Targeting super-enhancer activity for colorectal cancer therapy
Bibliographic record
Abstract
In addition to genetic variants and copy number alterations, epigenetic deregulation of oncogenes and tumor suppressors is a major contributor in cancer development and propagation. Regulatory elements for gene transcription regulation can be found in promoters which are located in the vicinity of transcription start sites but also at a distance, in enhancer sites, brought to interact with proximal sites when occupied by enhancer protein complexes. These sites provide most of the specific regulatory sequences recognized by transcription factors. A sub-set of enhancers characterized by a longer structure and stronger activity, called super-enhancers, are critical for the expression of specific genes, usually associated with individual cell type identity and function. Super-enhancers show deregulation in cancer, which may have profound repercussions for cancer cell survival and response to therapy. Dysfunction of super-enhancers may result from multiple mechanisms that include changes in their sequence, alterations in the topological neighborhoods where they belong, and alterations in the proteins that mediate their function, such as transcription factors and epigenetic modifiers. These can become potential targets for therapeutic interventions. Genes that are targets of super-enhancers are cell and cancer type specific and could also be of interest for therapeutic targeting. In colorectal cancer, a super-enhancer regulated and over-expressed oncogene is MYC, under the influence of the WNT/β-catenin pathway. Identification and targeting of additional oncogenes regulated by super-enhancers in colorectal cancer may pave the way for combination therapies targeting the super-enhancer machinery and signal transduction pathways that regulate the specific transcription factors operative on them.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".