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Updated: Jul 12, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell
Tao Wu1, Hong-Feng Tang2, Wei-Liang Wang3
1Department of Dermatology, Yangjiang People's Hospital affiliated to Guangdong Medical University, Yangjiang, Guangdong, China; The Eighth Affiliated Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China.
The DRIVE database offers a comprehensive resource on drug-induced cancer cell changes, revealing context-dependent responses and identifying potential immunomodulatory drugs and splicing modulators for combination immunotherapy.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Pharmacotherapy causes significant molecular changes in cancer, including altered gene expression and alternative splicing.
- Existing resources lack comprehensive data on drug-induced transcriptomic and splicing alterations, hindering neoantigen landscape analysis.
- Aberrant splicing can create neoantigens, but integrating drug effects with splicing and neoantigen data is limited.
Purpose of the Study:
- To create the DRIVE database, a resource detailing drug-induced transcriptomic and splicing responses in cancer.
- To systematically analyze drug perturbations, splicing dynamics, and neoantigen generation.
- To provide a platform for understanding drug mechanisms and discovering novel therapeutic strategies.
Main Methods:
- Processed thousands of public transcriptomic datasets from drug-treated and control cancer cell lines.
- Utilized large language models for metadata curation and a standardized processing pipeline.
- Quantified differential gene expression, alternative splicing events, and predicted HLA-binding peptides.
Main Results:
- The DRIVE database contains 3,911 samples across 278 drugs and 272 cell lines.
- Drug-induced transcriptomic reprogramming is context-dependent and correlates with chemical structure.
- Identified Osimertinib as a potential immunomodulator and KB-0742 as a splicing modulator.
- Predicted numerous drugs as candidates for combination immunotherapy based on neoantigen generation.
Conclusions:
- DRIVE enhances understanding of pharmacotherapy-induced molecular reprogramming in cancer.
- The database serves as a platform for deciphering drug mechanisms and drug repurposing.
- Identified specific drugs with potential for immunomodulation and combination immunotherapy.
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