Next Article in Journal
Development of New Pyrazolo [3,4-b]Pyridine Derivatives as Potent Anti-Leukemic Agents and Topoisomerase IIα Inhibitors with Broad-Spectrum Cytotoxicity
Next Article in Special Issue
BTLA: An Emerging Immune Checkpoint Target in Cancer Immunotherapy
Previous Article in Journal
Emulgel with Origanum vulgare L. Oil: A New Therapeutic Proposal in Case of Dermal Bacterial Infections
Previous Article in Special Issue
Therapeutic Impact of Zanubrutinib in Chronic Lymphocytic Leukemia: Evidence from a Systematic Review and Single-Arm Meta-Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 18
Issue 11
10.3390/ph18111769
pharmaceuticals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy

by
Olivia J. Cheng
1,2,†,
T.T.T. Tran
2,3,†,
Y. Ann Chen
2,3,*,‡ and
Aik Choon Tan
1,2,*,‡
1
Department of Oncological Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
2
Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
3
Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
*
Authors to whom correspondence should be addressed.
†
Co-first authors.
‡
Co-corresponding authors.
Pharmaceuticals 2025, 18(11), 1769; https://doi.org/10.3390/ph18111769
Submission received: 12 September 2025 / Revised: 1 November 2025 / Accepted: 18 November 2025 / Published: 20 November 2025
(This article belongs to the Special Issue Comprehensive Strategies in Cancer Immunotherapy)
Browse Figures
Versions Notes

Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and show notable success in some cancer types such as non-small cell lung cancer, melanoma and colorectal cancers, while they demonstrate relatively low response rate in others, such as esophageal cancers. Due to the heterogeneous nature of the tumor microenvironment and patient-to-patient variability, there remains a need to improve ICI response rates. Combining ICIs with therapies that can overcome resistance is a promising strategy. Compared to de novo drug development, drug repurposing offers a faster and more cost-effective approach to identifying such combination candidates. A variety of computational drug repurposing tools leverage genomics and/or transcriptomic data. As single-cell RNA sequencing (scRNA-seq) technology becomes available, it enables precise targeting of cancer-driving cellular components. In this review, we highlight current computational drug repurposing tools utilizing scRNA-seq data and demonstrate the application of two such tools, scDrug and scDrugPrio, on an esophageal squamous cell carcinoma dataset to identify potential drug candidates for combination with ICI therapy to enhance treatment response. scDrug focuses on predicting tumor cell-specific cytotoxicity, while scDrugPrio prioritizes drugs by reversing gene signatures associated with ICI non-responsiveness across diverse tumor microenvironment cell types. Together, this review underscores the importance of a multi-faceted approach in computational drug repurposing and highlights its potential for identifying drugs that enhance ICI treatment. Future work can expand the application of these strategies to multi-omics and spatial transcriptomics datasets, as well as personalized patient samples, to further refine drug repurposing involving ICI therapy.

Graphical Abstract

1. Introduction

1.1. Immune Checkpoint Inhibitors: Efficacy and Resistance Mechanisms

Immune checkpoint inhibitors (ICIs), such as monoclonal antibodies targeting Programmed Cell Death Protein 1 (PD-1), Programmed Death-Ligand 1 (PD-L1) and Cytotoxic T-Lymphocyte-Associated Protein 4 (CTLA-4), have revolutionized cancer therapy by reactivating suppressed anti-tumor immune responses. In both clinical trials and real-world settings, cancers such as melanoma, non-small cell lung cancer (NSCLC), colorectal cancer, kidney cancer, bladder cancer, and head and neck cancer demonstrate relatively higher response rates to ICIs [1]. However, the overall average response remains substantially low, with considerable variation driven by cancer subtype, patient heterogeneity, and treatment context [1,2,3,4]. These cancers are sometimes referred to as “immune cold” tumors. Limited efficacy is largely attributed to the complexity and heterogeneity of tumors and their microenvironments, which give rise to a wide range of ICI resistance mechanisms. These include both tumor intrinsic factors, such as impaired antigen presentation, dysregulated signaling and metabolic pathways, and tumor-extrinsic mechanisms, such as the loss or dysfunction of effector immune populations, along with the enrichment of immunosuppressive populations and signaling pathways [5,6,7]. To circumvent these obstacles that dampen the efficacy of ICIs, ongoing research focuses on developing strategies that target and modulate these resistance pathways, often through combination therapies involving ICIs and other therapies [8,9,10].

1.2. Drug Repurposing to Enhance ICI Efficacy

Compared to de novo drug discovery, repurposing existing drugs approved for other indications offers a faster and more cost-effective path to clinical application, owing to their established safety profiles [11]. In the United States, drug repurposing is supported by streamlined development and approval process through the 505(b)(2) regulatory submission pathway. This pathway allows for changes such as new indications to previously approved drugs, partly based on established safety data, thereby reducing the need redundant preclinical and clinical studies [12,13,14]. Rather than replacing current immunotherapies, drug repurposing can also be leveraged to identify synergistic therapies to complement ICIs and improve response rates [8,9,15,16,17,18].
To overcome resistance, ongoing efforts have been focusing on identifying and testing drug combinations to enhance ICI treatment efficacy. In addition to dual ICIs, various drugs are also being explored in combination with ICIs to improve the efficacy of treatments [8,10,19,20,21]. Drugs targeting various cancer hallmarks for ICI resistance—such as metabolic modulators, hormone receptor inhibitors, immunomodulators, and anti-inflammatory agents, as well as microbial therapy—have been studied in preclinical studies, and these same agents, or others targeting similar pathways, are currently being evaluated in clinical trials [22,23,24,25,26,27] (Table 1).
Table 1. Selected clinical trials investigating the use of various therapies in combination with immune checkpoint inhibitors. CFH—Complement factor H.
Table 1. Selected clinical trials investigating the use of various therapies in combination with immune checkpoint inhibitors. CFH—Complement factor H.
Drug TypeRepurposed DrugTumor Intrinsic vs. Extrinsic MechanismsClinicalTrials.gov Number
(NCT)		Cancer TypeICIPhaseStatus
Metabolic Modulators
AMPK activator and mitochondrial complex I inhibitorMetforminBoth [9,28,29]NCT04414540Head and Neck CancerPembrolizumab2Active, not recruiting
NCT03800602Colorectal CancerNivolumab2Completed
NCT03618654Head and Neck CancerDurvalumabEarly 1Completed
NCT03311308MelanomaPembrolizumab1Recruiting
StatinLovastatinIntrinsic [30,31]NCT06636734Head and Neck CancerPembrolizumab2Recruiting
Hormone modulators
Androgen Receptor InhibitorDarolutamideIntrinsic [32]NCT07016399Triple-Negative Breast CancerPembrolizumab2Not yet recruiting
Aromatase InhibitorsAnastrozole, Letrozole, or ExemestaneIntrinsic [33]NCT02648477Triple-Negative or Hormone-Receptor Positive Breast CancerPembrolizumab2Completed
Immunomodulators
IDO1 inhibitorBMS986205Both [34]NCT03854032Head and Neck CancerNivolumab2Active, not recruiting
EpacadostatNCT03358472Head and Neck CancerPembrolizumab3Active, not recruiting
NCT03322540Non-Small Cell Lung CancerPembrolizumab2Completed
CSF1R inhibitorAxatilimabExtrinsic [35]NCT07015853Triple-Negative Breast CancerPembrolizumab2Not yet recruiting
CXCR4 inhibitorBL-8040Extrinsic [36]NCT02907099Pancreatic CancerPembrolizumab2Completed
Galectin-3 inhibitorGB1211Extrinsic [37]NCT05913388Melanoma,
Head and Neck Cancer		Pembrolizumab2Recruiting
CFH inhibitorGT103Extrinsic
(tumor-derived) [38]		NCT07017829Non-Small Cell Lung CancerPembrolizumab2Not yet recruiting
Cancer vaccineIMA970AExtrinsic [39]NCT06218511Hepatocellular carcinomaDurvalumab1Recruiting
p53MVANCT02432963Solid TumorsPembrolizumab1Active, not recruiting
Live biotherapeutic products
Lactobacillus johnsoniiExtrinsic [40,41]NCT06823323Colorectal CancerPembrolizumabNANot yet recruiting
CBM588
(Clostridium butyricum)		Both [41]NCT06399419Kidney CancerNivolumab + Ipilimumab1Recruiting
Microbial Ecosystem Therapeutic 4, MET4 (30 microbials)Extrinsic [42]NCT03686202Solid TumorsICIs1Active, not recruiting
Anti-Inflammatory Agents/NSAIDs/COX inhibitors
NSAIDDicofenacIntrinsic [9,43] NCT06731270Non-Small Cell Lung CancerMultiple2Recruiting
COX inhibitorAspirinBoth [9,44,45] NCT02659384Ovarian CancerAtezolizumab2Completed
NCT03638297Colorectal Cancera-PD-12Recruiting
NCT03396952MelanomaPembrolizumab + Ipilimumab2Completed
COX inhibitor + platelet inhibitorClopidogrel/acetylsalicylic acidBoth [44,45,46]NCT03245489Head and Neck CancerPembrolizumab1Completed
Kinase and Receptor Inhibitors
FGFR4 inhibitorIrpagratinibBoth [47]NCT07010497Hepatocellular CarcinomaAtezolizumab + Bevacizumab2Not yet recruiting
mTOR inhibitornab-rapamycin (ABI-009)Both [48,49]NCT03190174MultipleNivolumab1/2Completed
PI3K- α inhibitorAlpelisibIntrinsic [50]NCT06545682Breast Cancer and MelanomaPembrolizumab1/2Recruiting
Multi-target tyrosine kinase inhibitorLenvatinibBoth [51]NCT07011849Renal Cell CarcinomaPembrolizumab2Not yet recruiting
CabozantinibBoth [52]NCT06900595Adrenocortical CancerCemiplimab2Not yet recruiting
CabozantinibExtrinsic [53]NCT03468218Head and Neck CancerPembrolizumab2Active, not recruiting
a-VEGFR2 antibodyRamucirumabBoth [54]NCT04120454Non-Small Cell Lung CancerPembrolizumab2Completed
VEGFR2 inhibitorAnlotinibNCT05218629Pancreatic Cancera-PD-12Recruiting
a-VEGF antibodyBevacizumabNCT03141684Alveolar Soft Part SarcomaAtezolizumab2Active, not recruiting
a-TNFInfliximab or CertolizumabBoth [55,56,57]NCT03293784MelanomaNivolumab + Ipilimumab1Completed
Anti-helminth drugs
IvermectinBoth [58]NCT05318469Breast CancerPembrolizumab1/2Recruiting

