Abstract
Background/Objectives: Multiple myeloma (MM) is a challenging, B cell
malignancy characterised by the uncontrolled proliferation of plasma
cells within the bone marrow. Despite significant advances in treatment
options nowadays, MM remains an incurable malignancy, with the majority
of patients succumbing to the disease. MM develops from a pre-malignant
state known as monoclonal gammopathy of unknown significance (MGUS),
which then has the potential to evolve either into smouldering
(asymptomatic) multiple myeloma (SMM) or into MM. Since novel drug
discovery takes years to reach the clinic, drug repurposing, which
concerns the detection of existing drugs for a novel disease, can be
applied. Methods: To address this critical and still unmet medical
need, we present a comprehensive signature-based drug-repurposing
approach using all the publicly available bulk transcriptomics datasets
on mGUS, sMM, and MM. Results: Our study included an in-house scoring
scheme approach enabling further filtering and prioritisation,
resulting in 25 candidate repurposed drugs for mGUS, 23 for sMM, and 66
for MM. The corresponding gene targets and the related functional terms
have been analysed, providing extra information for stage-specific
underlying mechanisms in myeloma. Lastly, enabled by a specific
computational workflow, we propose drug combinations between our top
candidate repurposed drugs and FDA-approved drugs for MM. Conclusions:
Together, these results deliver a stage-specific, transparent resource
for MM drug repurposing and combination design, intended to accelerate
translation toward earlier disease intervention and improved patient
outcomes.
1. Introduction
Multiple myeloma, the second-most common haematological malignancy
after non-Hodgkin lymphoma, originates from terminally differentiated
plasma cells primarily found in the bone marrow. The disease is
characterised by the secretion of a monoclonal immunoglobulin protein
(M protein) in most patients, driving clinical manifestations such as
hypercalcemia, renal insufficiency, anaemia, and bone disease [[32]1].
Monoclonal gammopathy of undetermined significance (MGUS) represents an
asymptomatic precursor to multiple myeloma, involving clonal plasma
cells and M protein secretion. Approximately 15% of MGUS cases progress
to multiple myeloma over 25 years, and early detection has shown
positive effects on overall survival [[33]2]. Smouldering multiple
myeloma (sMM) serves as an intermediate stage between MGUS and MM, with
recent updates considering ultra-high-risk sMM as part of multiple
myeloma and suggesting that early treatment for high-risk sMM may delay
progression to full-blown MM [[34]3]. More specifically, MGUS is an
asymptomatic clonal plasma cell disorder (M protein < 3 g/dL; marrow
plasma cells < 10%; no common symptoms of MM). Progression to MM
averages ~1%/year but varies with isotype, M protein level, and free
light chain ratio [[35]4]. sMM shows higher tumour burden (≥10% marrow
plasma cells and/or higher M protein) without end organ damage; risk
stratification identifies subsets for early intervention [[36]5].
Symptomatic MM is defined by clonal plasma cells (≥10%) plus
myeloma-defining events, reflecting genomic lesions and
microenvironmental cues driving proliferation, immune evasion, and bone
disease [[37]6]. Established diagnostic and prognostic biomarkers in MM
include detecting M protein, free immunoglobulin light chain,
β2-microglobulin and albumin, creatinine and calcium, and FISH analysis
for cytogenetic abnormalities. These biomarkers are used in the
disease’s staging system. Additionally, imaging is also used to detect
the degree of bone marrow infiltration. Emerging diagnostic biomarkers
include next-generation sequencing and next-generation flow cytometry,
extracellular vesicles, miRNAs, and circulating tumour cells [[38]7].
Current management includes regular monitoring for MGUS and low-risk
sMM, which avoids drug toxicity but carries a small ongoing risk of
progression [[39]4]. In selected high-risk sMM, early treatment (e.g.,
daratumumab- or lenalidomide-based) can delay progression and improve
outcomes, but increases infection risk and cost, and patient-selection
criteria are still evolving [[40]8]. In symptomatic MM, proteasome
inhibitors (bortezomib, carfilzomib, ixazomib) yield high response
rates, but neuropathy with bortezomib and cardiopulmonary events with
carfilzomib remain key limitations, alongside eventual resistance
[[41]9]. Immunomodulatory drugs (IMiDs; lenalidomide, pomalidomide)
provide durable benefit and underpin maintenance therapy yet cause
cytopenia and venous thromboembolism [[42]10]. Anti-CD38 monoclonal
antibodies (daratumumab, isatuximab) achieve rapid cytoreduction and
synergise with proteasome inhibitor and IMiD-based backbones, but are
associated with infusion-related reactions, infections,
hypogammaglobulinemia, and higher treatment cost [[43]11].
