Abstract 881: Dissecting chromatin dynamics in malignant progression
Bibliographic record
Abstract
Abstract Tumor-initiating cells within a cancer exhibit distinct patterns of transcription factors and gene expression compared to stem cells within their healthy tissue. Little is known about the key transcription factors that dictate chromatin remodeling and the accompanying transcriptional changes that ultimately hardwire the malignant behavior of tumor-initiating stem cells. Here, by in vivo chromatin and transcription profiling, we show that dramatic shifts in large open-chromatin (super-enhancer) landscapes underlie these differences. Focusing on one of the most common and life-threatening cancers world-wide, squamous cell carcinoma (SCC), we show that super-enhancers of SCC-stem cells contain the binding sites for a distinct set of putative master transcription factors. Many of their genes, including Ets2 and Elk3, are themselves regulated by SCC super-enhancers, suggesting a cooperative feed-forward loop. Malignant progression requires these genes, whose knockdown severely impairs tumor growth and prohibits progression from benign papillomas to SCCs. Interestingly, ETS2 is known to be phosphorylated and activated by RAS/MAPK signaling, and knockdown of ETS2 results in loss of expression of SCC super-enhancer-associated genes. Conversely, in vivo forced expression of an active ETS2 version harboring a phosphomimetic substitution at the MAPK-activation residue is sufficient to trigger cellular transformation without oncogenic RAS. Moreover, ETS2-overactivation in epidermal progenitors rewires the super-enhancer landscape and induces SCC super-enhancer associated genes including Fos, Junb, Klf5 and Elk3. Finally, we identify dramatic remodeling of the Cxcl1/2 locus from an H3K27me3-repressed to an ETS-regulated, super-enhancer-activated state, and provide evidence for their autocrine oncogenic role in SCCs through a cognate receptor Cxcr2. Together, our findings unearth an essential regulatory network required for the SCC chromatin landscape and unveil its importance in malignant progression. This work was funded by grants from the National Institutes of Health (R01-AR31737) and NYSTEM #CO29559. Citation Format: Hanseul Yang, Daniel Schramek, Rene Adam, John Levorse, Brice Keyes, Ping Wang, Deyou Zheng, Elaine Fuchs. Dissecting chromatin dynamics in malignant progression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 881.
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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) | 0.003 | 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".