Chromatin Looping Shapes KLF5-Dependent Transcriptional Programs in Human Epithelial Cancers
Bibliographic record
Abstract
Activation of transcription factors is a key driver event in cancer. We and others have recently reported that the Krüppel-like transcription factor KLF5 is activated in multiple epithelial cancer types including squamous cancer and gastrointestinal adenocarcinoma, yet the functional consequences and the underlying mechanisms of this activation remain largely unknown. Here we demonstrate that activation of KLF5 results in strongly selective KLF5 dependency for these cancer types. KLF5 bound lineage-specific regulatory elements and activated gene expression programs essential to cancer cells. HiChIP analysis revealed that multiple distal KLF5 binding events cluster and synergize to activate individual target genes. Immunoprecipitation-mass spectrometry assays showed that KLF5 interacts with other transcription factors such as TP63 and YAP1, as well as the CBP/EP300 acetyltransferase complex. Furthermore, KLF5 guided the CBP/EP300 complex to increase acetylation of H3K27, which in turn enhanced recruitment of the bromodomain protein BRD4 to chromatin. The 3D chromatin architecture aggregated KLF5-dependent BRD4 binding to activate polymerase II elongation at KLF5 target genes, which conferred a transcriptional vulnerability to proteolysis-targeting chimera-induced degradation of BRD4. Our study demonstrates that KLF5 plays an essential role in multiple epithelial cancers by activating cancer-related genes through 3D chromatin loops, providing an evidence-based rationale for targeting the KLF5 pathway. SIGNIFICANCE: An integrative 3D genomics methodology delineates mechanisms underlying the function of KLF5 in multiple epithelial cancers and suggests potential strategies to target cancers with aberrantly activated KLF5.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".