Practical Application of Lineage-Specific Immunohistochemistry Markers: Transcription Factors (Sometimes) Behaving Badly
Bibliographic record
Abstract
CONTEXT.—: Transcription factors (TFs) are proteins that regulate gene expression and control RNA transcription from DNA. Lineage-specific TFs have increasingly been used by pathologists to determine tumor lineage, especially in the setting of metastatic tumors of unknown primary, among other uses. With experience gathered from its daily application and increasing pitfalls reported from immunohistochemical studies, these often-touted highly specific TFs are not as reliable as once thought. OBJECTIVES.—: To summarize the established roles of many of the commonly used TFs in clinical practice and to discuss known and potential sources for error (eg, false-positivity from cross-reactivity, aberrant, and overlap "lineage-specific" expression) in their application and interpretation. DATA SOURCES.—: Literature review and the authors' personal practice experience were used. Several examples selected from the University Health Network (Toronto, Ontario, Canada) are illustrated. CONCLUSIONS.—: The application of TF diagnostic immunohistochemistry has enabled pathologists to better assess the lineage/origin of primary and metastatic tumors. However, the awareness of potential pitfalls is essential to avoid misdiagnosis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".