Assessing the Differential Expression of Molecular Biomarkers in Breast Cancer.
Bibliographic record
Abstract
Abstract Background: Molecular profiling of breast cancer has identified multiple biomarkers with potential to better predict clinical outcomes and response to treatment than existing clinicopathological indices. We focused on 8 biomarkers of particular relevance as suggested by the literature: CD44, methyl guanine methyltransferase (MGMT), epidermal growth factor receptor (EGFR), cyclooxygenase-2 (COX2), cytokeratin-5 (CK5), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This study aims to clarify the prognostic value of these breast cancer biomarkers as well as to define their relationships with each other.Methods: Formalin fixed paraffin embedded (FFPE) breast tumor samples from 140 patients diagnosed with breast cancer from Jan 2001 to Dec 2005 at St. Michael's Hospital in Toronto, Canada were examined retrospectively using tissue microarray analysis. Samples were stained for CD44, COX2, MGMT, EGFR, CK5, ER, PR, and HER2 by immunohistochemistry. Biomarkers including histological features and staining patterns were then evaluated. A manual chart review documenting relevant clinical and pathological features was also conducted. Subsequent statistical analysis utilized Kaplan-Meier survival curves to identify biomarker-survival associations and simple linear regression analysis to determine biomarker cluster groups.Results: Median patient age was 56 (range 31-86). Median follow-up time was 62 months (range 5-102). Tumors were of various pathological types and stages (I – IV). No significant associations between biomarkers and disease-free survival (DFS) were found. DFS (median = 53.5 months) did correlate with tumor stage, nuclear grade, and lymphovascular invasion (LVI) as expected. However, only LVI was found to correlate with DFS in triple negative patients (n=24). Regression analysis yielded two cluster groups of biomarkers; Cluster group 1 includes ER, PR, Cox2, CD44; and Cluster group 2 includes HER2, EGFR, CK5. Expression of biomarkers within one cluster group relate directly with those in the same group, but inversely with biomarkers from the other cluster group. In addition, biomarkers in Cluster group 1 relate inversely to the mitotic count, tumor size, and nuclear grade, while biomarkers from Cluster group 2 relate directly with these indices. For example, CK5 positive tumors with HER2 and EGFR overexpression tend to have a high mitotic count, large tumor size, and high nuclear grade. MGMT did not show association with any histological feature or DFS.Conclusions: This retrospective observational study of 140 breast cancer patients focused on the prognostic implications and relationships of 8 biomarkers: CD44, COX2, MGMT, EGFR, CK5, ER, PR, and HER2. Two cluster groups of biomarkers have been identified. Cluster group 1 (ER, PR, COX2, CD44) correlates with a less aggressive tumor morphology, while Cluster group 2 (HER2, EGFR, CK5) correlates with a more aggressive one. No associations have yet been found between biomarkers and DFS. This study has been extended to include 373 patients with further analysis underway. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6041.
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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 | 0.001 | 0.002 |
| 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".