主页 文献库文献详情
PMID: 25274085 已发表 · epublish 英语

Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

Prosperi Mattia C F, Ingham Sarah L, Howell Anthony, Lalloo Fiona, Buchan Iain E, Evans Dafydd Gareth

摘要

Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.,Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).,548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.,Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.

文献信息
期刊
BMC medical informatics and decision making
期刊简称
BMC Med Inform Decis Mak
发表日期
2015-04-17
收录日期
2014-10-14
更新日期
2016-11-22
语言
英语
国家/地区
England
NLM ID
101088682
分析服务
分析服务

联系地址

山东省济南市章丘区文博路2号

齐鲁师范学院 genelibs生信实验室

山东省济南市高新区舜华路750号

大学科技园北区F座4单元2楼

电话: 0531-88819269

微信公众号

关注微信订阅号,实时查看信息,关注医学生物学动态。


商务邮箱

E-mail: product@genelibs.com