Novel therapeutic strategy for advanced prostate cancer using antisense oligodeoxynucleotides targeting anti‐apoptotic genes upregulated after androgen withdrawal to delay androgen‐independent progression and enhance chemosensitivity
Bibliographic record
Abstract
Progression to androgen-independence remains the main obstacle to improving survival for patients with advanced prostate cancer. In this review, findings are summarized that have recently been demonstrated to establish novel therapeutic strategy targeting several genes playing functionally important roles after androgen withdrawal and during androgen-independent progression. The authors initially characterized changes in gene expression after androgen withdrawal in the androgen-dependent Shionogi and LNCaP tumor models using cDNA arrays. Based on these results, they focused on genes highly upregulated after androgen ablation (i.e. bcl-2, bcl-xL, TR.PM-2, IGFBP-5), which have anti-apoptotic or mitogenic activities, and thereby confer a resistance to androgen withdrawal as well as cytotoxic chemotherapy. The authors further demonstrated the efficacy of an antisense oligodeoxynucleotide (ODN) strategy for patients with advanced prostate cancer through the inhibition of target gene expression, resulting in a delay in the progression to androgen-independence by enhancing apoptotic cell death induced by androgen ablation and chemotherapy. The authors also showed the effectiveness of combined antisense ODN therapy and cytotoxic chemotherapy by achieving additive or synergistic effects. These findings provide a basic significance for the design of clinical studies using antisense ODN either alone or in combination with chemotherapeutic agents in patients with advanced prostate cancer.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".