RITA requires eIF2α-dependent modulation of mRNA translation for its anti-cancer activity
Bibliographic record
Abstract
Tumor protein 53 (p53, encoded by the TP53 gene) is a key tumor suppressor regulating cell fates in response to internal and external stresses. As TP53 is mutated or silenced in a majority of tumors, reactivation of p53 by small molecules represents a promising strategy in cancer therapeutics. One such agent is RITA (reactivation of p53 and induction of tumor cell apoptosis), which restores p53 expression in cells with hyperactive HDM2 and induces apoptosis. Yet, mechanisms underlying the anticancer activity of RITA are incompletely understood. Here we show that RITA suppresses mRNA translation independently of p53 by inducing eIF2α phosphorylation. Surprisingly, reactivation of p53 following RITA treatment is critically dependent on eIF2α phosphorylation. Moreover, inhibition of eIF2α phosphorylation attenuates pro-apoptotic and anti-neoplastic effects of RITA, while inducing phosphorylation of eIF2α enhances the anticancer activity of RITA. Collectively, these findings demonstrate that the translational machinery plays a major role in determining the antineoplastic activity of RITA, and suggest that combining p53 activators and translation modulators may be beneficial.
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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) | 0.001 | 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".