RT Journal Article
SR Electronic
T1 Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis
JF Cancer Epidemiology Biomarkers & Prevention
JO Cancer Epidemiol Biomarkers Prev
FD American Association for Cancer Research
SP 506
OP 521
DO 10.1158/1055-9965.EPI-18-0810
VO 28
IS 3
A1 Fung, Si Ming
A1 Wong, Xin Yi
A1 Lee, Shi Xun
A1 Miao, Hui
A1 Hartman, Mikael
A1 Wee, Hwee-Lin
YR 2019
UL http://cebp.aacrjournals.org/content/28/3/506.abstract
AB Background: SNP risk information can potentially improve the accuracy of breast cancer risk prediction. We aim to review and assess the performance of SNP-enhanced risk prediction models.Methods: Studies that reported area under the ROC curve (AUC) and/or net reclassification improvement (NRI) for both traditional and SNP-enhanced risk models were identified. Meta-analyses were conducted to compare across all models and within similar baseline risk models.Results: Twenty-six of 406 studies were included. Pooled estimate of AUC improvement is 0.044 [95% confidence interval (CI), 0.038–0.049] for all 38 models, while estimates by baseline models ranged from 0.033 (95% CI, 0.025–0.041) for BCRAT to 0.053 (95% CI, 0.018–0.087) for partial BCRAT. There was no observable trend between AUC improvement and number of SNPs. One study found that the NRI was significantly larger when only intermediate-risk women were included. Two other studies showed that majority of the risk reclassification occurred in intermediate-risk women.Conclusions: Addition of SNP risk information may be more beneficial for women with intermediate risk.Impact: Screening could be a two-step process where a questionnaire is first used to identify intermediate-risk individuals, followed by SNP testing for these women only.This article is featured in Highlights of This Issue, p. 425