1.3. Computational Tools for Drug Repurposing

Computational drug repurposing offers an in silico alternative to traditional experimental approaches, allowing for rapid hypothesis generation and drug prioritization through the analysis of large-scale, high-throughput multi-omics data while requiring less labor, cost, and physical resources [11,18,59]. Several comprehensive reviews provide detailed overviews on the diverse computational approaches used in drug repurposing, including literature and text mining, docking, structural similarity, gene perturbation, etc. [60,61]. Specifically, drug repurposing and combination therapies have been explored as approaches to overcome resistance to ICIs by targeting various biological mechanisms and pathways [62]. In this review, we discuss how computational strategies that utilize single-cell RNA sequencing (scRNA-seq) data can be used to identify potential therapies that may enhance ICI response.
Reference databases are the key foundations for drug repurposing. The Kyoto Encyclopedia of Genes and Genomes (KEGG) [63] provides a reference of curated knowledge, integrating genes and genome, molecular functions and pathways, disease and drug-gene target information. In addition to such knowledge-based repositories, data-driven pharmacological perturbation resources provide empirical, cell-specific transcriptional responses to drug treatment. Many transcriptomics-based drug repurposing tools build upon the foundational concept introduced by the Connectivity Map (CMap), which matches disease-associated gene expression signatures with drug-induced transcriptional responses [64]. CMap, along with complementary and expanded databases such as Drug Signatures Database (DSigDB), DrugSig, the Drug Repurposing Hub, the more recent L1000-based resources, and curated interaction databases like DrugBank and DGIdb (Table 2), serve as foundational references for several drug prediction tools and exploration platforms that leverage the concept of signature matching on disease-specific gene signatures generated from arrays or bulk RNA-seq, including iLINCS, DrInsight, L1000FWD and L1000CDS2 (Table 3).
Table 2. Key databases and libraries of drug-gene interactions and annotations.
Table 2. Key databases and libraries of drug-gene interactions and annotations.
ResourcesDescriptionWebsite
DrugMAP [65]A comprehensive database providing a molecular interaction atlas and pharmacological information for over 30,000 drugs and candidates, supporting drug discovery and AI-driven network analyses.https://drugmap.idrblab.net/
(accessed on 21 August 2025)
L1000 [66]A high-throughput, low-cost gene expression profiling method that enables large-scale mapping of cellular responses to genetic and chemical perturbations, expanding the Connectivity Map resource.https://lincsproject.org/LINCS/tools
(accessed on 21 August 2025)
DrugSig [67]A manually curated database of drug response gene signatures from microarray data, designed to facilitate computational drug repositioning by providing comprehensive drug, gene, and target information.http://biotechlab.fudan.edu.cn/database/drugsig
(accessed on 21 August 2025)
The Drug Repurposing Hub [68]A curated collection of over 4700 clinically tested compounds with detailed annotations, designed to enable systematic and large-scale drug repurposing efforts by providing resource for rapid identification of new therapeutic uses.https://repo-hub.broadinstitute.org/repurposing
(accessed on 21 August 2025)
DSigDB [69]A manually curated database of drug and small molecule-related gene sets designed to integrate seamlessly with Gene Set Enrichment Analysis (GSEA), enabling researchers to analyze drug-induced gene expression changes and drug-target interactions.https://dsigdb.tanlab.org/DSigDBv1.0/
(accessed on 21 August 2025)
ChEMBL [70,71]A comprehensive database that curates and standardizes bioactivity, binding for over a million drug-like compounds and thousands of protein targets to support drug discovery and chemical biology research.https://www.ebi.ac.uk/chembl/
(accessed on 21 August 2025)
DGIDB [72,73]A comprehensive resource that integrates drug-gene interactions and potential druggable gene categories from multiple sources, enabling researchers to explore therapeutic targets for mutated genes and prioritize candidates for drug development.https://dgidb.org/
(accessed on 21 August 2025)
DrugBank [74]A comprehensive, searchable database combining detailed drug and target information to support drug discovery and pharmacological research.https://go.drugbank.com/releases/latest
(accessed on 21 August 2025)
CMap (Connectivity Map) [64]A reference collection of gene-expression profiles from human cells treated with bioactive small molecules, enabling discovery of functional connections among drugs, diseases, and genetic perturbations.https://clue.io/
(accessed on 21 August 2025)
Kyoto Encyclopedia of Genes and Genomes (KEGG) [63]An encyclopedia of genes and genomes that integrates additional information regarding molecular functions and pathways, drugs and disease. http://www.genome.jp/kegg/
(accessed on 14 October 2025)
Table 3. Computational drug prediction tools using gene expression signatures.
Table 3. Computational drug prediction tools using gene expression signatures.
ToolPlatformDescriptionWebsite
RepurposeDrugs [75]Web-basedA web platform that combines a comprehensive drug–indication database with machine learning to predict the approval potential of mono- and combination therapies for new disease indications.https://repurposedrugs.aittokallio.group/
(accessed on 21 August 2025)
TxGNN [76]Web-basedA graph-based AI model for zero-shot drug repurposing that predicts new drug–disease associations, including for diseases with no known treatments, using interpretable reasoning over a medical knowledge graph.http://txgnn.org/
(accessed on 21 August 2025)
DRIE [77]RA framework to identify candidate drugs for cancer based on inhibition effect on disease-specific gene regulatory network of KEGG pathways.
SigCom LINCS [78]Web-basedA webserver offering over a million searchable gene expression signatures from LINCS, GTEx, and GEO to support drug and target discovery through signature similarity and metadata analysishttps://maayanlab.cloud/sigcom-lincs/#/SignatureSearch/UpDown
(accessed on 21 August 2025)
DRviaSPCN [79]RA tool for cancer drug repurposing that prioritizes candidate drugs by analyzing drug-induced sub-pathway crosstalk networks and their influence on tumor pathways.
iLINCS [80]Web-basedA web platform that integrates large-scale omics data and tools for analysis, visualization, mechanism of action studies, and drug repositioning without requiring programming skills. http://ilincs2018.ilincs.org/ilincs/
(accessed on 21 August 2025)
DrInsight [81]PythonA method for drug repurposing that uses genome-wide concordantly expressed genes to improve disease-drug matching, outperforming existing methods and enabling comprehensive drug-target network analysis.
SAveRUNNER [82]RA network-based tool for drug repurposing that predicts new drug-disease associations by analyzing the proximity of drug targets and disease proteins in the human interactome
DREIMT [83]Web-basedA web tool that prioritizes immunomodulatory drugs targeting up to 70 immune cell subtypes by integrating thousands of drug profiles and immune gene expression signatures, helping to identify potential therapies based on user-provided gene expression data.https://dreimt.org/
(accessed on 21 August 2025)
DrugCell [84]PythonAn interpretable deep learning model that predicts cancer drug responses by integrating tumor genotypes and drug structures, enabling accurate therapy predictions and the design of synergistic drug combinations.
L1000FWD [85]Web-basedAn interactive web tool for visualizing and exploring over 16,000 drug- and small-molecule-induced gene expression signatures to aid in understanding drug mechanisms of action and discovering novel compound functions.https://maayanlab.cloud/l1000fwd/
(accessed on 21 August 2025)
L1000CDS2 [86]Web-basedA web-based search engine that prioritizes small molecules predicted to mimic or reverse gene expression signatures, enabling drug prediction and target identification from the LINCS L1000 dataset.https://maayanlab.cloud/L1000CDS2/#/index
(accessed on 21 August 2025)
Since its breakthrough development in 2009 and broader adoption in 2017, scRNA-seq has rapidly expanded in use [87,88,89], especially in cancer research to understand the tumor heterogeneity and tumor-immune microenvironment. Consequently, there is a growing number of computational drug repurposing tools and methods that aim to harness the benefits from scRNA-seq and the rapidly expanding availability of single-cell datasets (Table 4). Compared to bulk RNA-seq, scRNA-seq offers transcriptomic profiling at increased cellular resolution, allowing for granular analysis using gene expression at the level of individual cell types and states. This is particularly informative in cancer research, where tumors often display marked heterogeneity both within and across patients, including key disease contributing factors that would otherwise be masked in bulk transcriptomic data due to their rarity and low abundance [90,91]. Specifically, scRNA-seq provides insights into the tumor microenvironment and clonal diversity—a complex network of interactions among cancer and non-cancer cells that plays an important role in tumor development, immune evasion, and cancer therapy response and resistance [89,91,92,93,94,95,96]. While earlier tools using bulk transcriptomic input for signature matching can be adapted to use the scRNA-derived differentially expressed gene (DEG) profile, newer tools and frameworks designed to use scRNA-seq data more directly have also been released, including scDR, scDEAL, scDrug, ASGARD, scDrug+, DrugReSC, scTherapy, scDrugPrio, and retriever (Table 4). These high-resolution, targeted approaches allow researchers to target the specific cell types and states, improving the precision and biological relevance of drug predictions.
Most single-cell-informed drug repurposing tools, including scTherapy, DrugReSC, and scDrug, utilize the drug response profile from resources like LINCS-L1000, GDSC, or PRISM, which are predominantly derived from cancer cell lines. As a result, these tools primarily focus on identifying drugs that act directly on malignant cell populations [97,98,99]. In contrast, tools such as drexml and scDrugPrio use drug-target databases like DrugBank. These tools are not cancer-specific and support drug prediction for non-malignant or diverse cell types, including immune cells [100,101]. While ASGARD utilizes drug response data from LINCS-L1000, the original publication demonstrated its capacity in drug repurposing for multiple cancers as well as COVID-19 [102]. Furthermore, while most tools provide monotherapy predictions, scTherapy, ComboSC, and scDrug support combination therapy prediction for optimal treatment by targeting multiple pathways. In addition, these tools also differ in their computational approaches. Tools like scTherapy, scDEAL, and drexml leverage machine learning models, including deep learning and explainable machine learning, while others such as scDrugPrio and ComboSC rely on network-based strategies to capture cellular interactions [100,103]. Together, these tools highlight the growing potential of single-cell transcriptomics to inform precision drug repurposing across a range of diseases, enabling more targeted therapeutic strategies to address interpatient and cellular-level heterogeneity in complex conditions such as cancer (Table 4).
Table 4. Computational drug repurposing tools and pipelines utilizing single-cell data input.
Table 4. Computational drug repurposing tools and pipelines utilizing single-cell data input.
ToolPlatformDescriptionCancer-Focused?
retriever [104]RA tool that identifies disease-specific transcriptional drug response profiles from LINCS-L1000 data to predict effective drug combinations for personalized cancer treatmentYes
scDrugPrio [100]RA framework that uses scRNA-seq data and drug-target information to build disease network models for prioritizing and ranking drugs in immune-mediated inflammatory diseasesNo
drexml [101]PythonA command line tool and package for data-driven drug repurposing that combines mechanistic signal transduction modeling with explainable machine learning to characterize disease-specific regulatory networks and rank drug targets based on their functional impact on disease signaling.No
SuperFeat [105]PythonAn artificial neural network–based framework that learns and scores canonical cellular features from single-cell RNA-seq data to identify disease progression markers and potential drug targets.Yes
Note: Can perform cellular status./feature scoring on all cell types but drug search focuses on malignant cells.
scTherapy [99]RA machine learning framework that uses single-cell transcriptomics to prioritize personalized multi-target drug combinations selectively targeting cancer cells while sparing normal cells.Yes
DrugReSC [98]RA method that leverages single-cell RNA-seq data to identify drugs targeting disease-critical cell subpopulations through transcriptional relationship modeling.Yes
Note: Identifies disease-central cells (not restricted to tumor) but uses LINCS for drug matching.
scDrug+ [106]PythonAn integrated pipeline combining single-cell transcriptomics and molecular structure analysis to predict drug responses, including for novel drugs, enabling precision medicine.Yes
ComboSC [103]RA pipeline that uses single-cell transcriptomes and bipartite graph optimization to predict personalized synergistic drug combinations, enhancing precision cancer immunotherapy.No
ASGARD [102]RA pipeline that accounts for cellular heterogeneity to improve personalized drug recommendations, outperforming bulk-based methodsNo
scDrug [97]PythonAn integrated workflow that streamlines scRNA-seq analysis and drug response prediction to facilitate tumor subpopulation identification and drug repurposing.Yes
DREEP [107]RA tool that predicts drug sensitivity at the single-cell level using transcriptomic data and pharmacogenomic references to guide personalized cancer treatment and drug repurposing.Yes
scDR [108]RA method that predicts drug response at single-cell resolution by integrating drug-response genes with scRNA-seq data, enabling prognosis prediction and exploration of drug resistance mechanismsYes
scDEAL [109]PythonA deep transfer learning framework that predicts cancer drug responses at the single-cell level by integrating bulk RNA-seq data with scRNA-seq and offers interpretable gene signatures linked to drug resistance.Yes
Beyondcell [110]RA method that identifies tumor subpopulations with distinct drug responses from scRNA-seq data and ranks cancer-specific treatments using drug signature enrichment.Yes

2. Case Study: Two Computational Strategies for Identifying Drug Repurposing Candidates to Enhance ICI Response

In this section, we review the overall workflows and application of two computational tools listed in Table 4 that utilize scRNA-seq as input data, scDrug and scDrugPrio. These two workflows represent distinct methodological approaches: scDrug aims to selectively target tumor cells, whereas scDrugPrio aims to counteract disease-associated transcriptomic signatures across all cell types (Figure 1).
To demonstrate their application in the context of ICI response, we applied both frameworks to a publicly available scRNA-seq dataset derived from esophageal squamous cell carcinoma (ESCC) patients. Compared to the more ICI-responsive cancers such as melanoma, the objective response rate (ORR) to anti-PD-(L)1 monotherapy in ESCC is relatively low (~10–20% vs. ~30–40%) [1,111]. Furthermore, ESCC remains underrepresented in immunotherapy research, receiving less attention in clinical trials compared to more common cancers like melanoma and NSCLC, highlighting a critical opportunity for novel insights [112]. The dataset was obtained from OMIX, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (accession number: OMIX005710) [113]. Patients in the cohort received neoadjuvant chemo-immunotherapy (NAT), which included rislelizumab or camrelizumab, in combination with carboplatin or nedaplatin, and albumin-bound paclitaxel. To illustrate the utility of the computational methods, we used the baseline (pre-treatment) scRNA-seq tumor samples and associated clinical outcome data of this cohort. Treatment response was determined in the original study based on the presence of viable residual tumor cells (VRTCs) in the resected tumor and lymph nodes. VRTCs ≥ 1% and ≤ 10% was considered as major pathological response (MPR), ≤ 1% was defined as pathological complete response (pCR) and >10% was defined as incomplete pathological response (IPR). In our study, pCR and MPR were grouped as response, and IPR was considered as no response. While traditional applications of drug repurposing tools focus on identifying therapies for a given disease or cancer, here we adapt the approach to enhance ICI response by treating non-responder samples as the “disease” condition.

