The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it