Stage of Breast Cancer Progression Influences Cellular Response to Activation of the WNT/Planar Cell Polarity Pathway
Bibliographic record
Abstract
Planar cell polarity (PCP) signaling has been shown in different studies to either promote or inhibit the malignancy of breast cancer. Using the 21T cell lines, which were derived from an individual patient and represent distinct stages of progression, we show that the prototypical PCP ligand, WNT5A, is expressed highest in 21MT-1 cells (invasive mammary carcinoma) and lowest in 21PT (atypical ductal hyperplasia) and 21NT (ductal carcinoma in situ) cells. Overexpression of WNT5A decreased spherical colony formation and increased invasion and in vivo extravasation only in 21NT cells; whereas overexpression increased migration of both 21PT and 21NT cells. WNT5A overexpression also increased RHOA expression of both cell lines and subsequent RHOA knockdown blocked WNT5A-induced migration, but only partially blocked WNT5A-induced invasion of 21NT cells. PCP can signal through VANGL1 to modulate AP-1 target genes (e.g. MMP3) and induce invasion. VANGL1 knockdown inhibited WNT5A-induced invasion of 21NT cells, but had no effect on WNT5A-induced migration of either 21PT or 21NT cells. WNT5A-induced MMP3 expression was seen only in 21NT cells, an effect that was VANGL1 dependent, but independent of AP-1. We thus provide evidence that PCP signaling can act in a context dependent manner to promote breast cancer progression.
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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) | 0.002 | 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".