Abstract
Background
Combination therapies play a crucial role in the treatment of complex
diseases, such as cancer. They enhance efficacy, minimize resistance,
and reduce toxicity by leveraging synergistic effects. However,
identifying effective combinations is challenging due to the vast
number of possible pairings and the high-priced costs of experimental
validation. Machine learning (ML) and deep learning (DL) models have
advanced drug synergy prediction by integrating diverse datasets and
modeling the interactions between drugs and cell lines. Despite these
advancements, most algorithms primarily rely on drug-specific features,
such as chemical structures, with limited incorporation of functional
drug information and cellular content features.
Methods:
We propose a novel approach that integrates Drug Resistance Signatures
(DRS) as a biologically informed representation of drug information.
This approach provides a more comprehensive framework for identifying
effective combination therapies. We evaluated the predictive power of
DRS features across various machine learning models (LASSO, Random
Forest, AdaBoost, and XGBoost) and the deep learning model SynergyX. We
compared their performance with that of conventional drug signatures
and chemical structure-based descriptors.
Results:
Our results demonstrate that models incorporating DRS features
consistently outperform traditional approaches across all evaluated
algorithms. Validation on independent datasets, including ALMANAC,
O’Neil, OncologyScreen, and DrugCombDB, confirms the robustness and
generalizability of the proposed framework.
Discussion
These findings emphasize the importance of integrating
resistance-informed transcriptomic features into computational models.
By capturing drug functionality in a biologically relevant context, DRS
improves both the accuracy and interpretability of drug synergy
prediction, offering a powerful strategy for guiding the discovery of
effective combination therapies.
Keywords: drug combination, synergy, drug signature, drug resistance,
gene expression
1 Introduction
Drug resistance occurs in over 90% of cancer patients, where cancer
cells develop tolerance to treatment. Therefore, combination therapy
has proven to be an effective method for combating drug resistance
([28]Yardley, 2013). Genetic mutations, epigenetic changes, increased
drug efflux, and other complex cellular and molecular mechanisms cause
his resistance. Drug resistance can be classified into intrinsic and
acquired types based on when it develops ([29]Wang et al., 2023).
Intrinsic resistance occurs prior to patient exposure to drugs, which
may reduce the drug efficacy from the beginning ([30]Wang et al., 2023;
[31]Wang et al., 2019; [32]Holohan et al., 2013). However, acquired
resistance develops over time during treatment and is characterized by
a decrease in the drug’s effectiveness over time. Acquired resistance
can be caused by the activation of a proto-oncogene, which becomes the
newly emerging driver gene, mutations, changing expression levels of
drug targets, or changes in the tumor microenvironment after therapy.
Both intrinsic and acquired resistance are common, with each occurring
in roughly 50% of cancer patients who develop drug resistance
([33]Holohan et al., 2013). Therefore, drug combination therapies have
become important as promising methods to overcome resistance by
simultaneously targeting multiple targets or biological pathways. In
addition, the lower dose prescriptions of a single drug can reduce the
potential risks of toxicity and side effects.
The increasing availability of large-scale, high-throughput data and
drug combination databases has enabled the development of numerous
machine learning (ML) and deep learning (DL) computational methods for
predicting drug synergy. These methods vary in their representation of
biological systems, how they integrate diverse data, and their modeling
of the complex interactions between drugs and cellular contexts
([34]Pan et al., 2023; [35]Besharatifard and Vafaee, 2024).
Conventional ML algorithms such as Random Forest (RF), Support Vector
Machines (SVM), Gradient Boosting Machines (GBM), K-Nearest Neighbors
(KNN), and logistic regression, have been widely used to predict drug
combination outcomes ([36]Güvenç et al., 2021; [37]Li et al., 2020).
These models typically rely on engineered features such as chemical
fingerprints, gene expression profiles, and drug-target interaction
data. While they are computationally efficient and relatively
interpretable, their capacity to capture nonlinear and higher-order
biological interactions is limited. Some ensemble-based variants
combine predictions from different feature spaces to enhance robustness
and predictive performance ([38]Li et al., 2020; [39]Xia et al., 2018).
The early DL-based methods, such as DeepSynergy ([40]Preuer et al.,
2018) and MatchMaker ([41]Kuru et al., 2021), utilize fully connected
deep neural networks to learn complex patterns from chemical
descriptors and transcriptomic features. To further enhance
performance, feature fusion models such as WRFEN-XGBoost ([42]Lu et
al., 2021) integrate drug-induced expression perturbations to better
model drug interaction effects. Recent developments have introduced
multi-view DL models that assign a separate sub-network to each type of
input data (such as gene expression, drug structure, or protein
abundance), followed by a shared prediction layer. These architectures
reduce noise and leverage the complementary strengths of heterogeneous
datasets. Models such as BestComboScore ([43]Xia et al., 2018) and
DeepDDS ([44]Wang et al., 2022) are key examples of this approach.
