Data from Reorganization of the 3D Genome Pinpoints Noncoding Drivers of Primary Prostate Tumors
Bibliographic record
Abstract
Abstract Prostate cancer is a heterogeneous disease whose progression is linked to genome instability. However, the impact of this instability on the noncoding genome and its three-dimensional organization to aid progression is unclear. Using primary benign and tumor tissue, we find a high concordance in higher-order three-dimensional genome organization. This concordance argues for constraints to the topology of prostate tumor genomes. Nonetheless, we identified changes in focal chromatin interactions, typical of loops bridging noncoding cis-regulatory elements, and showed how structural variants can induce these changes to guide cis-regulatory element hijacking. Such events resulted in opposing differential expression of genes found at antipodes of rearrangements. Collectively, these results argue that changes to focal chromatin interactions, as opposed to higher-order genome organization, allow for aberrant gene regulation and are repeatedly mediated by structural variants in primary prostate cancer. Significance: This work showcases how the noncoding genome can be hijacked by focal insults to its three-dimensional organization that contribute to prostate cancer oncogenesis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".