Updates on therapeutic targets and agents in castration-resistant prostate cancer.
Bibliographic record
Abstract
Prostate cancer (PCa) is the most commonly diagnosed noncutaneous cancer in men accounting for 28% of all newly diagnosed cancer cases and it is the second to third most common cause of cancer death in the Western world. Nearly all patients with metastatic disease will eventually experience disease progression despite castration as the median duration of response is between 18-24 months. Hence, development of castration-resistant prostate cancer (CRPC) is only a matter of time in these patients. CRPC is defined by disease progression despite androgen-deprivation therapy. CRPC presents a spectrum of disease ranging from rising PSA levels to metastases and significant debilitation from cancer symptoms. Prognosis is associated with several factors, including performance status, presence of bone pain, extent of disease on bone scan, and serum levels of alkaline phosphatase. Based on our enhanced understanding of tumor biology, including the role of tumor, host, and hormonal signaling, there has been rational development of new therapies for CRPC. Over the last decade, several clinical trials have been launched to study novel agents targeting different mechanisms of PCa progression, and have culminated success of new agents for CRPC (docetaxel, cabazitaxel, sipuleucel-T, denosumab, and abiraterone acetate) and several more molecules are on the horizon. The purpose of this review is to discuss the new therapeutic targets in CRPC focusing on new promising agents.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".