Graph convolutional approaches, including DRSPRING ([45]Han et al.,
2024) and MFSynDCP ([46]Dong et al., 2024), model drugs, targets, and
pathways as interconnected networks. By embedding this structure into
the learning framework, these models capture relational patterns that
are often missed by flat feature vectors. Similarly, knowledge graph
and hypergraph-based methods, such as KGANSynergy ([47]Zhang et al.,
2023) and HypergraphSynergy ([48]Liu et al., 2022), are designed to
account for higher-order interactions beyond drug pairs. These models
are particularly effective in sparse or noisy settings, where they
leverage biological priors to infer missing links. Recent graph
attention models, like SynergyX ([49]Guo et al., 2024), further
prioritize explainability by highlighting the most influential features
in synergy prediction.
To overcome the limitations of any single modality or model type,
hybrid systems integrate multiple data sources (chemical, genomic, and
phenotypic) and learning paradigms (e.g., deep learning, graph neural
networks, and ensemble learning). These approaches have demonstrated
improved generalizability and predictive stability across diverse
datasets. For instance, multi-modal frameworks incorporating
drug-pathway-cell line graphs, attention-guided embedding fusion, and
pathway-enriched transcriptomic features provide both predictive power
and biological insight ([50]Zhang et al., 2023; [51]Peng et al., 2024).
While recent hybrid and deep learning models have started to
incorporate functional drug data, such as transcriptional profiles and
drug-induced gene expression changes, these applications are often
limited to general drug signatures or pathway activation scores.
Methods such as DeepMDS ([52]Lu et al., 2021) and DRUGSYNC ([53]Zhao
and Luo, 2024) utilize transcriptomic features, including drug-induced
expression profiles or pathway-level summaries to represent drug
function. However, most of these models rely on broad or averaged gene
expression data without explicitly modeling resistance-specific
transcriptional adaptations. Despite these advances, the detailed
integration of drug resistance-specific transcriptional information
remains largely unexplored, particularly in the context of drug synergy
prediction. In particular, transcriptomic DRS can reveal molecular
adaptations that contribute to cancer drug resistance, enabling more
accurate predictions of treatment efficacy. To address this gap, To
address this gap, we utilize a novel feature class, DRS, which captures
transcriptomic changes associated with drug resistance mechanisms, as
illustrated in [54]Figure 1. Unlike traditional models that rely
primarily on chemical structures or general drug-induced
transcriptional responses, DRS features provide a functional
perspective by highlighting gene expression differences between
drug-sensitive and drug-resistant cancer cell lines. To evaluate the
generalizability and effectiveness of this feature type, we analyzed
its performance across various modeling strategies. These included four
classical machine learning algorithms: LASSO, Random Forest, AdaBoost,
and XGBoost, as well as the deep learning framework SynergyX. Our
findings suggest that incorporating functional drug data, particularly
resistance-related signatures, substantially improves predictive
performance in drug combination modeling.
FIGURE 1.
[55]Diagram illustrating the process of drug-induced gene profiling for
combination scoring. It shows drugs affecting genes and cell lines,
leading to drug signatures and resistance signatures through
differentially expressed genes (DEG). Two drugs (A and B), along with
cell lines, are analyzed using machine learning (ML) and deep learning
(DL) models to generate a combination score. The drug Erlotinib is
highlighted.
[56]Open in a new tab
Workflow for predicting drug synergy using transcriptomic signatures
features.
2 Materials and methods
2.1 Datasets
The Drug Combination Database aggregates experimental data on drug pair
interactions, including synergy scores derived from in vitro assays
conducted across various cell lines. We utilized five datasets:
DrugComb ([57]Zheng et al., 2021), O’Neil ([58]O’Neil et al., 2016),
Oncology Screen ([59]O’Neil et al., 2016), DrugCombDB ([60]Liu et al.,
2020), and Alamanac ([61]Holbeck et al., 2017) for benchmarking
predictive models against experimentally validated drug combinations.
Among them, DrugComb is the primary dataset in this study, and it
included 739,964 drug combination experiments and introduces a novel
synergy metric, the S score ([62]Malyutina et al., 2019). This metric
quantifies drug synergy by measuring the disparity between the
dose-response curves of a drug combination and its constituent single
agents. [63]Table 1 summarizes the drug combination synergy data in
different datasets with various synergy types.
TABLE 1.
Overview of datasets utilized for drug synergy prediction analysis.
Datasets Drugs Cell lines Combination
DrugComb 354 170 330917
DrugCombDB 600 68 60932
O’Neil 38 39 23062
Oncology Screen 21 29 4176
Alamanac 118 118 296503
[64]Open in a new tab
2.2 Drug signature features
The LINCS database provided extensive gene expression data from diverse
cell lines exposed to various drugs ([65]Subramanian et al., 2017). Its
large-scale repository included transcriptomic signatures across
various experimental conditions, such as different drug concentrations
and time points. We used LINCS data 24 h after treatment with 10 μM
drug concentration, as it was the most common condition in the LINCS
dataset.
