Abstract B20: Endometrial cancer molecular risk stratification in endometrioid ovarian cancers: A novel application of precision medicine
Bibliographic record
Abstract
Abstract Objective: Endometrioid ovarian carcinoma (ENOC) is associated with a generally more favorable prognosis compared to other ovarian carcinoma histotypes. Nonetheless, patients are still treated with a “one size fits all” approach. While tumor staging offers some stratification, the development of personalized treatment concepts remains elusive. Our group has recently validated the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), to distinguish clinically relevant prognostic groups amongst endometrial carcinomas. ENOC shares risk factors, genomics, and histology with its endometrial counterpart. The aim of our study was to apply and test ProMisE on ENOC. Methods: ProMisE was applied to 509 ENOC after biomarker-assisted review of ENOC histotype. Cases were aligned into low-risk POLE mutant (POLE), moderate-risk mismatch repair deficient (MMRd), high-risk p53 abnormal (p53abn), and a final moderate-risk category lacking these biomarkers (p53wt). Kaplan-Meier and multivariate survival analysis were performed. Results: 4% of cases were POLE, 16% MMRd, 71% p53wt, and 10% p53abn. Groups showed distinct progression-free and overall survival (p <0.001), near identical to profiles of endometrial cancers. 5-year PFS was 54% in p53abn, 82% in MMRd, 85% in p53wt, and 100% in POLEmut cases. Median overall survival was reached in p53 mutant cases only. ProMisE classes of ENOC were independent of stage and residual disease in multivariable analysis. Conclusion: ProMisE risk classification provides additional prognostic information in a large cohort of ENOC. Our findings support introduction of ProMisE-stratified treatment strategies to improve patient care across ENOC. Further, ENOC may benefit from parallel efforts under investigation in endometrial carcinoma. Citation Format: Pauline Krämer, Aline Talhouk, Tjalling Bosse, Florian Heitz, Naveena Singh, Felix Kommoss, Sara Brucker, Jessica McAlpine, Stefan Kommoss, Martin Koebel, Michael Anglesio. Endometrial cancer molecular risk stratification in endometrioid ovarian cancers: A novel application of precision medicine [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 B20.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".