BCMA-directed therapies—including CAR-T cells, bispecific antibodies,
and antibody–drug conjugates—can induce deep remissions in
relapsed/refractory MM, yet are limited by cytokine-release syndrome,
immune-effector cell-associated neurotoxicity, infections with
prolonged hypogammaglobulinemia, manufacturing/access constraints, and
relapse driven by antigen escape [[44]11].
Early standard treatment for MM included alkylating agents such as
melphalan and/or cyclophosphamide, combined with corticosteroids in
most cases. This treatment was followed by autologous stem cell
transplantation, a standard treatment option still used today.
Compounds such as thalidomide, lenalidomide, and pomalidomide also
became available soon afterward. During the last two decades, the
therapeutic pool of MM expanded even further, with the discovery of
proteasome inhibitors (bortezomib), histone deacetylase inhibitors
(panobinostat), and more recently, nuclear export inhibitors
(selinexor). In addition, approval of immunotherapies for MM in 2015
starting with daratumumab and elotuzumab monoclonal antibodies and
later on with the antibody–drug conjugate belantamab mafodotin, the
bispecific teclistamab, and chimeric antibody receptor (CAR)-T cell
products, such as idecabtagene and vicleucel, are standard treatment
options nowadays. Despite the outstanding discoveries and improvements
that were made regarding the treatment for MM, almost all patients
become refractory to treatment and relapse. Because of that, there is a
high need to detect or develop drugs for the treatment of relapsed
and/or refractory MM or for halting the progression to MM [[45]12]. One
such approach is drug repurposing. Drug repurposing concerns the
detection of novel indications for drugs that are already approved for
another disease. Specifically, in silico drug repurposing offers the
ability to detect potential drugs using computational methodologies in
a cost and time-efficient way.
Motivated by the lack of a cure for MM or drugs that could halt the
progression to MM, we focused on applying the avenue of computational
drug-repurposing to highlight promising drug candidates and to study
the underlying mechanisms of the drugs and pathways related to the
disease and the stages that precede the disease. Since each MM stage
(MGUS, sMM, and MM) is characterised by different symptoms and
molecular mechanisms, we stratified the patient samples based on the
different stages to pinpoint stage-specific candidate repurposed drugs.
Here, we present a novel approach to use the transcriptomic signatures
of the molecular basis of the different stages of MM to detect
candidate repurposed drugs per severity stage. Initially, we performed
drug-repurposing analysis for different MM stages using publicly
available transcriptomic data. Moreover, we show the pathways that our
proposed repurposed drugs are involved in and their structural
similarities with current clinical trial drugs of MM.
2. Objectives
* To analyse publicly available transcriptomic datasets across the
full spectrum of multiple myeloma (MGUS, sMM, and MM) to identify
stage-specific differentially expressed genes (DEGs) and perform
pathway enrichment analysis.
* To apply a computational drug-repurposing pipeline aimed at
identifying candidate therapeutic compounds for each disease stage
and uncovering shared and distinct candidate drugs across the MM
progression.
* To investigate the molecular targets and associated pathways of the
repurposed drug candidates, offering insight into the biological
complexity and heterogeneity of myeloma at each stage.
* To propose rational drug combination strategies, integrating
FDA-approved treatments for MM with newly identified repurposed
candidates.
3. Materials and Methods
On 2 September 2024, we collected all the available bulk transcriptomic
data of the 3 stages of MM progression: MGUS, SMM, and MM. We performed
DE analysis per dataset and MM stage, using the Limma R package
(version 3.64.0). Enrichment analysis of the DE genes and in silico
drug repurposing using 3 different drug-repurposing tools then
followed. After collecting the candidate repurposed drugs, we used an
already established in-house scoring scheme to further filter the
drugs. A structural comparison of the highlighted proposed drugs with
the drugs that are currently in clinical trials was then followed. The
targets of the proposed candidate repurposed drugs were found and
enrichment analysis of those targets was then performed. Lastly,
suggestions of candidate drug combinations of the MM FDA-approved drugs
with our proposed candidate repurposed drugs were followed. The
detailed pipeline is shown in [46]Figure 1.
Figure 1.
