Comparison of Quantitative Immunofluorescence With Conventional Methods for HER2/neu Testing With Respect to Response to Trastuzumab Therapy in Metastatic Breast Cancer
Bibliographic record
Abstract
Abstract Context. —Selection for trastuzumab therapy depends on a companion diagnostic assessment of HER2 by either immunohistochemistry (IHC) for protein overexpression or fluorescence in situ hybridization (FISH) to detect gene amplification. Although many studies have compared IHC to FISH, few have compared the tests to the true gold standard, tumor response. Objective. —To compare HER2 testing by FISH and IHC along with a third immunofluorescence-based assay (automated quantitative analysis–tissue microarray [AQUA-TMA]) and to assess the value of each test for prediction of response to trastuzumab. Design. —Immunohistochemistry and FISH assays were done on both whole slides (IHC-WS and FISH-WS) and on TMAs (IHC-TMA and FISH-TMA). AQUA was only done on TMAs (AQUA-TMA). Response was assessed according to modified Response Evaluation Criteria in Solid Tumors. Results. —AQUA-TMA scores showed a significant linear relationship to both the FISH signal ratio and IHC scores on whole sections and TMAs. Assay assessment by outcome showed no association between response and FISH-WS ratio ( P = .96), FISH-TMA ( P = .55), IHC-WS ( P = .75), or IHC-TMA ( P = .06), but a significant relationship between AQUA score and categoric response was observed ( P = .01). Assessed as a function of outcome using models of logistic regression, both AQUA-TMA and IHC-TMA were equally significant ( P = .01). FISH-WS was the most sensitive assay, with a significantly higher true-positive fraction than all other tests except AQUA-TMA, although it was the least specific. IHC-TMA was the most specific assay. The lowest misclassification rate was achieved using AQUA-TMA (0.30). Conclusions. —Both AQUA-TMA and IHC-TMA were substantially more predictive than the FISH or IHC-WS tests. Although these results are derived from a small retrospective series, they suggest that accurate measurement of protein expression and unbiased selection of tissue for measurement may be key factors in prediction of response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".