P4-09-26: Prognostic Significance of Tissue Inhibitor of Metalloproteinase-1 (TIMP-1) in Breast Cancer.
Bibliographic record
Abstract
Abstract Background: Despite advances of systemic treatment in breast cancer guided by hormonal status and HER2 amplification, new prognostic and predictive factors are still warranted to optimize treatments among these patients. Tissue Inhibitor of Metalloproteinase-1 (TIMP-1), a physiologic inhibitor of Matrix Metalloproteinases (MMPs), can act in both pro- and anti-tumoral effects. The prognostic significance of TIMP-1 in breast cancer is still controversial. This study aims to determine the prognostic significance of TIMP-1 in breast cancer. Material and Methods: One-hundred and seventy-six primary breast cancers from women with early stage disease treated with standard adjuvant therapy were analyzed by gene expression microarrays and immunohistochemistry for TIMP-1. Immunohistochemical analysis was independently reviewed by two pathologists. Results: At the optimized cut-off point, patients with high TIMP-1 RNA levels had a significantly shorter time to relapse, with a hazard ratio (HR) of 1.6 (p = 0.039), but without significant differences in overall survival (HR 1.29, p = 0.37). Although cytoplasmic overexpression of TIMP-1 protein was not correlated with early relapse (HR 1.2, p = 0.35), high expression was associated with shorter overall survival (HR 1.73, p = 0.027). In multivariate analysis, when considering stage, histologic grade, hormonal and HER2 status, TIMP-1 RNA levels remained independently prognostic for early relapse (HR 1.68, p = 0.04). Discussion: Elevated TIMP-1 RNA levels are independently prognostic for early recurrence, whereas protein overexpression of TIMP-1 is correlated with short overall survival in primary breast cancer. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-09-26.
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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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".