Transcription Factor Dynamics in Neuroendocrine Prostate Cancer Development
Bibliographic record
Abstract
Abstract Treatment-induced neuroendocrine prostate cancer (NEPC) represents a lethal evolution of prostate adenocarcinoma under androgen receptor pathway inhibition, posing a significant clinical challenge. In a recent landmark study, Wang et al . introduced an innovative internal Z-score based approach to comprehensively characterize the transcription factor (TF) landscape in prostate cancer progression, uncovering distinct TF profiles associated with adenocarcinoma and NEPC lineages. Notably, the study proposes a three-phase model of NEPC transdifferentiation—comprising de-differentiation, dormancy, and re-differentiation—revealing dynamic shifts in TF expression that underpin lineage plasticity and therapeutic resistance. This commentary critically evaluates the methodological advancements, the functional significance of the identified TF signatures, and the broader implications of these findings for developing novel therapeutic strategies. By delineating the molecular events driving the transition from androgen receptor (AR)-dependent adenocarcinoma to treatment-resistant NEPC, this work underscores the potential of targeting early and dormant phases of transdifferentiation to improve patient outcomes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".