population.
However,
it
remains
unclear
how
different
intra-
inter-cellular
processes
shape
heterogeneity.
Here,
using
Cell
Population
Balance
model,
we
capture
density
phenotypic
resulting
from
interplay
between-(a)
intracellular
regulatory
interaction
among
biomolecules,
(b)
division
death
(c)
stochastic
cell-state
transition.
We
find
that
while
existence
depends
regulation,
gets
enhanced
with
transitions
diminished
by
growth
rate
differences.
Further,
resource
competition
cells
can
lead
both
bi-phasic
total
population
and/or
bi-stability
composition.
Overall,
our
model
highlights
complex
between
cellular
shaping
dynamic
patterns
iScience,
Journal Year:
2024,
Volume and Issue:
27(7), P. 110116 - 110116
Published: May 27, 2024
Highlights•Luminal
signature
is
closely
associated
with
epithelial
in
breast
cancer•Basal
correlates
well
a
hybrid
epithelial-mesenchymal
signature•Basal
cancer
exhibits
higher
heterogeneity
patterns•Mathematical
modeling
of
underlying
gene
networks
explains
observed
heterogeneitySummaryIntra-tumoral
phenotypic
promotes
tumor
relapse
and
therapeutic
resistance
remains
an
unsolved
clinical
challenge.
Decoding
the
interconnections
among
different
biological
axes
plasticity
crucial
to
understand
molecular
origins
heterogeneity.
Here,
we
use
multi-modal
transcriptomic
data—bulk,
single-cell,
spatial
transcriptomics—from
cell
lines
primary
samples,
identify
associations
between
transition
(EMT)
luminal-basal
plasticity—two
key
processes
that
enable
We
show
luminal
strongly
associates
state,
but
basal
epithelial/mesenchymal
phenotype(s)
Mathematical
core
regulatory
representative
crosstalk
elucidate
mechanistic
underpinnings
from
data.
Our
systems-based
approach
integrating
data
analysis
mechanism-based
offers
predictive
framework
characterize
intra-tumor
interventions
restrict
it.Graphical
abstract
iScience,
Journal Year:
2024,
Volume and Issue:
27(7), P. 110310 - 110310
Published: June 19, 2024
Cancer
cell
populations
comprise
phenotypes
distributed
among
the
epithelial-mesenchymal
(E-M)
spectrum.
However,
it
remains
unclear
which
population-level
processes
give
rise
to
observed
experimental
distribution
and
dynamical
changes
in
E-M
heterogeneity,
including
(1)
differential
growth,
(2)
cell-state
switching,
(3)
population
density-dependent
growth
or
state-transition
rates.
Here,
we
analyze
necessity
of
these
three
explaining
dynamics
distributions
as
PMC42-LA
HCC38
breast
cancer
cells.
We
find
that,
while
transition
is
necessary
reproduce
observations
fractions,
interactions
(cooperation
suppression)
better
explains
data.
Further,
our
models
predict
that
treatment
cells
with
transforming
factor
β
(TGF-β)
signaling
Janus
kinase
2/signal
transducer
activator
transcription
3
(JAK2/3)
inhibitors
enhances
rate
mesenchymal-epithelial
(MET)
instead
lowering
(EMT).
Overall,
study
identifies
shaping
spontaneous
heterogeneity
Bulletin of Mathematical Biology,
Journal Year:
2025,
Volume and Issue:
87(2)
Published: Jan. 3, 2025
Abstract
Linear
compartmental
models
are
often
employed
to
capture
the
change
in
cell
type
composition
of
cancer
populations.
Yet,
these
populations
usually
grow
a
nonlinear
fashion.
This
begs
question
how
linear
can
successfully
describe
dynamics
types.
Here,
we
propose
general
modeling
framework
with
part
capturing
growth
and
transitions.
We
prove
that
this
model
asymptotically
equivalent
those
governed
only
by
its
under
wide
range
assumptions
for
growth.
