Journal of Clinical Medicine,
Год журнала:
2024,
Номер
13(11), С. 3337 - 3337
Опубликована: Июнь 5, 2024
Cancer
cells,
like
all
other
organisms,
are
adept
at
switching
their
phenotype
to
adjust
the
changes
in
environment.
Thus,
phenotypic
plasticity
is
a
quantitative
trait
that
confers
fitness
advantage
cancer
cell
by
altering
its
suit
environmental
circumstances.
Until
recently,
new
traits,
especially
cancer,
were
thought
arise
due
genetic
factors;
however,
it
now
amply
evident
such
traits
could
also
emerge
non-genetically
plasticity.
Furthermore,
of
cells
contributes
heterogeneity
population,
which
major
impediment
treating
disease.
Finally,
impacts
group
behavior
since
competition
and
cooperation
among
multiple
clonal
groups
within
population
interactions
they
have
with
tumor
microenvironment
contribute
evolution
drug
resistance.
understanding
mechanisms
exploit
tailor
phenotypes
systems
level
can
aid
development
novel
therapeutics
treatment
strategies.
Here,
we
present
our
perspective
on
team
medicine-based
approach
gain
deeper
phenomenon
develop
therapeutic
Annals of Oncology,
Год журнала:
2023,
Номер
34(10), С. 867 - 884
Опубликована: Сен. 28, 2023
Cancer
research
has
traditionally
focused
on
developing
new
agents,
but
an
underexplored
question
is
that
of
the
dose
and
frequency
existing
drugs.
Based
modus
operandi
established
in
early
days
chemotherapies,
most
drugs
are
administered
according
to
predetermined
schedules
seek
deliver
maximum
tolerated
only
adjusted
for
toxicity.
However,
we
believe
complex,
evolving
nature
cancer
requires
a
more
dynamic
personalized
approach.
Chronicling
milestones
field,
show
impact
schedule
choice
crucially
depends
processes
driving
treatment
response
failure.
As
such,
heterogeneity
evolution
dictate
one-size-fits-all
solution
unlikely-instead,
each
patient
should
be
mapped
strategy
best
matches
their
current
disease
characteristics
objectives
(i.e.
'tumorscape').
To
achieve
this
level
personalization,
need
mathematical
modeling.
In
perspective,
propose
five-step
'Adaptive
Dosing
Adjusted
Personalized
Tumorscapes
(ADAPT)'
paradigm
integrate
data
understanding
across
scales
derive
schedules.
We
conclude
with
promising
examples
model-guided
personalization
call
action
address
key
outstanding
challenges
surrounding
collection,
model
development,
integration.
PLoS ONE,
Год журнала:
2025,
Номер
20(1), С. e0310844 - e0310844
Опубликована: Янв. 9, 2025
Mathematical
modeling
plays
an
important
role
in
our
understanding
and
targeting
therapy
resistance
mechanisms
cancer.
The
polymorphic
Gompertzian
model,
analyzed
theoretically
numerically
by
Viossat
Noble
to
demonstrate
the
benefits
of
adaptive
metastatic
cancer,
describes
a
heterogeneous
cancer
population
consisting
therapy-sensitive
therapy-resistant
cells.
In
this
study,
we
that
model
successfully
captures
trends
both
vitro
vivo
data
on
non-small
cell
lung
(NSCLC)
dynamics
under
treatment.
Additionally,
for
tumor
patients
undergoing
treatment,
compare
goodness
fit
classical
oncologic
models,
which
were
previously
identified
as
models
best.
We
show
can
capture
U-shape
trend
size
during
relapse,
not
be
fitted
with
models.
general,
corresponds
well
real-world
data,
suggesting
it
candidate
improving
efficacy
therapy,
example,
through
evolutionary/adaptive
therapies.
Computers & Industrial Engineering,
Год журнала:
2024,
Номер
191, С. 110078 - 110078
Опубликована: Март 24, 2024
Cancer
is
a
leading
cause
of
death
and
cost
burden
on
healthcare
systems
worldwide.
