Mislocalization of the Cell Polarity Protein Scribble Promotes Mammary Tumorigenesis and Is Associated with Basal Breast Cancer
Bibliographic record
Abstract
Scribble (SCRIB) localizes to cell-cell junctions and regulates establishment of epithelial cell polarity. Loss of expression of SCRIB functions as a tumor suppressor in Drosophila and mammals; conversely, overexpression of SCRIB promotes epithelial differentiation in mammals. Here, we report that SCRIB is frequently amplified, mRNA overexpressed, and protein is mislocalized from cell-cell junctions in human breast cancers. High levels of SCRIB mRNA are associated with poor clinical prognosis, identifying an unexpected role for SCRIB in breast cancer. We find that transgenic mice expressing a SCRIB mutant [Pro 305 to Leu (P305L)] that fails to localize to cell-cell junctions, under the control of the mouse mammary tumor virus long terminal repeat promoter, develop multifocal hyperplasia that progresses to highly pleomorphic and poorly differentiated tumors with basal characteristics. SCRIB interacts with phosphatase and tensin homolog (PTEN) and the expression of P305L, but not wild-type SCRIB, promotes an increase in PTEN levels in the cytosol. Overexpression of P305L, but not wild-type SCRIB, activates the Akt/mTOR/S6K signaling pathway. Human breast tumors overexpressing SCRIB have high levels of S6K but do not harbor mutations in PTEN or PIK3CA, identifying SCRIB amplification as a mechanism of activating PI3K signaling in tumors without mutations in PIK3CA or PTEN. Thus, we demonstrate that high levels of mislocalized SCRIB functions as a neomorph to promote mammary tumorigenesis by affecting subcellular localization of PTEN and activating an Akt/mTOR/S6kinase signaling pathway.
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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 | 0.001 |
| 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".