Risks of Breast, Ovarian, and Contralateral Breast Cancer for $\textit{BRCA1}$ and $\textit{BRCA2}$ Mutation Carriers
Bibliographic record
Abstract
$\textbf{Importance}$ The clinical management of $\textit{BRCA1}$ and $\textit{BRCA2}$ mutation carriers requires accurate, prospective cancer risk estimates. $\textbf{Objectives}$ To estimate age-specific risks of breast, ovarian, and contralateral breast cancer for mutation carriers and to evaluate risk modification by family cancer history and mutation location. $\textbf{Design, Setting, and Participants}$ Prospective cohort study of 6036 $\textit{BRCA1}$ and 3820 $\textit{BRCA2}$ female carriers (5046 unaffected and 4810 with breast or ovarian cancer or both at baseline) recruited in 1997-2011 through the International $\textit{BRCA1/2}$ Carrier Cohort Study, the Breast Cancer Family Registry and the Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer, with ascertainment through family clinics (94%) and population-based studies (6%). The majority were from large national studies in the United Kingdom (EMBRACE), the Netherlands (HEBON), and France (GENEPSO). Follow-up ended December 2013; median follow-up was 5 years. $\textbf{Exposures}$ $\textit{BRCA1/2}$ mutations, family cancer history, and mutation location. $\textbf{Main Outcomes and Measures}$ Annual incidences, standardized incidence ratios, and cumulative risks of breast, ovarian, and contralateral breast cancer. $\textbf{Results}$ Among 3886 women (median age, 38 years; interquartile range [IQR], 30-46 years) eligible for the breast cancer analysis, 5066 women (median age, 38 years; IQR, 31-47 years) eligible for the ovarian cancer analysis, and 2213 women (median age, 47 years; IQR, 40-55 years) eligible for the contralateral breast cancer analysis, 426 were diagnosed with breast cancer, 109 with ovarian cancer, and 245 with contralateral breast cancer during follow-up. The cumulative breast cancer risk to age 80 years was 72% (95% CI, 65%-79%) for $\textit{BRCA1}$ and 69% (95% CI, 61%-77%) for $\textit{BRCA2}$ carriers. Breast cancer incidences increased rapidly in early adulthood until ages 30 to 40 years for $\textit{BRCA1}$ and until ages 40 to 50 years for $\textit{BRCA2}$ carriers, then remained at a similar, constant incidence (20-30 per 1000 person-years) until age 80 years. The cumulative ovarian cancer risk to age 80 years was 44% (95% CI, 36%-53%) for $\textit{BRCA1}$ and 17% (95% CI, 11%-25%) for $\textit{BRCA2}$ carriers. For contralateral breast cancer, the cumulative risk 20 years after breast cancer diagnosis was 40% (95% CI, 35%-45%) for $\textit{BRCA1}$ and 26% (95% CI, 20%-33%) for $\textit{BRCA2}$ carriers (hazard ratio [HR] for comparing $\textit{BRCA2}$ vs $\textit{BRCA1}$, 0.62; 95% CI, 0.47-0.82; P=.001 for difference). Breast cancer risk increased with increasing number of first- and second-degree relatives diagnosed as having breast cancer for both $\textit{BRCA1}$ (HR for ≥2 vs 0 affected relatives, 1.99; 95% CI, 1.41-2.82; P<.001 for trend) and $\textit{BRCA2}$ carriers (HR, 1.91; 95% CI, 1.08-3.37; P=.02 for trend). Breast cancer risk was higher if mutations were located outside vs within the regions bounded by positions c.2282-c.4071 in $\textit{BRCA1}$ (HR, 1.46; 95% CI, 1.11-1.93; P=.007) and c.2831-c.6401 in $\textit{BRCA2}$ (HR, 1.93; 95% CI, 1.36-2.74; P<.001). $\textbf{Conclusions and Relevance}$ These findings provide estimates of cancer risk based on $\textit{BRCA1}$ and $\textit{BRCA2}$ mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mutation location in risk assessment.
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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".