We also obtained drug response metrics for a wide array of cancer cell
lines from the GDSC database ([66]Iorio et al., 2016), including IC[50]
values and dose-response curves for several thousand anticancer agents.
We extracted Level 5 transcriptomic signatures from the LINCS database
and associated these profiles with cell viability data from the GDSC to
analyze drug-induced responses across multiple cell lines. The
integration of LINCS and GDSC datasets involved identifying overlapping
drugs by cross-referencing drug identifiers and filtering for matches,
resulting in a final set of common drugs.
To characterize drug sensitivity and resistance, cell lines have been
grouped from the GDSC based on their IC[50] values, using the median
IC[50] across all cell lines as a threshold, following established
methodologies ([67]Wang et al., 2019). We define the sensitivity status
[MATH: Si :MATH]
of the
[MATH: i :MATH]
cell line as:
[MATH: Si=if
IC50i<medianIC50i sensitive if IC50i≥medianIC50i resistance :MATH]
Where
[MATH:
IC50i :MATH]
denotes the IC[50] value of cell line j for a given drug.
Differential gene expression analysis was performed using two different
approaches: Conventional Drug Signature (DS): This signature compares
gene expression between treated and untreated conditions for a given
drug across a fixed cell line. For each gene
[MATH: i :MATH]
, the differential expression score is computed as:
[MATH:
μiS−μiR=∆iSR :MATH]
Where:
[MATH:
μiR=μ∑jϵT1T<
msubsup> μiS=μ∑jϵU1U<
/mrow> :MATH]
Let
[MATH: T :MATH]
be the set of samples treated with a specific drug,
[MATH: U :MATH]
be the set of control (untreated) samples. For each gene
[MATH: i :MATH]
, the mean expression level under treated
[MATH: μiR
:MATH]
and control
[MATH: μiS
:MATH]
conditions are calculated by averaging the normalized expression values
across all samples in each group. The differential expression score
[MATH:
∆iSR :MATH]
is then defined as the difference between these two means. A
statistical test (e.g., t-test or moderated t-statistic) is used to
compute the significance (
[MATH:
p−value
:MATH]
) for each
[MATH:
∆iSR :MATH]
and a threshold on adjusted
[MATH:
p−value
s :MATH]
is used to identify significantly up/downregulated genes.
Drug Resistance Signature: This signature compares gene expression
between resistant and sensitive cell lines in response to the same
drug.
[MATH:
μiR−μiS=∆iSRD
:MATH]
For each gene
[MATH: i :MATH]
, the mean expression in resistance
[MATH: μiR
:MATH]
and sensitive
[MATH: μiS
:MATH]
samples are computed. The resistance-associated differential expression
score
[MATH:
∆iSRD
:MATH]
reflects the gene expression changes between resistance and sensitive
contexts. These scores represent resistance-specific transcriptomic
patterns that serve as functional drug features in our modeling
framework.
2.3 Comparative analysis of models
To evaluate the predictive value of different drug signature
representations, we conducted a comparative analysis using four widely
adopted machine learning algorithms—LASSO, AdaBoost, Random Forest
(RF), and XGBoost—as well as the deep learning model SynergyX, a recent
attention-guided multi-modal model. This evaluation aimed to assess how
effectively each model leverages structural features, DS, and DRS to
predict drug combination synergy.
All models were trained and evaluated on the same datasets under
identical conditions, utilizing the same input features, model
architectures, and training parameters as specified in their respective
original studies. This standardized approach ensured a fair and
unbiased comparison of performance across methods. In this study,
SynergyX is utilized in the main model to predict drug synergy by
leveraging functional data, such as drug resistance signatures. Built
on a multi-modal architecture, it integrates diverse feature spaces,
including drug features, gene expression, and functional cell-level
data. A key component, its Cross-Modal Fusion Encoder, captures complex
interactions between different data modalities, such as molecular
properties and cellular response features ([68]Guo et al., 2024).
2.4 Model evaluation
We employed a stratified 5-fold cross-validation strategy to evaluate
the performance of all models. This method ensured that the
distribution of the target variable (synergy scores) was preserved
across training and testing splits, reducing the potential for data
imbalance to affect model performance. Each experiment was repeated ten
times with different random seeds, ensuring that the results were
robust and not sensitive to initialization or sampling variability.
We used Mean Squared Error (MSE), Root Mean Squared Error (RMSE),
R-squared (R^2), and correlation as evaluation metrics for the
regression prediction task. These metrics provided a comprehensive
assessment of the models’ predictive accuracy, ability to capture
variance, and rank-order relationships between predicted and observed
synergy scores. Additionally, 95% confidence intervals (CIs) were
computed for each metric to assess the reliability and variability of
the results.
