Abstract C025: Endometrial cancer mutation patterns by age with possible increased TP53 and decreased PTEN mutations in patients 50+ years old
Bibliographic record
Abstract
Abstract Introduction: Tumor molecular profiles have contributed to our understanding of the development and behavior of uterine cancer with varying mutational patterns across differing patient populations. Age is an important factor that may influence the microenvironment and molecular landscape of EC. Objectives: Our objective was to explore the relationships of the endometrial cancer (EC) biomarkers TP53, Her2, PTEN, PIK3CA, POle, CCNE1, and KRAS and their relationship with patient age <50 vs age ≥ 50 years old. Methods: The University Institutional Review Board reviewed and exempted this study. We identified patients diagnosed with EC between January 2014 – 2023 within the University of Pennsylvania Health System’s cancer registry and electronic medical record with tumor histology and molecular profile with institutional personalized diagnostics and CARIS Life Sciences©. Only those with confirmation of presence or absence of all mutations were included in this analysis. Analyses were stratified by patient age <50 and ≥50 years old threshold. Results: 3,418 patients were identified, and 211 patients were included in this analysis. 17(8%) were <50 years old and 194 (92%) patients were ≥50 years old. 51(24%) patients were identified as Black/Afr. Amer., 7(3%) Asian, 4 (2%) Other, and 149(71%) as White in their medical records. 102 (48%) were diagnosed at Stage I/II, 88(42%) at Stage III/IV and 21(10%) with an unknown stage. Patients with age ≥50 years old had a greater risk of TP53 mutation (OR 1.54; 95% CI: 1.21, 1.96; p=0.0005). There was no difference with regards to Her2 mutation status (OR=0.99; 95%CI: 0.89, 1.11; p=0.898). However, for PTEN, those ≥50 years old had a lower risk of PTEN mutations (OR=0.69, 95% CI: 0.54, 0.88; p=0.003). There was no difference in age risk for PIK3CA mutation (OR=1.12; 95%CI: 0.87, 1.43; p=0.38), POLe mutation (OR=1.13, 95%CI: 0.97, 1.32; p=0.13) and KRAS mutation (OR=1.04; 95%CI: 0.87, 1.24; p=0.69). No CCNE1 mutations were identified in this patient sample. Conclusions: Patients under <50 years were more likely to have PTEN mutation than patients ≥50 years old. Citation Format: Camille McCallister, Caroline Shermoen, Sneha Kadiyala, Anna Liu, Nathanael Koelper, Ronny Drapkin, Mary Boland, Emily Ko1, Kimberly Lee, Anna Jo Bodurtha Smith. Endometrial cancer mutation patterns by age with possible increased TP53 and decreased PTEN mutations in patients 50+ years old [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C025.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".