Dataset Information

Performance of BRCA1/2 mutation prediction models in Asian Americans.

ABSTRACT:

Purpose

There are established differences in breast cancer epidemiology between Asian and white individuals, but little is known about hereditary breast cancer in Asian populations. Although increasing numbers of Asian individuals are clinically tested for BRCA1/2 mutations, it is not known whether computer models that predict mutations work accurately in Asian individuals. We compared the performance in Asian and white individuals of two widely used BRCA1/2 mutation prediction models, BRCAPRO and Myriad II.

Patients and methods

We evaluated BRCAPRO and Myriad II in 200 Asian individuals and a matched control group of 200 white individuals who were tested for BRCA1/2 mutations at four cancer genetics clinics, by comparing numbers of observed versus predicted mutation carriers and by evaluating area under the receiver operating characteristic curve (AUC) for each model.

Results

BRCAPRO and Myriad II accurately predicted the number of white BRCA1/2 mutation carriers (25 observed v 24 predicted by BRCAPRO; 25 predicted by Myriad II, P > or = .69), but underpredicted Asian carriers by two-fold (49 observed v 25 predicted by BRCAPRO; 26 predicted by Myriad II; P < or = 3 x 10(-7)). For BRCAPRO, this racial difference reflects substantial underprediction of Asian BRCA2 mutation carriers (26 observed v 4 predicted; P = 1 x 10(-30)); for Myriad II, separate mutation predictions were not available. For both models, AUCs were nonsignificantly lower in Asian than white individuals, suggesting less accurate discrimination between Asian carriers and noncarriers.

Conclusion

Both BRCAPRO and Myriad II underestimated the proportion of BRCA1/2 mutation carriers, and discriminated carriers from noncarriers less well, in Asian compared with white individuals.

SUBMITTER: Kurian AW

PROVIDER: S-EPMC2653135 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Publications

Performance of BRCA1/2 mutation prediction models in Asian Americans.

Kurian Allison W AW Gong Gail D GD Chun Nicolette M NM Mills Meredith A MA Staton Ashley D AD Kingham Kerry E KE Crawford Beth B BB Lee Robin R Chan Salina S Donlon Susan S SS Ridge Yolanda Y Panabaker Karen K West Dee W DW Whittemore Alice S AS Ford James M JM

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20080908 29

<h4>Purpose</h4>There are established differences in breast cancer epidemiology between Asian and white individuals, but little is known about hereditary breast cancer in Asian populations. Although increasing numbers of Asian individuals are clinically tested for BRCA1/2 mutations, it is not known whether computer models that predict mutations work accurately in Asian individuals. We compared the performance in Asian and white individuals of two widely used BRCA1/2 mutation prediction models, B ...[more]

PMID: 18779604

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Project description:BACKGROUND: Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient's class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. METHODS: We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. RESULTS: A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1) For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2) The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3) Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. CONCLUSIONS: 1) Different performance metrics for evaluation of a survival prediction model may give different conclusions in its discriminatory ability. 2) Evaluation using a high-risk versus low-risk group comparison depends on the selected risk-score threshold; a plot of p-values from all possible thresholds can show the sensitivity of the threshold selection. 3) A randomization test of the significance of Somers' rank correlation can be used for further evaluation of performance of a prediction model. 4) The cross-validated power of survival prediction models decreases as the training and test sets become less balanced.

Dataset Information

Performance of BRCA1/2 mutation prediction models in Asian Americans.

Purpose

Patients and methods

Results

Conclusion

Publications

Performance of BRCA1/2 mutation prediction models in Asian Americans.

Similar Datasets

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