[47]Figure 1
[48]Open in a new tab
General pipeline of the study. Step 1: Transcriptomics data collection
from Gene expression omnibus (GEO). Step 2: Pre-processing of data and
detection of differentially expressed genes in R using the limma R
package. Step 3: Transcriptomics-based drug repurposing—3 different
tools were used: Connectivity Map (CMap) CLUE, L1000CDS^2, and
SigComLINCS. The top 150 over-expressed and 150 under-expressed genes
based on log[2]FC from the gene list with an adjusted p value of <0.05
were used as an input. Gene ontology enrichment analysis of the
differentially expressed genes and drug targets was followed. Kept the
top pathways per dataset to keep a unique list for each stage using an
in-house scoring scheme. Step 4: Collection of clinical trials drugs
and structural similarity was performed for candidate repurposed drugs
and drugs in clinical trials. Step 5: Proposal of drug combinations
through 4 different models, using the FDA-approved drugs for MM and our
proposed candidate repurposed drugs.
3.1. Data
Six different microarray MM datasets were retrieved from the Gene
expression omnibus (GEO) [[49]13]—a transcriptional data repository.
Some of the datasets did not include all disease stages. The selection
of the datasets was based on the disease staging: MGUS, sMM, and MM
([50]Table 1). To our knowledge, these were the only publicly available
datasets on the 2 September 2024 that clearly used this progression
staging for the disease.
Table 1.
The experimental design of the transcriptomic datasets used in this
study. All stages were compared to controls.
No Ref. GEO Accession Number Stage
1 [[51]14] [52]GSE36474 MM
2 [[53]15] [54]GSE5900 MGUS
sMM
3 [[55]16] [56]GSE6477 MGUS
sMM
MM
4 [[57]17] [58]GSE13591 MGUS
MM
5 [[59]18] [60]GSE47552 MGUS
sMM
MM
6 [[61]19] [62]GSE80608 MGUS
MM
[63]Open in a new tab
3.2. Pre-Processing of Data
Each dataset selected was quantile-normalised and log[2]-transformed
where necessary. Subsequent analysis was performed in R statistical
environment ([64]http://www.R-project.org/, accessed 12 September 2024)
[[65]20]. Each of the six datasets and for each stage was processed
using the Limma R package [[66]21], a linear model that calculates a
moderated t-statistic from gene expression experiments.
3.3. Detection of Differentially Expressed Genes
After the dataset pre-processing, probe-set IDs were matched to gene
symbols according to each platform’s annotation file. We maintained the
most differentially expressed ones in cases of gene symbol
correspondence to multiple probe-sets ([67]Tables S1–S6). Explicitly
stage-labelled cohorts across the MGUS → sMM → MM spectrum are rare and
span different microarray platforms with unequal stage composition.
Thus, we adopted a conservative strategy: within-study normalisation
and limma DE, followed by rank-level aggregation across studies per
stage.
From the Limma analysis result, we kept the top 150 over-expressed and
150 under-expressed genes based on log[2]FC from the gene list with an
adjusted p value of <0.05. All comparisons were made using the disease
state (i.e., MGUS, sMM, or MM) vs. control samples. The selected number
of genes (150 over-expressed and 150 under-expressed) corresponds to
the input number limit of the drug-repurposing tools we used in the
sequel. Many widely used signature-reversal tools perform best with
balanced up/down lists of limited size. Using a fixed symmetric cutoff
ensures comparability across tools and stages and focuses on the
high-confidence perturbation while avoiding noise from long tails where
directionality is least stable across datasets. To keep the study
tractable and avoid tool-specific optimisation (which risks
overfitting), we retained 150/150 as a pre-specified setting.
3.4. Pathway Analysis of DEGs
The Gene ontology (GO) enrichment analysis was conducted using the
differentially expressed genes (DEGs) identified in the transcriptomics
study. The focus was on biological processes (BPs), and the
clusterProfiler R package (accessed on 18 April 2025) [[68]22] was
employed for the analysis. This procedure was applied individually to
each stage and dataset, and a scoring method was used to combine the
results, leading to a single list of pathways for each stage of the
disease ([69]Tables S7–S9).
To rank the pathways for each stage and dataset, leading to a unified
list per stage, we ranked the pathways within each dataset according to
their adjusted p values. Hence, the pathways from each dataset for each
stage were combined as a union of unique pathways and ranked by
calculating the weighted sum of normalised average rankings and the
normalised number of appearances according to Equation (1):
[MATH:
Score i= w1<
mtext> ∗ R<
mrow>i +
w2 ∗ Ai<
/mi>, i <
mo>= 1, …
, N pathways :MATH]
(1)
where R[i] is the average ranking score from each of the three tools,
A[i] is the number of appearances of each pathway for the different
datasets per stage, and w[1] and w[2] are set to 0.7 and 0.3,
respectively. This scoring scheme was adapted from [[70]23].
