The joint effect of smoking and AIB1 on breast cancer risk in BRCA1 mutation carriers
Bibliographic record
Abstract
Women with BRCA1 mutations are at an elevated risk for breast cancer. AIB1 (NCOA3/SRC3) genotype and smoking may alter this risk. We examined the differences in breast cancer risk by AIB1 polyglutamine repeat polymorphism and pre-diagnosis smoking habits for BRCA1 mutation carriers to determine if there was an interaction between smoking and AIB1 genotype. Multivariate Cox proportional hazards regression was used with 316 female BRCA1 mutation carriers to model breast cancer risk. Ever having smoked was associated with a decreased breast cancer risk [Hazard Ratio (HR) = 0.63, 95% CI, 0.47-0.87]. A dose-response relationship between number of pack-years smoked and breast cancer risk was also found for women who smoked <20 pack years of cigarettes (HR = 0.72, 95% CI, 0.52-1.00) and for women who smoked >/=20 pack years (HR = 0.41, 95% CI, 0.23-0.71; P for trend = 0.0007). Women with a 28 repeat allele for AIB1 had a significantly reduced risk of breast cancer (HR = 0.72, 95% CI, 0.51-1.00). Women who smoked >/=20 pack-years with a 28 repeat allele had an even greater reduced risk of breast cancer (HR = 0.19, 95% CI, 0.07-0.54) compared to women who were never smokers with no 28 allele. Since AIB1 appears to modulate the effect of endogenous hormones via the estrogen receptor, and smoking affects circulating hormone levels, these results support evidence that steroid hormones play an important role in breast carcinogenesis in BRCA1 mutation carriers, and suggest mechanisms for developing novel cancer prevention strategies for BRCA1 mutation carriers.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".