The epigenetic and transcriptional landscape of neuroendocrine prostate cancer
Bibliographic record
Abstract
Tumours adapt to increasingly potent targeted therapies by transitioning to alternative lineage states. In prostate cancer, the widespread clinical application of androgen receptor (AR) pathway inhibitors has led to the insurgence of tumours relapsing with a neuroendocrine phenotype, termed neuroendocrine prostate cancer (NEPC). Recent evidence suggests that this lineage reprogramming is driven largely by dysregulation of the epigenome and transcriptional networks. Indeed, aberrant DNA methylation patterning and altered expression of epigenetic modifiers, such as EZH2, transcription factors, and RNA-modifying factors, are hallmarks of NEPC tumours. In this review, we explore the nature of the epigenetic and transcriptional landscape as prostate cancer cells lose their AR-imposed identity and transition to the neuroendocrine lineage. Beyond addressing the mechanisms underlying epithelial-to-neuroendocrine lineage reprogramming, we discuss how oncogenic signaling and metabolic shifts fuel epigenetic/transcriptional changes as well as the current state of epigenetic therapies for NEPC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".