应用单核苷酸多态性和乳腺 X 线密度与经典危险因素预测乳腺癌风险。
Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction.
机构信息
Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, England.
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, England.
出版信息
JAMA Oncol. 2018 Apr 1;4(4):476-482. doi: 10.1001/jamaoncol.2017.4881.
IMPORTANCE
Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models.
OBJECTIVE
To determine whether a panel of 18 SNPs (SNP18) may be used to predict breast cancer in combination with classic risk factors and mammographic density.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study enrolled a subcohort of 9363 women, aged 46 to 73 years, without a previous breast cancer diagnosis from the larger prospective cohort of the PROCAS study (Predicting Risk of Cancer at Screening) specifically to evaluate breast cancer risk-assessment methods. Enrollment took place from October 2009 through June 2015 from multiple population-based screening centers in Greater Manchester, England. Follow-up continued through January 5, 2017.
EXPOSURES
Genotyping of 18 SNPs, visual-assessment percentage mammographic density, and classic risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry.
MAIN OUTCOMES AND MEASURES
The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk.
RESULTS
A total of 9363 women were enrolled in this study (mean [range] age, 59 [46-73] years). Of these, 466 were found to have breast cancer (271 prevalent; 195 incident). SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classic factors (odds ratios per interquartile range, respectively, 1.56; 95% CI, 1.38-1.77 and 1.53; 95% CI, 1.35-1.74), with observed risks being very close to expected (adjusted observed-to-expected odds ratio, 0.98; 95% CI, 0.69-1.28). A combined risk assessment indicated 18% of the subcohort to be at 5% or greater 10-year risk, compared with 30% of all cancers, 35% of interval-detected cancers, and 42% of stage 2+ cancers. In contrast, 33% of the subcohort were at less than 2% risk but accounted for only 18%, 17%, and 15% of the total, interval, and stage 2+ breast cancers, respectively.
CONCLUSIONS AND RELEVANCE
SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.
重要性
单核苷酸多态性（SNPs）已证明与乳腺癌易感性有关，但关于如何将其纳入当前的乳腺癌风险预测模型的证据有限。
目的
确定 18 个单核苷酸多态性（SNP18）是否可用于结合经典风险因素和乳房 X 线照相密度来预测乳腺癌。
设计、地点和参与者：这项队列研究纳入了来自更大的 PROCAS 研究（预测筛查中的癌症风险）的一个子队列中的 9363 名年龄在 46 至 73 岁之间、无先前乳腺癌诊断的女性，专门用于评估乳腺癌风险评估方法。招募工作于 2009 年 10 月至 2015 年 6 月在英格兰大曼彻斯特的多个基于人群的筛查中心进行。随访一直持续到 2017 年 1 月 5 日。
暴露情况
在队列入组时，通过自我完成的问卷评估 18 个 SNP 的基因分型、视觉评估百分比乳房 X 线照相密度和 Tyrer-Cuzick 风险模型评估的经典风险。
主要结果和测量
使用逻辑回归优势比，每 1 个四分位距预测风险，评估 SNP18 对乳腺癌诊断（浸润性和导管原位癌）的预测能力。
结果
这项研究共纳入 9363 名女性（平均[范围]年龄，59[46-73]岁）。其中，466 人被发现患有乳腺癌（271 例为现患乳腺癌；195 例为新发病例）。未调整或调整乳房 X 线照相密度和经典因素后，SNP18 的预测能力相似（未调整的四分位距比值比，分别为 1.56；95%CI，1.38-1.77 和 1.53；95%CI，1.35-1.74），观察到的风险与预期非常接近（调整后的观察到的与预期的比值比，0.98；95%CI，0.69-1.28）。综合风险评估表明，子队列中有 18%的人存在 5%或更高的 10 年风险，而所有癌症的这一比例为 30%，间期发现癌症的这一比例为 35%，2 期+癌症的这一比例为 42%。相比之下，子队列中有 33%的人处于风险小于 2%的水平，但仅占总癌症、间期癌症和 2 期+癌症的 18%、17%和 15%。
结论和相关性
SNP18 为基于 Tyrer-Cuzick 模型和乳房 X 线照相密度的风险评估提供了大量信息。综合风险可能有助于分层筛查和预防策略。
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