Targeting the Apoptosis Pathway in Prostate Cancer
Bibliographic record
Abstract
Important inroads have been made in the understanding and treatment of metastatic prostate cancer in recent years. However, the need for agents targeting novel pathways remains ever present. One such area with promise is through apoptosis or programmed cell death. Many perturbations within the apoptotic process have been associated with treatment resistance and progression in castration-resistant prostate cancer; thus, therapeutic potential exists with agents that can restore an effective apoptotic response to cellular stressors. This article focuses on agents in clinical development targeting apoptosis through the intrinsic and extrinsic pathways. We review the current status of agents that intervene at the Bcl2 checkpoints, humanized antibodies to death receptors, agents that target the inhibitors of apoptosis proteins, mimetics of small mitochondria-derived activator of caspases, and antisense therapies targeting cytoprotective chaperones. Although single-agent activity has been demonstrated with some of these agents, the clinical development path forward will see them coupled with standard hormonal therapy and chemotherapy. OGX-011 (custirsen), which inhibits expression of the cytoprotective chaperone protein clusterin, is the most mature of these agents and is being tested in combination with chemotherapy in phase III clinical trials for castration-resistant prostate cancer, and results are eagerly awaited.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".