Multiple pathways counteract cell death induced by RB1 loss: Implications for cancer
Bibliographic record
Abstract
Inactivation of the tumor suppressor RB1 leads to cell proliferation, cell death and abortive differentiation in certain tissues and physiological contexts. Anti-apoptotic signals are thought to be the most important mechanism by which RB1-mutant cells escape cell death. Indeed, in the course of neoplastic transformation RB1 is often inactivated in conjunction with a mutation in the pro-apoptotic tumor suppressor p53. We have previously devised a biological framework to identify factors that maintain survival of differentiating Rb-deficient muscle fibers. We showed that differentiating Rb-deficient myoblasts fuse to form short myotubes that degenerate in a process associated with enhanced autophagy, and that degeneration was rescued by antagonists of apoptosis or autophagy, induction of mitochondrial-biogenesis or hypoxia-induced glycolytic shift, leading to long, twitching myotubes. Here, we also show that lithium slows the collapse of Rb-deficient myotubes and surprisingly, this is independent of autophagy, cyclin D3 and β-catenin. Thus, several distinct processes can suppress cell death induced by RB1 loss. We discuss these pathways and how they may cooperate with RB1 inactivation in the course of cancer initiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".