Data from Anticancer drugs affect the alternative splicing of <i>Bcl-x</i> and other human apoptotic genes
Bibliographic record
Abstract
Abstract Inducing an apoptotic response is the goal of most current chemotherapeutic interventions against cancer. However, little is known about the effect of chemotherapeutic agents on the alternative splicing of apoptotic genes. Here, we have tested 20 of the mainstream anticancer drugs for their ability to influence the production of Bcl-x splice isoforms. We find that many drugs shift splicing toward the proapoptotic Bcl-xS splice variant in 293 cells. The drugs modulate splicing decisions most likely through signaling events because the splicing switch is not compromised by inhibiting de novo protein synthesis or the activity of caspases. Several drugs also shift Bcl-x splicing in cancer cell lines (MCF-7, HeLa, PC-3, PA-1, and SKOV-3), but the set of active drugs varies between cell lines. We also examined the effect of anticancer agents on the alternative splicing of 95 other human apoptotic genes in different cell lines. Almost every drug can alter a subset of alternative splicing events in each cell line. Although drugs of the same class often influence the alternative splicing of the same units in individual cell lines, these units differ considerably between cell lines, indicating cell line–specific differences in the pathways that control splicing. [Mol Cancer Ther 2008;7(6):1398–409]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".