Loss of periostin/OSF-2 in ErbB2/Neu-driven tumors results in androgen receptor-positive molecular apocrine-like tumors with reduced Notch1 activity
Bibliographic record
Abstract
INTRODUCTION: Periostin (Postn) is a secreted cell adhesion protein that activates signaling pathways to promote cancer cell survival, angiogenesis, invasion, and metastasis. Interestingly, Postn is frequently overexpressed in numerous human cancers, including breast, lung, colon, pancreatic, and ovarian cancer. METHODS: Using transgenic mice expressing the Neu oncogene in the mammary epithelium crossed into Postn-deficient animals, we have assessed the effect of Postn gene deletion on Neu-driven mammary tumorigenesis. RESULTS: Although Postn is exclusively expressed in the stromal fibroblasts of the mammary gland, Postn deletion does not affect mammary gland outgrowth during development or pregnancy. Furthermore, we find that loss of Postn in the mammary epithelium does not alter breast tumor initiation or growth in mouse mammary tumor virus (MMTV)-Neu expressing mice but results in an apocrine-like tumor phenotype. Surprisingly, we find that tumors derived from Postn-null animals express low levels of Notch protein and Hey1 mRNA but increased expression of androgen receptor (AR) and AR target genes. We show that tumor cells derived from wild-type animals do not proliferate when transplanted in a Postn-null environment but that this growth defect is rescued by the overexpression of active Notch or the AR target gene prolactin-induced protein (PIP/GCDFP-15). CONCLUSIONS: Together our data suggest that loss of Postn in an ErbB2/Neu/HER2 overexpression model results in apocrine-like tumors that activate an AR-dependent pathway. This may have important implications for the treatment of breast cancers involving the therapeutic targeting of periostin or Notch signaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".