2.1. Method 1: scDrug—Targeting Tumor Cells

In 2023, Hsieh et al. introduced scDrug (https://github.com/ailabstw/scDrug, accessed on 13 October 2025), a Python based one-step computational pipeline designed to link single-cell transcriptomics with drug response prediction, particularly focusing on tumor cell subpopulations. scDrug integrates clustering, functional annotation, and pharmacogenomic modeling into a single framework.
The workflow begins with scRNA-seq data processing and analysis using SCANPY [114] (Figure 1). For quality control, cells with less than 200 genes and more than 30% mitochondrial content were excluded. Normalization was performed as described in https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html (accessed on 16 October 2025) and batch integration was performed using Harmony algorithm [14]. The scDrug pipeline identifies tumor clusters from the scRNA-seq dataset through analysis with the Louvain algorithm for unsupervised clustering and resolution was determined empirically via silhouette-based optimization using repeated subsampling. scMATCH was used for cell type annotation using the FANTOM5 reference dataset [97,115,116,117]. Tumor populations are then selected, re-clustered and used as the input for drug response prediction using public pharmacogenomic datasets with two independent predictive models. One prediction approach utilizes CaDRReS-Sc [118], a machine learning framework trained on the GDSC dataset (includes drug response data for 1047 cancer cell lines across 266 anti-cancer drugs) and the PRISM repurposing dataset (covering approximately 1448 drugs across 480 cell lines, quantified by 1 − AUC) to predict drug responses [119,120]. The second approach employs the Premnas framework, which leverages LINCS L1000 perturbation signature data to predict potential treatment combinations [121].
The aim of scDrug is to identify drugs and drug combinations that target and kill tumor clusters identified using scRNA-seq data. To tailor this to enhance ICI response, we applied the scDrug pipeline to the pre-treatment ESCC tumor samples from patients who later were classified as non-responders to neoadjuvant chemo-immunotherapy. The goal was to identify drug candidates that could potentially improve cancer killing in these treatment-resistant cases.
As a demonstration, we present one of the drug response analyses from scDrug workflow using the GDSC dataset as a reference. After selecting tumor populations, scDrug re-clustered them into seven distinct tumor subclusters. Drug response (IC50 and estimated killing percentage) was predicted for each cluster across all 171 compounds from the GDSC reference dataset.
Among the 171 drugs analyzed, 12 compounds—including EGFR inhibitors, Src family kinase inhibitors, and metabolic modulators—exhibited more than 60% of killing in four or more tumor subclusters (Figure 2). Notably, the Src family kinase inhibitor Dasatinib showed the highest predicted efficacy, exceeding 80% predicted killing in all seven tumor clusters.
In ESCC, dasatinib has been shown to enhance cisplatin sensitivity by modulating key resistance pathways, such as PI3K/AKT and STAT3, and downregulating resistance-associated molecules such as ERCC1 and BRCA1 [122], supporting its therapeutic relevance in this tumor type. Specifically in the context of ICI treatments, Dasatinib has demonstrated synergistic effects when combined with anti-PD-L1 in breast cancer models, reducing cancer cell viability, colony formation, and invasion [123]. Moreover, Dasatinib combined with anti-PD-1 has been shown to reduce tumor burden and improve overall survival in preclinical models of Philadelphia chromosome-positive acute lymphoblastic leukemia, colorectal cancer, and non-small-cell lung cancer [124,125,126]. These reports support that other Src family kinase inhibitors with high predicted efficacy, such as saracatinib and WH-4-023, may also warrant further investigation as potential ICI-enhancing agents.
Additional candidates identified by scDrug include rapamycin, an mTOR inhibitor that has been reported to enhance anti-PD-1 efficacy in colorectal cancer models [127] and has been investigated clinically for its efficacy in combination with anti-PD-1 (Table 1). Similarly, epidermal growth factor receptor (EGFR) inhibitors such as erlotinib have been evaluated in early-phase clinical trials (NCT02039674, NCT02013219) in combination with pembrolizumab or atezolizumab, with preliminary results demonstrating partial responses in some patients, while no complete responses were observed [128,129].
Together, these examples serve as a proof-of-concept that top candidates identified by scDrug analysis of ESCC non-responder data align with drugs that have either demonstrated or are emerging as potentials to enhance ICI response in preclinical or clinical settings. This supports the feasibility and relevance of scDrug as a strategy for selecting promising drug candidates for ICI-based combination therapy.
Despite this utility, there are important limitations to consider. First, drug response predictions are constrained by the coverage of drug sensitivity data available in the reference databases, which may not comprehensively represent all clinically relevant compounds. Second, the module for treatment combination selection depends heavily on the availability of drug-induced gene expression profiles from the LINCS L1000 dataset. The 2017 release of the LINCS lacked ESCC cell line data entirely. Although the updated release expanded coverage to 98 cancer cell lines [130], including the ESCC cell line KYSE30.311, key dose–response information required for combination analysis remains unavailable. As a result, treatment combination prediction was not feasible for this dataset, underscoring the limitations of applying scDrug to less well-characterized cancer types. Thirdly, since scDrug focuses primarily on tumor cell populations, tumor-extrinsic factors within the tumor microenvironment—such as immune cell and stromal interactions—that are known to influence treatment outcomes are not accounted for.

2.2. Method 2: scDrugPrio—Targeting All Cell Types

Published in 2024, scDrugPrio (available as an R package version 1.0.0; https://github.com/SDTC-CPMed/scDrugPrio, accessed on 21 August 2025) is a network-based drug repurposing tool originally developed for inflammatory disease datasets. It identifies compounds from DrugBank [75] that may counteract cell-type-specific disease-associated gene signatures based on DEGs in each cell cluster between disease state and control. scDrugPrio then ranks the compounds based on proximity between drug targets and DEGs in protein–protein interaction network (PPIN) and centrality of drug targets across various disease-central cell populations.
The pipeline begins with preprocessing, clustering, and annotation of scRNA-seq data using Seurat [131] (Figure 1). Quality control is performed for each individual patient dataset, excluding cells with fewer than 200 detected genes (nFeature_RNA < 200). Mitochondrial content is capped at 30%. Normalization and integration follow the publicly available Seurat vignette (https://satijalab.org/seurat/articles/integration_introduction; accessed on 13 May 2025). Disease-associated differential gene signature for each cell type is identified using the FindMarkers function of Seurat.
To assess intercellular centrality score for each cluster, scDrugPrio integrates cell–cell interaction analysis via NicheNet [132], allowing for identification of cell types that are more influential in the disease state. Within each cell type, drugs are assigned intracellular centrality score based on the network centrality of their targets in the disease-associated subnetworks. The final drug ranking is based on a composite score of intra-and intercellular centralities for each drug across cell types, with intercellular centrality weighed more heavily. Specifically, the compound score is calculated as the sum of a drug’s intercellular centrality plus 0.1 times the sum of its intracellular centralities across all cell types. By combining these layers of information, scDrugPrio provides a prioritization framework that outputs a ranked list of drug candidates most likely to modulate the observed disease-associated gene signatures in disease-driving cell populations.
To demonstrate how scDrugPrio can be applied to identify drug candidates to enhance ICI response, we analyzed the ESCC pre-treatment samples from both responders and non-responders. scDrugPrio filters drugs from DrugBank based on network distance between cell type-specific DEGs and drugs targets in the PPIN for each cell type-drug pair, selecting only drugs with targets significantly closed to DEGs and frequently target DEGs directly. Using the resulting unranked list of 129 unique candidate drugs from 622 cell type-drug pairs, manual evaluation of each drug’s mechanism of actions allows for the identification of compounds that may counteract or mimic the gene expression patterns associated with non-responsiveness in each cell type (Figure 3). This preliminary drug set offers a rich starting point for hypothesis generation and biological interpretation.
Out of the 129 unique drug candidates, 60 of them are counteracting upregulated genes associated with no response. Using these 60 drugs as input into DrugEnrichr [133,134,135], 19 Drug Repurposing Hub mechanisms of action (MOA) are significantly enriched (Appendix A Table A1). The most significant MOA, cyclooxygenase (COX) inhibitor, is represented by six scDrugPrio predicted drugs. While the effects of COX inhibitors demonstrate mixed results in their effect on ICI response and outcome in various cancer types and patient cohorts [136,137,138,139,140], several are being evaluated in current clinical trials (Table 1). Additional enriched mechanisms include inhibitors of KIT, PDGFR, and VEGFR, which are shared targets of lenvatinib and dasatinib. Lenvatinib is being investigated in an ongoing clinical trial (Table 1), and a retrospective clinical study has demonstrated its concurrent use with ICI associates with improved survival compared to sequential administration of treatments [141]. Dasatinib has been shown to potentiate a-CTLA-4 treatment in an in vivo mouse, supporting their use in combination with ICI treatments [142].
For individual drugs, several of the top hits aligned with previously published reports on their use in modulating ICI response, supporting the utility of scDrugPrio for drug repurposing to enhance ICI response. For example, several androgen receptor (AR) antagonists—including drospirenone, dienogest, and enzacamene—were identified and listed (Figure 4, Appendix A Table A2), consistent with approach to counteract the upregulated AR expression observed in non-responders in the ESCC cohort. Prior in vitro and in vivo studies in mouse models of hepatocellular carcinoma and prostate cancer have demonstrated that AR overexpression negatively attenuates anti-PD-L1, and that AR and PD-L1 dual blockade enhances the overall survival and T cell function [23,24]. Tumor necrosis factor (TNF) inhibitors and neutralizing antibodies, such as infliximab and certolizumab, were also identified by scDrugPrio (Figure 4, Appendix A Table A2). Both have been evaluated in a clinical trial for their potential to enhance ICI efficacy (NCT03293784, Table 1). On the other hand, Endothelin blocking has also been shown to potentiate responses to anti-CTLA4 and anti-PD1 therapies in a breast cancer model [143] and is also currently being investigated in a clinical trial (NCT07016399, Table 1). Notably, an endothelin receptor antagonist ambrisentan, which has been reported to inhibit cancer progression [144], was also among the scDrugPrio suggested candidates (Figure 4, Appendix A Table A2). Together, these examples highlight the potential of scDrugPrio to identify biologically relevant drug candidates for ICI efficacy enhancement.
Beyond identifying drugs that may help overcome resistance to ICI, the results from scDrugPrio can also be used to help flag compounds that may exacerbate non-responsiveness from the “disease” mimicking drugs in the unranked drug list from scDrugPrio analysis. For example, IL1R1 was downregulated in tumor-associated macrophages, mast cells, and dendritic cells in the non-responder group. While IL1R1 perturbation has been shown to sensitize resistant tumors in a lung cancer model, myeloid-specific ablation of IL1R1 can promote colorectal cancer progression [145,146]. Thus, targeting IL1R1 with inhibitory drugs like Anakinra might worsen ICI resistance and may not be applicable in the context of ESCC. Similarly, since AR expression levels was elevated in non-responders, use of an AR agonist Methyltestosterone would be counteractive and worsen response to treatment. These examples illustrate how scDrugPrio, by identifying compounds that mimic the gene expression signatures of non-responsiveness, can also help avoid compounds that may inadvertently worsen patient outcomes in a case-specific manner.
Like scDrug, the performance of scDrugPrio is also limited by the scope of drug information available in DrugBank. Most compounds are annotated primarily based on their original intended targets, while potential off-target effects—such as modulation of secondary genes or pathways—may not be captured in current annotations [9,17,147]. These unannotated effects may offer new therapeutic opportunities, particularly in the context of drug repurposing, but may be overlooked due to the limited extent of database annotations. Similarly, undocumented off-target effects may exacerbate disease or side effects, warranting future investigations into the toxicities of the predicted drug candidates.

3. Conclusions and Lessons Learned

The ability of several drug candidates identified by scDrug and scDrugPrio to enhance ICI efficacy is supported by existing preclinical and clinical evidence involving either the same or mechanistically related compounds. This highlights the potential of applying these computational drug repurposing tools to scRNA-seq datasets to uncover strategies to circumvent ICI treatment resistance. Although many of the identified drugs have been studied in cancer types other than ESCC, their prior use in combination with ICIs suggests the broader applicability and relevance of this approach. Despite the support of these predicted candidates from previous studies in their clinical and biological relevance in use with ICIs, in vitro and in vivo experiments are required to validate their capacity in enhancing ICI response.
Although scDrug and scDrugPrio employ distinct drug repurposing approaches and rely on reference databases with limited overlap in drug coverage, five drugs from the GDSC dataset were also identified in scDrugPrio: cabozantinib, dasatinib, etoposide, midostaurin, and ponatinib. Among these, dasatinib was ranked as the top candidate in scDrug based on its high predicted killing percentage across all tumor subclusters. The remaining four drugs, while exhibiting low predicted cytotoxicity in scDrug (Figure 5), may act on non-tumor components of the tumor microenvironment in non-responders, such as immune and non-malignant stromal cells, based on the scDrugPrio results (Table 5).
This limited overlap highlights the complementary nature of the two tools, although each captures different aspects of tumor biology and therapeutic potentials through distinct strategies (Figure 6). scDrug integrates single-cell data into drug screening and focuses on cytotoxicity predictions on tumor populations, whereas scDrugPrio integrates single-cell data with network-based drug-gene interaction to reverse gene signatures associated with non-responsiveness across all cell types in the tumor microenvironment. Importantly, each tool has its limitations: scDrug does not account for non-tumor compartments, potentially overlooking critical interactions in the tumor microenvironment, while scDrugPrio lacks pharmacodynamic information, which is essential for understanding how different drugs modulate different cell populations. This means that even if a drug theoretically targets disease-associated gene expression in a given cell population, it may not be effective due to factors like drug uptake, metabolism, or resistance mechanisms specific to that cell type. However, taken together, these tools offer complementary insights for drug prioritization.
These workflow examples support a multi-tool, integrative approach for computational drug repurposing. Combining strategies used in scDrug and scDrugPrio enables the identification of drug candidates that target both tumor-intrinsic and -extrinsic hallmarks of ICI resistance while also incorporating drug response data. Such a combined approach may provide a more comprehensive and robust strategy for selecting drug candidates to enhance ICI treatment response. While computational approaches offer a time- and cost-effective means of drug screening, experimental validation of the results remains essential.
To further expand this application of computational drug repurposing, future work could apply these methods to multi-omics and spatial transcriptomics datasets or individual patient samples for personalized treatment design. Additionally, beyond enhancing efficacy, some of these approaches may be adapted to address other aspects of ICI therapy, such as mitigating immune-related adverse events, and could also be applied to other types of immunotherapies, including adoptive cell therapies.

