Deneddylation of ribosomal proteins promotes synergy between MLN4924 and chemotherapy to elicit complete therapeutic responses
Bibliographic record
Abstract
The neddylation inhibitor MLN4924/Pevonedistat is in clinical trials for multiple cancers. Efficacy is generally attributed to cullin RING ligase (CRL) inhibition, but the contribution of non-CRL targets is unknown. Here, CRISPR screens map MLN4924-monotherapy sensitivity in retinoblastoma to a classic DNA damage-induced p53/E2F3/BAX-dependent death effector network, which synergizes with Nutlin3a or Navitoclax. In monotherapy-resistant cells, MLN4924 plus standard-of-care topotecan overcomes resistance, but reduces DNA damage, instead harnessing ribosomal protein nucleolar-expulsion to engage an RPL11/p21/MYCN/E2F3/p53/BAX synergy network that exhibits extensive cross-regulation. Strikingly, unneddylatable RPL11 substitutes for MLN4924 to perturb nucleolar function and enhance topotecan efficacy. Orthotopic tumors exhibit complete responses while preserving visual function. Moreover, MLN4924 plus melphalan deploy this DNA damage-independent strategy to synergistically kill multiple myeloma cells. Thus, MLN4924 synergizes with standard-of-care drugs to unlock a nucleolar death effector network across cancer types implying broad therapeutic relevance.
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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 | 0.000 |
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".