Super-enhancer impairment is a link between MLL4-inactivated lung tumors and their vulnerability to glycolysis pathway inhibition
Bibliographic record
Abstract
Abstract Epigenetic modifiers often harbor loss-of-function mutations in lung cancer, but their tumor-suppressive roles are poorly characterized. Here we show that lung-specific loss of the gene encoding the histone methyltransferase MLL4 (alias KMT2D; a COMPASS-like enzyme), which is ranked the most highly inactivated epigenetic modifier in lung cancer, strongly promotes lung adenocarcinoma in mice. Mll4 loss upregulated tumor-promoting programs, including glycolysis. The pharmacological inhibition of glycolysis preferentially impeded tumorigenic growth of human lung cancer cells bearing MLL4- inactivating mutations. Mll4 loss widely impaired epigenomic signals for super-enhancers and enhancers, including the super-enhancer for the circadian rhythm repressor gene Per2 , and decreased Per2 expression. Per2 downregulated several glycolytic pathway genes. These findings uncover a distinct tumor-suppressive epigenetic mechanism in which MLL4 enhances Per2-mediated repression of pro-tumorigenic glycolytic genes via super-enhancer activation to suppress lung adenocarcinoma tumorigenesis and also implicate a glycolysis-targeting strategy as a therapeutic intervention for the treatment of MLL4- mutant lung cancer.
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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) | 0.003 | 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".