Enhancer alterations in cancer: a source for a cell identity crisis
Bibliographic record
Abstract
Enhancers are selectively utilized to orchestrate gene expression programs that first govern pluripotency and then proceed to highly specialized programs required for the process of cellular differentiation. Whereas gene-proximal promoters are typically active across numerous cell types, distal enhancer activation is cell-type-specific and central to cell fate determination, thereby accounting for cell identity. Recent studies have highlighted the diversity of enhancer usage, cataloguing millions of such elements in the human genome. The disruption of enhancer activity, through genetic or epigenetic alterations, can impact cell-type-specific functions, resulting in a wide range of pathologies. In cancer, these alterations can promote a 'cell identity crisis', in which enhancers associated with oncogenes and multipotentiality are activated, while those promoting cell fate commitment are inactivated. Overall, these alterations favor an undifferentiated cellular phenotype. Here, we review the current knowledge regarding the role of enhancers in normal cell function, and discuss how genetic and epigenetic changes in enhancer elements potentiate oncogenesis. In addition, we discuss how understanding the mechanisms regulating enhancer activity can inform therapeutic opportunities in cancer cells and highlight key challenges that remain in understanding enhancer biology as it relates to oncology.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".