1. Evaluating BRCA mutation risk predictive models in a Chinese cohort in Taiwan.
- Author
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Hung FH, Wang YA, Jian JW, Peng HP, Hsieh LL, Hung CF, Yang MM, and Yang AS
- Subjects
- Adult, Asian People genetics, Carcinoma, Ovarian Epithelial genetics, Cohort Studies, Female, Genes, BRCA1 physiology, Genes, BRCA2 physiology, Genetic Counseling, Genetic Predisposition to Disease genetics, Heterozygote, Humans, Middle Aged, Models, Statistical, Mutation genetics, Ovarian Neoplasms genetics, Probability, ROC Curve, Risk Assessment, Risk Factors, Taiwan epidemiology, BRCA1 Protein genetics, BRCA2 Protein genetics, Breast Neoplasms genetics, Genetic Testing methods
- Abstract
Accurate estimation of carrier probabilities of cancer susceptibility gene mutations is an important part of pre-test genetic counselling. Many predictive models are available but their applicability in the Asian population is uncertain. We evaluated the performance of five BRCA mutation risk predictive models in a Chinese cohort of 647 women, who underwent germline DNA sequencing of a cancer susceptibility gene panel. Using areas under the curve (AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comparably well as in western cohorts (BOADICEA 0.75, BRCAPRO 0.73, Penn II 0.69, Myriad 0.68). For unaffected women with family history of breast or ovarian cancer (n = 144), BOADICEA, BRCAPRO, and Tyrer-Cuzick models had excellent performance (AUC 0.93, 0.92, and 0.92, respectively). For women with both personal and family history of breast or ovarian cancer (n = 241), all models performed fairly well (BOADICEA 0.79, BRCAPRO 0.79, Penn II 0.75, Myriad 0.70). For women with personal history of breast or ovarian cancer but no family history (n = 262), most models did poorly. Between the two well-performed models, BOADICEA underestimated mutation risks while BRCAPRO overestimated mutation risks (expected/observed ratio 0.67 and 2.34, respectively). Among 424 women with personal history of breast cancer and available tumor ER/PR/HER2 data, the predictive models performed better for women with triple negative breast cancer (AUC 0.74 to 0.80) than for women with luminal or HER2 overexpressed breast cancer (AUC 0.63 to 0.69). However, incorporating ER/PR/HER2 status into the BOADICEA model calculation did not improve its predictive accuracy.
- Published
- 2019
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