Abstract P2-10-07: Ep-ICD overexpression associates with poor prognosis in invasive ductal carcinoma
Bibliographic record
Abstract
Abstract Background: Despite improvements in treatment strategies recurrence rates are still high among breast cancer patients. This may be attributed to heterogeneous nature of breast cancers representing varied morphologic and biological features, behavior, and response to therapy. Even among breast tumors of similar histologic type and grade, prognosis varies. Currently, breast cancer prognosis assessment methods have limited accuracy, are expensive, and in 20-30% of cases lead to over-treatment with adverse effects. None of the currently known prognostic factors has the ability to predict accurately which breast cancer patients are at high risk of recurrence. Thus, there is an increasing need for identification and validation of prognostic markers for assessment of risk for disease recurrence in breast cancer patients. Epithelial cell adhesion molecule (EpCAM) is a glycosylated, 30- to 40-kDa type I membrane protein, expressed in several human epithelial tissues and overexpressed in cancers, as well as in progenitors, normal and cancer stem cells, and is implicated in epithelial mesenchymal transition (EMT). Regulated intra-membrane proteolysis (RIP) of EpCAM by tumor-necrosis-factor alpha converting enzyme (TACE) results in shedding of its extracellular domain (EpEx) and release of intracellular domain, Ep-ICD, into the cytoplasm. Ep-ICD can signal into the cell nucleus by engagement of components of the Wnt pathway proteins including four and one half LIM domains protein 2 (FHL2), β-catenin and Lef, leading to activation of its oncogenic activity. Objective. Evaluate the prognostic significance of Ep-ICD overexpression in invasive ductal carcinoma (IDC). Methodology: Formalin fixed paraffin embedded (FFPE) tissue sections obtained from IDCs (n = 180) and normal breast tissues (n = 45) were used for immunostaining for Ep-ICD using specific monoclonal antibody. A semi-quantitative visual scoring of the immunostaining results for Ep-ICD based on percentage of tumor cells stained and intensity of scoring was used to compare the expression in breast cancers and normal tissues. Statistical analysis was carried out to determine the association of Ep-ICD expression with clinical outcome. Results: Among the 180 IDCs analyzed, nuclear Ep-ICD was observed in 75 tissues (41.7%) while cytoplasmic positivity was observed in 145 tissues (80.6%). In comparison, nuclear Ep-ICD localization was observed only in 11 normal tissues (23.9%) and cytoplasmic positivity was observed in 39 normal tissues (86.7%). The nuclear / cytoplasmic Ep-ICD expression has been correlated with at least 5 years follow-up data of 180 breast cancer patients after primary treatment. Kaplan Meier survival analysis showed significantly reduced 5 year disease free survival in IDC patients showing nuclear positivity (p < 0.001) or cytoplasmic positivity (p = 0.048). In Cox multivariate regression analysis, nuclear Ep-ICD overexpression emerged as an independent indicator of poor prognosis in IDCs (p = 0.008, H.R. = 81.18). Conclusion: Among invasive ductal carcinomas of the breast, nuclear Ep-ICD overexpression predicts reduced 5-year disease free survival. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-10-07.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".