The Prolactin Inducible Protein Modulates Antitumor Immune Responses and Metastasis in a Mouse Model of Triple Negative Breast Cancer
Bibliographic record
Abstract
The prolactin inducible protein (PIP) is expressed to varying degrees in more than 90% of breast cancers (BCs). Although high levels of PIP expression in BC has been shown to correlate with better prognosis and patient response to chemotherapy, some studies suggest that PIP may also play a role in metastasis. Here, we investigated the role of PIP in BC using the well-established 4T1 and E0771 mouse BC cell lines. Stable expression of PIP in both cell lines did not significantly alter their proliferation, migration, and response to anticancer drugs in vitro compared to empty vector control. To assess the effect of PIP expression on breast tumorigenesis in vivo , the 4T1 syngeneic transplantable mouse model was utilized. In immunocompetent syngeneic BALB/c mice, PIP-expressing 4T1 primary tumors displayed delayed tumor onset and reduced tumor growth, and this was associated with higher percentages of natural killer cells and reduced percentages of type 2 T-helper cells in the tumor environment. The delayed tumor onset and growth were abrogated in immunodeficient mice, suggesting that PIP-mediated modulation of primary tumor growth involves an intact immune system. Paradoxically, we also observed that PIP expression was associated with a higher number of 4T1 colonies in the lungs in both the immunocompetent and immunodeficient mice. Gene expression analysis of PIP-expressing 4T1 cells (4T1-PIP) revealed that genes associated with tumor metastasis such as CCL7, MMP3 and MMP13, were significantly upregulated in 4T1-PIP cells when compared to the empty vector control (4T1-EV) cells. Collectively, these studies strongly suggest that PIP may possess a double-edge sword effect in BC, enhancing both antitumor immunity as well as metastasis.
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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 | 0.000 | 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) | 0.000 | 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".