Nuclear Ep-ICD accumulation predicts aggressive clinical course in early stage breast cancer patients
Bibliographic record
Abstract
BACKGROUND: Regulated intramembrane proteolysis of Epithelial cell adhesion molecule (EpCAM) results in release of its intracellular domain (Ep-ICD) which triggers oncogenic signalling. The clinical significance of Ep-ICD in breast cancer remains to be determined. Herein, we examined the expression of nuclear and cytoplasmic Ep-ICD, and membranous extracellular domain of EpCAM (EpEx) in breast cancer patients, to determine its potential utility in predicting aggressive clinical course of the disease. METHODS: In this retrospective study, 266 breast cancers and 45 normal breast tissues were immunohistochemically analyzed to determine the expression patterns of nuclear and cytoplasmic Ep-ICD and membranous EpEx and correlated with clinicopathological parameters and follow up. Disease-free survival was determined by Kaplan-Meier method and multivariate Cox regression analysis. RESULTS: Nuclear Ep-ICD was more frequently expressed in breast cancers compared to normal tissues. Significant association was observed between increased nuclear Ep-ICD expression and reduced disease-free survival in patients with ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) (p < 0.001). Nuclear Ep-ICD was positive in all the 13 DCIS and 25 IDC patients who had reduced disease-free survival, while none of the nuclear Ep-ICD negative DCIS or IDC patients had recurrence during the follow up period. Notably, majority of IDC patients who had recurrence had early stage tumors. Multivariate Cox regression analysis identified nuclear Ep-ICD as the most significant predictive factor for reduced disease-free survival in IDC patients (p = 0.011, Hazard ratio = 80.18). CONCLUSION: Patients with nuclear Ep-ICD positive breast cancers had poor prognosis. The high recurrence of disease in nuclear Ep-ICD positive patients, especially those with early tumor stage suggests that nuclear Ep-ICD accumulation holds the promise of identifying early stage patients with aggressive disease who are likely to be in need of more rigorous post-operative surveillance and/or treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".