HER2 Status in Gastric and Gastroesophageal Junction Cancer: Results of the Large, Multinational HER-EAGLE Study
Bibliographic record
Abstract
Human epidermal growth factor receptor 2 (HER2) dysregulation is associated with tumorigenesis in gastric/gastroesophageal junction cancer; however, the number of patients with HER2-positive disease is unclear, possibly due to differing scoring criteria/assays. Data are also lacking for early disease. We aimed to assess the HER2-positivity rate using approved testing criteria in a large, real-life multinational population. HER2-positivity was defined as an immunohistochemistry staining score of 3+, or immunohistochemistry 2+ and HER2 amplification detected by in situ hybridization. A total of 4949 patients were enrolled and results showed that 14.2% of 4920 samples with immunohistochemistry results were HER2-positive. HER2-positivity was significantly higher in males (16.1% vs. 9.6% in females), in gastroesophageal versus stomach tumors (22.1% vs. 12.9%), in biopsy versus surgical samples (18.3% vs. 13.0%), in intestinal tumor subtypes versus diffuse (21.5% vs. 4.8%) and mixed types (21.5% vs. 8.5%) (P<0.001), in mixed versus diffuse types (8.5% vs. 4.8%), and in "other" versus diffuse types (11.7% vs. 4.8%; P=0.002). There were no significant differences between stages. Patients in the youngest age percentile had significantly lower HER2-positivity rates than patients in the remaining percentiles (9.2% vs. 15.9%, 15.7%, and 15.1%; P<0.001). HER2-positivity was highest in France (20.2%) and lowest in Hong Kong (10.4%). In conclusion, HER-EAGLE, the first study of its kind to be conducted in a large, multinational population of almost 5000 patients, gives valuable insights into the real-world HER2-positivity rate in a gastric/gastroesophageal junction cancer patient population not selected for disease stage or histology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".