Inhibition of autotaxin delays breast tumor growth and lung metastasis in mice
Bibliographic record
Abstract
ABSTRACT Autotaxin is a secreted enzyme that produces most extracellular lysophosphatidate, which stimulates 6 G‐protein‐coupled receptors. Lysophosphatidate promotes cancer cell survival, growth, migration, invasion, metastasis, and resistance to chemotherapy and radiotherapy. The present work investigated whether inhibiting autotaxin could decrease breast tumor growth and metastasis. We used a new autotaxin inhibitor (ONO‐8430506; IC 90 =100 nM), which decreased plasma autotaxin activity by >60% and concentrations of unsaturated lysophosphatidates by >75% for 24 h compared with vehicle‐treated mice. The effects of ONO‐8430506 on tumor growth were determined in a syngeneic orthotopic mouse model of breast cancer following injection of 20,000 BALB/c mouse 4T1 or 4T1‐12B cancer cells. We show for the first time that inhibiting autotaxin decreases initial tumor growth and subsequent lung metastatic nodules both by 60% compared with vehicle‐treated mice. Significantly, 4T1 cells express negligible autotaxin compared with the mammary fat pad. Autotaxin activity in the fat pad of nontreated mice was increased 2‐fold by tumor growth. Our results emphasize the importance of tumor interaction with its environment and the role of autotaxin in promoting breast cancer growth and metastasis. We also established that autotaxin inhibition could provide a novel therapeutic approach to blocking the adverse effects of lysophosphatidate in cancer.—Benesch, M. G. K., Tang, X., Maeda, T., Ohhata, A., Zhao, Y. Y., Kok, B. P. C., Dewald, J., Hitt, M., Curtis, J. M., McMullen, T. P. W., Brindley, D. N. Inhibition of autotaxin delays breast tumor growth and lung metastasis in mice. FASEB J . 28, 2655–2666 (2014). www.fasebj.org
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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) | 0.000 | 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) | 0.002 | 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".