To further validate the generalizability and robustness of our
approach, we tested all models on four independent benchmark datasets:
ALMANAC, DrugCombDB, Oncology Screen, and O’Neil’s dataset. This
evaluation aimed to demonstrate the model’s ability to perform well on
unseen datasets and across diverse experimental conditions, a critical
requirement for real-world applications in predicting drug synergy.
3 Results
3.1 Evaluation of model performance across classes
The following analysis compares the predictive performance of various
machine learning and deep learning models across three distinct feature
categories: structural drug descriptors, DS, and DRS. As shown in
[69]Table 2, the SynergyX model trained with DRS features consistently
achieves the lowest Mean Squared Error (MSE) and Root Mean Squared
Error (RMSE), outperforming traditional models such as LASSO, Random
Forest (RF), AdaBoost, and XGBoost (XGB). Notably, in the DRS feature
category, SynergyX achieves the best performance with an MSE of 92.16 ±
1.82, significantly better than other models.
TABLE 2.
Comparative performance of machine learning models across three feature
categories: structure, drug resistance (DR) and DRS.
Feature Category Measure LASSO AdaBoost RF XGB SynergyX
Structure MSE 331.40 ± 5.40 346.22 ± 4.99 163.58 ± 1.82 147.15 ± 1.19
92.81 ± 1.29
95%Cl [316.39 346.40] [332.38 360.07] [158.51 168.64] [143.86 150.45]
[89.23 96.38]
P-value 4.24E-07 2.58E-07 9.29E-08 2.55E-08 2.22E-07
DS MSE 345.83 ± 4.86 351.57 ± 5.16 346.16 ± 4.85 346.15 ± 4.79 106.18 ±
1.90
95%Cl [332.34 359.33] [337.25 365.88] [332.70 359.61] [332.85 359.45]
[100.92 111.45]
P-value 2.34E-07 2.34E-07 2.34E-07 2.34E-07 6.10E-07
DRS MSE 324.59 ± 3.99 341.57 ± 4.89 165.65 ± 2.07 144.51 ± 3.09 92.16 ±
1.82
95%Cl [313.51 335.67] [327.99 355.15] [159.91 171.40] [135.93 153.09]
[87.10 97.22]
P-value 1.37E-07 2.52E-07 1.46E-07 1.25E-06 9.15E-07
[70]Open in a new tab
Bold values indicate the best performance (i.e., lowest error or
highest correlation) among the compared models within each feature
category.
In addition to accuracy metrics, the Pearson correlation coefficients
of greater than 0.70 for most models in the DRS category indicate that
drug resistance signatures capture biologically relevant patterns in
synergy prediction more effectively than drug signatures (0.72) and
structural features (0.68).
Similarly, Spearman correlations were more consistent in the DRS class,
with values remaining above 0.80. This result indicates that functional
drug-response data not only improves predictive accuracy but also
enhances the model’s ability to effectively capture rank-order
relationships between drug combinations. The DRS feature class
exhibited narrower confidence intervals (CIs) for both the mean squared
error (MSE) and root mean squared error (RMSE) metrics, indicating
higher model stability and reliability. For example, the 95% CI for
RMSE in the DRS class 9.73 ± 0.10 is significantly tighter than that of
the structural feature class, as shown in [71]Figure 2, indicating
reduced variability and more consistent predictions.
FIGURE 2.
[72]Bar chart comparing RMSE values across five methods: LASSO,
AdaBoost, RF, GXB, and SynergyX. Each method has three bars: Structure,
DS, and DRS. SynergyX shows the lowest RMSE at 9.6 for DRS.
[73]Open in a new tab
Comparison of RMSE across different machine learning and deep learning
models utilizing three feature categories: Structure, Drug Signature
(DS), and DRS.
The R^2 metric, a crucial measure of predictive accuracy, further
highlights the importance of DRS features. In the structural feature
class, the highest R^2 value of 0.67 ± 0.07 was achieved by SynergyX,
indicating moderate predictive performance. Within the drug signature
(DS) class, SynergyX again outperformed other models with an R^2 of
0.71 ± 0.04, compared to 0.61 ± 0.03 for XGBoost (XGB) and 0.55 ± 0.03
for Random Forest (RF). The DRS feature class demonstrated the highest
R^2 values, highlighting the superior predictive capability of
functional features. This strong R^2 score reinforces the importance of
functional drug-response data in accurately modeling complex drug
interactions.
