Human Epidermal Growth Factor Receptor 2 Testing: Where Are We?
Bibliographic record
Abstract
In this issue of Journal of Clinical Oncology, Baehner et al have reported from a Kaiser Permanente case-control study that there is a high degree of concordance (97%) between quantitative reverse transcription polymerase chain reaction (qRT-PCR) and central laboratory fluorescent in situ hybridization (FISH) assessment of HER2 status. This provides us the opportunity to examine the status of human epidermal growth factor receptor 2 (HER2) testing today. For the most part, cancer is a genetic disease, and successful treatment of breast cancer is particularly dependent on a number of complex factors, including detection of the tumor early in the course of development, accurate assessment of the right biomarker, and the biology of the underlying disease. Amplification and overexpression of the HER2/ERBB2 oncogenes are observed in 15% to 25% of invasive breast cancers. HER2-positive tumors define a clinically important breast cancer subgroup that is generally associated with poor prognosis and variable response to conventional systemic cytotoxic therapy. HER2 testing is routinely performed in patients with a new diagnosis of invasive breast cancer. Accurate testing to identify HER2 status for patients with breast cancer who can benefit from anti-HER2 treatment (eg, trastuzumab, lapatinib) is a clinical and economic necessity (Fig 1). As a consequence, issues relating to accurate and reliable laboratory assessment of HER2 status in patients with breast cancer are a matter of significant concern to patients, pathologists, and oncologists.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".