3.5. Transcriptomics-Based Drug Repurposing
The transcriptomic-based drug repurposing was performed using three
different drug-repurposing tools: Connectivity Map (CMap) CLUE
[[71]24], L1000CDS^2 [[72]25], and SigComLINCS [[73]26]. The 300
differentially expressed genes (based on their log[2]FC value) from the
six different datasets were used as transcriptomic signatures. Next,
each set was used as an input to the aforementioned repurposing tools.
These tools use transcriptional expression data from multiple human
cell lines to probe relationships between diseases and therapeutic
agents. Drugs are sorted according to a score (inhibition score), which
characterises if a drug can reverse (drugs with a strong negative score
value) or mimic (drugs with a strong positive score value) the
expression levels of a disease based on a given set of genes. For each
stage and each dataset, we obtained a list of candidate repurposed
drugs predicted by each of the three tools, ranked based on their
inhibition score. Since the output of L1000CDS^2 is limited to 50
drugs, we applied the same cutoff for all the other repurposed drug
lists. Hence, the top 50 drugs from each of the three tools were
combined as a union of unique drugs and ranked by calculating the
weighted sum of normalised average rankings and the normalised number
of appearances according to Equation (2):
[MATH:
Score i = w1 ∗ Ri +
w2 ∗ Ai, i<
mtext> = 1, … , N drugs
:MATH]
(2)
where R[i] is the average ranking score from each of the three tools,
A[i] is the number of appearances of each drug in the three DR tools,
and w[1] and w[2] are set to 0.7 and 0.3, respectively. The drug lists
obtained from all datasets were combined and re-ranked using Equation
(1) to conclude a single drug list from all the datasets. This scoring
scheme was adapted from [[74]23].
After the scoring was completed, we retained drugs with a score of 0.75
or higher for each disease stage.
3.6. Collection of the Currently Running Clinical Trials of MM and Its Stages
All listed clinical studies related to the three stages were collected
from [75]www.clinicaltrials.gov, accessed on 5 January 2025. The
downloaded file was filtered separately for MGUS, sMM, and MM and
entries that did not match these terms were removed. Additionally,
clinical trials that were either suspended, withdrawn, unknown, or
terminated were removed in order to keep the entries that are active,
completed, or will be recruiting soon. Specifically, only
small-molecule drugs and biologicals were obtained from the studies,
and everything else was removed.
3.7. Structural Similarity
The structures of the candidate repurposed drugs and the clinical trial
drugs we collected were downloaded in the form of the Simplified
Molecular Input Line Entry Systems (SMILES) through the PubChem
Identifier Exchange Service of the PubChem database
([76]https://pubchem.ncbi.nlm.nih.gov/idexchange/idexchange.cgi,
accessed on 21 March 2025) [[77]27]. We then converted the SMILES
format into a single 2D structure data file (SDF) using the OpenBabel
software [[78]28]. The Rcpi R package (accessed on 27 March 2025)
[[79]29] was then used to perform structural similarity across the
different drug groups collected, using an in-house script. We used an
80% Tanimoto similarity as a threshold. Additionally, we used a merged
SDF of the shortlisted repurposed drugs of the three stages (MGUS, sMM,
and MM) as input in the ChemBioServer 2.0
([80]https://chembioserver.vi-seem.eu/, accessed on 30 March 2025)
[[81]30], a publicly available tool that provides filtering,
clustering, comparison of drug structures, and networking of chemical
compounds to facilitate both drug discovery and repurposing. Drugs were
clustered using the Soergel distance ≤ 0.15 corresponding to a Tanimoto
similarity 87% (Tanimoto similarity = 1/(1 + Soergel distance))
[[82]31].
3.8. Drug Target Pathway Analysis
To further explore the candidate repurposed drugs, we extracted the
corresponding gene targets of each drug through mainly the Drug
repurposing hub database
([83]https://repo-hub.broadinstitute.org/repurposing, accessed on 30
March 2025) [[84]32] per stage. In cases where no target was found,
DrugBank ([85]https://go.drugbank.com/, accessed on 30 March 2025) and
PubChem ([86]https://pubchem.ncbi.nlm.nih.gov/, accessed on 30 March
2025) were also used. ([87]Tables S13–S15). The Gene ontology (GO)
enrichment analysis was conducted using the drug target genes of the
top candidate repurposed drugs, as chosen using the scoring scheme
mentioned above. The focus was on biological processes (BPs), and the
clusterProfiler R package was employed for the analysis. This procedure
was applied individually to each stage, and the top pathways were kept
using an adjusted p value of <0.05.
