Evaluation of HER2 immunohistochemistry expression in non-standard solid tumors from a Single-Institution Prospective Cohort
Bibliographic record
Abstract
Aim: Human epidermal growth factor receptor-2 (HER2) is a well-established prognostic and predictive biomarker. It is an FDA-approved therapeutic target for HER2 positive breast, gastroesophageal, and more recently, lung and colon cancers. It is an emerging biomarker in biliary tract, bladder, cervical, endometrial, ovarian, and pancreatic cancers. The emergence of new indications warrants further characterization of HER2 expression in diverse cancer populations. This study investigated HER2 expression in solid tumour samples and the feasibility of obtaining these results. Methods: Prospective consent was obtained at a Canadian tertiary academic cancer center from adult oncology patients who were referred for molecular genetic testing of malignant tissue samples. Standard HER2-targeted malignancies were considered breast and gastroesophageal, and were excluded from this study. Between July 2020 and November 2023, 499 samples of solid tumors underwent immunohistochemistry (IHC) HER2 staining. A median turnaround time (TAT) of 14 days would be considered feasible for clinical decision making. Results: The mean age (± SD) of participants was 67 ± 12.5 years, with 270 (54%) male and 229 (46%) female. HER2 protein expression was measured in 42 unique cancer types. IHC levels of 0, 1+, 2+, and 3+ were reported and were 43%, 12%, 35%, and 10% of all analyzable samples respectively (tissue inadequate in 3% of samples). The median TAT for HER2 expression results from time of request to result in release was 18 (interquartile range, 11 to 30) days. Conclusions: HER2 protein expression varies widely between different cancer types. TAT for HER2 IHC results was a median of 18 days, which is close to our feasibility cut-off.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".