Targeting the Cytoprotective Chaperone, Clusterin, for Treatment of Advanced Cancer
Bibliographic record
Abstract
Many strategies used to kill cancer cells induce stress-responses that activate survival pathways to promote emergence of a treatment resistant phenotype. Secretory clusterin (sCLU) is a stress-activated cytoprotective chaperone up-regulated by many varied anticancer therapies to confer treatment resistance when overexpressed. sCLU levels are increased in several treatment recurrent cancers including castrate resistant prostate cancer, and therefore sCLU has become an attractive target in cancer therapy. sCLU is not druggable with small molecule inhibitors, therefore nucleotide-based strategies to inhibit sCLU at the RNA level are appealing. Preclinical studies have shown that antisense oligonucleotide (ASO) or siRNA knockdown of sCLU have preclinical activity in combination with hormone- and chemotherapy. Phase I and II clinical trial data indicate that the second generation ASO, custirsen (OGX-011), has biologic and clinical activity, suppressing sCLU expression in prostate cancer tissues by more than 90%. A randomized study comparing docetaxel-custirsen to docetaxel alone in men with castrate resistant prostate cancer reported improved survival by 7 months from 16.9 to 23.8 months. Strong preclinical and clinical proof-of-principle data provide rationale for further study of sCLU inhibitors in randomized phase III trials, which are planned to begin in 2010.
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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) | 0.002 | 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".