HER-3 Overexpression Is Prognostic of Reduced Breast Cancer Survival
Bibliographic record
Abstract
In Brief Introduction: Advances in molecular biology have led to the identification of potential markers of prognostic and therapeutic importance in human cancers. HER-2 testing and targeted therapy now represents a critical cornerstone in the management of breast cancer. The objectives of the current study were to determine the frequency and prognostic significance of HER-3 over-expression and HER-4 over-expression by invasive breast cancer. Methods: Tissue microarrays were constructed using clinically annotated formalin-fixed and paraffin-embedded tumor samples from 4046 patients diagnosed with invasive breast carcinoma with a median 12.5 years of follow-up. Type 1 growth factor receptor family members HER-1, HER-2, HER-3, and HER-4 expression levels were determined by immunohistochemistry, and HER-2 status was further resolved by fluorescent in-situ hybridization. The study cohort was randomly divided and analyzed as a core data set and a validation data set. Results: HER-3 over-expression was identified in 10.0% of tumors and was a significant marker of reduced patient breast cancer-specific survival on univariate analysis (P = 1.32 × 10−5). Furthermore, in tumors with normal expression levels of HER-1 and HER-2, the overexpression of HER-3 had a significant negative prognostic effect on disease-specific survival (HR: 1.541, 95% CI: 1.166–2.036, P = 2.37 × 10−3) independent of patient age at diagnosis, Estrogen receptor status, tumor grade, tumor size, nodal status, and the presence of lymphatic or vascular invasion by cancer. HER-4 overexpression was identified in 78.2% of breast cancers and was not a significant marker of patient survival (P = 0.214). Results of all statistical tests were positively confirmed in the validation data set analysis. Conclusions: HER-3 status is an important prognostic marker of disease-specific survival in patients with invasive breast cancer. Accordingly, evaluation of the HER-3 expression level may identify a subset of patients with a poor disease prognosis, and who could undergo further evaluation for the efficacy of HER-3 targeted anticancer agents. Type 1 growth factor receptor family members HER-1, HER-2, HER-3, and HER-4 expression levels were determined utilizing tissue microarrays constructed of tumor samples from 4046 patients diagnosed with invasive breast cancer. HER-3 overexpression was identified in 10.0% of tumors and was a significant marker of reduced patient breast cancer-specific survival on univariate and multivariate analysis. Supplemental digital content is available in the article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".