Journal of the National Cancer Institute Advance Access originally published online on July 8, 2008
JNCI Journal of the National Cancer Institute 2008 100(14):1037-1041; doi:10.1093/jnci/djn180
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Published by Oxford University Press 2008.
BRIEF COMMUNICATION |
Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk
Affiliation of author: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
Correspondence to: Mitchell H. Gail, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Rm 8032, Bethesda, MD 20892-7244 (e-mail: gailm{at}mail.nih.gov).
One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.
| CONTEXT AND CAVEATS Prior knowledge Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. Study design Estimates of relative risks and allele frequencies from these studies were used to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). The discriminatory accuracy of these seven SNPs and a hypothetical model with 14 such SNPs were then compared with that of the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT). Contribution The seven-SNP model (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than the National Cancer Institute's BCRAT (AUC = 0.607). Adding the seven SNPs to BCRAT increased the AUC to 0.632. Implications Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy. Limitations Individual-level data on case patients and control subjects are needed to investigate interactions that may improve the models. The data used to estimate SNP effects did not permit estimation of interactions among SNPs or between SNPs and risk factors in BCRAT. From the Editors
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Manuscript received February 11, 2008; revised May 2, 2008; accepted May 6, 2008.
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  M. S. Pepe and H. E. Janes Gauging the Performance of SNPs, Biomarkers, and Clinical Factors for Predicting Risk of Breast Cancer J Natl Cancer Inst, July 16, 2008; 100(14): 978 - 979. [Full Text] [PDF]
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