NF-κB at the Crossroads of Normal Mammary Gland Biology and the Pathogenesis and Prevention of <i>BRCA1</i>-Mutated Breast Cancer
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".