Abstract A020: TP53 mutation and prediction of platinum response in BRCA-mutated ovarian cancer: A prospective case-series analysis
Bibliographic record
Abstract
Abstract Background and aim of the study: Currently, platinum sensitivity (PS) is a prerequisite for first-line PARP inhibitors (PARPi) in locally advanced and relapsed high grade serous ovarian cancer (HGS-OC). BRCA mutations are recognized as predictive of PS and, therefore, of response to PARPi . Notably, platinum and PARPi cytotoxic action is mainly related by their ability of inducing p53-mediated apoptosis. Therefore, the integrity of p53 machinery is crucial for platinum-related activity whereas the presence of p53 mutation is a fairly frequent event in ovarian cancer, particularly in HGS-OC and in BRCA mutated ones. Patients and methods: We prospectively analyzed 208 women with primary ovarian cancer undergoing surgery at the Department of Gynecologic Oncology, ARNAS G. Brotzu, Cagliari, Italy, between 2019 and 2023. Somatic NGS analysis was performed to detect BRCA and HRD mutations. TP53 mutations were classified according to according to hotspot, structural (missense or nonsense) and functional classification as “gain of function” (GOF) or “loss of function” (LOF), based on the IARC TP53 database. Comparative testing with Fisher's exact test was used to examine TP53 mutation distribution and associations with clinicopathologic factors and PS. The BRCA mutation status was further used to stratify the analysis. Results: Globally, we included 127 adult HGS-OC pts. 84.2% had stage III-IV disease. TP53 mutation was found in 83.4 % of the entire cohort. Somatic BRCA mutations were found in 28.3% pts. Overall, HGS-OC with somatic BRCA mutations had higher TP53 mutation frequency (88.8%) when compared to BRCA WT (81.3%, p=0.1510). Employing the structural classification scheme, most cancers harbored a missense TP53 mutation (76.5%). LOF TP53 mutations were found in 59.4% while GOF in 31.2%. No significant disparity was observed in the distribution of specific TP53 mutations within each classification scheme between cases with BRCA mutations and those without. As for BRCA mutated pts, TP53 WT were all PS. Among those p53 mutated, GOF mutations were associated with PS in 7 pts and platinum resistance in 3 pts; LOF mutations were associated with PS in 7 pts and platinum resistance in 12 pts. The difference in distribution of PS between functional categories of p53 mutations was significant (p=0.0291). As for BRCA WT pts, TP53 WT were all PS. Among TP53 mutated, GOF mutations were associated with PS in 14 pts and platinum resistance in 10 pts; viceversa, LOF mutations were associated with PS in 19 pts and platinum resistance in 25 pts, even if these findings were not statistically significant (p=0.2357). Of relevance, in 5 cases where LOF mutations of p53 was associated with null expression of HIC p53, patients were refractory to platinum-based chemotherapy. Conclusions: Even if preliminary, our data show that HGS-OC harboring p53 null mutations are the poorest prognostic subgroup, especially in terms of PS. Further studies are needed to confirm our findings and the role of TP53 mutation as a biomarker of inherent or acquired platinum resistance. Citation Format: Eleonora Lai, Manuela Neri, Elisabetta Sanna, Sonia Nemolato, Fabio Bardanzellu, Mario Scartozzi, Sabrina Giglio, Antonio Macciò, Clelia Madeddu. TP53 mutation and prediction of platinum response in BRCA-mutated ovarian cancer: A prospective case-series analysis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A020.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".