3.9. Drug Combination Synergies
The DrugComb database [[88]33] was used to extract and analyse
experimental drug combination data across multiple cancer cell lines.
DrugComb ([89]https://drugcomb.fimm.fi, accessed on 30 March 2025) is
an open access data portal containing drug combination studies, which
are standardised and harmonised. In total, 437,932 drug combinations
were tested on a variety of cancer cell lines. The data were downloaded
and filtered to include only MM-related cell lines and FDA-approved
drugs for MM. The analysis was performed in R. Four synergy models were
used:
* ZIP (Zero Interaction Potency): Measures interactions across
different doses [[90]34].
* Loewe Additivity: Compares observed combination effects to the
expected additive effects.
* HSA (Highest Single Agent): Evaluates whether the combination is
superior to the best-performing single agent.
* Bliss Independence: Assesses interactions based on independent
probabilities of drug effects.
We used four complementary reference models. HSA benchmarks against the
best single agent (conservative). Bliss assumes probabilistic
independence. Loewe assumes dose equivalence (appropriate for similar
mechanism pairs) and is typically most stringent when mechanisms
diverge. ZIP integrates potency and effect shifts across the response
surface. Therefore, model divergence, especially lower Loewe synergy
for mechanistically distinct pairs, is expected and informative.
4. Results
The pipeline adopted in this study and the main steps are illustrated
in [91]Figure 1. The overall process entails the analysis of
stage-specific MM-related transcriptomics datasets to identify
significant genes, with the subsequent identification and shortlisting
of candidate repurposed drugs and the pathways they target.
4.1. Differential Expression Analysis
The first part of this study included the collection and analysis of
publicly available transcriptomics datasets of MGUS, sMM, and MM
patients and controls. Following the pre-processing of these datasets,
we performed differential analysis to identify differentially expressed
genes (DEGs) between patients in each stage vs. controls. We used a
cutoff of adjusted p values < 0.05 and then sorted the differentially
expressed genes based on their log[2] fold-change (log[2]FC) value. All
differential expression analysis comparisons are presented in
[92]Tables S1–S6. The top five differentially expressed genes for each
comparison are listed in [93]Table 2.
Table 2.
The top 5 differentially expressed genes for each comparison.
MGUS sMM MM
[94]GSE36474 MAB21L1
XG
EMX2
HOXB-AS3
FAM3A
[95]GSE5900 KIT
IGLC1
PRR15
C7orf55
LOC100293211 KIT
CYAT1
LOC100293211
IGHV3-73
C7orf55
[96]GSE6477 CLC
PRG2
LOC100293211
RNASE2
PRG3 IGHD
IGLJ3
LOC100293211
CKAP2
IGHA1 IGHD
IGLJ3
LOC100293211
CKAP2
IGHA1
[97]GSE13591 IGLV1-44
IGLC1
LOC100293211
IGK
CKAP2 IGHD
LOC100293211
AbParts
IGLV1-44
IGHM
[98]GSE47552 IGKV2D-40
SNORD115-1
SNORD115-6
GPR15
SNORD115-44 IGKV2D-40
IGKV2D-26
IGKV1D-27
IGHV1OR15-1
IGKV1OR2-3 IGKV2D-40
IGKV2D-26
IGKV1OR2-3
IGKV6-21
IGKV1D-27
[99]GSE80608 SFRP2
H19
SLC14A1
F2R
SCIN FLG
H19
SLC14A1
F2R
SCIN
[100]Open in a new tab
4.2. Pathway Analysis of Differentially Expressed Genes
GO enrichment analysis was performed to detect the statistically
significant pathways involving the DEGs from the different datasets per
stage. In the MGUS stage, several biological processes (BPs) related to
the immune response were significantly detected ([101]Table S7). The
highest scores were associated with processes like the adaptive immune
response, particularly those involving immune receptor recombination-
and leukocyte-mediated immunity. This indicates heightened immune
activity at this early stage, where processes such as regulation of
leukocyte and lymphocyte proliferation, cell chemotaxis, and immune
responses to hydrogen peroxide are prominent. Notably, positive
regulation of cytokine production, reactive oxygen species (ROS)
metabolic process, and cell–cell adhesion suggest an active environment
where immune cells are mobilised and interact to control abnormal cell
growth.
In the sMM stage, immune system regulation intensifies, as seen in the
overrepresentation of GO terms related to mononuclear and lymphocyte
proliferation, positive regulation of cell activation, and T cell
differentiation ([102]Table S8). Nearly all immune-related pathways
show high scores (0.99), indicating robust immune modulation. Key
processes such as immune response-activating cell surface receptor
signalling and antigen receptor-mediated signalling pathways are highly
active, suggesting a pre-cancerous state where immune surveillance
attempts to combat disease progression. Additionally, terms involving
positive regulation of leukocyte and lymphocyte adhesion imply strong
immune cell communication and activation during this transitional
phase.
