<i>BRCA1</i> secondary splice-site mutations drive exon-skipping and PARP inhibitor resistance
Bibliographic record
Abstract
Abstract BRCA1 splice isoforms Δ11 and Δ11q can contribute to PARP inhibitor (PARPi) resistance by splicing-out the mutation-containing exon, producing truncated, partially-functional proteins. However, the clinical impact and underlying drivers of BRCA1 exon skipping remain undetermined. We analyzed nine ovarian and breast cancer patient derived xenografts (PDX) with BRCA1 exon 11 frameshift mutations for exon skipping and therapy response, including a matched PDX pair derived from a patient pre- and post-chemotherapy/PARPi. BRCA1 exon 11 skipping was elevated in PARPi resistant PDX tumors. Two independent PDX models acquired secondary BRCA1 splice site mutations (SSMs), predicted in silico to drive exon skipping. Predictions were confirmed using qRT-PCR, RNA sequencing, western blots and BRCA1 minigene modelling. SSMs were also enriched in post-PARPi ovarian cancer patient cohorts from the ARIEL2 and ARIEL4 clinical trials. We demonstrate that SSMs drive BRCA1 exon 11 skipping and PARPi resistance, and should be clinically monitored, along with frame-restoring secondary mutations. Statement of significance Few PARPi resistance mechanisms have been confirmed in the clinical setting. While secondary/reversion mutations typically restore a gene’s reading frame, we have identified secondary mutations in patient cohorts that hijack splice sites to enhance mutation-containing exon skipping, resulting in the overexpression of BRCA1 hypomorphs, which in turn promote PARPi resistance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".