Association between Basal-Like Phenotype and BRCA1/2 Germline Mutations in Korean Breast Cancer Patients
Bibliographic record
Abstract
Introduction: BRCA mutation testing allows index patients and their families to be provided with appropriate cancer risk-reduction strategies. Because of the low prevalence of BRCA mutations in unselected breast cancer patients and the high cost of genetic testing, it is important to identify the subset of women who are likely to carry BRCA mutations. In the present study, we examined the association between BRCA1/2 germline mutations and the immunohistochemical features of breast cancer. Methods: In a retrospective review of 498 breast cancer patients who had undergone BRCA testing at Seoul National University Bundang Hospital between July 2003 and September 2012, we gathered immunohistochemical information on estrogen receptor (ER), progesterone receptor (PR), HER2 (human epidermal growth factor receptor 2), cytokeratin 5/6, EGFR (epidermal growth factor receptor), and p53 status. Results: Among the 411 patients eligible for the study, 50 (12.2%) had germline mutations in BRCA1 or BRCA2. Of the 93 patients with triple-negative breast cancer (TNBC), 25 with BRCA1/2 mutations were identified (BRCA1, 20.4%; BRCA2, 6.5%). On univariate analysis, ER, PR, cytokeratin 5/6, EGFR, and TNBC were found to be related to BRCA1 mutations, but on multivariate analysis, only TNBC was significantly associated with BRCA1 mutations. Among patients with early-onset breast cancer or with a family history of breast or ovarian cancer, BRCA1 mutations were significantly more prevalent in the tnbc group than in the non-TNBC group. Conclusions: In the present study, TNBC was the only independent predictor of BRCA1 mutation in patients at high risk of hereditary breast and ovarian cancers. Other histologic features of basal-like breast cancer did not improve the estimate of BRCA1 mutation risk.
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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) | 0.002 | 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".