In the MM stage, there is still emphasis in immune-related processes,
with some unique changes ([103]Table S9). The B cell receptor
signalling pathway and B cell activation become more prominent,
suggesting the involvement of B cells in the disease’s progression.
Additionally, cytokine production, particularly interleukin-6 (IL-6),
and processes related to leukocyte migration and myeloid leukocyte
cytokine production highlight the advanced immune dysregulation. These
factors are likely contributing to the chronic inflammatory
environment.
Across the three stages (MGUS, sMM, and MM), there is a clear
progression in immune response activities. In the MGUS stage, the
immune system is highly active with processes focused on immune cell
proliferation and activation. As the disease progresses to the sMM
stage, the immune system continues to play a central role, with
heightened regulation of lymphocyte activation and immune signalling
pathways. By the MM stage, however, there is a shift towards immune
dysfunction, with an emphasis on B cell activation and cytokine
production, particularly IL-6, a known contributor to MM progression.
This progression reflects how the immune system, initially attempting
to control the abnormal cells, becomes increasingly compromised,
allowing for tumour growth and proliferation in later stages. Full
lists of pathways for each stage are presented in [104]Tables S7–S9.
4.3. Identification of the Shortlisted Candidate Repurposed Drugs for MM and
Its Stages
To perform in silico drug-repurposing analysis, we selected the top 150
over- and 150 under-expressed genes as they are required for most
repurposing tools. Using the DEG sets, we performed a series of in
silico drug-repurposing analyses with existing computational tools (see
Methods); Connectivity Map (CLUE), L1000CDS^2, and SigCom LINCS,
leading to three lists of candidate repurposed drugs, for each stage
and dataset, followed by a scoring process yielding to three lists of
proposed repurposed drugs for each stage (MGUS, sMM, and MM). The top
repurposed drugs selected are shown in [105]Figure 2. Our in silico
drug-repurposing analysis identified several candidate drugs with the
potential for repurposing in MM. These drugs were evaluated based on
their clinical trial status, preclinical evidence, mechanisms of
action, and gene targets ([106]Table S16). While some drugs have been
previously investigated in MM, others have only been tested in other
cancer types. Key mechanisms of action included cyclin-dependent kinase
(CDK) inhibition, histone deacetylase (HDAC) inhibition, and selective
estrogen receptor modulation.
Figure 2.
[107]Figure 2
[108]Open in a new tab
Top-scored candidate repurposed drugs for each disease state (MGUS,
sMM, and MM) according to our scoring scheme [[109]23]. (A). MGUS, (B).
sMM, and (C). MM. Bortezomib, which is detected in the sMM stage, is an
already FDA drug for MM.
For MGUS, the top candidate repurposed drugs proposed regarding the
in-house scoring scheme used were geldanamycin, roscovitine, PP-30,
mitomycin C, and collybolide. For sMM, the top candidate repurposed
drugs proposed regarding the scoring scheme used were olprinone,
lamotrigine, diprotin A, collybolide, and 112726-66-6 (BTCP). For MM,
the top candidate repurposed drugs proposed regarding the scoring
scheme used in this study were radicicol, piperlongumine, entinostat,
vecuronium, and terreic acid ([110]Tables S10–S12).
4.4. Investigation of Structural Similarity Concerning Ongoing Clinical
Trials
The shortlisted candidate repurposed drugs were screened for structural
similarity with the MGUS-, sMM-, and MM-related drugs in clinical
trials, and FDA-approved drugs of MM. Pairwise structural similarity
was calculated using a Tanimoto score threshold of 80%. As shown in
[111]Figure 3, heatmaps for each disease state suggest that the
majority of all examined drugs are not very similar, indicating a lack
of redundancy and a wide range of structural diversity in both our
shortlist and clinical trials. However, some similarities are present,
particularly for MM. For instance, ten candidate repurposed drugs are
also used in ongoing clinical trials, therefore showing a Tanimoto
similarity of 100%. Additionally, the candidate repurposed drug
exemestane, was found to have a similarity score of 82% against
dehydroepiandrosterone, a clinical trial drug. The repurposed drug
fluocinolone acetonide was also found to have a similarity score of 82%
against the clinical trial drug dexamethasone. Lastly, the repurposed
drug ivermectin b1a showed an 81% similarity with the clinical trial
drug bryostatin 1.
