A recurrent pathogenic BRCA2 truncating variant reveals a role for BRCA2-PCAF complex in modulating NF-κB-driven transcription
Bibliographic record
Abstract
Germline monoallelic truncating mutations in BRCA2, a key mediator of homologous recombination (HR), predispose individuals to breast and ovarian cancer. Tumorigenesis is typically attributed to biallelic inactivation, yet evidence suggests haploinsufficiency can suffice in some contexts. We model two pathogenic BRCA2 truncating variants in heterozygosis in non-tumorigenic breast epithelial cells. One variant is not expressed and confers PARP inhibitor (PARPi) sensitivity and reduced HR, indicating haploinsufficiency. In contrast, the other produces a truncated protein that rewires transcription in cells and tumors. Mechanistically, this truncated product acts as a dominant negative by forming abnormal oligomers with full-length BRCA2 and sequestering the PCAF acetyltransferase. This interaction reduces global histone H4 acetylation and suppresses NF-κB transcriptional activity, ultimately altering epithelial migration. Our findings reveal a BRCA2–PCAF axis that modulates NF-κB signaling, a process co-opted by a recurrent BRCA2 pathogenic variant. Pathogenic BRCA2 truncating variants in heterozygosis drive distinct cancer-linked mechanisms. Here the authors show that one causes PARPi sensitivity and HR loss via haploinsufficiency, while another expresses a truncated protein that rewires transcription by hijacking PCAF, reducing H4 acetylation and NF-κB activity.
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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 | 0.001 | 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".