Additionally, The AUC (Area Under the Curve) metric, used to assess
model classification power in synergy prediction, further supports the
superior performance of DRS features. Although this is a regression
study, a commonly used synergy threshold of 10 was applied for
classification purposes. SynergyX achieved the highest AUC in the DRS
class at 0.74 ± 0.01, followed by XGBoost (XGB) at 0.72 ± 0.01, while
traditional models like AdaBoost and Random Forest (RF) scored lower at
0.69 ± 0.00 and 0.70 ± 0.01, respectively. Results from the drug
signature (DS) and Structure classes revealed performance limitations
when using less detailed features, with a peak AUC of 0.72 ± 0.01 for
SynergyX in the DS class and 0.74 ± 0.01 in the Structure class. While
structure-based features performed well for classification tasks, their
regression performance was less consistent. In contrast, DRS features
not only improved regression accuracy but also enhanced classification
robustness and interpretability.
To further evaluate the effectiveness of the DRS, we applied the
SynergyX model to four widely used benchmark datasets: ALMANAC,
DrugCombDB, OncologyScreen, and O’Neil. As summarized in [74]Table 3,
we compared the predictive performance of models trained with DRS
versus those trained with DS across multiple evaluation metrics.
TABLE 3.
Performance comparison of SynergyX using Drug Signature (DS) and DRS
features across four benchmark datasets.
Models MSE RMSE R2 Pearson Spearman
Alamanac DR 3,219.46 56.74 0.02 0.15 0.17
DRS 1,273.28 35.68 0.61 0.78 0.73
DrugCombDB DR 25.73 5.07 0.06 0.25 0.27
DRS 10.80 3.28 0.60 0.78 0.86
OncologyScreen DR 530.33 23.02 0.07 0.28 0.27
DRS 230.83 15.19 0.59 0.80 0.76
Oneils DR 428.70 20.70 0.02 0.19 0.18
DRS 163.35 12.78 0.62 0.79 0.79
[75]Open in a new tab
Bold values indicate the best performance (i.e., lowest error or
highest correlation) among the compared models within each feature
category.
In the ALMANAC dataset, the DRS-based model achieved substantially
lower error rates (MSE: 1,273.28, RMSE: 35.68) and markedly higher
correlation scores (Pearson: 0.78; Spearman: 0.73) than the DS-based
model, which showed weak correlations (Pearson: 0.15; Spearman: 0.17)
despite reporting a marginally higher R^2.
In DrugCombDB, while the DS model yielded slightly lower MSE and RMSE,
the DRS-based model demonstrated significantly superior rank-order
consistency, with a Spearman correlation of 0.78 compared to 0.25 for
the DS model. For the OncologyScreen dataset, DRS again outperformed
DS, achieving better error metrics (MSE: 230.83; RMSE: 15.19) and
higher correlations (Pearson: 0.80; Spearman: 0.76), while DS showed
weaker predictive performance (MSE: 530.34; Pearson: 0.28; Spearman:
0.27).
Similarly, in O’Neil’s dataset, the DRS-based model outperformed DS
across all metrics, achieving an MSE of 163.35, RMSE of 12.78, and
strong correlation values (Pearson and Spearman: 0.79), indicating
robust predictive accuracy and rank-order reliability.
3.2 Comparative analysis between drug and drug resistance signatures
The gene expression profiles related to Erlotinib in both the DS and
DRS are shown in [76]Figure 3. The volcano plot for the DS ([77]Figure
3A) reveals significant upregulation of genes like MAPKAPK3, CSNK2A2,
and EIF4G1, which are involved in cell cycle regulation, signal
transduction, and translation initiation. These genes suggest enhanced
cellular adaptability and survival mechanisms in response to Erlotinib
treatment. Downregulated genes such as GRB10 and FAT1 are linked to
growth signaling and cell adhesion, indicating potential inhibition of
survival pathways commonly associated with EGFR signaling. The broader
gene distribution in the DS suggests a less specific but more
comprehensive representation of drug response, capturing both direct
and indirect effects of Erlotinib exposure.
FIGURE 3.
[78]Two volcano plots labeled A and B compare gene expression changes.
Both plots display log2 fold change (x-axis) against negative log10
P-value (y-axis). In plot A, blue dots represent downregulated genes
and red dots upregulated genes. In plot B, similar distinctions exist
with blue and red dots indicating downregulation and upregulation,
respectively. Both plots have dotted lines indicating significance
thresholds. Gene names are labeled next to several dots, highlighting
specific genes with significant changes in expression.
[79]Open in a new tab
Volcano plots of differential gene expression analysis. (A) DS
highlights general drug-induced expression changes in response to
Erlotinib. Significantly upregulated genes include MAPKAPK3, CSNK2A2,
and EIF4G1, associated with cell cycle regulation, signal transduction,
and translation initiation. (B) DRS displays a distinct expression
profile with significant upregulation of resistance-associated genes,
including EIF4EBP1, TRIB3, and SLC1A4, which are linked to EGFR
signaling modulation and metabolic adaptation. Dashed lines indicate
the thresholds for log2 Fold Change (logFC) and −log10 (P-value).
