BRCA1/2 mutations and cancer risk in Asian-Americans
Bibliographic record
Abstract
10512 Background: There are significant differences in breast cancer epidemiology between Caucasian and Asian-Americans and even between different Asian groups. These cancer risks and associated BRCA1/2 mutation prevalence have not been well defined in Asians; BRCA1/2 mutation penetrance might differ due to different risk modifiers. We report on a case-control study of BRCA1/2 mutation prevalence and cancer risk in Asian-American women. Methods: Clinical Data Collection: Chart review from cancer genetics services of 4 North American centers with highest Asian volume. BRCA1/2 Mutation Risk Assessment: BRCAPRO and Myriad II models, CancerGene version 4.3 (University of Texas). BRCA1/2 Mutation Testing: Full sequencing and large rearrangement panel (Myriad Genetics Inc.). Endpoints: BRCA1/2 mutation prevalence and predictive model accuracy (observed versus predicted mutations). Results: 43 of 181 Asians (23.8%) had a BRCA1/2 mutation; 36 (19.9%) had a variant of uncertain significance. The observed prevalence of BRCA1/2 mutations was 23.8% of women, which differs significantly from the predicted prevalence of 12.9% using BRCAPRO (p = 5.6 × 10-9), and the predicted prevalence of 12.6% using Myriad II. This 2-fold difference existed for Chinese, Japanese, and Filipina women (the ethnic sub-groups with enough cases available for comparisons), even though the percent with observed and predicted mutations varied for these three groups. Conclusions: One in 4 clinically tested Asian-Americans has a BRCA1/2 mutation. Standard models significantly under-predict mutations in Asians; consequently, Asians are likely under-tested for BRCA1/2 mutations. These results may reflect lower BRCA1/2-associated cancer risk in Asians compared to Caucasians. Comparison to Caucasian controls and to Asians in Hong Kong is underway, to investigate potential genetic and lifestyle modifiers of BRCA1/2-associated cancer risk. [Table: see text] No significant financial relationships to disclose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".