Notch-Induced Expression of FZD7 Requires Noncanonical NOTCH3 Signaling in Human Breast Epithelial Cells
Bibliographic record
Abstract
The evolutionarily conserved Notch and Wnt signaling pathways have demonstrated roles in normal mammary gland development and in breast carcinogenesis. We previously reported that in human mammary gland, signaling through NOTCH3 alone regulates the commitment of the undifferentiated bipotential progenitors to the luminal cell fate, indicating that NOTCH3 may regulate the expression of unique genes apart from the other Notch receptors. In this study, we used gain of function and loss of function experiments and found that a Wnt signaling receptor, Frizzled7 (FZD7), is a unique and nonredundant target of NOTCH3 in human breast epithelial cells. Interestingly, neither the constitutively active forms of NOTCH1-2, 4 nor loss of expression of these receptors were able to alter expression of FZD7 in human breast epithelial cells. We further show that FZD7-expressing cells are found more frequently in the luminal progenitor-enriched subpopulation of cells obtained from breast reduction samples compared with the undifferentiated bipotent progenitors. Also, we show that NOTCH3-induced expression of FZD7 occurs in the absence of CSL (CBF1-Suppressor of Hairless-Lag-1). Our data suggest that noncanonical Notch signaling through NOTCH3 could modulate Wnt signaling via FZD7 and in this way, might be involved in luminal cell differentiation.
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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 | 0.001 |
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".