Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.
Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.
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DOI：
10.1186/bcr3110
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发表时间：
2012-02-07
期刊：
影响因子：
--
通讯作者：
Humphreys K
中科院分区：
文献类型：
--
作者：
Darabi H;Czene K;Zhao W;Liu J;Hall P;Humphreys K
在过去的十年中，已经确定了几个乳腺癌风险等位基因，这导致了对临床目的的个体化风险预测的兴趣增加。我们根据经验，调查了最新的18种乳腺癌风险单核苷酸多态性（SNP）以及乳房X线摄影百分比密度（PD）、体重指数（BMI）和临床风险因素在预测乳腺癌绝对风险方面的表现。在一项针对绝经后妇女的充分表征的瑞典病例对照研究中。我们通过扩展最近提出的用于估计捕获的病例数的分析方法，在人群水平上检查了各种预测模型用于个体化筛查的效率。基于7个乳腺癌风险SNP的初始组的风险预测模型的性能通过另外包括11个最近建立的乳腺癌风险SNP而得到改善（P = 4.69 × 10-4）。将乳腺摄影PD，BMI和所有18个SNP添加到Swedish Gail模型中，将区分准确性（AUC统计量）从55%提高到62%。净重新分类改善用于评估将女性分为低、中和高5年风险类别的改善（P = 8.93 × 10-9）。对于我们考虑的情况，我们估计，基于风险模型的个体化筛查策略，包括临床风险因素，乳房X线摄影密度和SNP，比使用相同资源的筛查策略多捕获10%的病例，仅基于年龄。通过按年龄分层的筛查捕获的病例数的估计值提供了对个性化筛查计划在实践中可能出现的情况的深入了解。总的来说，遗传风险因素和乳腺摄影密度为预测乳腺癌的临床风险因素模型提供了适度的改善。
Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes. We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured. The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice. Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
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DOI：
10.1093/jnci/djq388
发表时间：
2010-11-03
期刊：
Journal of the National Cancer Institute
影响因子：
--
作者：
Mealiffe ME;Stokowski RP;Rhees BK;Prentice RL;Pettinger M;Hinds DA
通讯作者：
Hinds DA
影响因子：
3.5
作者：
Easton, Douglas F.;Eeles, Rosalind A.
通讯作者：
Eeles, Rosalind A.
影响因子：
4.5
作者：
So HC;Sham PC
通讯作者：
Sham PC
影响因子：
8.8
作者：
Antoniou, A. C.;Cunningham, A. P.;Peto, J.;Evans, D. G.;Lalloo, F.;Narod, S. A.;Risch, H. A.;Eyfjord, J. E.;Hopper, J. L.;Southey, M. C.;Olsson, H.;Johannsson, O.;Borg, A.;Passini, B.;Radice, P.;Manoukian, S.;Eccles, D. M.;Tang, N.;Olah, E.;Anton-Culver, H.;Warner, E.;Lubinski, J.;Gronwald, J.;Gorski, B.;Tryggvadottir, L.;Syrjakoski, K.;Kallioniemi, O-P;Eerola, H.;Nevanlinna, H.;Pharoah, P. D. P.;Easton, D. F.
通讯作者：
Easton, D. F.
DOI：
10.1093/jnci/djj332
发表时间：
2006-09-06
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Chen, Jinbo;Pee, David;Gail, Mitchell H.
通讯作者：
Gail, Mitchell H.