Recurrent BRCA1 and BRCA2 germline mutations in ovarian cancer: A founder mutation of BRCA1 identified in the Chinese population
Bibliographic record
Abstract
Previous mutational analysis for BRCA gene mutations in sporadic ovarian cancer occurring in Chinese patients in Hong Kong identified six germline BRCA1 mutations and one germline BRCA2 mutation, six of which were novel (Khoo et al., 2000). Knowledge of BRCA gene mutations in the Chinese population is relatively scant. In this study, we focussed on whether any of these mutations could be recurrent in our Chinese population, making use of archival paraffin embedded tissue. A consecutive series of 214 ovarian cancer cases, half of Southern Chinese origin from Hong Kong whilst the other half of Northern Chinese origin from Beijing were used for the study. We identified one further novel mutation, 1081delG, in BRCA1. This was found to occur in two unrelated individuals with shared haplotype as revealed by allelotype analysis, thus demonstrating founder effect. Two other recurrent mutations were also identified, the 2371-2372delTG mutation in BRCA1 and the 3337C>T mutation in BRCA2 recurring in two and three unrelated individuals respectively, giving an overall prevalence 4.7% of recurrent BRCA mutations in ovarian cancer in the Southern Chinese population. Most importantly, all our recurrent mutation carriers were identified from Southern Chinese patients from Hong Kong whilst such mutations were absent in samples from the Northern Chinese. Our findings indicate possible heterogeneity in the BRCA genotype between Northern and Southern Chinese. The identification of a founder mutation and two recurrent mutations moreover, has important implications towards screening strategies for breast and ovarian cancer among Chinese of southern ancestral origin who are now dispersed throughout the world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".