Abstract B30: The effect of OCP use on the incidence of precancerous p53 lesions in fallopian tube fimbria
Bibliographic record
Abstract
Abstract Background: High-grade serous ovarian carcinoma (HGSOC) accounts for >70% of ovarian cancer-related deaths and is the most common ovarian cancer histotype, most originating from precancerous p53 lesions in the fallopian tube (FT) fimbria. Use of oral contraceptive pills (OCPs) for 5 years or more is associated with a >40% reduction in risk of HGSOC, but the mechanism is unknown. We hypothesize that OCP use reduces the incidence of p53 lesions. Our preliminary data show higher incidence of p53 lesions in post- compared to premenopausal women; therefore we aim to quantify p53 lesions in postmenopausal women who previously did or did not use OCPs. This will provide insight into the protective effects of OCPs against HGSOC. Preliminary Results: We determined the presence of p53 lesions by immunohistochemistry (IHC) in FT of women up to 40 years old (n=27) and >60 years old (n=24) who underwent salpingectomies for noncancer reasons. p53 lesions were identified in 3/27 cases of the younger cohort (11%) and in 10/24 of the older cohort (42%). Thus, we conclude an increased incidence of p53 lesions in older compared to younger women. Proposed Design: IHC for p53 will be performed on FT fimbria of women >55 years old who received salpingectomy for noncancer reasons. Based on an assumed reduction in p53 lesions of 35% in women who used OCPs for 5 years or more compared to nonusers (25 vs. 42%), analysis of 190 cases from each group will provide >80% power (p<0.05). Cases will be identified through Population Data BC and blind analysis by p53 IHC will be performed at the Vancouver General Hospital. Data will be flowed back to Pop Data BC to compare to OCP data. Conclusion: Our preliminary study found that 42% of postmenopausal women had p53 lesions, informing this study design. The study registered through this abstract will be the first to examine the impact of OCPs on the earliest known precursors of HGSOC. Citation Format: Kendall Greening, Anthony Karnezis, Dawn Cochrane, David Farnell, Lien Hoang, Gillian Hanley, David Huntsman. The effect of OCP use on the incidence of precancerous p53 lesions in fallopian tube fimbria [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B30.
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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 | 0.002 |
| 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) | 0.005 | 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".