Author Contributions

Conceptualization, O.J.C., A.C.T. and Y.A.C.; methodology, O.J.C. and T.T.T.T.; validation, O.J.C., T.T.T.T., Y.A.C. and A.C.T.; formal analysis, O.J.C. and T.T.T.T.; investigation, O.J.C. and T.T.T.T.; resources, A.C.T. and Y.A.C.; data curation, O.J.C. and T.T.T.T.; writing—original draft preparation, O.J.C. and T.T.T.T.; writing—review and editing, Y.A.C. and A.C.T.; visualization, O.J.C. and T.T.T.T.; supervision, Y.A.C. and A.C.T.; project administration, A.C.T.; funding acquisition, Y.A.C. and A.C.T. All authors have read and agreed to the published version of the manuscript.

Funding

Part of this research was funded by the Jon M. and Karen Huntsman Endowed Chair in Cancer Data Science (A.C.T.).

Data Availability Statement

All datasets used in the case studies are publicly available and cited within the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AUCArea under the curve
ARAndrogen receptor
CaDRReS-ScCancer Drug Response prediction using a Recommender System-Single cell
CAFCancer-associated fibroblast
GDSCGenomics of Drug Sensitivity in Cancer
CFHComplement factor H
CTLA-4Cytotoxic T-Lymphocyte-Associated Protein 4
DEGDifferentially expressed genes
EGFREpidermal growth factor receptor
ESCCEsophageal squamous cell carcinoma
IC50Half maximal inhibitory concentration
ICIImmune checkpoint inhibitors
NATNeoadjuvant chemo-immunotherapy
NRNon-responder
NSCLCNon-small cell lung cancer
ORRObjective response rate
scRNA-seqSingle-cell RNA sequencing
TMETumor microenvironment
TNFTumor necrosis factor
PD-1Programmed Cell Death Protein 1
PD-L1Programmed Death-Ligand 1
PPINProtein–protein interaction network
RResponder

Appendix A

Table A1. Significant mechanisms of action associated with scDrugPrio-predicted drugs counteracting DEGs linked to non-response in ESCC patients, identified via DrugEnrichR enrichment analysis. Adjusted p-values were rounded to four decimal places.
Table A1. Significant mechanisms of action associated with scDrugPrio-predicted drugs counteracting DEGs linked to non-response in ESCC patients, identified via DrugEnrichR enrichment analysis. Adjusted p-values were rounded to four decimal places.
Drug Repurpose Hub Mechanisms of ActionOverlapAdjusted p-ValueAssociated Drugs
Cyclooxygenase inhibitor5/750.0001Ketoprofen
Propacetamol
Bromfenac
Nabumetone
Mefenamic acid
KIT inhibitor2/160.0114Dasatinib
Lenvatinib
PDGFR tyrosine kinase receptor inhibitor2/200.0119Dasatinib
Lenvatinib
VEGFR inhibitor2/290.0188Lenvatinib
Cabozantinib
Phosphodiesterase inhibitor2/400.0282Amrinone theobromine
Prostaglandin inhibitor1/70.0367Grapiprant
PKC inhibitor1/90.0367Dequalinium
Endothelin receptor antagonist1/80.0367Ambrisentan
CC chemokine receptor antagonist1/70.0367Plerixafor
RET tyrosine kinase inhibitor1/70.0367Cabozantinib
Tyrosine kinase inhibitor1/80.0367Dasatinib
Potassium channel blocker1/90.0367Amiodarone
FGFR inhibitor1/90.0367Lenvatinib
Bcr-Abl kinase inhibitor1/70.0367Dasatinib
src inhibitor1/70.0367Dasatinib
angiogenesis inhibitor1/60.0367Tranilast
dihydrofolate reductase inhibitor1/100.0383Trimethoprim
progesterone receptor agonist1/110.0397Dienogest
FLT3 inhibitor1/120.0410Gilteritinib
Table A2. Selected drugs with upregulated target DEGs and their corresponding DrugBank information from scDrugPrio analysis to counteract treatment resistance in ESCC patient dataset.
Table A2. Selected drugs with upregulated target DEGs and their corresponding DrugBank information from scDrugPrio analysis to counteract treatment resistance in ESCC patient dataset.
Drug DrugBank IDPharmacological EffectTargeted Upregulated DEGsCell Type Clusters
DrospirenoneDB01395PGR (agonist),
NR3C2 (antagonist),
AR (antagonist)		ARB cells
DienogestDB09123PGR (agonist),
AR (antagonist)		ART cells
EnzacameneDB11219PGR (antagonist),
AR (antagonist)		ARDendritic cells,
T cells
EtanerceptDB00005TNF (antibody)TNFEndothelial cells,
Fibroblast,
Cancer-associated fibroblast (CAF)
AdalimumabDB00051TNF (antibody)TNFEndothelial cells,
Fibroblast,
CAF
InfliximabDB00065TNF (inhibitor)TNFEndothelial cells,
Fibroblast,
CAF
Certolizumab
pegol		DB08904TNF (neutralizer)TNFFibroblast
AmrinoneDB01427PDE3A (inhibitor),
PDE4B (inhibitor),
TNF (inhibitor)		PDE4BB cells
AmrinoneDB01427PDE3A (inhibitor),
PDE4B (inhibitor),
TNF (inhibitor)		PDE3A,
PDE4B,
TNF		Fibroblast
AmbrisentanDB06403EDNRA (antagonist),
EDNRB (antagonist)		EDNRBTumor-associated macrophages (TAM)
EDNRA,
EDNRB		B cells
EDNRBEndothelial cells
EDNRAFibroblast
EDNRBEpithelial cells
EDNRATAM
EDNRA,
EDNRB		Inflammatory CAF
EDNRBEpithelial cells

