The RB Protein Family in Retinal Development and Retinoblastoma: New Insights from New Mouse Models
Bibliographic record
Abstract
The Rb gene was isolated almost 20 years ago, but fundamental questions regarding its role in retinal development and retinoblastoma remain. What is the normal function of RB protein in retinogenesis? What is the cell-of-origin of retinoblastoma? Why do retinoblastoma tumors have recurrent genetic lesions other than Rb inactivation? Why is retinoblastoma not induced by defects in cell cycle regulators other than Rb? Why is the retina so sensitive to Rb loss? Recently developed conditional Rb knockout models provide new insight into some of these issues. The data suggest that RB protein may not control the rate of progenitor division, but is critical for cell cycle exit when dividing retinal progenitors differentiate into postmitotic transition cells. This finding focuses attention on the ectopically dividing transition cell, rather than the progenitor, as the cell-of-origin. Cell-specific analyses in the RB-deficient retina reveal that ectopically dividing photoreceptors, bipolar and ganglion cells die, but amacrine, horizontal and Muller cells survive and stop dividing when they terminally differentiate. Rare amacrine transition cells escape cell cycle exit and generate tumors. These data suggest that post-Rb mutations are required to overcome growth arrest associated with terminal differentiation, rather than apoptosis as previously suggested. To explain why perturbing cell cycle regulators other than RB does not initiate retinoblastoma, we speculate that mutations in other components of the RB pathway perturb cell cycle arrest, but only RB loss triggers genome instability in retinal transition cells, which may be critical to facilitate post-Rb mutations necessary for transformation. Cell-specific differences in the effect of Rb loss on genome stability may contribute to the tremendous sensitivity of retinal transition cells to tumorigenesis. The new mouse models of retinoblastoma will be invaluable for testing these possibilities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".