Adverse association of expressed vascular endothelial growth factor (VEGF) with long-term outcome of stage I-III breast cancer (BrCa), with co-expression data of VEGF and Her2, Cox2, uPA and ER. Results from the British Columbia Tissue Microarray Project
Bibliographic record
Abstract
524 Background. Several prior studies have shown association of VEGF with inferior outcome of BrCa. However, only a few past studies had a large number of patients, or availability of multiple markers and prognostic factors.Methods. In this study, impact of VEGF was correlated with the outcome in a large cohort of Breast Cancer (BrCa) patients, enrolled between 1978–1990 in phase II-III British Columbia trials, who had tissue microarrays (TMA) built from paraffin, with 877 pts having VEGF expression tested by Immunohistochemistry (IHC, Biogenix polyclonal anti-VEGF Ab, Dako). For detection, the LSAB2 kit with the DAB as the chromogen, was used. Event at 15 years was any death (expressed as OS%), with the median survival and 95% confidence intervals (CI) expressed for 4 groups according to the stain intensity. Other markers (Her2, Cox2, uPA, ER, etc.) were tested for co-expression. Results. At a median follow up of 17.4 years (ranges 9.8, 28.7 years), 586 patients died (any death), and 502 had BrCa recurrence. Expression of VEGF showed progressively worse outcome according to intensity of expression. VEGF expression showed a positive coexpression with Her2 (49 vs 61% for Her2-ve vs +ve, p=0.006); Cox2 (47 vs 57%, for −ve vs +ve, p=0.004); uPA (43 vs 59%, for −ve vs +ve, p<0.001) and a negative coexpression with ER (55 vs 47%, for −ve vs +ve).Conclusion: Expression of VEGF, and its intensity, are associated with a significantly inferior outcome of early BrCa, and with co-expression of several markers of relevance in BrCa biology. VEGF, Her2, Cox2 and other markers can be successfully tested on TMA permitting interactive testing of multiple markers. These data indicate that diagnostic and therapeutic trials exploiting VEGF expression interacting with other markers are indicated for early BrCa. No significant financial relationships to disclose.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".