Molecular model for neuroendocrine prostate cancer progression
Bibliographic record
Abstract
Prostate cancer (PCa) is the most common form of cancer in men in the developed world and the second leading cause of cancer-related deaths. While advanced PCa is initially controlled with hormonal therapies targeting the androgen receptor (AR) pathway, recurrence occurs because of the emergence of lethal castration-resistant PCa (CRPC). Despite newer AR pathway inhibitors that prolong survival, resistance still emerges, most often with rising PSA levels indicative of AR-driven activity, but increasingly as non-AR-driven cancer. Treatment resistance mechanisms include AR-signalling pathway alterations, AR-signalling bypass mechanisms, and AR-independent clonal evolution. The latter mechanism can lead to the emergence of neuroendocrine prostate cancer (NEPC), an aggressive lethal subtype of PCa. The incidence of treatment-induced NEPC is rising because of the widespread use of more potent AR pathway inhibitors. This comprehensive review of major NEPC drivers and facilitators defines three coordinated processes contributing to NEPC progression. Specifically, castration-resistant adenocarcinoma cells gain lineage plasticity under selective pressures of potent AR suppression to transform into AR-independent tumour cells. In concert, neuroendocrine (NE)-specific transdifferentiation factors induce NE lineage of these PCa cells, which, with support of increased proliferation factors, contributes to clonal expansion and tumour repopulation into NEPC. We examine the roles of each of the major NEPC contributors during the disease progression and identify potential therapeutic opportunities for targeted therapies.
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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) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".