The retinoblastoma tumor suppressor limits ribosomal readthrough during oncogene induced senescence
Bibliographic record
Abstract
Abstract The origin and evolution of cancer cells is considered to be mainly fueled by mutations affecting the DNA sequence. Although errors in translation could also expand the cellular proteome, their role in cancer biology remains poorly understood. Tumor suppressors called “caretakers” block cancer initiation and progression by preventing DNA mutations and/or stimulating DNA repair. If translational errors contribute to tumorigenesis, then caretakers genes will prevent such errors in normal cells in response to oncogenic stimuli. Here, we show that the retinoblastoma protein (RB) acts as caretaker tumor suppressor by preventing the readthrough of termination codons, a process that allows proteins to be synthetized with additional domains. In particular, we show that expression of oncogenic ras in normal human cells triggers a cellular senescence response characterized by a significant reduction of basal ribosomal readthrough. However, inactivation of the RB tumor suppressor pathway in these cells, using the viral oncoprotein E7 or the oncogenic kinase CDK4 increased readthrough. Conversely, activation of the RB pathway by the tumor suppressor PML, the ribosomal proteins RPS14/uS11 and RPL22/eL22 or the CDK4/6 inhibitor palbociclib reduced readthrough. We thus reveal a novel function for the RB pathway as a caretaker of translational errors with implications for tumor suppression and cancer treatment.
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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) | 0.002 | 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".