CpG Island Tumor Suppressor Promoter Methylation in Non-BRCA-Associated Early Mammary Carcinogenesis
Bibliographic record
Abstract
BACKGROUND: Only 5% of all breast cancers are the result of BRCA1/2 mutations. Methylation silencing of tumor suppressor genes is well described in sporadic breast cancer; however, its role in familial breast cancer is not known. METHODS: CpG island promoter methylation was tested in the initial random periareolar fine-needle aspiration sample from 109 asymptomatic women at high risk for breast cancer. Promoter methylation targets included RARB (M3 and M4), ESR1, INK4a/ARF, BRCA1, PRA, PRB, RASSF1A, HIN-1, and CRBP1. RESULTS: Although the overall frequency of CpG island promoter methylation events increased with age (P<0.0001), no specific methylation event was associated with age. In contrast, CpG island methylation of RARB M4 (P=0.051), INK4a/ARF (P=0.042), HIN-1 (P=0.044), and PRA (P=0.032), as well as the overall frequency of methylation events (P=0.004), was associated with abnormal Masood cytology. The association between promoter methylation and familial breast cancer was tested in 40 unaffected premenopausal women in our cohort who underwent BRCA1/2 mutation testing. Women with BRCA1/2 mutations had a low frequency of CpG island promoter methylation (15 of 15 women had <or=4 methylation events), whereas women without a mutation showed a high frequency of promoter methylation events (24 of 25 women had 5-8 methylation events; P<0.0001). Of women with a BRCA1/2 mutation, none showed methylation of HIN-1 and only 1 of 15 women showed CpG island methylation of RARB M4, INK4a/ARF, or PRB promoters. CONCLUSIONS: This is the first evidence of CpG island methylation of tumor suppressor gene promoters in non-BRCA1/2 familial breast cancer.
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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 | 0.000 | 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".