References

  1. Mao, Y.; Xie, H.; Lv, M.; Yang, Q.; Shuang, Z.; Gao, F.; Li, S.; Zhu, L.; Wang, W. The Landscape of Objective Response Rate of Anti-PD-1/L1 Monotherapy across 31 Types of Cancer: A System Review and Novel Biomarker Investigating. Cancer Immunol. Immunother. 2023, 72, 2483–2498. [Google Scholar] [CrossRef]
  2. Haslam, A.; Olivier, T.; Prasad, V. How Many People in the Are Eligible for and Respond to Checkpoint Inhibitors: An Empirical Analysis. Int. J. Cancer 2025, 156, 2352–2359. [Google Scholar] [CrossRef] [PubMed]
  3. Shiravand, Y.; Khodadadi, F.; Kashani, S.M.A.; Hosseini-Fard, S.R.; Hosseini, S.; Sadeghirad, H.; Ladwa, R.; O’Byrne, K.; Kulasinghe, A. Immune Checkpoint Inhibitors in Cancer Therapy. Curr. Oncol. 2022, 29, 3044–3060. [Google Scholar] [CrossRef] [PubMed]
  4. Yao, L.; Jia, G.; Lu, L.; Bao, Y.; Ma, W. Factors Affecting Tumor Responders and Predictive Biomarkers of Toxicities in Cancer Patients Treated with Immune Checkpoint Inhibitors. Int. Immunopharmacol. 2020, 85, 106628. [Google Scholar] [CrossRef] [PubMed]
  5. Nagasaki, J.; Ishino, T.; Togashi, Y. Mechanisms of Resistance to Immune Checkpoint Inhibitors. Cancer Sci. 2022, 113, 3303–3312. [Google Scholar] [CrossRef]
  6. Zhang, C.; Zhang, C.; Wang, H. Immune-Checkpoint Inhibitor Resistance in Cancer Treatment: Current Progress and Future Directions. Cancer Lett. 2023, 562, 216182. [Google Scholar] [CrossRef]
  7. Alsaafeen, B.H.; Ali, B.R.; Elkord, E. Resistance Mechanisms to Immune Checkpoint Inhibitors: Updated Insights. Mol. Cancer 2025, 24, 20. [Google Scholar] [CrossRef]
  8. Xia, Y.; Sun, M.; Huang, H.; Jin, W.-L. Drug Repurposing for Cancer Therapy. Signal Transduct. Target. Ther. 2024, 9, 92. [Google Scholar] [CrossRef]
  9. Al Khzem, A.H.; Gomaa, M.S.; Alturki, M.S.; Tawfeeq, N.; Sarafroz, M.; Alonaizi, S.M.; Al Faran, A.; Alrumaihi, L.A.; Alansari, F.A.; Alghamdi, A.A. Drug Repurposing for Cancer Treatment: A Comprehensive Review. Int. J. Mol. Sci. 2024, 25, 12441. [Google Scholar] [CrossRef]
  10. Stolfi, C.; Pacifico, T.; Luiz-Ferreira, A.; Monteleone, G.; Laudisi, F. Anthelmintic Drugs as Emerging Immune Modulators in Cancer. Int. J. Mol. Sci. 2023, 24, 6446. [Google Scholar] [CrossRef]
  11. Park, K. A Review of Computational Drug Repurposing. Transl. Clin. Pharmacol. 2019, 27, 59. [Google Scholar] [CrossRef]
  12. Crittenden, D.; Gallagher, R.; Milans del Bosch, F.; Fox, D.M.; Kleiman, L.B. Pathways for Non-Manufacturers to Drive Generic Drug Repurposing for Cancer in the U.S. Front. Pharmacol. 2024, 15, 1419772. [Google Scholar] [CrossRef]
  13. Sharma, V.D.; Bhattaram, V.A.; Krudys, K.; Li, Z.; Marathe, A.; Mehrotra, N.; Wang, X.; Liu, J.; Stier, E.; Florian, J.; et al. Driving Efficiency: Leveraging Model-Informed Approaches in 505(b)(2) Regulatory Actions. J. Clin. Pharmacol. 2024, 64, 1484–1490. [Google Scholar] [CrossRef]
  14. Korsunsky, I.; Millard, N.; Fan, J.; Slowikowski, K.; Zhang, F.; Wei, K.; Baglaenko, Y.; Brenner, M.; Loh, P.; Raychaudhuri, S. Fast, Sensitive and Accurate Integration of Single-Cell Data with Harmony. Nat. Methods 2019, 16, 1289–1296. [Google Scholar] [CrossRef]
  15. Zhang, Z.; Zhou, L.; Xie, N.; Nice, E.C.; Zhang, T.; Cui, Y.; Huang, C. Overcoming Cancer Therapeutic Bottleneck by Drug Repurposing. Signal Transduct. Target. Ther. 2020, 5, 113. [Google Scholar] [CrossRef] [PubMed]
  16. Mohi-ud-din, R.; Chawla, A.; Sharma, P.; Mir, P.A.; Potoo, F.H.; Reiner, Ž.; Reiner, I.; Ateşşahin, D.A.; Sharifi-Rad, J.; Mir, R.H.; et al. Repurposing Approved Non-Oncology Drugs for Cancer Therapy: A Comprehensive Review of Mechanisms, Efficacy, and Clinical Prospects. Eur. J. Med. Res. 2023, 28, 345. [Google Scholar] [CrossRef] [PubMed]
  17. Ayyar, P.; Subramanian, U. Repurposing—Second Life for Drugs. Pharmacia 2022, 69, 51–59. [Google Scholar] [CrossRef]
  18. Pinzi, L.; Bisi, N.; Rastelli, G. How Drug Repurposing Can Advance Drug Discovery: Challenges and Opportunities. Front. Drug Discov. 2024, 4, 1460100. [Google Scholar] [CrossRef]
  19. Walsh, R.J.; Sundar, R.; Lim, J.S.J. Immune Checkpoint Inhibitor Combinations—Current and Emerging Strategies. Br. J. Cancer 2023, 128, 1415–1417. [Google Scholar] [CrossRef]
  20. Vafaei, S.; Zekiy, A.O.; Khanamir, R.A.; Zaman, B.A.; Ghayourvahdat, A.; Azimizonuzi, H.; Zamani, M. Combination Therapy with Immune Checkpoint Inhibitors (ICIs); a New Frontier. Cancer Cell Int. 2022, 22, 2. [Google Scholar] [CrossRef]
  21. Birnboim-Perach, R.; Benhar, I. Using Combination Therapy to Overcome Diverse Challenges of Immune Checkpoint Inhibitors Treatment. Int. J. Biol. Sci. 2024, 20, 3911–3922. [Google Scholar] [CrossRef] [PubMed]
  22. Scharping, N.E.; Menk, A.V.; Whetstone, R.D.; Zeng, X.; Delgoffe, G.M. Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia. Cancer Immunol. Res. 2017, 5, 9–16. [Google Scholar] [CrossRef] [PubMed]
  23. Jiang, G.; Shi, L.; Zheng, X.; Zhang, X.; Wu, K.; Liu, B.; Yan, P.; Liang, X.; Yu, T.; Wang, Y.; et al. Androgen Receptor Affects the Response to Immune Checkpoint Therapy by Suppressing PD-L1 in Hepatocellular Carcinoma. Aging 2020, 12, 11466–11484. [Google Scholar] [CrossRef] [PubMed]
  24. Guan, X.; Polesso, F.; Wang, C.; Sehrawat, A.; Hawkins, R.M.; Murray, S.E.; Thomas, G.V.; Caruso, B.; Thompson, R.F.; Wood, M.A.; et al. Androgen Receptor Activity in T Cells Limits Checkpoint Blockade Efficacy. Nature 2022, 606, 791–796. [Google Scholar] [CrossRef]
  25. Xiao, X.; Zeng, S.; Li, Y.; Li, L.; Zhang, J. Aspirin Suppressed PD-L1 Expression through Suppressing KAT5 and Subsequently Inhibited PD-1 and PD-L1 Signaling to Attenuate OC Development. J. Oncol. 2022, 2022, 4664651. [Google Scholar] [CrossRef]
  26. Renner, K.; Bruss, C.; Schnell, A.; Koehl, G.; Becker, H.M.; Fante, M.; Menevse, A.-N.; Kauer, N.; Blazquez, R.; Hacker, L.; et al. Restricting Glycolysis Preserves T Cell Effector Functions and Augments Checkpoint Therapy. Cell Rep. 2019, 29, 135–150.e9. [Google Scholar] [CrossRef]
  27. Coleman, M.; Mascialino, S.J.; Panjwani, A.; Edwards, E.; Sukhatme, V.V.; Gavegnano, C.; Sukhatme, V.P. Readily Available Drugs and Other Interventions to Potentially Improve the Efficacy of Immune Checkpoint Blockade in Cancer. Front. Immunol. 2024, 14, 1281744. [Google Scholar] [CrossRef]
  28. Pujalte-Martin, M.; Belaïd, A.; Bost, S.; Kahi, M.; Peraldi, P.; Rouleau, M.; Mazure, N.M.; Bost, F. Targeting Cancer and Immune Cell Metabolism with the Complex I Inhibitors Metformin and IACS-010759. Mol. Oncol. 2024, 18, 1719–1738. [Google Scholar] [CrossRef]
  29. Galal, M.A.; Al-Rimawi, M.; Hajeer, A.; Dahman, H.; Alouch, S.; Aljada, A. Metformin: A Dual-Role Player in Cancer Treatment and Prevention. Int. J. Mol. Sci. 2024, 25, 4083. [Google Scholar] [CrossRef]
  30. Xie, L.; Zhu, G.; Shang, J.; Chen, X.; Zhang, C.; Ji, X.; Zhang, Q.; Wei, Y. An Overview on the Biological Activity and Anti-Cancer Mechanism of Lovastatin. Cell. Signal. 2021, 87, 110122. [Google Scholar] [CrossRef]
  31. Stryjkowska-Góra, A.; Karczmarek-Borowska, B.; Góra, T.; Krawczak, K. Statins and Cancers. Współczesna Onkol. 2015, 3, 167–175. [Google Scholar] [CrossRef] [PubMed]
  32. Sugawara, T.; Baumgart, S.J.; Nevedomskaya, E.; Reichert, K.; Steuber, H.; Lejeune, P.; Mumberg, D.; Haendler, B. Darolutamide Is a Potent Androgen Receptor Antagonist with Strong Efficacy in Prostate Cancer Models. Int. J. Cancer 2019, 145, 1382–1394. [Google Scholar] [CrossRef] [PubMed]
  33. Chumsri, S.; Howes, T.; Bao, T.; Sabnis, G.; Brodie, A. Aromatase, Aromatase Inhibitors, and Breast Cancer. J. Steroid Biochem. Mol. Biol. 2011, 125, 13–22. [Google Scholar] [CrossRef]
  34. Tang, K.; Wu, Y.-H.; Song, Y.; Yu, B. Indoleamine 2,3-Dioxygenase 1 (IDO1) Inhibitors in Clinical Trials for Cancer Immunotherapy. J. Hematol. Oncol. 2021, 14, 68. [Google Scholar] [CrossRef]
  35. Lv, Q.; Zhang, Y.; Gao, W.; Wang, J.; Hu, Y.; Yang, H.; Xie, Y.; Lv, Y.; Zhang, H.; Wu, D.; et al. CSF1R Inhibition Reprograms Tumor-Associated Macrophages to Potentiate Anti-PD-1 Therapy Efficacy against Colorectal Cancer. Pharmacol. Res. 2024, 202, 107126. [Google Scholar] [CrossRef]
  36. Ciavattone, N.G.; Bevoor, A.; Farfel, A.; Rehman, A.; Ho, K.K.Y.; Rock, E.C.; Chen, Y.-C.; Luker, K.E.; Humphries, B.A.; Luker, G.D. Inhibiting CXCR4 Reduces Immunosuppressive Effects of Myeloid Cells in Breast Cancer Immunotherapy. Sci. Rep. 2025, 15, 5204. [Google Scholar] [CrossRef]
  37. Farhad, M.; Rolig, A.S.; Redmond, W.L. The Role of Galectin-3 in Modulating Tumor Growth and Immunosuppression within the Tumor Microenvironment. Oncoimmunology 2018, 7, e1434467. [Google Scholar] [CrossRef]
  38. Saxena, R.; Gottlin, E.B.; Campa, M.J.; Bushey, R.T.; Guo, J.; Patz, E.F.; He, Y.-W. Complement Factor H: A Novel Innate Immune Checkpoint in Cancer Immunotherapy. Front. Cell Dev. Biol. 2024, 12, 1302490. [Google Scholar] [CrossRef]
  39. Ruzzi, F.; Riccardo, F.; Conti, L.; Tarone, L.; Semprini, M.S.; Bolli, E.; Barutello, G.; Quaglino, E.; Lollini, P.-L.; Cavallo, F. Cancer Vaccines: Target Antigens, Vaccine Platforms and Preclinical Models. Mol. Asp. Med. 2025, 101, 101324. [Google Scholar] [CrossRef]
  40. Jia, D.; Wang, Q.; Qi, Y.; Jiang, Y.; He, J.; Lin, Y.; Sun, Y.; Xu, J.; Chen, W.; Fan, L.; et al. Microbial Metabolite Enhances Immunotherapy Efficacy by Modulating T Cell Stemness in Pan-Cancer. Cell 2024, 187, 1651–1665.e21. [Google Scholar] [CrossRef]
  41. Jia, D.; Wang, L. Modulating Commensal Bacteria to Intervene in Cancer Immunotherapy. Clin. Transl. Med. 2024, 14, e1704. [Google Scholar] [CrossRef] [PubMed]
  42. Spreafico, A.; Heirali, A.A.; Araujo, D.V.; Tan, T.J.; Oliva, M.; Schneeberger, P.H.H.; Chen, B.; Wong, M.K.; Stayner, L.-A.; Hansen, A.R.; et al. First-in-Class Microbial Ecosystem Therapeutic 4 (MET4) in Combination with Immune Checkpoint Inhibitors in Patients with Advanced Solid Tumors (MET4-IO Trial). Ann. Oncol. 2023, 34, 520–530. [Google Scholar] [CrossRef] [PubMed]
  43. Choi, S.; Kim, S.; Park, J.; Lee, S.E.; Kim, C.; Kang, D. Diclofenac: A Nonsteroidal Anti-Inflammatory Drug Inducing Cancer Cell Death by Inhibiting Microtubule Polymerization and Autophagy Flux. Antioxidants 2022, 11, 1009. [Google Scholar] [CrossRef] [PubMed]
  44. Yang, J.; Yamashita-Kanemaru, Y.; Morris, B.I.; Contursi, A.; Trajkovski, D.; Xu, J.; Patrascan, I.; Benson, J.; Evans, A.C.; Conti, A.G.; et al. Aspirin Prevents Metastasis by Limiting Platelet TXA2 Suppression of T Cell Immunity. Nature 2025, 640, 1052–1061. [Google Scholar] [CrossRef]
  45. Lei, J.; Zhou, Z.; Fang, J.; Sun, Z.; He, M.; He, B.; Chen, Q.; Paek, C.; Chen, P.; Zhou, J.; et al. Aspirin Induces Immunogenic Cell Death and Enhances Cancer Immunotherapy in Colorectal Cancer. Int. Immunopharmacol. 2023, 121, 110350. [Google Scholar] [CrossRef]
  46. Denslow, A.; Świtalska, M.; Jarosz, J.; Papiernik, D.; Porshneva, K.; Nowak, M.; Wietrzyk, J. Clopidogrel in a Combined Therapy with Anticancer Drugs—Effect on Tumor Growth, Metastasis, and Treatment Toxicity: Studies in Animal Models. PLoS ONE 2017, 12, e0188740. [Google Scholar] [CrossRef]
