Treatment with docetaxel in combination with Aneustat leads to potent inhibition of metastasis in a patient-derived xenograft model of advanced prostate cancer
Bibliographic record
Abstract
BACKGROUND: Docetaxel used for first-line treatment of advanced prostate cancer (PCa) is only marginally effective. We previously showed, using the LTL-313H subrenal capsule patient-derived metastatic PCa xenograft model, that docetaxel combined with Aneustat (OMN54), a multivalent plant-derived therapeutic, led to marked synergistic tumour growth inhibition. Here, we investigated the effect of docetaxel+Aneustat on metastasis. METHODS: C4-2 cells were incubated with docetaxel, Aneustat and docetaxel+Aneustat to assess effects on cell migration. The LTL-313H model, similarly treated, was analysed for effects on lung micro-metastasis and kidney invasion. The LTL-313H gene expression profile was compared with profiles of PCa patients (obtained from Oncomine) and subjected to IPA to determine involvement of cancer driver genes. RESULTS: Docetaxel+Aneustat markedly inhibited C4-2 cell migration and LTL-313H lung micro-metastasis/kidney invasion. Oncomine analysis indicated that treatment with docetaxel+Aneustat was associated with improved patient outcome. The drug combination markedly downregulated expression of cancer driver genes such as FOXM1 (and FOXM1-target genes). FOXM1 overexpression reduced the anti-metastatic activity of docetaxel+Aneustat. CONCLUSIONS: Docetaxel+Aneustat can inhibit PCa tissue invasion and metastasis. This activity appears to be based on reduced expression of cancer driver genes such as FOXM1. Use of docetaxel+Aneustat may provide a new, more effective regimen for therapy of metastatic PCa.
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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".