Breast cancer biomarkers in clinical testing: analysis of a UK national external quality assessment scheme for immunocytochemistry and in situ hybridisation database containing results from 199 300 patients
Bibliographic record
Abstract
We describe a collated data set of results from clinical testing of breast cancers carried out between 2009 and 2016 in the United Kingdom and Republic of Ireland. More than 199 000 patient biomarker data sets, together with clinicopathological parameters were collected. Our analyses focused on human epidermal growth factor receptor-2 (HER2), oestrogen receptor (ER) and progesterone receptor (PR), with the aim of the study being to provide robust confirmatory evidence on known associations in these biomarkers and to uncover new data on previously undescribed or unconfirmed associations, thus strengthening the evidence-base in clinical breast cancer testing. Overall, 13.1% of tumours were HER2-positive; 10.6% in ER-positive tumours, and 25.5% in ER-negative tumours. Higher rates of HER2 positivity were significantly associated with patient age <56 years versus age ≥56 years, symptomatic versus screen-detected tumours, testing of involved axillary node versus primary breast cancer, invasive ductal carcinoma (not otherwise specified) versus other histological types, higher histological grade, increasing tumour size, increasing nodal involvement, ER-negative versus ER-positive tumour status, PR-negative versus PR-positive tumour status. Where ER status was known, 82.7% of tumours were ER-positive; 80.9% in women age <56 years, and 83.6% in those age ≥56 years (ER-positive cut-off ≥1.0% positive tumour cells or equivalent). Where PR status was known, 64.9% of tumours were PR-positive; 65.8% in women age <56 years, and 64.4% in women age ≥56 years (PR-positive cut off ≥10.0% or equivalent). These analyses of clinical test results provide contemporary benchmarking data for HER2, ER and PR positive rates.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".