Genetic and Epigenetic Reprogramming of Transposable Elements Drives ecDNA-Mediated Metastatic Prostate Cancer
Bibliographic record
Abstract
Abstract Extrachromosomal DNAs (ecDNAs), which replicate and segregate in a non-Mendelian manner, serve as vectors for accelerated tumor evolution. By integrating chromatin accessibility, whole-genome sequencing, and Hi-C-based genome topology data from a cohort of metastatic Castration-Resistant Prostate Cancer (mCRPC) cases, we show that epigenetically activated repeat DNA, amplified in ecDNAs, drive oncogene overexpression. Specifically, we identify a subgroup of mCRPCs (20%) characterized by clusters of accessible LINE1 repeat DNA elements flanking the androgen receptor (AR) gene. These LINE1 elements are co-amplified with AR and provide binding sites for prostate-lineage transcription factors, including AR, FOXA1 and HOXB13. Accessible LINE1 elements establish novel 3D chromatin interactions with the AR gene, forging a new regulatory plexus driving AR overexpression and confers resistance to androgen signaling inhibitors. Our findings indicate how tumor evolution is driven by the convergence of genetic and epigenetic alterations on repeat DNA, activating and amplifying them to allow oncogene overexpression. Statement of significance We show how tumor evolution is driven by the convergence of genetic and epigenetic alterations on repeat DNA elements, resulting in their activation as regulatory elements and co-amplification in ecDNAs with oncogenes in mCRPC.
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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".