  47. Ruan, R.; Li, L.; Li, X.; Huang, C.; Zhang, Z.; Zhong, H.; Zeng, S.; Shi, Q.; Xia, Y.; Zeng, Q.; et al. Unleashing the Potential of Combining FGFR Inhibitor and Immune Checkpoint Blockade for FGF/FGFR Signaling in Tumor Microenvironment. Mol. Cancer 2023, 22, 60. [Google Scholar] [CrossRef]
  48. Xie, J.; Wang, X.; Proud, C.G. MTOR Inhibitors in Cancer Therapy. F1000Research 2016, 5, 2078. [Google Scholar] [CrossRef]
  49. Liu, Y.; Feng, M.; Chen, H.; Yang, G.; Qiu, J.; Zhao, F.; Cao, Z.; Luo, W.; Xiao, J.; You, L.; et al. Mechanistic Target of Rapamycin in the Tumor Microenvironment and Its Potential as a Therapeutic Target for Pancreatic Cancer. Cancer Lett. 2020, 485, 1–13. [Google Scholar] [CrossRef]
  50. Xu, H.; Chen, K.; Shang, R.; Chen, X.; Zhang, Y.; Song, X.; Evert, M.; Zhong, S.; Li, B.; Calvisi, D.F.; et al. Alpelisib Combination Treatment as Novel Targeted Therapy against Hepatocellular Carcinoma. Cell Death Dis. 2021, 12, 920. [Google Scholar] [CrossRef]
  51. Chen, Y.; Dai, S.; Cheng, C.; Chen, L. Lenvatinib and Immune-Checkpoint Inhibitors in Hepatocellular Carcinoma: Mechanistic Insights, Clinical Efficacy, and Future Perspectives. J. Hematol. Oncol. 2024, 17, 130. [Google Scholar] [CrossRef]
  52. Su, J.; Zhang, J.; Wu, Y.; Ni, C.; Ding, Y.; Cai, Z.; Xu, M.; Lai, M.; Wang, J.; Lin, S.; et al. Cabozantinib in Combination with Immune Checkpoint Inhibitors for Renal Cell Carcinoma: A Systematic Review and Meta-Analysis. Front. Pharmacol. 2024, 15, 1322473. [Google Scholar] [CrossRef]
  53. Saba, N.F.; Steuer, C.E.; Ekpenyong, A.; McCook-Veal, A.; Magliocca, K.; Patel, M.; Schmitt, N.C.; Stokes, W.; Bates, J.E.; Rudra, S.; et al. Pembrolizumab and Cabozantinib in Recurrent Metastatic Head and Neck Squamous Cell Carcinoma: A Phase 2 Trial. Nat. Med. 2023, 29, 880–887. [Google Scholar] [CrossRef] [PubMed]
  54. Zhao, Y.; Guo, S.; Deng, J.; Shen, J.; Du, F.; Wu, X.; Chen, Y.; Li, M.; Chen, M.; Li, X.; et al. VEGF/VEGFR-Targeted Therapy and Immunotherapy in Non-Small Cell Lung Cancer: Targeting the Tumor Microenvironment. Int. J. Biol. Sci. 2022, 18, 3845–3858. [Google Scholar] [CrossRef] [PubMed]
  55. Chen, A.Y.; Wolchok, J.D.; Bass, A.R. TNF in the Era of Immune Checkpoint Inhibitors: Friend or Foe? Nat. Rev. Rheumatol. 2021, 17, 213–223. [Google Scholar] [CrossRef] [PubMed]
  56. Scirocchi, F.; Napoletano, C.; Pace, A.; Rahimi Koshkaki, H.; Di Filippo, A.; Zizzari, I.G.; Nuti, M.; Rughetti, A. Immunogenic Cell Death and Immunomodulatory Effects of Cabozantinib. Front. Oncol. 2021, 11, 755433. [Google Scholar] [CrossRef]
  57. Huang, D.; Xue, J.; Li, S.; Yang, D. Oxaliplatin and Infliximab Synergize to Induce Regression of Colon Cancer. Oncol. Lett. 2017, 11, 755433. [Google Scholar] [CrossRef]
  58. Draganov, D.; Han, Z.; Rana, A.; Bennett, N.; Irvine, D.J.; Lee, P.P. Ivermectin Converts Cold Tumors Hot and Synergizes with Immune Checkpoint Blockade for Treatment of Breast Cancer. NPJ Breast Cancer 2021, 7, 22. [Google Scholar] [CrossRef]
  59. Shaker, B.; Ahmad, S.; Lee, J.; Jung, C.; Na, D. In Silico Methods and Tools for Drug Discovery. Comput. Biol. Med. 2021, 137, 104851. [Google Scholar] [CrossRef]
  60. Tanoli, Z.; Fernández-Torras, A.; Özcan, U.O.; Kushnir, A.; Nader, K.M.; Gadiya, Y.; Fiorenza, L.; Ianevski, A.; Vähä-Koskela, M.; Miihkinen, M.; et al. Computational Drug Repurposing: Approaches, Evaluation of in Silico Resources and Case Studies. Nat. Rev. Drug Discov. 2025, 24, 521–542. [Google Scholar] [CrossRef]
  61. Dash, R.; Yadav, M.; Biswal, J.; Samanta, S.; Sharma, T.; Mohapatra, S. Drug Repurposing a Compelling Cancer Strategy with Bottomless Opportunities: Recent Advancements in Computational Methods and Molecular Mechanisms. Indian J. Pharmacol. 2023, 55, 322–331. [Google Scholar] [CrossRef] [PubMed]
  62. To, K.K.W.; Cho, W.C. Drug Repurposing to Circumvent Immune Checkpoint Inhibitor Resistance in Cancer Immunotherapy. Pharmaceutics 2023, 15, 2166. [Google Scholar] [CrossRef] [PubMed]
  63. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef]
  64. Lamb, J.; Crawford, E.D.; Peck, D.; Modell, J.W.; Blat, I.C.; Wrobel, M.J.; Lerner, J.; Brunet, J.-P.; Subramanian, A.; Ross, K.N.; et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 2006, 313, 1929–1935. [Google Scholar] [CrossRef]
  65. Li, F.; Yin, J.; Lu, M.; Mou, M.; Li, Z.; Zeng, Z.; Tan, Y.; Wang, S.; Chu, X.; Dai, H.; et al. DrugMAP: Molecular Atlas and Pharma-Information of All Drugs. Nucleic Acids Res. 2023, 51, D1288–D1299. [Google Scholar] [CrossRef]
  66. Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K.; et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017, 171, 1437–1452.e17. [Google Scholar] [CrossRef]
  67. Wu, H.; Huang, J.; Zhong, Y.; Huang, Q. DrugSig: A Resource for Computational Drug Repositioning Utilizing Gene Expression Signatures. PLoS ONE 2017, 12, e0177743. [Google Scholar] [CrossRef]
  68. Corsello, S.M.; Bittker, J.A.; Liu, Z.; Gould, J.; McCarren, P.; Hirschman, J.E.; Johnston, S.E.; Vrcic, A.; Wong, B.; Khan, M.; et al. The Drug Repurposing Hub: A next-Generation Drug Library and Information Resource. Nat. Med. 2017, 23, 405–408. [Google Scholar] [CrossRef]
  69. Yoo, M.; Shin, J.; Kim, J.; Ryall, K.A.; Lee, K.; Lee, S.; Jeon, M.; Kang, J.; Tan, A.C. DSigDB: Drug Signatures Database for Gene Set Analysis. Bioinformatics 2015, 31, 3069–3071. [Google Scholar] [CrossRef]
  70. Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; et al. ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery. Nucleic Acids Res. 2012, 40, D1100–D1107. [Google Scholar] [CrossRef]
  71. Bento, A.P.; Gaulton, A.; Hersey, A.; Bellis, L.J.; Chambers, J.; Davies, M.; Krüger, F.A.; Light, Y.; Mak, L.; McGlinchey, S.; et al. The ChEMBL Bioactivity Database: An Update. Nucleic Acids Res. 2014, 42, D1083–D1090. [Google Scholar] [CrossRef]
  72. Griffith, M.; Griffith, O.L.; Coffman, A.C.; Weible, J.V.; McMichael, J.F.; Spies, N.C.; Koval, J.; Das, I.; Callaway, M.B.; Eldred, J.M.; et al. DGIdb: Mining the Druggable Genome. Nat. Methods 2013, 10, 1209–1210. [Google Scholar] [CrossRef]
  73. Cannon, M.; Stevenson, J.; Stahl, K.; Basu, R.; Coffman, A.; Kiwala, S.; McMichael, J.F.; Kuzma, K.; Morrissey, D.; Cotto, K.; et al. DGIdb 5.0: Rebuilding the Drug–Gene Interaction Database for Precision Medicine and Drug Discovery Platforms. Nucleic Acids Res. 2024, 52, D1227–D1235. [Google Scholar] [CrossRef] [PubMed]
  74. Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M. DrugBank: A Knowledgebase for Drugs, Drug Actions and Drug Targets. Nucleic Acids Res. 2008, 36 (Suppl. S1), D901–D906. [Google Scholar] [CrossRef] [PubMed]
  75. Ianevski, A.; Kushnir, A.; Nader, K.; Miihkinen, M.; Xhaard, H.; Aittokallio, T.; Tanoli, Z. RepurposeDrugs: An Interactive Web-Portal and Predictive Platform for Repurposing Mono- and Combination Therapies. Brief. Bioinform. 2024, 25, bbae328. [Google Scholar] [CrossRef]
  76. Huang, K.; Chandak, P.; Wang, Q.; Havaldar, S.; Vaid, A.; Leskovec, J.; Nadkarni, G.N.; Glicksberg, B.S.; Gehlenborg, N.; Zitnik, M. A Foundation Model for Clinician-Centered Drug Repurposing. Nat. Med. 2024, 30, 3601–3613. [Google Scholar] [CrossRef]
  77. Li, X.; Liao, M.; Wang, B.; Zan, X.; Huo, Y.; Liu, Y.; Bao, Z.; Xu, P.; Liu, W. A Drug Repurposing Method Based on Inhibition Effect on Gene Regulatory Network. Comput. Struct. Biotechnol. J. 2023, 21, 4446–4455. [Google Scholar] [CrossRef]
  78. Evangelista, J.E.; Clarke, D.J.B.; Xie, Z.; Lachmann, A.; Jeon, M.; Chen, K.; Jagodnik, K.M.; Jenkins, S.L.; Kuleshov, M.V.; Wojciechowicz, M.L.; et al. SigCom LINCS: Data and Metadata Search Engine for a Million Gene Expression Signatures. Nucleic Acids Res. 2022, 50, W697–W709. [Google Scholar] [CrossRef]
  79. Wu, J.; Li, X.; Wang, Q.; Han, J. DRviaSPCN: A Software Package for Drug Repurposing in Cancer via a Subpathway Crosstalk Network. Bioinformatics 2022, 38, 4975–4977. [Google Scholar] [CrossRef]
  80. Pilarczyk, M.; Fazel-Najafabadi, M.; Kouril, M.; Shamsaei, B.; Vasiliauskas, J.; Niu, W.; Mahi, N.; Zhang, L.; Clark, N.A.; Ren, Y.; et al. Connecting Omics Signatures and Revealing Biological Mechanisms with ILINCS. Nat. Commun. 2022, 13, 4678. [Google Scholar] [CrossRef]
  81. Chan, J.; Wang, X.; Turner, J.A.; Baldwin, N.E.; Gu, J. Breaking the Paradigm: Dr Insight Empowers Signature-Free, Enhanced Drug Repurposing. Bioinformatics 2019, 35, 2818–2826. [Google Scholar] [CrossRef] [PubMed]
  82. Fiscon, G.; Paci, P. SAveRUNNER: An R-Based Tool for Drug Repurposing. BMC Bioinform. 2021, 22, 150. [Google Scholar] [CrossRef] [PubMed]
  83. Troulé, K.; López-Fernández, H.; García-Martín, S.; Reboiro-Jato, M.; Carretero-Puche, C.; Martorell-Marugán, J.; Martín-Serrano, G.; Carmona-Sáez, P.; Glez-Peña, D.; Al-Shahrour, F.; et al. DREIMT: A Drug Repositioning Database and Prioritization Tool for Immunomodulation. Bioinformatics 2021, 37, 578–579. [Google Scholar] [CrossRef] [PubMed]
  84. Kuenzi, B.M.; Park, J.; Fong, S.H.; Sanchez, K.S.; Lee, J.; Kreisberg, J.F.; Ma, J.; Ideker, T. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell 2020, 38, 672–684.e6. [Google Scholar] [CrossRef]
  85. Wang, Z.; Lachmann, A.; Keenan, A.B.; Ma’ayan, A. L1000FWD: Fireworks Visualization of Drug-Induced Transcriptomic Signatures. Bioinformatics 2018, 34, 2150–2152. [Google Scholar] [CrossRef]
  86. Duan, Q.; Reid, S.P.; Clark, N.R.; Wang, Z.; Fernandez, N.F.; Rouillard, A.D.; Readhead, B.; Tritsch, S.R.; Hodos, R.; Hafner, M.; et al. L1000CDS2: LINCS L1000 Characteristic Direction Signatures Search Engine. NPJ Syst. Biol. Appl. 2016, 2, 16015. [Google Scholar] [CrossRef]
  87. Tang, F.; Barbacioru, C.; Wang, Y.; Nordman, E.; Lee, C.; Xu, N.; Wang, X.; Bodeau, J.; Tuch, B.B.; Siddiqui, A.; et al. MRNA-Seq Whole-Transcriptome Analysis of a Single Cell. Nat. Methods 2009, 6, 377–382. [Google Scholar] [CrossRef]
  88. Jovic, D.; Liang, X.; Zeng, H.; Lin, L.; Xu, F.; Luo, Y. Single-Cell RNA Sequencing Technologies and Applications: A Brief Overview. Clin. Transl. Med. 2022, 12, e694. [Google Scholar] [CrossRef]
  89. Li, X.; Wang, C.-Y. From Bulk, Single-Cell to Spatial RNA Sequencing. Int. J. Oral Sci. 2021, 13, 36. [Google Scholar] [CrossRef]
  90. Zhang, Y.; Wang, D.; Peng, M.; Tang, L.; Ouyang, J.; Xiong, F.; Guo, C.; Tang, Y.; Zhou, Y.; Liao, Q.; et al. Single-cell RNA Sequencing in Cancer Research. J. Exp. Clin. Cancer Res. 2021, 40, 81. [Google Scholar] [CrossRef]
  91. Huang, D.; Ma, N.; Li, X.; Gou, Y.; Duan, Y.; Liu, B.; Xia, J.; Zhao, X.; Wang, X.; Li, Q.; et al. Advances in Single-Cell RNA Sequencing and Its Applications in Cancer Research. J. Hematol. Oncol. 2023, 16, 98. [Google Scholar] [CrossRef] [PubMed]
