Clusterin inhibition using OGX-011 synergistically enhances antitumour activity of sorafenib in a human renal cell carcinoma model
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to investigate whether the therapeutic activity of sorafenib could be enhanced by combining with OGX-011, an antisense oligodeoxynucleotide (ODN) targeting clusterin, in renal cell carcinoma (RCC). METHODS: We investigated the effects of combined treatment with OGX-011 and sorafenib on a human RCC ACHN model both in vitro and in vivo. RESULTS: Although clusterin expression was increased by sorafenib, additional treatment of ACHN with OGX-011 significantly blocked the upregulation of clusterin induced by sorafenib. Despite the lack of a significant effect on the growth of ACHN, OGX-011 synergistically enhanced the sensitivity to sorafenib, reducing the IC(50) by >50%. Apoptotic changes were intensively detected in ACHN after combined treatment with OGX-011 and a sublethal dose of sorafenib, but not either agent alone. Furthermore, this combined treatment resulted in the marked downregulation of phosphorylated Akt and p44/42 mitogen-activated protein kinase in ACHN compared with treatment with either agent alone. In vivo systemic administration of OGX-011 plus sorafenib significantly decreased the ACHN tumour volume compared with control ODN plus sorafenib. CONCLUSION: Combined use with OGX-011 may be useful in enhancing the cytotoxic effect of sorafenib on RCC by inducing apoptosis and inactivating major signal transduction pathways.
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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) | 0.000 | 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) | 0.001 | 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".