bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 15, 2024
Abstract
In
this
study,
we
developed
and
evaluated
Machine
Learning
(ML)
models
aimed
at
predicting
the
stage
of
multiple
myeloma
(MM)
progression
monoclonal
gammopathy
undetermined
significance
(MGUS)
to
MM.
Accurate
staging
MM
is
critical
for
determining
appropriate
treatment
strategies,
our
models,
employing
algorithms
such
as
ElasticNet,
Random
Forest,
Boosting,
Support
Vector
Machines,
demonstrated
high
efficacy
in
capturing
biological
differences
across
disease
stages.
Among
these,
ElasticNet
model
exhibited
strong
generalizability,
achieving
consistent
multiclass
AUC
values
various
datasets
data
transformations.
Predicting
MGUS
presents
a
significant
challenge
due
scarcity
cases
that
have
progressed.
We
employed
two-pronged
approach
address
this:
developing
using
limited
dataset
containing
progressing
patients
training
on
combined
datasets.
The
achieved
slightly
above
0.8,
particularly
with
Boosting
indicating
their
potential
stratifying
by
risk.
This
study
original
integrating
enhance
predictive
accuracy
progression,
offering
novel
methodology
clinical
applications
patient
monitoring
early
intervention.
Our
feature
selection
enrichment
analyses
further
revealed
identified
genes
are
involved
key
signaling
pathways,
including
PI3K-Akt,
MAPK,
Wnt,
mTOR,
all
which
play
crucial
roles
pathogenesis.
These
findings
align
established
knowledge,
suggest
possible
therapeutic
targets
increase
explainability
models.
Cancers,
Journal Year:
2025,
Volume and Issue:
17(7), P. 1081 - 1081
Published: March 24, 2025
In
recent
years,
efforts
by
the
scientific
community
to
elucidate
underlying
mechanisms
of
clonal
expansion
and
selection
within
tumors
have
led
theory
"tumor
ecosystems",
implicating,
among
other
factors,
role
microenvironment
in
therapy
resistance
tumor
progression.
this
context,
contribution
development
multiple
myeloma
(MM)
is
being
investigated,
imparting
great
emphasis
on
continuous
evolution.
This
process
gives
rise
aggressive
clones
with
potential
spread
extramedullary
sites,
rendering
any
treatment
strategy
practically
ineffective.
systematic
review
aimed
gather
knowledge
about
immune
(IME)
plasma
cell
differences
synthesis
between
medullary
disease
(EMD).
A
search
according
PRISMA
guidelines
was
conducted
seven
databases,
six
articles
meeting
inclusion
criteria
were
encompassed
study.
Results
obtained
from
molecular
analysis
as
well
flow
cytometry
immunofluorescence
indicated
profound
genetic
instability
at
EMD
sites
along
spatial
temporal
heterogeneity
IME,
implying
a
possible
correlation
them.
Both
variability
notably
greater
compared
disease.
The
establishment
an
immunosuppressive
rule,
exhausted
CD8+
natural
killer
(NK)
cells,
M2
macrophages,
inactivated
dendritic
cells
found
co-localized
neoplastic
whereas
cytotoxic
M1
active
congregated
tumor-free
areas.
Post-therapy
alterations
milieu
also
noted
concerned
mostly
percentages
Tregs
MDSCs.
recognition
microenvironment-myeloma
interplay
essential
for
designing
specific
therapeutic
strategies
ameliorating
prognosis.
Aging,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 1, 2025
Multiple
myeloma
(MM)
is
a
cancer
that
difficult
to
be
diagnosed
and
treated.
This
study
aimed
identify
programmed
cell
death
(PCD)-related
molecular
subtypes
of
MM
assess
their
impact
on
patients'
prognosis,
immune
status,
drug
sensitivity.
We
used
the
ConsensusClusterPlus
method
classify
with
prognostically
relevant
PCD
genes
from
patients
screened.
A
prognostic
model
nomogram
were
established
applying
one-way
COX
regression
analysis
LASSO
Cox
analysis.
sensitivity
chemotherapeutic
agents
was
predicted
for
at-risk
populations.
Six
classified
employing
PCD-related
genes,
notably,
three
them
had
higher
tendency
escape
two
correlated
worse
prognosis
MM.
Furthermore,
C3
subtype
activated
pathways
such
as
oxidative
phosphorylation
DNA
repair,
while
C2
C4
related
apoptosis.
The
Risk
score
showed
can
correctly
predict
OS
patients,
in
particular,
high-risk
group
low
overall
survival
(OS).
Pharmacovigilance
analyses
revealed
low-risk
groups
greater
IC50
values
drugs
SB505124_1194
AZD7762_1022,
respectively.
12-gene
developed
accurately
patients.
Our
provided
potential
targets
strategies
individualized
treatment
Biomedicines,
Journal Year:
2025,
Volume and Issue:
13(4), P. 885 - 885
Published: April 5, 2025
Background:
Multiple
myeloma
(MM)
is
a
hematological
malignancy
originating
from
the
plasma
cells
present
in
bone
marrow.
Despite
significant
therapeutic
advancements,
relapse
and
drug
resistance
remain
major
clinical
challenges,
highlighting
urgent
need
for
novel
targets.
Methods:
To
identify
potential
druggable
genes
associated
with
MM,
we
performed
Mendelian
randomization
(MR)
analysis.
Causal
candidates
were
further
validated
using
single-tissue
transcriptome-wide
association
study
(TWAS),
colocalization
analysis
was
conducted
to
assess
shared
genetic
signals
between
gene
expression
disease
risk.
Potential
off-target
effects
assessed
through
an
MR
phenome-wide
(MR-PheWAS).
Additionally,
molecular
docking
functional
assays
used
evaluate
candidate
efficacy.