In contrast, the DRS ([80]Figure 3B) focuses on a more refined set of
genes directly tied to resistance mechanisms and Erlotinib’s targeted
pathways. Upregulated genes, including EIF4EBP1, TRIB3, and SLC1A4, are
associated with modulation of EGFR signaling, stress response, and
metabolic adaptation, emphasizing their direct role in driving
resistance. Additionally, the downregulation of genes such as XBP1 and
TSC22D3, involved in stress response and apoptotic regulation,
highlights altered cellular pathways that reduce sensitivity to
Erlotinib. This refined gene expression pattern underscores key
mechanisms that contribute to the development of drug resistance.
To elucidate the molecular pathways driving Erlotinib’s therapeutic
effects and resistance mechanisms, we performed pathway enrichment
analysis on gene expression profiles derived from DS and DRS analyses.
This approach allowed us to identify distinct biological processes
associated with Erlotinib sensitivity and acquired resistance.
The results, presented in [81]Figure 4, show the most significantly
enriched pathways based on adjusted p-values (on a-log10 scale). In the
DS analysis ([82]Figure 4A), the most enriched pathways included
Colorectal cancer, Proteoglycans in cancer, Hepatocellular carcinoma,
and Kaposi sarcoma-associated herpesvirus infection. Notably, pathways
such as Chronic myeloid leukemia, Pancreatic cancer, and Cell cycle
regulation also show significant enrichment. These pathways are
consistent with Erlotinib’s known mechanism of action as an EGFR
inhibitor, influencing cancer proliferation, senescence, and
stress-response pathways, which likely contribute to Erlotinib
sensitivity.
FIGURE 4.
[83]Bar charts comparing pathways associated with DR and DRS. Chart A
highlights the cell cycle, pancreatic cancer, and chronic myeloid
leukemia among others. Chart B emphasizes the cell cycle, cellular
senescence, and p53 signaling pathway. Both charts use varying shades
of blue to indicate adjusted p-values, with darker shades representing
lower p-values. Bars depict the count of occurrences.
[84]Open in a new tab
Pathway enrichment analysis comparing DRS and Drug Signature (DS) for
Erlotinib. (A) DS highlights pathways associated with cancer
progression and tumor signaling. These pathways suggest Erlotinib’s
impact on tumor biology and its potential involvement in regulating
cancer-associated processes. (B) DRS shows pathways mainly related to
cell cycle control, p53 signaling, cellular senescence, and apoptosis,
underscoring mechanisms of resistance and cellular survival. The x-axis
represents either pathway count or adjusted p-values (−log10),
reflecting the statistical significance of pathway enrichment.
In contrast, the DRS analysis ([85]Figure 4B) revealed a different
enrichment profile. Pathways such as Apoptosis, Colorectal cancer,
Viral carcinogenesis, p53 signaling pathway, Cellular senescence, and
Cell cycle are among the top enriched pathways. These findings suggest
a prominent role of genomic stability and cell survival mechanisms in
resistance. The upregulation of DNA damage response and cell cycle
regulation pathways indicates that Erlotinib-resistant cells may
activate compensatory mechanisms to enhance their survival and promote
resistance. These results highlight the distinct biological processes
involved in Erlotinib sensitivity versus resistance. While sensitive
cells show enrichment in cancer-related and stress response pathways,
resistant cells exhibit pathways associated with survival, apoptosis,
and immune-related processes related to viral infections, potentially
facilitating their adaptation and resistance to treatment.
3.3 Drug synergy predictions based on drug resistance signature
To further evaluate the performance of our model, we assessed its
ability to identify novel and biologically meaningful drug
combinations. For this purpose, we selected a set of 68 FDA-approved
anticancer drugs commonly used in breast cancer treatment. To capture
the influence of cellular context on drug synergy, we selected two
biologically distinct yet estrogen receptor-positive (ER+) human breast
cancer cell lines: MCF7 and T47D. This selection enabled us to evaluate
both the consistency of predicted drug combinations across different
cellular environments and the discriminative power of the proposed
feature space. The chosen drugs were identified based on their overlap
within the LINCS and GDSC databases, ensuring compatibility for
downstream analyses. We then generated all possible drug pairs and
employed the SynergyX deep learning model, enhanced with DRS features,
to predict synergy scores. This framework enabled a robust,
context-specific evaluation of model performance and biological
relevance.
The top-ranking pairs for each cell line are presented in [86]Tables 4,
[87]5, emphasizing the influence of cell line specificity on the
predicted results. [88]Table 4 displays the top 5 predicted drug
combinations for the MCF7 cell line. These findings indicate a
potential role for Methotrexate in mediating synergistic interactions
that are specific to the MCF7 cell line. In contrast, [89]Table 5
presents the top five predicted drug combinations for the T47D cell
line, where Anastrozole-based combinations consistently achieved the
highest synergy scores—particularly Anastrozole in combination with
Methotrexate or Lapatinib. Notably, predicted synergy scores were
consistently higher in T47D than in MCF7, emphasizing the importance of
cell line-specific biological context in influencing combination
outcomes. These results demonstrate that drug synergy predictions are
highly dependent on the underlying cellular background, with the T47D
model exhibiting generally stronger synergistic responses. This
variation highlights the significant impact of factors such as genetic
profiles, molecular signaling networks, and baseline resistance
phenotypes on influencing drug-drug interactions.
