BRCA mutations lead to XIAP overexpression and sensitise ovarian cancer to inhibitor of apoptosis (IAP) family inhibitors
Bibliographic record
Abstract
BACKGROUND: We tested the hypothesis that inhibitor of apoptosis family (IAP) proteins may be altered in BRCA1-mutated ovarian cancers and that could affect the sensitivity to IAP inhibitors. METHODS: The levels of IAP proteins were evaluated in human cancers and cell lines. Cell lines were used to determine the effects of IAP inhibitors. The in vivo effects of treatments were evaluated in PDX mouse models. RESULTS: Expression of X-linked inhibitor of apoptosis (XIAP) is increased in BRCA1-mutated cancers and high levels are associated with improved patient outcomes after platinum chemotherapy. XIAP overexpression is mediated by NF-kB activation and is associated with an optimisation of PARP. BRCA1-mutated cell lines are particularly sensitive to IAP inhibitors due to an inhibitory effect on PARP. Both a BRCA1-mutated cell line with acquired resistance to PARP inhibitors and one with restored BRCA1 remain sensitive to IAP inhibitors. Treatment with IAP inhibitors restores the efficacy of PARP inhibition in these cell lines. The IAP inhibitor LCL161 alone and in combination with a PARP inhibitor, exhibited antitumour effects in PDX mouse models of resistant BRCA2 and 1-mutated ovarian cancer, respectively. CONCLUSION: A clinical trial may be justified to further investigate the utility of IAP inhibitors.
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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 | 0.000 | 0.000 |
| 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".