Small modifier, big decision: switching to <scp>SUMO</scp> mode adds weight to cancer stemness in mammary tumors
Bibliographic record
Abstract
Protein SUMOylation is crucial for maintaining the hallmarks of cancer stem cells, including self-renewal and active pluripotency gene networks. While inhibiting key steps of the SUMOylation cascade has been shown to suppress tumorigenesis, the specific mechanisms of SUMO dependency in cancer have not been comprehensively characterized. Li et al. applied genetically engineered models of mammary gland tumorigenesis to demonstrate that SUMOylation of the transcription factor Etv1 is essential for maintaining cancer stem cell functions. Moreover, SUMO conjugation of Etv1 acts as a switch between stem and nonstem cancer cell states. Here, we discuss the implications of these findings regarding the role of SUMOylation-dependent mechanisms in the hierarchical organization of malignant cells and intratumor heterogeneity and highlight potential therapeutic approaches harnessing the SUMOylation cascade.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".