Super-enhancer acquisition drives oncogene expression in triple negative breast cancer
Bibliographic record
Abstract
Triple Negative Breast Cancer (TNBC) is a heterogeneous disease lacking known molecular drivers and effective targeted therapies. Cytotoxic chemotherapy remains the mainstay of treatment for TNBCs, which have significantly poorer survival rates compared to other breast cancer subtypes. In addition to changes within the coding genome, aberrant enhancer activity is a well-established contributor to tumorigenesis. Here we use H3K27Ac chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map the active cis-regulatory landscape in TNBC. We identify distinct disease subtypes associated with specific enhancer activity, and over 2,500 unique superenhancers acquired by tumor cells but absent from normal breast tissue. To identify potential actionable disease drivers, we probed the dependency on genes that associate with tumor-specific enhancers by CRISPR screening. In this way we identify a number of tumor-specific dependencies, including a previously uncharacterized dependency on the TGFβ pseudo-receptor BAMBI.
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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) | 0.002 | 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".