Abstract 337: Genetic analysis of tumors from a phase II trial evaluating AZD1775, carboplatin and paclitaxel in patients with TP53-mutant ovarian cancer
Bibliographic record
Abstract
Abstract Background: AZD1775 (formerly MK-1775) is a selective inhibitor of WEE1 kinase, which has been shown to sensitize TP53-mutant cancer cells to genotoxic agents such as platinum-based chemotherapies. Mutation of TP53 abrogates the G1/S checkpoint in cells, which may enhance their dependency on the G2/M checkpoint and WEE1 kinase activity for control of the cell cycle and effective repair of DNA damage. The results from a randomized Phase II trial (NCT01357161) evaluating the effects of adding AZD1775 to carboplatin and paclitaxel in patients with TP53-mutant ovarian cancer showed an increase in progression-free survival (PFS) for the AZD1775 arm versus placebo (enhanced RECIST: median PFS 34.14 vs 31.86 weeks; HR = 0.63, 80% CI: 0.45-0.89, P = 0.080; RECIST 1.1: median PFS 42.86 vs 34.86 weeks; HR = 0.55, 80% CI 0.39-0.79, P = 0.030; Oza et al, ASCO 2015). We investigated whether particular genetic factors were associated with an increased response to the combination of AZD1775 and chemotherapy in this trial. Methods: A retrospective analysis was performed to determine whether any specific subtype of TP53 alteration was associated with a greater response to the AZD1775 combination compared with placebo. In addition, next-generation sequencing (NGS) was performed on archival tumors from a subset of patients who provided consent in an effort to identify additional genetic alterations that may predict increased clinical benefit following the addition of AZD1775 to chemotherapy. Results: TP53 data were available for 136 patients (15 patients from an initial open-label safety run-in and 121 patients from the randomized trial) and 133 patients were evaluable for response. Fifty-five patients provided additional consent for NGS of tumor samples. The genetic aberrations observed in the NGS subset of tumors from this trial were heterogeneous, and the total mutational load in cancer-related genes was also variable across tumors. Although the small number of patients with a tumor BRCA mutation limited the statistical power of the comparison between randomized arms, the median PFS was longer in those patients treated with AZD1775 (53.86 weeks, 95% CI 24.43-66.57) versus placebo (45.86 weeks, 95% CI 35.71-55.86) in this BRCA-mutated subgroup. TP53 subgroup analyses showed a similar benefit for patients with missense mutations compared to those with splice site, nonsense and frameshift TP53 mutations. The heterogeneity of the G1/S checkpoint gene aberrations limited the statistical power of the subgroup analysis, but specific genes that warrant further investigation were identified. Conclusions: These results highlight a number of potential candidate genes for increased response to AZD1775 and chemotherapy compared with chemotherapy alone. Citation Format: Naomi Laing, Zhongwu Lai, J. Carl Barrett, Mark J. O’Connor, Amit M. Oza, David Lawrence. Genetic analysis of tumors from a phase II trial evaluating AZD1775, carboplatin and paclitaxel in patients with TP53-mutant ovarian cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 337.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".