Lipid phosphate phosphatase-1 expression in cancer cells attenuates tumor growth and metastasis in mice
Bibliographic record
Abstract
Lipid phosphate phosphatase-1 (LPP1) degrades lysophosphatidate (LPA) and attenuates receptor-mediated signaling. LPP1 expression is low in many cancer cells and tumors compared with normal tissues. It was hypothesized from studies with cultured cells that increasing LPP1 activity would decrease tumor growth and metastasis. This hypothesis has never been tested in vivo. To do this, we inducibly expressed LPP1 or a catalytically inactive mutant in cancer cells. Expressing active LPP1 increased extracellular LPA degradation by 5-fold. It also decreased the stimulation of Ca(2+) transients by LPA, a nondephosphorylatable LPA1/2 receptor agonist and a protease-activated receptor-1 peptide. The latter results demonstrate that LPP1 has effects downstream of receptor activation. Decreased Ca(2+) mobilization and Rho activation contributed to the effects of LPP1 in attenuating the LPA-induced migration of MDA-MB-231 breast cancer cells and their growth in 3D culture. Increasing LPP1 expression in breast and thyroid cancer cells decreased tumor growth and the metastasis by up to 80% compared with expression of inactive LPP1 or green fluorescent protein in syngeneic and xenograft mouse models. The present work demonstrates for the first time that increasing the LPP1 activity in three lines of aggressive cancer cells decreases their abilities to produce tumors and metastases in mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".