Clinical and Molecular Characteristics of High-Risk, Recurrent, or Metastatic Endometrial Cancer That Is Human Epidermal Growth Factor Receptor 2–Low
Bibliographic record
Abstract
PURPOSE: Recent success of human epidermal growth factor receptor 2 (HER2)-targeted antibody-drug-conjugate trastuzumab-deruxtecan in HER2-low and HER2-positive tumors has sparked interest in examining the HER2 status of tumors not traditionally associated with HER2 amplification. Despite the increasing number of systemic treatment options, patients with advanced endometrial cancer (EC) still face a poor prognosis. This study evaluates HER2-low status in over 800 EC, correlating HER2 with both molecular and clinical features. METHODS: HER2 status was determined by immunohistochemistry (IHC) and dual in situ hybridization (DISH) on four studies of previously classified high-risk EC (PORTEC-3 and Medical Spectrum Twente cohort), recurrent or metastatic EC (DOMEC), and a primary stage IV cohort. EC was classified as HER2-negative (IHC 0), HER2-low (IHC 1+/2+ without amplification), or HER2-positive (IHC 3+ or DISH-confirmed amplification). Survival analysis was performed using the Kaplan-Meier method. Cox proportional hazards models assessed the independence of any prognostic impact of HER2 status. RESULTS: HER2 status was determined in 806 EC: 74.8% were HER2-negative, 17.2% HER2-low, and 7.9% HER2-positive. HER2-low was found across all molecular classes and histotypes. The highest rates of HER2-low and HER2-positive tumors were in recurrent or metastatic EC (35.6% and 15.6%), followed by primary stage IV EC (29.9% and 12.4%) and high-risk EC (14.2% and 6.8%). HER2 status had no independent prognostic value. CONCLUSION: A quarter of high-risk, metastatic, or recurrent EC exhibited HER2 overexpression. The presence of HER2 overexpression in all clinical and molecular categories highlights the need for broad testing and offers treatment options for a wide range of patients.
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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 | 0.002 |
| 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) | 0.002 | 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".