Lessons from in-vivo models of castration-resistant prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although the treatment of castration-resistant prostate cancer (CRPC) has benefited from the use of increasingly potent androgen synthesis inhibitors and androgen receptor (AR) antagonists, it is only marginally effective. There is therefore a critical need for a better understanding of the mechanisms underlying the CRPC development and more effective therapeutic approaches. Here, we focus on the advancements reported in the last 18 months, particularly with regard to the mechanisms of castration resistance and potential therapeutic targets emerging from the studies with in-vivo models. RECENT FINDINGS: Recent findings indicate that AR-dependent mechanisms, for example, increased expression of CYP17A1 and AR splice variants, play important roles in in-vivo castration resistance to new antiandrogens and androgen synthesis inhibitors. Whereas current therapeutic approaches focus on AR-dependent CRPC, studies based on genetically engineered mouse models indicate that castration resistance can develop in the absence of robust AR signaling. Furthermore, increasing evidence suggests that cellular plasticity of prostate adenocarcinoma allows AR-independent CRPC development via various adaptive mechanisms. SUMMARY: Significant progress has been made in the understanding of AR-dependent and AR-independent mechanisms involved in the development of CRPC. This may lead to identification of new therapeutic targets and improved therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".