ACS Appl Mater Interfaces. 2025 Dec 1. doi: 10.1021/acsami.5c15987. Online ahead of print.

ABSTRACT

Gene therapy represents a promising strategy for treating lung cancer, with the potential to inhibit the proliferation of cancerous cells and induce apoptosis. However, current gene therapy for lung cancer encounters challenges with delivery, targeting, and safety, such as off-target effects, immune responses, and the necessity for better delivery methods. Here, we introduce gene therapy using the key regulator in lung adenocarcinoma, runt-related transcription factor 3 (RUNX3), within exosomes (Exos), which are known for their biocompatibility and ability to selectively target cancer cells. We packaged the RUNX3 plasmid DNA into human exosomes (hExo-Rs), designed to target and induce apoptosis in cancer cells, resulting in a viability decrease to 43.3%. Normal fibroblasts remained viable at 96.0%, confirming the safety of hExo-Rs for future therapies. We delivered hExo-Rs to cancer spheroids, examined their effects, and found that cytokines from treated cells promote M1 macrophage polarization, emphasizing their potential for immunotherapy. We developed a hydrogel platform for the targeted 14-day release of RUNX3 pDNA by attaching hExo-Rs to gelatin using microbial transglutaminase, which enables the selective decrease in cancer cell viability and confirms apoptosis. Our demonstration of RUNX3 gene therapy with Exos presents selective anticancer effectiveness and the promise of clinical use through localized, sustained release using the hydrogel.

PMID:41325015 | DOI:10.1021/acsami.5c15987

Pharmaceuticals (Basel). 2025 Nov 20;18(11):1769. doi: 10.3390/ph18111769.

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.

PMID:41305010 | PMC:PMC12655618 | DOI:10.3390/ph18111769

Pharmaceuticals (Basel). 2025 Nov 20;18(11):1769. doi: 10.3390/ph18111769.

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.

PMID:41305010 | DOI:10.3390/ph18111769