  92. Maggiorani, D.; Le, O.; Lisi, V.; Landais, S.; Moquin-Beaudry, G.; Lavallée, V.P.; Decaluwe, H.; Beauséjour, C. Senescence Drives Immunotherapy Resistance by Inducing an Immunosuppressive Tumor Microenvironment. Nat. Commun. 2024, 15, 2435. [Google Scholar] [CrossRef] [PubMed]
  93. Kawashima, S.; Togashi, Y. Resistance to Immune Checkpoint Inhibitors and the Tumor Microenvironment. Exp. Dermatol. 2023, 32, 240–249. [Google Scholar] [CrossRef] [PubMed]
  94. Yang, Y.; Li, S.; To, K.K.W.; Zhu, S.; Wang, F.; Fu, L. Tumor-Associated Macrophages Remodel the Suppressive Tumor Immune Microenvironment and Targeted Therapy for Immunotherapy. J. Exp. Clin. Cancer Res. 2025, 44, 145. [Google Scholar] [CrossRef]
  95. Dijkstra, K.K.; Vendramin, R.; Karagianni, D.; Witsen, M.; Gálvez-Cancino, F.; Hill, M.S.; Foster, K.A.; Barbè, V.; Angelova, M.; Hynds, R.E.; et al. Subclonal Immune Evasion in Non-Small Cell Lung Cancer. Cancer Cell 2025, 43, P1833–1849.e10. [Google Scholar] [CrossRef]
  96. Kamatani, T.; Umeda, K.; Iwasawa, T.; Miya, F.; Matsumoto, K.; Mikami, S.; Hara, K.; Shimoda, M.; Suzuki, Y.; Nishino, J.; et al. Clonal Diversity Shapes the Tumour Microenvironment Leading to Distinct Immunotherapy Responses in Metastatic Urothelial Carcinoma. Nat. Commun. 2025, 16, 7995. [Google Scholar] [CrossRef]
  97. Hsieh, C.-Y.; Wen, J.-H.; Lin, S.-M.; Tseng, T.-Y.; Huang, J.-H.; Huang, H.-C.; Juan, H.-F. ScDrug: From Single-Cell RNA-Seq to Drug Response Prediction. Comput. Struct. Biotechnol. J. 2023, 21, 150–157. [Google Scholar] [CrossRef]
  98. Liu, C.; Zhang, Y.; Liang, Y.; Zhang, T.; Wang, G. DrugReSC: Targeting Disease-Critical Cell Subpopulations with Single-Cell Transcriptomic Data for Drug Repurposing in Cancer. Brief Bioinform. 2024, 25, bbae490. [Google Scholar] [CrossRef]
  99. Ianevski, A.; Nader, K.; Driva, K.; Senkowski, W.; Bulanova, D.; Moyano-Galceran, L.; Ruokoranta, T.; Kuusanmäki, H.; Ikonen, N.; Sergeev, P.; et al. Single-Cell Transcriptomes Identify Patient-Tailored Therapies for Selective Co-Inhibition of Cancer Clones. Nat. Commun. 2024, 15, 8579. [Google Scholar] [CrossRef]
  100. Schäfer, S.; Smelik, M.; Sysoev, O.; Zhao, Y.; Eklund, D.; Lilja, S.; Gustafsson, M.; Heyn, H.; Julia, A.; Kovács, I.A.; et al. ScDrugPrio: A Framework for the Analysis of Single-Cell Transcriptomics to Address Multiple Problems in Precision Medicine in Immune-Mediated Inflammatory Diseases. Genome Med. 2024, 16, 42. [Google Scholar] [CrossRef]
  101. Esteban-Medina, M.; de la Oliva Roque, V.M.; Herráiz-Gil, S.; Peña-Chilet, M.; Dopazo, J.; Loucera, C. Drexml: A Command Line Tool and Python Package for Drug Repurposing. Comput. Struct. Biotechnol. J. 2024, 23, 1129–1143. [Google Scholar] [CrossRef]
  102. He, B.; Xiao, Y.; Liang, H.; Huang, Q.; Du, Y.; Li, Y.; Garmire, D.; Sun, D.; Garmire, L.X. ASGARD Is A Single-Cell Guided Pipeline to Aid Repurposing of Drugs. Nat. Commun. 2023, 14, 993. [Google Scholar] [CrossRef] [PubMed]
  103. Tang, C.; Fu, S.; Jin, X.; Li, W.; Xing, F.; Duan, B.; Cheng, X.; Chen, X.; Wang, S.; Zhu, C.; et al. Personalized Tumor Combination Therapy Optimization Using the Single-Cell Transcriptome. Genome Med. 2023, 15, 105. [Google Scholar] [CrossRef] [PubMed]
  104. Osorio, D.; Shahrouzi, P.; Tekpli, X.; Kristensen, V.N.; Kuijjer, M.L. Drug Combination Prediction for Cancer Treatment Using Disease-Specific Drug Response Profiles and Single-Cell Transcriptional Signatures. eLife 2025, 14, RP102442. [Google Scholar] [CrossRef]
  105. Zhong, J.; Yang, J.; Song, Y.; Zhang, Z.; Wang, C.; Tong, R.; Li, C.; Yu, N.; Zou, L.; Liu, S.; et al. SuperFeat: Quantitative Feature Learning from Single-Cell RNA-Seq Data Facilitates Drug Repurposing. Genom. Proteom. Bioinform. 2024, 22, qzae036. [Google Scholar] [CrossRef]
  106. Sun, Y.-Y.; Hsieh, C.-Y.; Wen, J.-H.; Tseng, T.-Y.; Huang, J.-H.; Oyang, Y.-J.; Huang, H.-C.; Juan, H.-F. ScDrug+: Predicting Drug-Responses Using Single-Cell Transcriptomics and Molecular Structure. Biomed. Pharmacother. 2024, 177, 117070. [Google Scholar] [CrossRef]
  107. Pellecchia, S.; Viscido, G.; Franchini, M.; Gambardella, G. Predicting Drug Response from Single-Cell Expression Profiles of Tumours. BMC Med. 2023, 21, 476. [Google Scholar] [CrossRef]
  108. Lei, W.; Yuan, M.; Long, M.; Zhang, T.; Huang, Y.; Liu, H.; Jiang, W. ScDR: Predicting Drug Response at Single-Cell Resolution. Genes 2023, 14, 268. [Google Scholar] [CrossRef]
  109. Chen, J.; Wang, X.; Ma, A.; Wang, Q.-E.; Liu, B.; Li, L.; Xu, D.; Ma, Q. Deep Transfer Learning of Cancer Drug Responses by Integrating Bulk and Single-Cell RNA-Seq Data. Nat. Commun. 2022, 13, 6494. [Google Scholar] [CrossRef]
  110. Fustero-Torre, C.; Jiménez-Santos, M.J.; García-Martín, S.; Carretero-Puche, C.; García-Jimeno, L.; Ivanchuk, V.; Di Domenico, T.; Gómez-López, G.; Al-Shahrour, F. Beyondcell: Targeting Cancer Therapeutic Heterogeneity in Single-Cell RNA-Seq Data. Genome Med. 2021, 13, 187. [Google Scholar] [CrossRef]
  111. Hirsch, L.; Zitvogel, L.; Eggermont, A.; Marabelle, A. PD-Loma: A Cancer Entity with a Shared Sensitivity to the PD-1/PD-L1 Pathway Blockade. Br. J. Cancer 2019, 120, 3–5. [Google Scholar] [CrossRef] [PubMed]
  112. Ye, Y.; Jing, Y.; Li, L.; Mills, G.B.; Diao, L.; Liu, H.; Han, L. Sex-Associated Molecular Differences for Cancer Immunotherapy. Nat. Commun. 2020, 11, 1779. [Google Scholar] [CrossRef] [PubMed]
  113. Ji, G.; Yang, Q.; Wang, S.; Yan, X.; Ou, Q.; Gong, L.; Zhao, J.; Zhou, Y.; Tian, F.; Lei, J.; et al. Single-Cell Profiling of Response to Neoadjuvant Chemo-Immunotherapy in Surgically Resectable Esophageal Squamous Cell Carcinoma. Genome Med. 2024, 16, 49. [Google Scholar] [CrossRef] [PubMed]
  114. Wolf, F.A.; Angerer, P.; Theis, F.J. SCANPY: Large-Scale Single-Cell Gene Expression Data Analysis. Genome Biol. 2018, 19, 15. [Google Scholar] [CrossRef]
  115. Blondel, V.D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks. J. Stat. Mech. Theory Exp. 2008, 2008, P10008. [Google Scholar] [CrossRef]
  116. Hou, R.; Denisenko, E.; Forrest, A.R.R. ScMatch: A Single-Cell Gene Expression Profile Annotation Tool Using Reference Datasets. Bioinformatics 2019, 35, 4688–4695. [Google Scholar] [CrossRef]
  117. Abugessaisa, I.; Noguchi, S.; Hasegawa, A.; Harshbarger, J.; Kondo, A.; Lizio, M.; Severin, J.; Carninci, P.; Kawaji, H.; Kasukawa, T. FANTOM5 CAGE Profiles of Human and Mouse Reprocessed for GRCh38 and GRCm38 Genome Assemblies. Sci. Data 2017, 4, 170107. [Google Scholar] [CrossRef]
  118. Suphavilai, C.; Bertrand, D.; Nagarajan, N. Predicting Cancer Drug Response Using a Recommender System. Bioinformatics 2018, 34, 3907–3914. [Google Scholar] [CrossRef]
  119. Yang, W.; Soares, J.; Greninger, P.; Edelman, E.J.; Lightfoot, H.; Forbes, S.; Bindal, N.; Beare, D.; Smith, J.A.; Thompson, I.R.; et al. Genomics of Drug Sensitivity in Cancer (GDSC): A Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res. 2012, 41, D955–D961. [Google Scholar] [CrossRef]
  120. Yu, C.; Mannan, A.M.; Yvone, G.M.; Ross, K.N.; Zhang, Y.-L.; Marton, M.A.; Taylor, B.R.; Crenshaw, A.; Gould, J.Z.; Tamayo, P.; et al. High-Throughput Identification of Genotype-Specific Cancer Vulnerabilities in Mixtures of Barcoded Tumor Cell Lines. Nat. Biotechnol. 2016, 34, 419–423. [Google Scholar] [CrossRef]
  121. Hsieh, C.-Y.; Tu, C.-C.; Hung, J.-H. Estimating Intraclonal Heterogeneity and Subpopulation Changes from Bulk Expression Profiles in CMap. Life Sci. Alliance 2022, 5, e202101299. [Google Scholar] [CrossRef]
  122. Chen, J.; Lan, T.; Zhang, W.; Dong, L.; Kang, N.; Fu, M.; Liu, B.; Liu, K.; Zhang, C.; Hou, J.; et al. Dasatinib Enhances Cisplatin Sensitivity in Human Esophageal Squamous Cell Carcinoma (ESCC) Cells via Suppression of PI3K/AKT and Stat3 Pathways. Arch. Biochem. Biophys. 2015, 575, 38–45. [Google Scholar] [CrossRef]
  123. Kheraldine, H.; Gupta, I.; Cyprian, F.S.; Vranic, S.; Al-Farsi, H.F.; Merhi, M.; Dermime, S.; Al Moustafa, A.-E. Targeting HER2-Positive Breast Cancer Cells by a Combination of Dasatinib and BMS-202: Insight into the Molecular Pathways. Cancer Cell Int. 2024, 24, 94. [Google Scholar] [CrossRef]
  124. Kadota, H.; Yuge, R.; Shimizu, D.; Miyamoto, R.; Otani, R.; Hiyama, Y.; Takigawa, H.; Hayashi, R.; Urabe, Y.; Kitadai, Y.; et al. Anti-Programmed Cell Death-1 Antibody and Dasatinib Combination Therapy Exhibits Efficacy in Metastatic Colorectal Cancer Mouse Models. Cancers 2022, 14, 6146. [Google Scholar] [CrossRef] [PubMed]
  125. Koller, P.; Baran, N.; Harutyunyan, K.; Cavazos, A.; Mallampati, S.; Chin, R.L.; Jiang, Z.; Sun, X.; Lee, H.-H.; Hsu, J.L.; et al. PD-1 Blockade in Combination with Dasatinib Potentiates Induction of Anti-Acute Lymphocytic Leukemia Immunity. J. Immunother. Cancer 2023, 11, e006619. [Google Scholar] [CrossRef] [PubMed]
  126. Redin, E.; Garmendia, I.; Lozano, T.; Serrano, D.; Senent, Y.; Redrado, M.; Villalba, M.; De Andrea, C.E.; Exposito, F.; Ajona, D.; et al. SRC Family Kinase (SFK) Inhibitor Dasatinib Improves the Antitumor Activity of Anti-PD-1 in NSCLC Models by Inhibiting Treg Cell Conversion and Proliferation. J. Immunother. Cancer 2021, 9, e001496. [Google Scholar] [CrossRef] [PubMed]
  127. Jia, M.; Yuan, Z.; Yu, H.; Feng, S.; Tan, X.; Long, Z.; Duan, Y.; Zhu, W.; Yan, P. Rapamycin Circumvents Anti PD-1 Therapy Resistance in Colorectal Cancer by Reducing PD-L1 Expression and Optimizing the Tumor Microenvironment. Biomed. Pharmacother. 2024, 176, 116883. [Google Scholar] [CrossRef]
  128. Yang, J.C.-H.; Gadgeel, S.M.; Sequist, L.V.; Wu, C.-L.; Papadimitrakopoulou, V.A.; Su, W.-C.; Fiore, J.; Saraf, S.; Raftopoulos, H.; Patnaik, A. Pembrolizumab in Combination With Erlotinib or Gefitinib as First-Line Therapy for Advanced NSCLC With Sensitizing EGFR Mutation. Journal of Thoracic Oncology 2019, 14, 553–559. [Google Scholar] [CrossRef]
  129. Rudin, C.M.; Cervantes, A.; Dowlati, A.; Besse, B.; Ma, B.; Costa, D.B.; Schmid, P.; Heist, R.; Villaflor, V.M.; Spahn, J.; et al. Safety and Clinical Activity of Atezolizumab plus Erlotinib in Patients with Non-Small-Cell Lung Cancer. ESMO Open 2023, 8, 101160. [Google Scholar] [CrossRef]
  130. Lim, N.; Pavlidis, P. Evaluation of Connectivity Map Shows Limited Reproducibility in Drug Repositioning. Sci. Rep. 2021, 11, 17624. [Google Scholar] [CrossRef]
  131. Hao, Y.; Stuart, T.; Kowalski, M.H.; Choudhary, S.; Hoffman, P.; Hartman, A.; Srivastava, A.; Molla, G.; Madad, S.; Fernandez-Granda, C.; et al. Dictionary Learning for Integrative, Multimodal and Scalable Single-Cell Analysis. Nat. Biotechnol. 2024, 42, 293–304. [Google Scholar] [CrossRef] [PubMed]
  132. Browaeys, R.; Saelens, W.; Saeys, Y. NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes. Nat. Methods 2020, 17, 159–162. [Google Scholar] [CrossRef] [PubMed]
  133. Kuleshov, M.V.; Jones, M.R.; Rouillard, A.D.; Fernandez, N.F.; Duan, Q.; Wang, Z.; Koplev, S.; Jenkins, S.L.; Jagodnik, K.M.; Lachmann, A.; et al. Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update. Nucleic Acids Res. 2016, 44, W90–W97. [Google Scholar] [CrossRef] [PubMed]
