Cancer risks in first degree relatives of <i>BRCA1</i> mutation carriers: effects of mutation and proband disease status
Bibliographic record
Abstract
BACKGROUND: Mutations in the BRCA1 (MIM 113705) gene are found in many families with multiple cases of breast and ovarian cancer, and women with a BRCA1 mutation are at significantly higher risk of developing breast and ovarian cancer than are the general public. METHODS: We obtained blood samples and pedigree information from 3568 unselected cases of early-onset breast cancer and 609 unselected patients with ovarian cancer from hospitals throughout Poland. Genetic testing was performed for three founder BRCA1 mutations. We also calculated the risk of breast and ovarian cancer to age 75 in the first degree relatives of carriers using Kaplan-Meier methods. RESULTS: The three founder BRCA1 mutations were identified in 273 samples (187 with 5382insC, 22 with 4153delA, and 64 with C61G). A mutation was present in 4.3% of patients with breast cancer and 12.3% of patients with ovarian cancer. The overall risk of breast cancer to age 75 in relatives was 33% and the risk of ovarian cancer was 15%. The risk for breast cancer was 42% higher among first degree relatives of carriers of the C61G missense mutation compared to other mutations (HR = 1.42; p = 0.10) and the risk for ovarian cancer was lower than average (OR = 0.26; p = 0.03). Relatives of women diagnosed with breast cancer had a higher risk of breast cancer than relatives of women diagnosed with ovarian cancer (OR = 1.7; p = 0.03). CONCLUSIONS: The risk of breast cancer in female relatives of women with a BRCA1 mutation depends on whether the proband was diagnosed with breast or ovarian cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".