Prediction of BRCA Mutations Using the BRCAPRO Model in Clinic-Based African American, Hispanic, and Other Minority Families in the United States

Department of Epidemiology, Columbia University, New York, New York, United States
Journal of Clinical Oncology (Impact Factor: 18.43). 03/2009; 27(8):1184-90. DOI: 10.1200/JCO.2008.17.5869
Source: PubMed


BRCAPRO, a BRCA mutation carrier prediction model, was developed on the basis of studies in individuals of Ashkenazi Jewish and European ancestry. We evaluated the performance of the BRCAPRO model among clinic-based minority families. We also assessed the clinical utility of mutation status of probands (the first individual tested in a family) in the recommendation of BRCA mutation testing for other at-risk family members.
A total of 292 minority families with at least one member who was tested for BRCA mutations were identified through the Breast Cancer Family Registry and the University of Chicago. Using the BRCAPRO model, the predicted likelihood of carrying BRCA mutations was generated. Area under the receiver operating characteristic curves (AUCs) were calculated.
There were 104 African American, 130 Hispanic, 37 Asian-American, and 21 other minority families. The AUC was 0.748 (95% CI, 0.672 to 0.823) for all minorities combined. There was a statistically nonsignificant trend for BRCAPRO to perform better in Hispanic families than in other minority families. After taking into account the mutation status of probands, BRCAPRO performance in additional tested family members was improved: the AUC increased from 0.760 to 0.902.
The findings support the use of BRCAPRO in pretest BRCA mutation prediction among minority families in clinical settings, but there is room for improvement in ethnic groups other than Hispanics. Knowledge of the mutation status of the proband provides additional predictive value, which may guide genetic counselors in recommending BRCA testing of additional relatives when a proband has tested negative.

Download full-text


Available from: Kisha Hope, Feb 05, 2014
  • Source
    • "We also found better performance for IBIS in almost all covariate-specific subgroups, except for Hispanic and nonwhite women, and women with a prior breast biopsy. Race is an important predictor of breast cancer risk [36], and hereditary patterns and mutation prevalences differ by race and ethnicity [37]. The BCRAT model was updated, in 2008, to incorporate revised estimates for African American women [8] and in 2011, to include projections for Asian and Pacific Islander Americans. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Introduction Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. Methods We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). Results The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Conclusions Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.
    Breast Cancer Research 11/2012; 14(6). DOI:10.1186/bcr3352 · 5.49 Impact Factor
  • Source
    • "A larger cohort of male probands is necessary to allow further confirmation of our studies’ findings. Consistent with previous reported studies, we found that both BOADCIEA and BRCAPRO models underestimated the number of mutations carriers at a lower threshold and overestimated at a higher threshold [53]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: BRCA1/2 mutation prediction models (BRCAPRO, Myriad II, Couch, Shattuck-Eidens, BOADICEA) are well established in western cohorts to estimate the probability of BRCA1/2 mutations. Results are conflicting in Asian populations. Most studies did not account for gender-specific prediction. We evaluated the performance of these models in a Chinese cohort, including males, before BRCA1/2 mutation testing. The five risk models were used to calculate the probability of BRCA mutations in probands with breast and ovarian cancers; 267 were non-BRCA mutation carriers (247 females and 20 males) and 43 were BRCA mutation carriers (38 females and 5 males). Mean BRCA prediction scores for all models were statistically better for carriers than noncarriers for females but not for males. BRCAPRO overestimated the numbers of female BRCA1/2 mutation carriers at thresholds ≥20% but underestimated if <20%. BRCAPRO and BOADICEA underestimated the number of male BRCA1/2 mutation carriers whilst Myriad II underestimated the number of both male and female carriers. In females, BRCAPRO showed similar discrimination, as measured by the area under the receiver operator characteristic curve (AUC) for BRCA1/2 combined mutation prediction to BOADICEA, but performed better than BOADICEA in BRCA1 mutation prediction (AUC 93% vs. 87%). BOADICEA had the best discrimination for BRCA1/2 combined mutation prediction (AUC 87%) in males. The variation in model performance underscores the need for research on larger Asian cohorts as prediction models, and the possible need for customizing these models for different ethnic groups and genders.
    World Journal of Surgery 01/2012; 36(4):702-13. DOI:10.1007/s00268-011-1406-y · 2.64 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Breast cancer is a complex disease caused by the progressive accumulation of multiple gene mutations combined with epigenetic dysregulation of critical genes and protein pathways. There is substantial interindividual variability in both the age at diagnosis and phenotypic expression of the disease. With an estimated 1,152,161 new breast cancer cases diagnosed worldwide per year, cancer control efforts in the postgenome era should be focused at both population and individual levels to develop novel risk assessment and treatment strategies that will further reduce the morbidity and mortality associated with the disease. The discovery that mutations in the BRCA1 and BRCA2 genes increase the risk of breast and ovarian cancers has radically transformed our understanding of the genetic basis of breast cancer, leading to improved management of high-risk women. A better understanding of tumor host biology has led to improvements in the multidisciplinary management of breast cancer, and traditional pathologic evaluation is being complemented by more sophisticated genomic approaches. A number of genomic biomarkers have been developed for clinical use, and increasingly, pharmacogenetic end points are being incorporated into clinical trial design. For women diagnosed with breast cancer, prognostic or predictive information is most useful when coupled with targeted therapeutic approaches, very few of which exist for women with triple-negative breast cancer or those with tumors resistant to chemotherapy. The immediate challenge is to learn how to use the molecular characteristics of an individual and their tumor to improve detection and treatment, and ultimately to prevent the development of breast cancer. The five articles in this edition of CCR Focus highlight recent advances and future directions on the pathway to individualized approaches for the early detection, treatment, and prevention of breast cancer.
    Clinical Cancer Research 01/2009; 14(24):7988-99. DOI:10.1158/1078-0432.CCR-08-1211 · 8.72 Impact Factor
Show more