Evaluation of human epidermal growth factor receptor 2 (HER2) single nucleotide polymorphisms (SNPs) in normal and breast tumor tissues and their link with breast cancer prognostic factors
Bibliographic record
Abstract
Amplification of the human epidermal growth factor receptor 2 (HER2) gene is associated with worse prognosis and decreased overall survival in breast cancer patients. The HER2 gene contains several polymorphisms; two of the best-characterized HER2 polymorphisms are Ile655Val and Ala1170Pro. The aim of this study was to evaluate the association between these two HER2 polymorphisms in normal breast and breast cancer tissues and known breast cancer prognostic factors in a retrospective cohort study of 73 women with non-metastatic HER2-positive breast cancer. HER2 polymorphisms were assessed in breast cancer tissue and normal breast tissue using TaqMan assay. Ala1170Pro polymorphism in normal breast tissue was associated with age at diagnosis (p = 0.007), tumor size (p = 0.004) and lymphovascular invasion (p = 0.06). Similar significant associations in cancer tissues were observed. No association between the Ile655Val polymorphism and prognostic factors were observed. However, we found significant differences in the distribution of Ile655Val (p = 0.03) and Ala1170Pro (p = 0.01) genotypes between normal breast and breast tumor tissues. This study demonstrates that only the Ala1170Pro polymorphism is associated with prognostic factors in HER2-positive breast cancer patients. Moreover, our results suggest that both HER2 polymorphisms could play a significant role in carcinogenesis in non-metastatic HER2-positive breast cancer women.
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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 | 0.001 | 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) | 0.001 | 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".