A genome-wide strategy to identify causes and consequences of retrotransposon expression finds activation by BRCA1 in ovarian cancer
Bibliographic record
Abstract
It is challenging to identify the causes and consequences of retrotransposon expression in human disease due to the hundreds of active genomic copies and their poor conservation across species. We profiled genomic insertions of retrotransposons in ovarian cancer. In addition, in ovarian and breast cancer we analyzed RNAs exhibiting Bayesian correlation with retrotransposon RNA to identify causes and consequences of retrotransposon expression. This strategy finds divergent inflammatory responses associated with retrotransposon expression in ovarian and breast cancer and identifies new factors inducing expression of endogenous retrotransposons including anti-viral responses and the common tumor suppressor BRCA1. In cell lines, mouse ovarian epithelial cells and patient-derived tumor spheroids, BRCA1 promotes accumulation of retrotransposon RNA. BRCA1 promotes transcription of active families of retrotransposons and their insertion into the genome. Intriguingly, elevated retrotransposon expression predicts survival in ovarian cancer patients. Retrotransposons are part of a complex regulatory network in ovarian cancer including BRCA1 that contributes to patient survival. The described strategy can be used to identify the regulators and impacts of retrotransposons in various contexts of biology and disease in humans.
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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 | 0.000 | 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 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".