Evaluating the Expression and Prognostic Value of TRAIL-R1 and TRAIL-R2 in Breast Cancer
Bibliographic record
Abstract
PURPOSE: The cell surface receptors tumor necrosis factor-related apoptosis-inducing ligand receptor 1 (TRAIL-R1) and TRAIL-R2 transmit apoptotic signals, and agents that activate these receptors are in clinical development. We sought to determine the expression and prognostic value of TRAIL-R1 and TRAIL-R2 in early-stage breast cancer. EXPERIMENTAL DESIGN: Tissue microarrays containing specimens from 655 breast cancer patients with 20-year follow-up were employed and evaluated with our automated quantitative analysis (AQUA) system. The system uses cytokeratin to define pixels as breast cancer (tumor mask) within the array spot, and measures intensity of TRAIL receptor expression using Cy5 conjugated antibodies within the mask. AQUA scores were correlated with clinical and pathologic variables. TRAIL-R1 and TRAIL-R2 expression were similarly studied on 95 unmatched normal breast specimens. RESULTS: TRAIL-R1 expression was not associated with survival. High TRAIL-R2 expression strongly correlated with decreased survival (P = 0.0005). On multivariate analysis, high TRAIL-R2 expression remained an independent prognostic marker, as did nodal status and tumor size. High TRAIL-R2 expression correlated strongly with lymph node involvement (P = 0.0003). TRAIL-R2 expression was stronger in malignant specimens than in normal breast epithelium (P < 0.0001). CONCLUSIONS: High TRAIL-R2 expression was independently associated with decreased survival in breast cancer. The biological basis and the sensitivity of high TRAIL-R2 expressing cells to TRAIL agonists and/or chemotherapy are subject to further investigation. Evaluation of TRAIL-R2 expression in early-stage breast cancer may identify a subset of patients requiring more aggressive or pathway-targeted adjuvant treatment. Clinical trials involving TRAIL-R2 agonists should stratify patients based on TRAIL-R2 expression.
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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 | 0.002 | 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) | 0.000 | 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".