Tumor Growth Inhibition by Olaparib in <i>BRCA2</i> Germline-Mutated Patient-Derived Ovarian Cancer Tissue Xenografts
Bibliographic record
Abstract
PURPOSE: Most patients with ovarian carcinomas succumb to their disease and there is a critical need for improved therapeutic approaches. Carcinomas arising in BRCA mutation carriers display defective DNA double-strand break repair that can be therapeutically exploited by inhibition of PARP-1, a key enzyme in the repair of DNA single-strand breaks, creating synthetic lethality in tumor cells. EXPERIMENTAL DESIGN: To investigate synthetic lethality in vivo, we established a BRCA2 germline-mutated xenograft model that was developed directly from human ovarian cancer tissue, treated with the PARP inhibitor olaparib (AZD2281) alone and in combination with carboplatin. RESULTS: We show that olaparib alone and in combination with carboplatin greatly inhibit growth in BRCA2-mutated ovarian serous carcinoma. This effect was not observed in a serous carcinoma with normal BRCA function, showing a specific antitumor effect of olaparib in mutation carriers. Immunohistochemistry (cleaved caspase-3 and Ki-67 stains) of remnant tissue after olaparib treatment revealed significantly decreased proliferation and increased apoptotic indices in these tumors compared with untreated controls. Furthermore, olaparib-treated tumors showed highly reduced PARP-1 activity that correlated with olaparib levels. CONCLUSIONS: We established a BRCA2-mutated human ovarian cancer xenograft model suitable for experimental drug testing. The demonstrated in vivo efficacy of olaparib extends on the preclinical rationale for further clinical trials targeting ovarian cancer patients with BRCA mutations.
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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 | 0.001 |
| 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".