An Anti-Urokinase Plasminogen Activator Receptor Antibody (ATN-658) Blocks Prostate Cancer Invasion, Migration, Growth, and Experimental Skeletal Metastasis In Vitro and In Vivo
Bibliographic record
Abstract
Urokinase plasminogen activator receptor (uPAR) is a multidomain protein that plays important roles in the growth, invasion, and metastasis of a number of cancers. In the present study, we examined the effects of administration of a monoclonal anti-uPAR antibody (ATN-658) on prostate cancer progression in vitro and in vivo. We examined the effect of treatment of ATN-658 on human prostate cancer cell invasion, migration, proliferation, and regulation of intracellular signaling pathways. For in vivo studies, PC-3 cells (1 x 10(6)) were inoculated into the right flank of male Balb C nu/nu mice through subcutaneous or through intratibial route (2 x 10(5)) of male Fox Chase severe combined immunodeficient mice to monitor the effect on tumor growth and skeletal metastasis. Treatment with ATN-658 resulted in a significant dose-dependent decrease in PC-3 cell invasion and migration without affecting cell doubling time. Western blot analysis showed that ATN-658 treatment decreased the phosphorylation of serine/threonine protein kinase B (AKT), mitogen-activated protein kinase (MAPK), and focal adhesion kinase (FAK) without affecting AKT, MAPK, and FAK total protein expression. In in vivo studies, ATN-658 caused a significant decrease in tumor volume and a marked reduction in skeletal lesions as determined by Faxitron x-ray and micro-computed tomography. Immunohistochemical analysis of subcutaneous and tibial tumors showed a marked decrease in the levels of expression of pAKT, pMAPK, and pFAK, consistent with the in vitro observations. Results from these studies provide compelling evidence for the continued development of ATN-658 as a potential therapeutic agent for the treatment of prostate and other cancers expressing uPAR.
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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) | 0.001 | 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".