Therapeutic Exploitation of Neuroendocrine Transdifferentiation Drivers in Prostate Cancer
Bibliographic record
Abstract
Neuroendocrine prostate cancer (NEPC), an aggressive and lethal subtype of prostate cancer (PCa), often arises as a resistance mechanism in patients undergoing hormone therapy for prostate adenocarcinoma. NEPC is associated with a significantly poor prognosis and shorter overall survival compared to conventional prostate adenocarcinoma due to its aggressive nature and limited response to standard of care therapies. This transdifferentiation, or lineage reprogramming, to NEPC is characterised by the loss of androgen receptor (AR) and prostate-specific antigen (PSA) expression, and the upregulation of neuroendocrine (NE) biomarkers such as neuron-specific enolase (NSE), chromogranin-A (CHGA), synaptophysin (SYP), and neural cell adhesion molecule 1 (NCAM1/CD56), which are critical for NEPC diagnosis. The loss of AR expression culminates in resistance to standard of care PCa therapies, such as androgen-deprivation therapy (ADT) which target the AR signalling axis. This review explores the drivers of NE transdifferentiation. Key genetic alterations, including those in the tumour suppressor genes RB1, TP53, and PTEN, and changes in epigenetic regulators, particularly involving EZH2 and cell-fate-determining transcription factors (TFs) such as SOX2, play significant roles in promoting NE transdifferentiation and facilitate the lineage switch from prostate adenocarcinoma to NEPC. The recent identification of several other key novel drivers of NE transdifferentiation, including MYCN, ASCL1, BRN2, ONECUT2, and FOXA2, further elucidates the complex regulatory networks and pathways involved in this process. We suggest that, given the multifactorial nature of NEPC, novel therapeutic strategies that combine multiple modalities are essential to overcome therapeutic resistance and improve patient outcomes.
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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) | 0.001 | 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".