Abstract B031: High risk no specific molecular profile (HR-NSMP) endometrial cancer can be stratified into three subgroups based on tumor grade and estrogen receptor status with differing clinicopathologic characteristics and outcomes
Bibliographic record
Abstract
Abstract Introduction: Recent publications have identified ‘low risk’ (LR-NSMP) and ‘high risk’ no specific molecular profile (HR-NSMP) endometrial cancers (ECs). Although LR-NSMP (low grade (G1/2) estrogen receptor (ER) positive tumors have extremely low rates (<2%) of death from disease and are considered candidates for de-escalation of adjuvant therapy, little is known about the newly defined entity of HR-NSMP (G3 and/or ER-negative), including optimal management, with 27% of HR-NSMP patients dying from their disease We aimed to perform in-depth characterization of a cohort of HR-NSMP ECs to identify additional prognostic or predictive features that may inform management. Methods: Clinicopathologic data collection, immunohistochemistry (IHC), and next generation sequencing was performed on a cohort of 148 HR-NSMP ECs testing for associations with outcomes. Three subgroups of HR-NSMP were assessed: i) low grade without any ER expression (G1/2ER-, n=40), ii) high grade with ER (G3ER+, n=67), and iii) high grade without ER expression (G3ER-, n=41). Results: In a univariate analysis, advanced stage (III/IV), positive lymph node (LN) status, no LN assessment, and PIK3CA mutations were associated with inferior progression-free survival (PFS), disease specific survival (DSS), and overall survival (OS). HR-NSMP patients with positive LNs had a hazard ratio (HR) of death of 7.55 (95% CI: 3.41−16.73) compared to node negative. Additionally, patients with no LN assessment had an increased risk of death with a HR of 2.33 (95% CI: 1.12−4.85) compared to node negative. Within the three HR-NSMP subgroups, the worst OS (HR 7.06, CI 4.12-12.09, p< 0.001), DSS (HR 20.43, CI 10.14-41.19, p< 0.001), and PFS (HR 3.69, CI 1.59-8.63, p<0.001) were observed in patients with both adverse features (G3ER-). PIK3CA mutations were found in 32.7% of all HR-NSMP tumors tested (G1/2ER- 45.5%, G3ER+ 42.3%, G3ER- 6.7%) and associated with inferior OS (HR 4.84, CI 1.21-19.41, p=0.014), DSS (HR 4.84, CI 1.21-19.41, p=0.014), and PFS (HR 3.22, CI=1.08-9.66, p=0.028). There was a trend towards decreased OS and DSS in HR-NSMP patients with overexpression of HER2 IHC. L1CAM IHC overexpression and CTNNB1 mutation status were not associated with outcomes when assessed across all HR-NSMP or within subgroups. Conclusion: Among HR-NSMP ECs, the worst clinical outcomes were observed in patients harboring both high grade and ER negative tumors. In contrast to LR-NSMP ECs where patients who had no lymph node assessment had excellent outcomes mirroring node negative status, HR-NSMP patients who had no nodal assessment had a high rate of recurrence and death from disease suggesting the importance of comprehensive staging in this cohort. PIK3CA appears to be a prognostic stratification feature within HR-NSMP and represents a potential therapeutic pathway. More work is needed to understand the association of HER2 expression and ER status and whether HER2 -targeted therapies represent actionable opportunities for these patients. Citation Format: Andrea Neilson, Amy Jamieson, Derek Chiu, Samuel Leung, Amy Lum, Jennifer Pors, Stefan Kommoss, Aline Talhouk, David G. Huntsman, Naveena Singh, C. Blake Gilks, Jessica N. McAlpine. High risk no specific molecular profile (HR-NSMP) endometrial cancer can be stratified into three subgroups based on tumor grade and estrogen receptor status with differing clinicopathologic characteristics and outcomes [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B031.
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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 | 0.000 | 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) | 0.002 | 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".