Bivalent and Broad Chromatin Domains Regulate Pro-metastatic Drivers in Melanoma
Bibliographic record
Abstract
ABSTRACT Chromatin deregulation is an emerging hallmark of cancer. However, the extent of epigenetic aberrations during tumorigenesis and their relationship with genetic aberrations are poorly understood. Using ChIP-sequencing for enhancers (H3K27ac and H3K4me1), promoters (H3K4me3), active transcription (H3K79me2) and polycomb (H3K27me3) or heterochromatin (H3K9me3) repression we generated chromatin state profiles in metastatic melanoma using 46 tumor samples and cell lines. We identified a strong association of NRAS, but not BRAF mutations, with bivalent states harboring H3K4me3 and H3K27me3 marks. Importantly, the loss and gain of bivalent states occurred on important pro-metastasis regulators including master transcription factor drivers of mesenchymal phenotype including ZEB1, TWIST1, SNAI1 and CDH1 . Unexpectedly, a subset of these and additional pro-metastatic drivers (e.g. POU3F2, SOX9 and PDGFRA) as well as melanocyte-specific master regulators (e.g. MITF, ZEB2 , and TFAP2A) were regulated by exceptionally wide H3K4me3 domains that can span tens of thousands of kilobases suggesting roles of this new epigenetic element in melanoma metastasis. Overall, we find that BRAF, NRAS and WT melanomas may use bivalent states and broad H3K4me3 domains in a specific manner to regulate pro-metastatic drivers. We propose that specific epigenetic traits – such as bivalent and broad domains – get assimilated in the epigenome of pro-metastatic clones to drive evolution of cancer cells to metastasis.
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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".