A Biomarker-enriched, Randomized Phase II Trial of Adavosertib (AZD1775) Plus Paclitaxel and Carboplatin for Women with Platinum-sensitive <i>TP53</i> -mutant Ovarian Cancer
Bibliographic record
Abstract
Abstract Purpose: Preclinical studies show that adavosertib, a WEE1 kinase inhibitor, sensitizes TP53-mutant cells to chemotherapy. We hypothesized that adavosertib, plus chemotherapy, would enhance efficacy versus placebo in TP53-mutated ovarian cancer. Patients and Methods: Following safety run-in, this double-blind phase II trial (NCT01357161) randomized women with TP53-mutated, platinum-sensitive ovarian cancer to oral adavosertib (225 mg twice daily for 2.5 days/21-day cycle) or placebo, plus carboplatin (AUC5) and paclitaxel (175 mg/m2), until disease progression or for six cycles. The primary endpoints were progression-free survival (PFS) by enhanced RECIST v1.1 [ePFS (volumetric)] and safety. Secondary/exploratory objectives included PFS by RECIST v1.1 (single dimension), objective response rate, overall survival, and analysis of tumor gene profile versus sensitivity to adavosertib. Results: A total of 121 patients were randomized to adavosertib (A+C; n = 59) and placebo (P+C; n = 62) plus chemotherapy. Adding adavosertib to chemotherapy improved ePFS [median, 7.9 (95% confidence interval (CI), 6.9–9.9) vs. 7.3 months (5.6–8.2); HR 0.63 (95% CI, 0.38–1.06); two-sided P = 0.080], meeting the predefined significance threshold (P &lt; 0.2). Clinical benefit was observed following A+C for patients with different TP53 mutation subtypes, identifying possible response biomarkers. An increase in adverse events was seen with A+C versus P+C: greatest for diarrhea (adavosertib 75%; placebo 37%), vomiting (63%; 27%), anemia (53%; 32%), and all grade ≥3 adverse events (78%; 65%). Conclusions: Establishing an optimal strategy for managing tolerability and identifying specific patient populations most likely to benefit from treatment may increase clinical benefit. Future studies should consider additional adavosertib doses within the chemotherapy treatment cycle and the potential for maintenance therapy.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".