Targeting MYC effector functions in pancreatic cancer by inhibiting the ATPase RUVBL1/2
Bibliographic record
Abstract
OBJECTIVE: The hallmark oncogene MYC drives the progression of most tumours, but direct inhibition of MYC by a small-molecule drug has not reached clinical testing. MYC is a transcription factor that depends on several binding partners to function. We therefore explored the possibility of targeting MYC via its interactome in pancreatic ductal adenocarcinoma (PDAC). DESIGN: To identify the most suitable targets among all MYC binding partners, we constructed a targeted shRNA library and performed screens in cultured PDAC cells and tumours in mice. RESULTS: Unexpectedly, many MYC binding partners were found to be important for cultured PDAC cells but dispensable in vivo. However, some were also essential for tumours in their natural environment and, among these, the ATPases RUVBL1 and RUVBL2 ranked first. Degradation of RUVBL1 by the auxin-degron system led to the arrest of cultured PDAC cells but not untransformed cells and to complete tumour regression in mice, which was preceded by immune cell infiltration. Mechanistically, RUVBL1 was required for MYC to establish oncogenic and immunoevasive gene expression identifying the RUVBL1/2 complex as a druggable vulnerability in MYC-driven cancer. CONCLUSION: One implication of our study is that PDAC cell dependencies are strongly influenced by the environment, so genetic screens should be performed in vitro and in vivo. Moreover, the auxin-degron system can be applied in a PDAC model, allowing target validation in living mice. Finally, by revealing the nuclear functions of the RUVBL1/2 complex, our study presents a pharmaceutical strategy to render pancreatic cancers potentially susceptible to immunotherapy.
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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) | 0.001 | 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".