The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
Bibliographic record
Abstract
Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterized treatment-emergent mCRPC by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing from 210 tumors. AR-/NE- tumors were clinically and molecularly distinct from other mCRPC subtypes, with the shortest survival, amplification of the chromatin remodeler CHD7, and PTEN loss. Methylation changes in CHD7 candidate enhancers were linked to elevated CHD7 expression in AR-/NE+ tumors. Genome-wide methylation analysis nominated Krüppel-like factor 5 (KLF5) as a driver of the AR-/NE- phenotype, and KLF5 activity was linked to RB1 loss. These observations reveal the aggressiveness of AR-/NE- mCRPC and could facilitate the identification of therapeutic targets in this highly aggressive disease. SIGNIFICANCE: Comprehensive characterization of the five subtypes of metastatic castration-resistant prostate cancer identified transcription factors that drive each subtype and showed that the double-negative subtype has the worst prognosis.
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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) | 0.000 | 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) | 0.001 | 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 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".