Proteogenomic Characterization of Patient-Derived Xenografts Highlights the Role of REST in Neuroendocrine Differentiation of Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: An increasing number of castration-resistant prostate cancer (CRPC) tumors exhibit neuroendocrine (NE) features. NE prostate cancer (NEPC) has poor prognosis, and its development is poorly understood. Experimental Design: We applied mass spectrometry–based proteomics to a unique set of 17 prostate cancer patient–derived xenografts (PDX) to characterize the effects of castration in vivo, and the proteome differences between NEPC and prostate adenocarcinomas. Genome-wide profiling of REST-occupied regions in prostate cancer cells was correlated to the expression changes in vivo to investigate the role of the transcriptional repressor REST in castration-induced NEPC differentiation. Results: An average of 4,881 proteins were identified and quantified from each PDX. Proteins related to neurogenesis, cell-cycle regulation, and DNA repair were found upregulated and elevated in NEPC, while the reduced levels of proteins involved in mitochondrial functions suggested a prevalent glycolytic metabolism of NEPC tumors. Integration of the REST chromatin bound regions with expression changes indicated a direct role of REST in regulating neuronal gene expression in prostate cancer cells. Mechanistically, depletion of REST led to cell-cycle arrest in G1, which could be rescued by p53 knockdown. Finally, the expression of the REST-regulated gene secretagogin (SCGN) correlated with an increased risk of suffering disease relapse after radical prostatectomy. Conclusions: This study presents the first deep characterization of the proteome of NEPC and suggests that concomitant inhibition of REST and the p53 pathway would promote NEPC. We also identify SCGN as a novel prognostic marker in prostate cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".