Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO.

Department of Biostatistics, School of Public Health, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX 76107-2699, USA.
Breast Cancer Research and Treatment (Impact Factor: 4.47). 01/2012; 133(1):347-55. DOI: 10.1007/s10549-012-1958-z
Source: PubMed

ABSTRACT The BRCAPRO model estimates carrier probabilities for the BRCA1 and BRCA2 genes, and was recently enhanced to use estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer. No independent assessment of the added value of these markers exists. Moreover, earlier versions of BRCAPRO did not use human epidermal growth factor receptor 2 (Her-2/neu) status of breast cancer. Here, we incorporate Her-2/neu in BRCAPRO and validate all the markers. We trained the enhanced model on 406 germline tested individuals, and validated on a separate clinical cohort of 796 individuals for whom test results and family history are available. For model-building, we estimated joint probabilities of ER, PR, and Her-2/neu status for carriers and non-carriers of BRCA1/2 mutations. For validation, we obtained BRCAPRO predictions with and without markers. We calculated area under the receiver operating characteristic curve (AUC), sensitivity, specificity, predictive values, and correct reclassification rates. The AUC for predicting BRCA1 status among individuals who are carriers of at least one mutation improved when ER and PR were used. The AUC for predicting the presence of either mutation improved when Her-2/neu was added. Use of markers also produced highly significant correct reclassification improvements in both cases. Breast tumor markers are useful for prediction of BRCA1/2 mutation status. ER and PR improve discrimination between BRCA1 and BRCA2 mutation carriers while Her-2/neu helps discriminate between carriers and non-carriers, particularly among women who are ER positive and Her-2/neu negative. These results support the use of the enhanced version of BRCAPRO in clinical settings.

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    Journal of Clinical Oncology 09/2014; · 17.88 Impact Factor
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    ABSTRACT: The recent release of version 2.0-8 of the BayesMendel package contains an updated BRCAPRO risk prediction model, which includes revised modeling of Contralateral Breast Cancer (CBC) penetrance, provisions for pedigrees of mixed ethnicity and an adjustment for mastectomies among family members. We estimated penetrance functions for contralateral breast cancer by a combination of parametric survival modeling of literature data and deconvolution of SEER9 data. We then validated the resulting updated model of CBC in BRCAPRO by comparing it with the previous release (BayesMendel 2.0-7), using pedigrees from the Cancer Genetics Network (CGN) Model Validation Study. Version 2.0-8 of BRCAPRO discriminates BRCA1/BRCA2 carriers from non-carriers with similar accuracy compared to the previous version (increase in AUC: 0.0043), is slightly more precise in terms of RMSE (decrease in RMSE: 0.0108), and it significantly improves calibration (ratio of observed to expected events of 0.9765 in version 2.0-8, compared to 0.8910 in version 2.0-7). We recommend that the new version be used in clinical counseling, particularly in settings where families with CBC are common.
    Cancer Epidemiology Biomarkers & Prevention 06/2014; 23(8). · 4.32 Impact Factor
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    ABSTRACT: Risk prediction models play an important role in prevention and treatment of several diseases. Models that are in clinical use are often refined and improved. In many instances, the most efficient way to improve a successful model is to identify subgroups for which there is a specific biological rationale for improvement and tailor the improved model to individuals in these subgroups, an approach especially in line with personalized medicine. At present, we lack statistical tools to evaluate improvements targeted to specific subgroups. Here, we propose simple tools to fill this gap. First, we extend a recently proposed measure, the Integrated Discrimination Improvement, using a linear model with covariates representing the subgroups. Next, we develop graphical and numerical tools that compare reclassification of two models, focusing only on those subjects for whom the two models reclassify differently. We apply these approaches to BRCAPRO, a genetic risk prediction model for breast and ovarian cancer, using data from MD Anderson Cancer Center. We also conduct a simulation study to investigate properties of the new reclassification measure and compare it with currently used measures. Our results show that the proposed tools can successfully uncover subgroup specific model improvements. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 12/2013; · 2.04 Impact Factor


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Aug 12, 2014