Altering Chemosensitivity by Modulating Translation Elongation
Bibliographic record
Abstract
BACKGROUND: The process of translation occurs at a nexus point downstream of a number of signal pathways and developmental processes. Modeling activation of the PTEN/AKT/mTOR pathway in the Emu-Myc mouse is a valuable tool to study tumor genotype/chemosensitivity relationships in vivo. In this model, blocking translation initiation with silvestrol, an inhibitor of the ribosome recruitment step has been showed to modulate the sensitivity of the tumors to the effect of standard chemotherapy. However, inhibitors of translation elongation have been tested as potential anti-cancer therapeutic agents in vitro, but have not been extensively tested in genetically well-defined mouse tumor models or for potential synergy with standard of care agents. METHODOLOGY/PRINCIPAL FINDINGS: Here, we chose four structurally different chemical inhibitors of translation elongation: homoharringtonine, bruceantin, didemnin B and cycloheximide, and tested their ability to alter the chemoresistance of Emu-myc lymphomas harbouring lesions in Pten, Tsc2, Bcl-2, or eIF4E. We show that in some genetic settings, translation elongation inhibitors are able to synergize with doxorubicin by reinstating an apoptotic program in tumor cells. We attribute this effect to a reduction in levels of pro-oncogenic or pro-survival proteins having short half-lives, like Mcl-1, cyclin D1 or c-Myc. Using lymphomas cells grown ex vivo we reproduced the synergy observed in mice between chemotherapy and elongation inhibition and show that this is reversed by blocking protein degradation with a proteasome inhibitor. CONCLUSION/SIGNIFICANCE: Our results indicate that depleting short-lived pro-survival factors by inhibiting their synthesis could achieve a therapeutic response in tumors harboring PTEN/AKT/mTOR pathway mutations.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".