Abstract IA20: Chromatin looping factors and breast cancer
Bibliographic record
Abstract
Abstract Estrogen signaling in breast cancer cells relies on chromatin interactions connecting distal regulatory elements bound by the estrogen receptor alpha (ER) to target gene promoters. This ensures stimulus and subtype-specific transcriptional responses. Chromatin looping factors, including CTCF, ZNF143 and RAD21, are genetically altered in breast cancer. However, the impact of these alterations on breast cancer development is ill defined. Here we demonstrate that ZNF143 directly regulates the formation of chromatin interactions by marking promoters connecting with distal regulatory elements. ZNF143 occupies the promoter of most early-response estrogen target genes in ER-positive breast cancer cells. Its chromatin occupancy is unaffected by estrogen stimulation suggesting that chromatin interactions are stable as opposed to modulated by stimulus. ZNF143 overexpression within ER-positive breast cancer patients associates with a worse outcome. Its loss abrogates the estrogen response in breast cancer cells. Overall, these results suggest that ZNF143 is a critical effector of the estrogen response and highlights the contribution of the chromatin looping machinery to ER-positive breast cancer development. Citation Format: Aislinn Treloar, Xue Wu, Nadia Penrod, Swneke D. Bailey, Xiaoyang Zhang, Kinjal Desai, Balazs Gyorffy, Mathieu Lupien. Chromatin looping factors and breast cancer. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr IA20.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".