NF-κB at the Crossroads of Normal Mammary Gland Biology and the Pathogenesis and Prevention of <i>BRCA1</i>-Mutated Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recent studies have shown that progesterone receptor (PR)–expressing cells respond to progesterone in part through the induction of the receptor activator of NF-κB ligand (RANKL), which acts in a paracrine manner to induce expansion of a RANK-expressing luminal progenitor cell population. The RANK+ population in human breast tissue from carriers of BRCA1 mutations (BRCA1mut/+) as well as the luminal progenitor population in Brca1-deficient mouse mammary glands is abnormally amplified. Remarkably, mouse Brca1+/− and human BRCA1mut/+ progenitor cells are able to form colonies in vitro in the absence of progesterone, demonstrating a hormone-independent proliferative capacity. Our research has demonstrated that proliferation in BRCA1-deficient cells results in a DNA damage response (DDR) that activates a persistent NF-κB signal, which supplants progesterone/RANKL signaling for an extended time period. Thus, the transcriptional targets normally activated by RANKL that promote a proliferative response in luminal progenitors can contribute to the susceptibility of mammary epithelial cells to BRCA1-mutated breast cancers as a consequence of DDR-induced NF-κB. Together, these latest findings mark substantial progress in uncovering the mechanisms driving high rates of breast tumorigenesis in BRCA1 mutation carriers and ultimately reveal possibilities for nonsurgical prevention strategies. Cancer Prev Res; 11(2); 69–80. ©2017 AACR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it