TABLE 4.
Top 5 Predicted Synergistic Drug Combinations for the MCF7 Cell line.
Rank Drug A Drug B Pred-Score
1 Anastrozole Methotrexate 12.59
2 Cyclophosphamide Methotrexate 9.91
3 Letrozole Methotrexate 8.25
4 Cyclophosphamide Lapatinib 7.42
5 Anastrozole Lapatinib 7.09
[90]Open in a new tab
TABLE 5.
Top 5 Predicted Synergistic Drug Combinations for the T47D Cell line.
Rank Drug A Drug B Pred-Score
1 Anastrozole Methotrexate 23.87
2 Anastrozole Lapatinib 17.41
3 Cyclophosphamide Methotrexate 14.60
4 Letrozole Methotrexate 13.38
5 Cyclophosphamide Lapatinib 12.83
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Among the top-ranked drug pairs, combinations involving Anastrozole and
Methotrexate consistently emerged across both MCF7 and T47D cell lines,
suggesting their potential as robust synergistic partners. Furthermore,
T47D-specific combinations such as Anastrozole–Lapatinib and
Letrozole–Olaparib represent promising candidates for novel combination
therapies.
The observed synergy between Anastrozole, an aromatase inhibitor, and
Methotrexate, a dihydrofolate reductase inhibitor, is likely driven by
their complementary mechanisms of action. Anastrozole suppresses
estrogen production, thereby inhibiting the growth of estrogen receptor
(ER)-positive breast cancer cells. In parallel, Methotrexate impairs
DNA synthesis, enhancing cytotoxic effects in rapidly proliferating
tumor cells.
Importantly, the ability of DRS to accurately predict this synergy
underscores their strength in capturing adaptive cellular responses,
particularly how tumor cells reprogram their survival pathways when
exposed to dual-targeting strategies. This finding highlights the
practical value of DRS-informed models in identifying drug combinations
that exploit functional vulnerabilities in resistant cancer phenotypes.
4 Discussion
This study demonstrates the effectiveness of DRS features in improving
the prediction of synergistic drug combinations by incorporating
functional transcriptomic responses to drug treatment. Unlike
conventional models that rely on chemical structures or general gene
expression data, DRS-guided models provide more mechanistic insight
into drug interactions, identifying combinations that either target
complementary biological processes or performance on the same
resistance pathway.
To evaluate the biological relevance of the DRS-based feature, we
conducted a case study using Erlotinib, a selective EGFR (epidermal
growth factor receptor) inhibitor. Comparing general drug signature
profiling with DRS analysis revealed key differences in the pathways
associated with Erlotinib resistance ([92]Harada et al., 2012;
[93]Kanda et al., 2013; [94]Liao et al., 2020; [95]Jakobsen et al.,
2017). While the DS approach identified a broad range of pathways,
including cellular stress responses and metabolic adaptations, the DRS
approach provided more specific mechanistic insights, directly linking
resistance to compensatory survival mechanisms. Both profiling methods
confirmed EGFR signaling as central to Erlotinib’s function. However,
DRS uniquely identified adaptive resistance pathways, such as the
PI3K-Akt and p53 signaling pathways, which promote cellular survival
and proliferation despite EGFR inhibition. These pathways were
significantly enriched in resistant profiles, suggesting that resistant
cells leverage compensatory signaling networks to bypass the inhibitory
effects of Erlotinib ([96]Zhou et al., 2021; [97]He et al., 2021).
Furthermore, DRS features identified specific upregulated genes
associated with Erlotinib resistance, including EF4BP1, TRIB3, and
SLC1A4, which are known to drive alternative survival pathways ([98]Wan
et al., 2020). These findings suggest that targeting compensatory
signaling pathways, such as the PI3K/Akt pathway, may enhance the
efficacy of Erlotinib when used in combination therapies. Conversely,
downregulated genes, such as XBP1 and TSC22D3, which are involved in
oxidative stress regulation and apoptosis, indicate a reduced apoptotic
response in resistant cells, further reinforcing the importance of
functional resistance profiling. These findings suggest the importance
of incorporating DRS-based profiling into resistance studies, as it
offers mechanistic clarity beyond general drug response signatures. The
identification of resistance-associated pathways, particularly the
PI3K/Akt signaling pathway, presents potential therapeutic targets.
Targeting these compensatory survival pathways in combination with
Erlotinib may enhance its efficacy and help overcome resistance.