Results:
The
identified
nine
(FDR
<
0.05),
among
which
Orosomucoid
1
(ORM1)
Oviductal
Glycoprotein
(OVGP1)
supported
by
both
TWAS
evidence
(PPH4
>
0.75).
Experimental
validation
demonstrated
downregulation
of
ORM1
OVGP1
MM
(p
0.05).
Pregnenolone
irinotecan,
as
agonists
OVGP1,
respectively,
significantly
inhibited
cell
viability,
while
upregulating
their
Conclusions:
Our
highlights
targets
MM.
efficacy
pregnenolone
irinotecan
suppressing
growth
suggests
application.
These
findings
provide
insights
into
pathogenesis
offer
promising
strategy
overcoming
resistance.
European Journal Of Haematology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 27, 2024
ABSTRACT
Relapsed
and
refractory
multiple
myeloma
(RRMM)
remains
a
challenging
condition
despite
advances
in
immunotherapies.
Novel
bispecific
antibodies
(BsAbs),
including
talquetamab,
have
shown
promising
efficacy
heavily
pretreated
patients,
even
those
with
triple‐
penta‐refractory
disease.
Talquetamab,
recently
approved
by
the
FDA
EMA,
is
indicated
for
patients
who
progressed
after
at
least
three
or
four
prior
lines
of
therapy
(LOTs).
Administered
following
step‐up
dosing
phase
to
manage
cytokine
release
syndrome
(CRS),
talquetamab
demonstrated
high
overall
response
rate
(ORR)
approximately
70%,
previously
treated
T‐cell
redirecting
therapies.
Its
safety
profile
consistent
other
BsAbs,
hematologic
adverse
events
such
as
anemia
neutropenia
commonly
reported,
alongside
unique
on‐target
off‐tumor
toxicities
like
dysgeusia
skin‐related
events.
Infections
were
less
frequent
compared
BsAbs.
The
optimal
sequencing
therapies,
CAR‐T
cell
treatments,
an
area
active
research,
resistance
anti‐BCMA
therapies
presents
ongoing
clinical
challenges.
Current
trials
are
exploring
use
combination
well
therapeutic
strategies
post‐treating
progression.
real‐world
data
further
support
talquetamab's
efficacy,
making
it
valuable
addition
RRMM
treatment
landscape.
European Journal of Medical and Health Research,
Journal Year:
2024,
Volume and Issue:
2(5), P. 10 - 26
Published: Sept. 1, 2024
The
review
examines
multiple
myeloma,
including
pathophysiology,
conventional
treatments,
current
management
strategies,
treatment
challenges,
and
emerging
therapies.
disease,
originating
from
malignant
plasma
cells,
leads
to
bone
marrow
infiltration
osteolytic
lesions.
Common
manifestations
include
anemia,
pain,
renal
dysfunction,
hypercalcemia.
Pathophysiological
aspects
involve
disrupted
signaling
pathways
conflicts
between
myeloma
cells
the
environment.
Conventional
such
as
chemotherapy
with
melphalan
cyclophosphamide,
corticosteroids
(e.g.,
dexamethasone),
autologous
stem
cell
transplantation
(ASCT),
have
improved
patient
outcomes
but
come
significant
side
effects,
myelosuppression
infection
risks.
Recent
advances
in
targeted
therapies
like
proteasome
inhibitors
bortezomib)
immunomodulatory
drugs
lenalidomide),
well
monoclonal
antibodies
daratumumab)
innovative
immunotherapies,
CAR
T-cell
therapy
bispecific
antibodies.
Precision
medicine
enhances
by
customizing
based
on
individual
genetic
molecular
profiles.
Despite
these
advancements,
challenges
drug
resistance,
relapse,
refractory
disease
persist.
Resistance
mechanisms,
upregulation
of
anti-apoptotic
proteins
mutations
affecting
metabolism,
hinder
effective
treatment.
Managing
relapsed
or
cases
frequently
requires
reassessing
strategies
exploring
novel
Current
treatments'
adverse
both
hematological
non-hematological,
impact
quality
life,
necessitating
supportive
care,
dose
adjustments,
proactive
education.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 15, 2024
Abstract
In
this
study,
we
developed
and
evaluated
Machine
Learning
(ML)
models
aimed
at
predicting
the
stage
of
multiple
myeloma
(MM)
progression
monoclonal
gammopathy
undetermined
significance
(MGUS)
to
MM.
Accurate
staging
MM
is
critical
for
determining
appropriate
treatment
strategies,
our
models,
employing
algorithms
such
as
ElasticNet,
Random
Forest,
Boosting,
Support
Vector
Machines,
demonstrated
high
efficacy
in
capturing
biological
differences
across
disease
stages.
Among
these,
ElasticNet
model
exhibited
strong
generalizability,
achieving
consistent
multiclass
AUC
values
various
datasets
data
transformations.
Predicting
MGUS
presents
a
significant
challenge
due
scarcity
cases
that
have
progressed.
We
employed
two-pronged
approach
address
this:
developing
using
limited
dataset
containing
progressing
patients
training
on
combined
datasets.
The
achieved
slightly
above
0.8,
particularly
with
Boosting
indicating
their
potential
stratifying
by
risk.
This
study
original
integrating
enhance
predictive
accuracy
progression,
offering
novel
methodology
clinical
applications
patient
monitoring
early
intervention.
Our
feature
selection
enrichment
analyses
further
revealed
identified
genes
are
involved
key
signaling
pathways,
including
PI3K-Akt,
MAPK,
Wnt,
mTOR,
all
which
play
crucial
roles
pathogenesis.
These
findings
align
established
knowledge,
suggest
possible
therapeutic
targets
increase
explainability
models.
