uPAR antibody (huATN-658) and Zometa reduce breast cancer growth and skeletal lesions
Bibliographic record
Abstract
Urokinase plasminogen activator receptor (uPAR) is implicated in tumor growth and metastasis due to its ability to activate latent growth factors, proteases, and different oncogenic signaling pathways upon binding to different ligands. Elevated uPAR expression is correlated with the increased aggressiveness of cancer cells, which led to its credentialing as an attractive diagnostic and therapeutic target in advanced solid cancer. Here, we examine the antitumor effects of a humanized anti-uPAR antibody (huATN-658) alone and in combination with the approved bisphosphonate Zometa (Zoledronic acid) on skeletal lesion through a series of studies in vitro and in vivo. Treatment with huATN-658 or Zometa alone significantly decreased human MDA-MB-231 cell proliferation and invasion in vitro, effects which were more pronounced when huATN-658 was combined with Zometa. In vivo studies demonstrated that huATN-658 treatment significantly reduced MDA-MB-231 primary tumor growth compared with controls. In a model of breast tumor-induced bone disease, huATN-658 and Zometa were equally effective in reducing skeletal lesions. The skeletal lesions were significantly reduced in animals receiving the combination of huATN-658 + Zometa compared with monotherapy treatment. These effects were due to a significant decrease in osteoclastic activity and tumor cell proliferation in the combination treatment group. Transcriptome analysis revealed that combination treatment significantly changes the expression of genes from signaling pathways implicated in tumor progression and bone remodeling. Results from these studies provide a rationale for the continued development of huATN-658 as a monotherapy and in combination with currently approved agents such as Zometa in patients with metastatic breast cancer.
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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".