Subtype-specific role for Jagged1 in promoting or inhibiting breast tumor formation
Bibliographic record
Abstract
Notch signaling is altered in breast cancer. Recent studies highlighted both tumor-suppressive and oncogenic roles for Notch in this tissue. The function of Jagged1, the most highly expressed Notch ligand in the mammary gland, is not well defined. Here we report that deletion of Jagged1 in the mammary epithelium of virgin mice led to expansion of the mammary stem cell (MaSC) compartment and defective luminal differentiation associated with decreased expression of the progesterone receptor (PR). In contrast, deletion of Jagged1 in alveolar cells of pregnant mice had no effect on alveolar and lactogenic differentiation or post-lactational involution. Interestingly, deletion of Jagged1 promoted mouse mammary tumor formation from luminal cells but suppressed them from basal cells, associated with downregulation of Notch target genes Hey1 and Hey2, respectively. In agreement with mouse experiments, high expression of JAG1 and HEY1 are associated with better overall survival among patients with luminal tumors, whereas high expression of JAG1 and HEY2 are both associated with worse overall survival in basal subtype of human breast cancer. These results identified Jagged1 as an important regulator of mammary epithelial hierarchy and revealed differential roles of Jagged1-mediated Notch signaling in different subtypes of breast cancer arising from distinct cell types.
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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".