Figure 3.
[112]Figure 3
[113]Open in a new tab
Structural similarity heatmaps of shortlisted repurposed drugs along
with the ongoing drugs in clinical trials for (A) MGUS, (B) sMM, and
(C) MM. For MM, the maximum similarity between a repurposed drug and a
clinical trial drug was kept, since too many drugs were available for
this stage. X-axis shows the candidate repurposed drugs. Y-axis shows
the drugs in clinical trials for each disease state.
Additionally, the shortlisted candidate repurposed drugs were screened
for structural similarity among the three stages: MGUS, sMM, and MM.
Pairwise structural similarity was calculated using a Tanimoto score
threshold of 87%. As shown in [114]Figure 4, again in this comparison,
the hierarchical clustering suggests that all drugs are not very
similar, indicating a lack of redundancy and a wide range of structural
diversity in our shortlisted candidate repurposed drugs. Ten of these
drugs were detected in two disease stages as shown by the asterisk.
These drugs include terreic acid, calyculin A, radicicol, sofalcone,
piperlongumine, vorinostat, 849234-64-6
(4-acetamido-N-(2-amino-5-thiophen-2-ylphenyl)benzamide), bortezomib,
elesclomol, and linifanib.
Figure 4.
[115]Figure 4
[116]Open in a new tab
Hierarchical clustering of the shortlisted candidate repurposed drugs
for MGUS, sMM, and MM. The different groups in each box are thresholded
at Soergel distance value 0.15. Candidate repurposed drugs that are
detected in two disease stages are marked with an asterisk (*).
To further explore the candidate repurposed drugs, we extracted the
corresponding gene targets of each drug through mainly the Drug
repurposing hub database, and in cases where no target was found,
DrugBank and PubChem were also used ([117]Tables S13–S15).
We then performed Gene ontology (GO) analysis (biological processes)
per disease stage, using the gene targets of the candidate drugs
through the ClusterProfiler R package. Additionally, hierarchical
clustering of the enriched terms was performed again using the
ClusterProfiler R package. This relies on the pairwise similarities of
the enriched terms calculated by the use of Jaccard’s similarity index
(JC) ([118]Supplementary Figures S1 and S2).
To further elucidate the functional differences and commonalities
between the three disease stages, we generated an enrichment map plot
using the ClusterProfiler R package, organising enriched terms into a
network with edges connecting overlapping gene sets. In this way,
mutually overlapping gene sets tend to cluster together, making it easy
to identify functional modules. These gene targets of the proposed
candidate repurposed drugs from our analysis, provide valuable insights
into the fundamental transcriptional characteristics underlying the
functional properties of MGUS, sMM, and MM.
This analysis highlights BPs involved in “cell signalling pathways”,
such as vascular endothelial growth factor signalling pathway,
brain-derived neurotrophic factor receptor signalling pathway, response
to insulin and others ([119]Figure 5, yellow cluster). Moreover, the
purple cluster is involved in cellular response to insulin stimulus,
cellular response to peptide hormone stimulus, response to macrophage
colony-stimulating factor, and others. Notably, the yellow and purple
clusters overlapped spatially, indicating that these functionally
coherent groups share biologically related terms. Additionally, BPs
involved in membrane and action potentials and potassium ion transport
were also highlighted ([120]Figure 5, red cluster). GO terms associated
with this cluster were highly enriched in all disease stages, and
particularly in MGUS and sMM. Another important cluster generated by
this analysis highlights BPs involved in epigenetic regulation of gene
expression and protein modification ([121]Figure 5, green cluster).
Additionally, GO terms associated with this cluster were highly
enriched in all disease stages. Moreover, a cluster highlighting
Circadian rhythms, protein localisation, mitochondrial function, and
others, has also been generated through this analysis. Most BPs of this
cluster have been highlighted in MM (blue cluster, [122]Figure 5). The
blue-green cluster is focused on hormone-related signalling pathways,
with the GO terms associated with this cluster being enriched in all
disease stages. Lastly, a small cluster associated with response to
oxidative stress has also been highlighted (yellow cluster, [123]Figure
5).
Figure 5.
[124]Figure 5
[125]Open in a new tab
Biological theme comparison of MGUS, sMM, and MM. The enrichment map
shows the top enriched terms in MGUS, sMM, and MM, organised into a
network with edges connecting overlapping gene sets. Coloured-based
clustering was performed using the emapplot function, which reflects
functional similarity among enriched terms.
4.5. Drug Combination Synergies
The drug combination data were extracted through the DrugComb database
(see Methods). In this work, we kept four synergy models: ZIP (Zero
Interaction Potency), Loewe Additivity, HSA, and Bliss Independence.