  134. Chen, E.Y.; Tan, C.M.; Kou, Y.; Duan, Q.; Wang, Z.; Meirelles, G.V.; Clark, N.R.; Ma’ayan, A. Enrichr: Interactive and Collaborative HTML5 Gene List Enrichment Analysis Tool. BMC Bioinform. 2013, 14, 128. [Google Scholar] [CrossRef]
  135. Kuleshov, M.V.; Diaz, J.E.L.; Flamholz, Z.N.; Keenan, A.B.; Lachmann, A.; Wojciechowicz, M.L.; Cagan, R.L.; Ma’ayan, A. ModEnrichr: A Suite of Gene Set Enrichment Analysis Tools for Model Organisms. Nucleic Acids Res. 2019, 47, W183–W190. [Google Scholar] [CrossRef]
  136. Kanai, O.; Ito, T.; Saito, Z.; Yamamoto, Y.; Fujita, K.; Okamura, M.; Hashimoto, M.; Nakatani, K.; Sawai, S.; Mio, T. Effect of Cyclooxygenase Inhibitor Use on Immunotherapy Efficacy in Non-small Cell Lung Cancer. Thorac. Cancer 2021, 12, 949–957. [Google Scholar] [CrossRef]
  137. Quandt, Z.; Jacob, S.; Fadlullah, M.Z.H.; Wu, C.; Wu, C.; Huppert, L.; Levine, L.S.; Sison, P.; Tsai, K.K.; Chow, M.; et al. Phase II Trial of Pembrolizumab, Ipilimumab, and Aspirin in Melanoma: Clinical Outcomes and Translational Predictors of Response. BJC Rep. 2024, 2, 46. [Google Scholar] [CrossRef]
  138. Wu, Z.; Zhang, Y.; Cheng, Y.; Li, J.; Li, F.; Wang, C.; Shi, L.; Qin, G.; Zhan, W.; Cai, Y.; et al. PD-1 Blockade plus COX Inhibitors in DMMR Metastatic Colorectal Cancer: Clinical, Genomic, and Immunologic Analyses from the PCOX Trial. Med 2024, 5, 998–1015.e6. [Google Scholar] [CrossRef]
  139. Wang, S.-J.; Khullar, K.; Kim, S.; Yegya-Raman, N.; Malhotra, J.; Groisberg, R.; Crayton, S.H.; Silk, A.W.; Nosher, J.L.; Gentile, M.A.; et al. Effect of Cyclo-Oxygenase Inhibitor Use during Checkpoint Blockade Immunotherapy in Patients with Metastatic Melanoma and Non-Small Cell Lung Cancer. J. Immunother. Cancer 2020, 8, e000889. [Google Scholar] [CrossRef]
  140. Zhang, Y.; Kumar, P.; Adashek, J.J.; Skelton, W.P.; Li, J.; Vosoughi, A.; Chahoud, J.; Manley, B.J.; Spiess, P.E. Adding Cyclooxygenase Inhibitors to Immune Checkpoint Inhibitors Did Not Improve Outcomes in Metastatic Renal Cell Carcinoma. Cells 2022, 11, 2505. [Google Scholar] [CrossRef]
  141. Wang, X.; Liu, C.; Wen, H.; Duan, X.; Jiao, Y.; Liu, Y.; Chen, M.; Zhu, K.; Mao, X.; Zhou, Q. Effectiveness of Lenvatinib plus Immune Checkpoint Inhibitors in Primary Advanced Hepatocellular Carcinoma beyond Oligometastasis. Clin. Transl. Med. 2023, 13, e1214. [Google Scholar] [CrossRef]
  142. Yu, G.-T.; Mao, L.; Wu, L.; Deng, W.-W.; Bu, L.-L.; Liu, J.-F.; Chen, L.; Yang, L.-L.; Wu, H.; Zhang, W.-F.; et al. Inhibition of SRC Family Kinases Facilitates Anti-CTLA4 Immunotherapy in Head and Neck Squamous Cell Carcinoma. Cell. Mol. Life Sci. 2018, 75, 4223–4234. [Google Scholar] [CrossRef] [PubMed]
  143. Voutouri, C.; Panagi, M.; Mpekris, F.; Stylianou, A.; Michael, C.; Averkiou, M.A.; Martin, J.D.; Stylianopoulos, T. Endothelin Inhibition Potentiates Cancer Immunotherapy Revealing Mechanical Biomarkers Predictive of Response. Adv. Ther. 2021, 4, 2000289. [Google Scholar] [CrossRef]
  144. Kappes, L.; Amer, R.L.; Sommerlatte, S.; Bashir, G.; Plattfaut, C.; Gieseler, F.; Gemoll, T.; Busch, H.; Altahrawi, A.; Al-Sbiei, A.; et al. Ambrisentan, an Endothelin Receptor Type A-Selective Antagonist, Inhibits Cancer Cell Migration, Invasion, and Metastasis. Sci. Rep. 2020, 10, 15931. [Google Scholar] [CrossRef]
  145. Cho, N.W.; Guldberg, S.M.; Nabet, B.Y.; Yu, J.Z.; Kim, E.J.; Hiam-Galvez, K.J.; Yee, J.L.; DeBarge, R.; Tenvooren, I.; Ashitey, N.A.; et al. T Cells Instruct Immune Checkpoint Inhibitor Therapy Resistance in Tumors Responsive to IL1 and TNFα Inflammation. Cancer Immunol. Res. 2025, 13, 229–244. [Google Scholar] [CrossRef]
  146. Dmitrieva-Posocco, O.; Dzutsev, A.; Posocco, D.F.; Hou, V.; Yuan, W.; Thovarai, V.; Mufazalov, I.A.; Gunzer, M.; Shilovskiy, I.P.; Khaitov, M.R.; et al. Cell-Type-Specific Responses to Interleukin-1 Control Microbial Invasion and Tumor-Elicited Inflammation in Colorectal Cancer. Immunity 2019, 50, 166–180.e7. [Google Scholar] [CrossRef]
  147. Parisi, D.; Adasme, M.F.; Sveshnikova, A.; Bolz, S.N.; Moreau, Y.; Schroeder, M. Drug Repositioning or Target Repositioning: A Structural Perspective of Drug-Target-Indication Relationship for Available Repurposed Drugs. Comput. Struct. Biotechnol. J. 2020, 18, 1043–1055. [Google Scholar] [CrossRef]
Pharmaceuticals 18 01769 g001
Figure 1. Summary workflows of drug repurposing analysis using scRNA-seq data of ESCC samples from treatment responders and non-responders with (A) scDrug and (B) scDrugPrio. CaDRReS-Sc—Cancer Drug Response prediction using a Recommender System-Single cell. GDSC—Genomics of Drug Sensitivity in Cancer. IC50—Half maximal inhibitory concentration. AUC—Area under the curve. TME—Tumor microenvironment. DEG—Differentially expressed genes. R—Responder. NR—Non-responder. PPIN—Protein–protein interaction network.
Figure 1. Summary workflows of drug repurposing analysis using scRNA-seq data of ESCC samples from treatment responders and non-responders with (A) scDrug and (B) scDrugPrio. CaDRReS-Sc—Cancer Drug Response prediction using a Recommender System-Single cell. GDSC—Genomics of Drug Sensitivity in Cancer. IC50—Half maximal inhibitory concentration. AUC—Area under the curve. TME—Tumor microenvironment. DEG—Differentially expressed genes. R—Responder. NR—Non-responder. PPIN—Protein–protein interaction network.
Pharmaceuticals 18 01769 g001
Pharmaceuticals 18 01769 g002
Figure 2. Top drugs from the GDSC database showing more than 60% predicted killing in four or more tumor subclusters, based on scDrug analysis (Paclitaxel and GW843682X (with <20% average killing) are included as comparators).
Figure 2. Top drugs from the GDSC database showing more than 60% predicted killing in four or more tumor subclusters, based on scDrug analysis (Paclitaxel and GW843682X (with <20% average killing) are included as comparators).
Pharmaceuticals 18 01769 g002
Pharmaceuticals 18 01769 g003
Figure 3. Proportion of (A) potential drug effects and (B) targeted cell types among the 622 entries in the unranked scDrugPrio list, comprising 129 unique drug candidates. Each entry represents a drug–cell type pair, where individual drugs may appear multiple times across different targeted cell types.
Figure 3. Proportion of (A) potential drug effects and (B) targeted cell types among the 622 entries in the unranked scDrugPrio list, comprising 129 unique drug candidates. Each entry represents a drug–cell type pair, where individual drugs may appear multiple times across different targeted cell types.
Pharmaceuticals 18 01769 g003
Pharmaceuticals 18 01769 g004
Figure 4. Selected drugs from scDrugPrio results on ESCC dataset and their corresponding pharmacological effects and targeted cell clusters. Colored flows represent each drug’s pathway from effect to gene target to cell cluster.
Figure 4. Selected drugs from scDrugPrio results on ESCC dataset and their corresponding pharmacological effects and targeted cell clusters. Colored flows represent each drug’s pathway from effect to gene target to cell cluster.
Pharmaceuticals 18 01769 g004
Pharmaceuticals 18 01769 g005
Figure 5. Predicted killing percentages across tumor subclusters for overlapped drugs between scDrug (GDSC) and scDrugPrio results.
Figure 5. Predicted killing percentages across tumor subclusters for overlapped drugs between scDrug (GDSC) and scDrugPrio results.
Pharmaceuticals 18 01769 g005
Pharmaceuticals 18 01769 g006
Figure 6. Selected drug candidates from scDrug and scDrugPrio predictions targeting tumor microenvironment components. This schematic illustrates the cellular composition of the tumor microenvironment, including cancer cells, immune cells, and stromal and endothelial cells. Predicted drug candidates identified by two drug repurposing tools—scDrug and scDrugPrio—are mapped to their respective cellular targets. Created in BioRender. CHENG, O. (2025) https://BioRender.com/lcev1pu (accessed on 9 September 2025).
Figure 6. Selected drug candidates from scDrug and scDrugPrio predictions targeting tumor microenvironment components. This schematic illustrates the cellular composition of the tumor microenvironment, including cancer cells, immune cells, and stromal and endothelial cells. Predicted drug candidates identified by two drug repurposing tools—scDrug and scDrugPrio—are mapped to their respective cellular targets. Created in BioRender. CHENG, O. (2025) https://BioRender.com/lcev1pu (accessed on 9 September 2025).
Pharmaceuticals 18 01769 g006
Table 5. Overlapped drugs with scDrug (GDSC) and their corresponding DrugBank information from scDrugPrio analysis of ESCC patient dataset.
Table 5. Overlapped drugs with scDrug (GDSC) and their corresponding DrugBank information from scDrugPrio analysis of ESCC patient dataset.
DrugDrugBank IDPharmacological EffectTargeted Upregulated DEGsCluster
DasatinibDB01254ABL1 (multitarget),
SRC (multitarget),
EPHA2 (antagonist),
LCK (multitarget),
YES1 (inhibitor),
KIT (antagonist),
PDGFRB (antagonist),
STAT5B (inhibitor),
ABL2 (multitarget),
FYN (multitarget)		PDGFRBT cells
Endothelial cells
Epithelial cells
CabozantinibDB08875MET (antagonist),
KDR (antagonist),
RET (antagonist)		KDRMast cells
Endothelial cells
EtoposideDB00773TOP2A (inhibitor),
TOP2B (inhibitor)		TOP2AT cells
Fibroblast
Endothelial cells
Mast cells
MidostaurinDB06595PRKCA (antagonist, inhibitor),
KDR (antagonist, inhibitor),
KIT (antagonist, inhibitor),
PDGFRA (antagonist, inhibitor),
PDGFRB (antagonist, inhibitor),
FLT3 (antagonist, inhibitor)		PDGFRA,
FLT3		Tumor epithelial cells
Endothelial cells
PonatinibDB08901ABL1 (inhibitor),
BCR (inhibitor),
KIT (inhibitor),
RET (inhibitor),
TEK (inhibitor),
FLT3 (inhibitor),
FGFR1 (inhibitor),
FGFR2 (inhibitor),
FGFR3 (inhibitor),
FGFR4 (inhibitor),
LCK (inhibitor),
SRC (inhibitor),
LYN (inhibitor),
KDR (inhibitor),
PDGFRA (inhibitor)		KIT,
PDGFRA		Cycling/proliferating epithelial cells
LYN,
KDR		Cancer-associated fibroblast (CAF)
FGFR3,
KDR,
PDGFRA		Mast cells
FLT3,
KDR,
PDGFRA		Epithelial cells
FLT3,
KDR,
PDGFRA		Endothelial cells
FLT3,
KDR,
PDGFRA		B cells
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Cheng, O.J.; Tran, T.T.T.; Chen, Y.A.; Tan, A.C. Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy. Pharmaceuticals 2025, 18, 1769. https://doi.org/10.3390/ph18111769

AMA Style

Cheng OJ, Tran TTT, Chen YA, Tan AC. Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy. Pharmaceuticals. 2025; 18(11):1769. https://doi.org/10.3390/ph18111769

Chicago/Turabian Style

Cheng, Olivia J., T.T.T. Tran, Y. Ann Chen, and Aik Choon Tan. 2025. "Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy" Pharmaceuticals 18, no. 11: 1769. https://doi.org/10.3390/ph18111769

APA Style

Cheng, O. J., Tran, T. T. T., Chen, Y. A., & Tan, A. C. (2025). Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy. Pharmaceuticals, 18(11), 1769. https://doi.org/10.3390/ph18111769

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop