Obesity promotes breast epithelium DNA damage in women carrying a germline mutation in <i>BRCA1 or BRCA2</i>
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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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 | 0.000 |
| 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) | 0.002 | 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".