ArticleNeoplasia (New York, N.Y.)2026
DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell.
Article in Neoplasia (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.