Excessive transcription-replication conflicts are a vulnerability of <i>BRCA1</i>-mutant cancers
Bibliographic record
Abstract
BRCA1 mutations are associated with increased breast and ovarian cancer risk. BRCA1-mutant tumors are high-grade, recurrent, and often become resistant to standard therapies. Herein, we performed a targeted CRISPR-Cas9 screen and identified MEPCE, a methylphosphate capping enzyme, as a synthetic lethal interactor of BRCA1. Mechanistically, we demonstrate that depletion of MEPCE in a BRCA1-deficient setting led to dysregulated RNA polymerase II (RNAPII) promoter-proximal pausing, R-loop accumulation, and replication stress, contributing to transcription-replication collisions. These collisions compromise genomic integrity resulting in loss of viability of BRCA1-deficient cells. We also extend these findings to another RNAPII-regulating factor, PAF1. This study identifies a new class of synthetic lethal partners of BRCA1 that exploit the RNAPII pausing regulation and highlight the untapped potential of transcription-replication collision-inducing factors as unique potential therapeutic targets for treating cancers associated with BRCA1 mutations.
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.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".