Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3).
Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3).
复制标题
DOI：
10.1158/1055-9965.epi-11-1038
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Kraft P
中科院分区：
文献类型：
--
作者：
Lindström S;Schumacher FR;Cox D;Travis RC;Albanes D;Allen NE;Andriole G;Berndt SI;Boeing H;Bueno-de-Mesquita HB;Crawford ED;Diver WR;Gaziano JM;Giles GG;Giovannucci E;Gonzalez CA;Henderson B;Hunter DJ;Johansson M;Kolonel LN;Ma J;Le Marchand L;Pala V;Stampfer M;Stram DO;Thun MJ;Tjonneland A;Trichopoulos D;Virtamo J;Weinstein SJ;Willett WC;Yeager M;Hayes RB;Severi G;Haiman CA;Chanock SJ;Kraft P
个性化医疗的目标之一是生成个体风险特征，从而可以识别出人群中表现出高风险的个体。在前列腺癌中发现了二十多个独立的SNP标记，这提高了这种风险分层的可能性。在这项研究中，我们评估了前列腺癌风险模型的区分和预测能力，该模型结合了25种常见的前列腺癌遗传标记物、前列腺癌家族史和年龄。我们拟合了一系列风险模型，并在NCI乳腺癌和前列腺癌队列联盟（BPC 3）的7，509例前列腺癌病例和7，652例对照中估计了它们的表现。我们还根据SEER发病率数据计算了绝对风险。最佳风险模型（C-统计量=0.642）包括个体遗传标记和前列腺癌家族史。我们观察到随着年龄的增长，辨别能力呈下降趋势（P=0.009），60岁以下男性的准确性最高（C统计量=0.679）。有家族史的50岁男性的绝对十年风险范围为1.6%（遗传风险的第10百分位数）至6.7%（遗传风险的第90百分位数）。对于无家族史的男性，风险范围为0.8%（第10百分位数）至3.4%（第90百分位数）。我们的研究结果表明，将遗传信息和家族史纳入前列腺癌风险模型，对于识别可能受益于PSA筛查的年轻男性特别有用。虽然添加遗传风险标志物提高了模型性能，但这些遗传风险模型的临床实用性有限。
One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent SNP markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer and age. We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data. The best risk model (C-statistic=0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P=0.009), with highest accuracy in men younger than 60 years (C-statistic=0.679). The absolute ten-year risk for 50-year old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile). Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from PSA screening. Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.