Direct Detection of Herceptin/Trastuzumab Binding on Breast Tissue Sections
Bibliographic record
Abstract
The protooncogene product HER-2/neu is the target of the humanized monoclonal antibody trastuzumab (Herceptin). Several tests are used clinically to identify patients with HER-2/neu overexpression based on evaluation by pathologists of gene amplification by fluorescence in situ hybridization or protein expression using immunohistochemistry (IHC). A simple technique has been developed for staining formalin-fixed, paraffin-embedded breast cancer tissue using unmodified Herceptin/trastuzumab as the primary antibody. Results were compared with staining with the commercial kit, HercepTest, as well as with polyclonal anti-HER-2/neu antibodies and with biotinylated trastuzumab. These procedures were tested using four breast cancer microarrays. There were 854 cores that were stained with all four antibodies, representing 325 cases. A standard 4-point scoring system (0-3) was used. A total of 156 cases (48%) were scored as 0 by all the methods used and 31 (9.5%) were positive (3+) by all methods. Of interest, three cases scored negative using polyclonal anti-HER-2/neu antibodies but were positive using unmodified trastuzumab. To clarify this discrepancy, whole sections of tumors were examined with both antibodies using double labeling. There were some tumors that demonstrated a mosaic pattern of staining with neighboring cells or groups of cells stained exclusively with one antibody or the other. These results demonstrate that unmodified humanized or human therapeutic antibodies could be used for preclinical testing or in a clinical laboratory setting for IHC-based selection of patients for treatment, and results of such selection could be different from those obtained using polyclonal antibody-based IHC procedure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".