Overall, DRS analysis offers a refined framework for understanding
acquired resistance mechanisms and informs the rational design of
combination therapies aimed at improving outcomes in EGFR-targeted
treatments.
The top-ranked combination of Anastrozole and Methotrexate exemplifies
how DRS features can identify drug interactions based on complementary
mechanisms of action. Anastrozole, an aromatase inhibitor, reduces
estrogen receptor (ER)-positive breast cancer growth by suppressing
estrogen synthesis, thereby limiting tumor proliferation ([99]Milani et
al., 2009). Methotrexate, a dihydrofolate reductase inhibitor, disrupts
nucleotide synthesis, leading to impaired DNA replication and enhanced
cytotoxicity ([100]Jolivet et al., 1983). This synergy highlights how
hormonal signaling inhibition and nucleotide depletion can work in
concert to enhance therapeutic efficacy, a pattern effectively captured
by DRS-based predictive models. The ability of DRS-based models to
predict this synergy suggests that transcriptomic resistance signatures
effectively capture adaptive survival responses in tumor cells,
enabling the identification of functionally relevant drug interactions
that may be ignored by traditional structure-based models ([101]Ma et
al., 2019). DRS-guided models also prioritize synergistic drug pairs
that target the same resistance pathway, as demonstrated by the synergy
between Cyclophosphamide and Methotrexate. Cyclophosphamide, an
alkylating agent, induces DNA crosslinking and replication stress,
leading to genomic instability. Methotrexate, by depleting nucleotide
pools, further exacerbates the accumulation of DNA damage, leading to
heightened cytotoxic effects and cell death ([102]Sahrayi et al.,
2021).
Conventional synergy prediction models, which primarily rely on
chemical properties or generalized transcriptional profiles, often lack
the resolution needed to identify pathway-specific interactions. As a
result, they may overlook critical mechanistic synergies that arise
from functional adaptations within resistant cancer cells. This
limitation leads to an incomplete understanding of compensatory
survival pathways, thereby restricting the ability of predictive models
to accurately identify effective drug combinations. By integrating DRS
features, our model addresses these challenges by effectively
identifying functional synergies that exploit shared resistance
mechanisms, thereby providing a more precise and biologically relevant
framework for predicting drug synergy.
While this study demonstrates the effectiveness of DRS in enhancing the
prediction of drug synergy, several limitations should be considered.
Although comprehensive validation using experimental assays would
enhance the confidence and translational relevance of the identified
drug combinations, our study relied exclusively on large-scale,
well-curated datasets for model training and evaluation. Additionally,
the dependence on the LINCS and GDSC databases introduces coverage
limitations and potential bias due to the incomplete overlap of drugs,
cell lines, and treatment conditions. Another limitation lies in
deriving resistance signatures from a single post-treatment time point
(24 h), which may not adequately capture the temporal complexity and
dynamic evolution of drug resistance.
In future work, we aim to integrate single-cell transcriptomics,
consider multi-time-point resistance profiling, and develop multi-modal
models that incorporate genomic and phenotypic context to improve
biological fidelity and clinical relevance.
5 Conclusion
This study highlights the importance of incorporating drug
resistance-specific functional data in predicting synergistic drug
combinations, demonstrating that DRS features enhance predictive
accuracy by capturing adaptive transcriptomic responses to therapy. By
systematically comparing DRS to structural and general drug signature
features across multiple machine learning and deep learning models,
SynergyX, we demonstrated that DRS consistently outperforms other
feature types in terms of predictive accuracy, rank-order stability,
and interpretability. Despite certain limitations, such as reliance on
pre-existing datasets and absence of experimental validation, the
proposed framework provides a scalable and mechanistically insightful
approach for prioritizing effective drug combinations. These findings
pave the way for future efforts to integrate multi-omic, temporal, and
single-cell data into resistance-aware synergy prediction models,
ultimately guiding the development of more precise and personalized
combination therapies in oncology.
Funding Statement
The author(s) declare that no financial support was received for the
research and/or publication of this article.
Data availability statement
Publicly available datasets were analyzed in this study. This data can
be found here:
[103]https://github.com/mozaffarilegha/DrugCombinationPredicrion_DRS.
Author contributions
MM: Conceptualization, Data curation, Formal Analysis, Investigation,
Methodology, Project administration, Validation, Visualization, Writing
– original draft. SG: Conceptualization, Resources, Supervision,
Writing – review and editing.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
The author(s) declared that they were an editorial board member of
Frontiers, at the time of submission. This had no impact on the peer
review process and the final decision.
Generative AI statement
The author(s) declare that Generative AI was used in the creation of
this manuscript. During the preparation of this work the authors used
ChatGPT in order to improve readability and language. After using this
tool, the authors reviewed and edited the content as needed and take
full responsibility for the content of the publication.
Publisher’s note
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References