The
mainstay
treatment
chemotherapy
which
most
often
administered
empirically.
Optimizing
the
frequency
drug
administration
would
benefit
patients
by
avoiding
overtreatment
reducing
costs.
In
this
work,
optimization
regimens
using
mathematical
programming
techniques
demonstrated
developing
simple
model
for
fictitious
drug.
question
to
be
answered
solution
how
should
so
that
tumor
size
does
not
exceed
predefined
reaches
minimum
value.
proposed
computer-implemented
well-established
system,
thus
keeping
effort
obtaining
results
low.
An
example
used
demonstrate
superiority
approach
over
approach.
Life,
Год журнала:
2023,
Номер
13(2), С. 410 - 410
Опубликована: Фев. 1, 2023
Mathematical
models
are
a
core
component
in
the
foundation
of
cancer
theory
and
have
been
developed
as
clinical
tools
precision
medicine.
Modeling
studies
for
applications
often
assume
an
individual's
characteristics
can
be
represented
parameters
model
used
to
explain,
predict,
optimize
treatment
outcomes.
However,
this
approach
relies
on
identifiability
underlying
mathematical
models.
In
study,
we
build
framework
observing-system
simulation
experiment
study
several
growth,
focusing
prognostic
each
model.
Our
results
demonstrate
that
frequency
data
collection,
types
data,
such
proxy,
accuracy
measurements
all
play
crucial
roles
determining
We
also
found
highly
accurate
allow
reasonably
estimates
some
parameters,
which
may
key
achieving
practice.
As
more
complex
required
identification,
our
support
idea
using
with
clear
mechanism
tracks
disease
progression
settings.
For
model,
subset
associated
naturally
minimizes
identifiability.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 25, 2023
Abstract
Drug
resistance
results
in
poor
outcomes
for
most
patients
with
metastatic
cancer.
Adaptive
Therapy
(AT)
proposes
to
address
this
by
exploiting
presumed
fitness
costs
incurred
drug-resistant
cells
when
drug
is
absent,
and
prescribing
dose
reductions
allow
fitter,
sensitive
re-grow
re-
sensitise
the
tumour.
However,
empirical
evidence
treatment-induced
change
lacking.
We
show
that
chemotherapy-resistant
ovarian
cancer
cause
selective
decline
apoptosis
of
resistant
populations
low-resource
conditions.
Moreover,
carboplatin
AT
caused
fluctuations
sensitive/resistant
tumour
population
size
vitro
significantly
extended
survival
tumour-bearing
mice.
In
sequential
blood-derived
cell-free
DNA
samples
obtained
longitudinally
from
during
treatment,
we
inferred
cell
through
therapy
observed
it
correlated
strongly
disease
burden.
These
data
have
enabled
us
launch
a
multicentre,
phase
2
randomised
controlled
trial
(ACTOv)
evaluate
Cancer
treatment
optimizations
select
the
most
optimum
combinations
of
drugs,
sequencing
schedules,
and
appropriate
doses
that
would
limit
toxicity
yield
an
improved
patient
quality
life.
However,
these
often
lack
adequate
consideration
cancer's
near-infinite
potential
for
evolutionary
adaptation
to
therapeutic
interventions.
Adapting
cancer
therapy
based
on
monitored
tumor
burden
clonal
composition
is
intuitively
sound
approach
as
inherently
complex
adaptive
system.
The
be
driven
by
clinical
outcome
setpoints
embodying
aims
thwart
resistance
maintain
a
long-term
management
disease
or
even
cure.
given
nonlinear,
stochastic
dynamics
response
interventions,
strategies
may
at
least
need
one-step-ahead
prediction
their
control
over
growth
dynamics.
article
explores
feasibility
state
feedback
assuming
cell
fitness
underlying
source
phenotypic
plasticity
pathway
entropy
biomarker
trajectory.
exploration
undertaken
using
deterministic
models
