Implementation of HER2 Testing in Endometrial Cancer, a Summary of Real-World Initial Experience in a Large Tertiary Cancer Center
Bibliographic record
Abstract
HER2-targeted therapies have transformed the management of advanced or recurrent serous endometrial cancer (EC), leading to an increased clinical demand for HER2 testing. Despite its adoption in select academic centers, the global extent of such tumor testing is unclear. In this study, we report on the initial two-year experience of HER2 testing at a major academic center with a reference gynecologic oncology service and biomarker reference laboratory. All patients who underwent HER2 testing based on physician discretion, reflex HER2 testing, and reference laboratory requests were included. From February 2021 to October 2023, HER2 testing was performed on 192 tumor tissue samples from 180 EC patients. Serous carcinoma constituted 52% of samples, reflecting diagnostic challenges and limited therapeutic options for advanced EC. HER2 positivity was found in 28% of all cases and 30% of p53-aberrant cases. An immunohistochemistry (IHC) score of 3+ was found in 15% of samples, while IHC 2+ was found in 45% (13% IHC 2+/ISH+ and 32% IHC 2+/ISH-). The newly identified 'HER2-low' category comprised 46% of the samples. Heterogeneity was noted in 42% of HER2-positive cases, with complex patterns in 3%. NGS and HER2 IHC-FISH showed a 24% discordance, attributed to intratumoral heterogeneity, tumor cellularity, a small number of amplified cells, and the HER2/CEP17 ratio near the cut-off. This study offers real-world insights into HER2 testing in EC, highlighting the challenges and underscoring the need for standardized guidelines in specimen handling, proficiency testing, and scoring criteria to enhance patient management and therapeutic decision-making.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".