Comprehensive molecular characterization of early stage grade 3 endometrioid endometrial adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: The treatment for stage IB grade 3 endometrioid endometrial adenocarcinoma is challenging with variable practice. Molecular characterization may help identify adjuvant therapy strategies beyond stage. We aimed to better understand the molecular features of these tumors by characterizing them by ProMisE classification, mutational signature, and commonly mutated genes. METHODS: Patients with stage IB grade 3 EEC at two institutions were included. Immunohistochemistry and whole exome sequencing were performed on archival FFPE tissue sections to determine ProMisE classification. Personal Cancer Genome Reporter was used for somatic variant annotation, and mutational signatures were generated based on COSMIC single base substitution mutational signatures. RESULTS: 46 patients were included with variable adjuvant treatment. Nine patients recurred (19.6%), most with extra-abdominal disease (n = 5, or 55.6%). 10 had POLE mutations (21.7%), 18 were MMR deficient (39.1%), 6 had abnormal p53 (13.0%), and 12 were p53 wildtype (26.1%). There were no recurrences in the POLE subgroup. A dominant mutational signature was identified in 38 patients: 17 SBS5 signature (44.7%), 10 SBS15 or SBS44 signature (26.3%), 7 SBS10a or SBS10b signature (18.4%), 3 SBS14 signature (7.9%), and 1 SBS40 signature (2.6%). The six patients that recurred had a SBS5 signature. Frequently mutated genes included ARID1A (n = 30, 65%), PTEN (n = 28, 61%), MUC16 (n = 27, 59%), and PIK3CA (n = 25, 54%). CONCLUSIONS: This comprehensive evaluation found a molecularly diverse cohort of tumors, despite the same histology, stage and grade. Mutational signature SBS5 correlated with a high risk of recurrence. Further refining of endometrial cancer classification may enable more precise patient stratification and personalized treatment approaches.
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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 | 0.000 |
| 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 | 0.001 | 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".