DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell
TL;DR
The DRIVE database was constructed as a comprehensive resource detailing drug-induced transcriptomic and splicing responses and identified Osimertinib as a potential immunomodulatory agent associated with transcriptional signatures of an activated tumor microenvironment, while KB-0742 emerged as an unappreciated candidate global splicing modulator.
Abstract
Pharmacotherapy induces complex molecular reprogramming in cancer, driving transcriptome-wide alterations and widespread dysregulation of alternative splicing. Despite these profound changes, there remain limited resources characterizing drug-induced whole-transcriptomic responses in cancer. Furthermore, while aberrant splicing can generate immunogenic neoantigens, existing resources fail to systematically integrate drug perturbations, splicing dynamics, and neoantigen landscapes. To address this gap, the DRIVE database was constructed as a comprehensive resource detailing drug-induced transcriptomic and splicing responses. Utilizing the large language models for rigorous metadata curation and construct the standardized processing pipeline, thousands of publicly available raw transcriptomic datasets from drug-treated and control cancer cell lines were systematically processed. The resulting repository encompasses 3,911 samples, involving 278 drugs and 272 cell lines, enabling the precise quantification of differential gene expression, differential alternative splicing events, and the prediction of splicing-derived human leukocyte antigen-binding peptides. Analysis of the data revealed that drug-induced transcriptomic reprogramming is highly context-dependent and correlated with chemical structural similarity. We identified Osimertinib as a potential immunomodulatory agent associated with transcriptional signatures of an activated tumor microenvironment, while KB-0742 emerged as an unappreciated candidate global splicing modulator. Furthermore, our large-scale prediction of differential splicing-derived neoantigens uncovered several drugs that warrant further investigation as candidates for combination immunotherapy. DRIVE also provides a user-friendly interface to browse datasets, perform drug enrichment and connectivity analysis. (https://componclab.com/DRIVE). This database could improve our understanding of molecular reprogramming under pharmacotherapy, and serve as a valuable platform for deciphering drug mechanisms, promoting virtual cell modeling and discovering novel strategies of drug repurposing.