Detection of IKKε by immunohistochemistry in primary breast cancer: association with EGFR expression and absence of lymph node metastasis
Bibliographic record
Abstract
BACKGROUND: IKKε is an oncogenic kinase that was found amplified and overexpressed in a substantial percentage of human breast cancer cell lines and primary tumors using genomic and gene expression analyses. Molecular studies have provided the rational for a key implication of IKKε in breast cancer cells proliferation and invasiveness through the phosphorylation of several substrates. METHODS: Here, we performed immunohistochemical detection of IKKε expression on tissue microarrays constituted of 154 characterized human breast cancer tumors. We further determined the association with multiple clinicopathological parameters and 5-years overall, disease-free and distant disease free survival. RESULTS: We observed expression of IKKε in 60.4% of the breast cancer tumors. IKKε expression status showed no association with a panel of markers used for molecular classification of the tumors, including ER/PR/HER2 status, or with the molecular subtypes. However, IKKε expression was inversely associated with lymph node metastasis status (p = 0.0032). Additionally, we identified a novel association between IKKε and EGFR expression (p = 0.0011). CONCLUSIONS: The unexpected observation of an inverse association between IKKε and lymph node metastasis advocates for larger scale immunohistochemical profiling of primary breast tumors to clarify the role of IKKε in metastasis. This study suggests that breast cancer tumors expressing EGFR and IKKε may be potential targets for drugs aiming at inhibiting IKKε activity or expression.
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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) | 0.002 | 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 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".