p53 mutations in epithelial ovarian cancers: possible role in predicting chemoresistance.
Bibliographic record
Abstract
PURPOSE: The investigators undertook a retrospective study to determine (1) whether p53 mutations are predictors of survival in patients with advanced epithelial ovarian cancer, (2) whether p53 status by sequencing is associated with established prognostic indicators, and (3) the agreement of results between direct sequencing of p53 mutations and immunohistochemistry. MATERIAL AND METHODS: This study was retrospective review of 43 patients with advanced epithelial ovarian cancer treated with surgery and by paclitaxel-based chemotherapy. Clinical data were extracted from the charts. By use of paraffin-embedded blocks, p53 analysis was carried out by (1) direct sequencing and (2) immunohistochemistry. Kaplan-Meier estimates were used for overall and disease-free survivals. To determine whether p53 mutation (sequencing) was related to prognosis and to determine the agreement between p53 abnormalities by sequencing and immunohistochemistry, risk ratios were calculated. RESULTS: Mean age at diagnosis was 57.4 years. Surgical stages were as follows: 5% were stage IIC, 79% were stage III, and 16% were stage IV. Seventy-seven percent of tumors were serous, and 56% of tumors were grade 3. All patients received paclitaxel-based chemotherapy. Mean disease-free and overall survivals were 16.4 and 22.6 months, respectively. p53 abnormalities were detected by sequencing in 53% of cases and by immunohistochemistry in 70%. Agreement between both techniques was 68%. Patients with stages IIC/IV had a risk of 1.7 of having a p53 mutation by sequencing; grade, histology, disease-free survival and overall survival were not predictive of p53 mutation status. DISCUSSION: The 54% mutation rate may be underestimated by limiting our analysis to exons 5 to 9. p53 mutation status was not predictive of survival (disease free and overall) or of chemoresistance; this suggests that paclitaxel-based apoptosis is independent of the p53 gene. The concomitant use of cisplatin, an inducer of apoptosis that is dependent on the normal function of p53, makes the interpretation of these results difficult. Histopathologic factors were not statistically associated with p53 status, but more advanced surgical stage and tumor grade were suggestive of higher rates of p53 mutations, implying more aggressive behavior. Finally, the lack of agreement between results obtained by sequencing and immunohistochemistry highlights the limitation of the latter technique and the possibility of underestimating abnormal p53 function. In conclusion, because discrepancies exist between the two techniques, we recommend direct sequencing of exons 4 through 10 to determine the true prevalence of p53 mutation. Furthermore, larger randomized studies are required to elucidate the role of p53 in predicting chemoresponse in advanced epithelial ovarian cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".