Drug combinations are considered synergistic if they exceed specific
thresholds: moderate synergy (>+5) and strong synergy (>+10)
([126]Supplementary Figure S3).
4.6. Top Drug Combinations Identified
To find the top synergies regarding our disease of interest, MM, a
systematic analysis of MM’s FDA-approved drugs was carried out to
detect synergies in general and, more specifically, synergies with our
proposed candidate repurposed drugs. When looking for drug combinations
common across all synergy models, 17 drug combinations were detected
([127]Supplementary Figures S4 and S5), from which, however, none
include any of our proposed candidate repurposed drugs. Therefore, we
stick to the drug combinations for each synergy model separately, which
are then combined in a final list.
A total of 181 drug combinations exhibited synergy across multiple,
with some combinations including drugs from our proposed candidate
repurposed drug list. The strongest synergies observed per model are
shown in [128]Figure 6, in a subset of 20 top-ranking combinations.
Notably, an analogue of one of the proposed candidate repurposed drugs,
erlotinib, was also identified, further supporting its potential
application in combination therapy. Top drug combinations of FDA drugs
along with our proposed candidate repurposed drugs include lenalidomide
+ retinoic acid (from the ZIP and Bliss models), erlotinib
hydrochloride + bortezomib (from the ZIP model), and 23541-50-6
(daunorubicin HCL) + thalidomide (from the HAS model). The total
significant drug combinations can be found in [129]Table S19.
Figure 6.
[130]Figure 6
[131]Open in a new tab
Summary of the top 20 drug combinations per model. The figure presents
a comparative analysis of synergy scores across different models,
highlighting the most promising combinations for MM treatment. (A)
Synergy Zip Score model, (B) Synergy Loewe Score model, (C) Synergy HSA
Score model and (D) Synergy Bliss Score Model.
The results suggest that several repurposed drugs could be explored
further for their potential in MM treatment. Additionally, the
identification of highly synergistic drug combinations underscores the
importance of combination therapy approaches. Future studies should
focus on validating these findings in preclinical and clinical settings
to assess their therapeutic potential.
5. Discussion
The search for effective treatments for MM continues to be a
substantial problem, requiring ongoing investigation into new
therapeutic approaches. Drug repurposing, the process of using existing
drugs for a novel disease, offers a promising approach due to its cost-
and time-effective nature. Drug repurposing is essential in the
advancement of innovative anti-cancer therapies. This study explores
the capabilities of transcriptomic signature-based drug repurposing
using all the publicly available bulk transcriptomics datasets on MGUS,
sMM, and MM. We filter and prioritise candidate repurposed drugs to be
shortlisted for further analysis in the future. Our study included a
scoring scheme from a previous work of our group [[132]23], resulting
in 25 candidate repurposed drugs for MMGUS, 23 for sMM, and 66 for MM.
From these, 18 candidate repurposed drugs for MGUS, 16 for sMM, and 52
for MM had available structure information. These highlighted drugs
have generally been shown to be structurally distinct from each other,
meaning that most of the proposed drugs do not belong to the same
structural subgroup. Additionally, Gene ontology terms (biological
processes) were detected using the DEGs of each dataset and disease
state. The same scoring scheme used for the candidate repurposed drugs
was also used here to select a single list of pathways for each disease
state. Lastly, we detected the gene targets of the proposed repurposed
drugs along with the associated Gene ontology terms (biological
processes).
According to MeSH, when analysing the top 15 proposed candidate
repurposed drugs for each disease stage—MGUS, sMM, and MM—three drugs
were characterised as antineoplastic for MGUS, including geldanamycin,
mitomycin, and roscovitine. Additionally, for sMM, two drugs were
detected as antineoplastic: linifanib and vorinostat. Lastly, for MM,
five out of the fifteen top proposed candidate repurposed drugs were
antineoplastic agents, including radicicol, entinostat, salermide,
temozolomide, and daunorubicin. Other drug categories among the top
proposed candidate repurposed drugs include immunosuppressants (e.g.,
cyclosporin A detected for sMM).
To synthesise the pathway-level signals, we summarise key therapeutic
families implicated by our stage-specific targets and drug candidates
([133]Table 3). This table highlights where candidates align with known
mechanisms versus under-explored processes enriched in earlier stages.
Table 3.
Mechanistic families for shortlisted repurposing candidates across the
MGUS, sMM, and MM spectrum.
Pathway Family Representative Targets/Mechanisms Example Candidates
from Our Shortlist Key References