Preclinical studies reveal MLN4924 is a promising new retinoblastoma therapy
Bibliographic record
Abstract
Abstract RB1 loss (RB1null) or MYCN amplification (MYCNamp) in fetal human retina causes retinoblastoma. SKP2 loss kills RB1null cells, but small molecule SKP2 inhibitors remain unexplored therapeutically. Whether SKP2 is synthetic lethal in MYCNamp retinoblastoma is unclear. SKP2 is the substrate recognition component of two Cullin-RING Ligase complexes (CRL1SKP2/SCFSKP2, and CRL4SKP2), a family of multiprotein E3 ubiquitin ligases. NEDD8 activating enzyme (NAE) is required for Cullin neddylation and thus CRL activation. Here, we show that the NAE inhibitor, Pevonedistat (MLN4924), potently inhibits RB1null and MYCNamp tumors. Intravitreal MLN4924 suppressed multiple human xenografts with EC80s from 20 ng to 3.5 μg. Maximum tolerated dose (MTD) was 10–30 μg, highlighting a favorable therapeutic window. Inhibition of Cullin neddylation was similar in all cases, but cellular effects ranged from G1 arrest with apoptosis to G2/M arrest with endoreplication. However, even in less sensitive lines (EC50 ≈ 1 μM), prolonged exposure was lethal or induced persistent cytostasis. Mechanistically, depleting any single Cullin did not fully recapitulate drug phenotypes, but sensitivity to SKP2 loss correlated with that of drug. Thus, intravitreal MLN4924 is a promising new retinoblastoma therapy, mimicking the cancer-specific lethality of eliminating SKP2 complexes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".