Obesity promotes breast epithelium DNA damage in women carrying a germline mutation in <i>BRCA1 or BRCA2</i>
Why this work is in the frame
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Bibliographic record
Abstract
Obesity, defined as a body mass index (BMI) ≥ 30, is an established risk factor for breast cancer among women in the general population after menopause. Whether elevated BMI is a risk factor for women with a germline mutation in BRCA1 or BRCA2 is less clear because of inconsistent findings from epidemiological studies and a lack of mechanistic studies in this population. Here, we show that DNA damage in normal breast epithelia of women carrying a BRCA mutation is positively correlated with BMI and with biomarkers of metabolic dysfunction. In addition, RNA sequencing showed obesity-associated alterations to the breast adipose microenvironment of BRCA mutation carriers, including activation of estrogen biosynthesis, which affected neighboring breast epithelial cells. In breast tissue explants cultured from women carrying a BRCA mutation, we found that blockade of estrogen biosynthesis or estrogen receptor activity decreased DNA damage. Additional obesity-associated factors, including leptin and insulin, increased DNA damage in human BRCA heterozygous epithelial cells, and inhibiting the signaling of these factors with a leptin-neutralizing antibody or PI3K inhibitor, respectively, decreased DNA damage. Furthermore, we show that increased adiposity was associated with mammary gland DNA damage and increased penetrance of mammary tumors in Brca1 +/− mice. Overall, our results provide mechanistic evidence in support of a link between elevated BMI and breast cancer development in BRCA mutation carriers. This suggests that maintaining a lower body weight or pharmacologically targeting estrogen or metabolic dysfunction may reduce the risk of breast cancer in this population.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it