Immunohistochemical Characterization of a Large Cohort of Triple Negative Breast Cancer
Bibliographic record
Abstract
Introduction. Triple negative breast carcinomas are characterized by a lack of hormone receptor and HER2 expression and inconsistent expression of breast-specific immunohistochemical markers. The expression of many site-specific markers in these tumors is largely unknown. The objective of the study was to examine the expression of widely used immunohistochemical markers on a large cohort of triple negative breast cancer. Methods. Sections from tissue microarrays were stained with 47 markers using routine protocols. Most markers were scored using a modified Allred method. ATRX, BAP1, SMAD4, e-cadherin, and beta-catenin were scored as retained or lost. Mammaglobin was considered positive if there was at least moderate intensity staining in any tumor cells. P16 was scored as overexpressed or not overexpressed; p53 was scored as wildtype, overexpressed, null, or cytoplasmic. Results. The cohort consisted of 639 tumors including 601 primary and 32 metastases. Overall, 96% expressed GATA3, mammaglobin, and/or SOX10 while 97% of no special type tumors expressed this panel. Carcinoma of apocrine differentiation demonstrated an AR positive, SOX10 negative, K5 negative/focal immunophenotype. PAX8 (SP348), WT1, Napsin A, and TTF1 (8G7G3/1) were never or rarely expressed while CA9, CDX2, NKX3.1, SATB2 (SATBA410), synaptophysin, and vimentin were variably expressed. Conclusions. Almost all TNBC express at least 1 of the 3 IHC markers: GATA3, mammaglobin, and/or SOX10. Carcinoma of apocrine differentiation is characterized by an AR positive, SOX10 negative, K5 negative or focal immunophenotype. Cautious interpretation of so-called site-specific markers, with knowledge of antibody clones, is required in excluding the diagnosis of triple negative breast cancer.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".