FRIZZLED7 (FZD7) is a NOTCH3 specific target in human mammary epithelial cells
Bibliographic record
Abstract
The current paradigm of Notch signaling indicates that upon ligand binding each of the four NOTCH receptors (NRs) indiscriminately form complexes with a DNA binding complex and regulate the transcription of target genes. Knockout mouse models of individual Notch receptors have no mammary phenotype, suggesting that in mouse mammary glands Notch receptors likely have redundant biological functions. Recent evidence however, indicates that signaling through NR3 alone is essential for maintaining the luminal cell differentiation potential of the bipotential human mammary epithelial progenitors. This observation suggests that NRs play non-redundant roles in regulating the growth and differentiation of mammary epithelial cells and that they may have unique target genes. The focus of my thesis is to identify specific, non-redundant target(s) of Notch receptor 3, a member of the Notch signaling pathway. This project led to identification of FRIZZED-7 (FZD7), a trans-membrane receptor in the Wnt signaling pathway, as a specific gene target of Notch receptor 3. I further demonstrate that NOTCH3 and FZD7 are highly expressed in the luminal progenitors compared to the bipotent progenitors, suggesting that the cross-talk between the WNT and Notch signaling may be involved in the luminal cell fate determination in the bipotent progenitor cells. Since Notch and the Wnt signaling are potent mammary oncogenes, understanding their precise roles and mechanisms of action in regulating the normal mammary gland development provides an insight into how their altered expressions can lead to breast cancer or contribute to pre-malignancy lesion. Furthermore, since FZD7 blocking antibodies are currently in clinical trials for colorectal and intestinal cancers, there might be potential therapeutic values for such antibodies in treating recurrent luminal-type breast cancers.
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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".