单核苷酸多态性（SNP）是同一物种不同个体的基因组脱氧核糖核酸（DNA）等位序列同一位置单个核苷酸变异（替代、插入或缺失）现象，有该变异的基因座、DNA序列等可以作为基因组作图的标志。人类基因组平均约每1000个核苷酸即可能出现1个SNP，其中有些可能与疾病有关，但是大多数可能与疾病无关。SNP是研究人类家族和动植物品系遗传变异的重要依据。虽然已经证实SNP与乳腺癌易感性相关，但是将其加入当前乳腺癌风险预测模型的证据有限。那么，SNP结合钼靶密度能否提高经典风险因素的乳腺癌预测水平？
2018年1月18日，《美国医学会杂志》肿瘤学分册在线发表英国曼彻斯特大学、伦敦玛丽王后大学、曼彻斯特大学医院、曼彻斯特肿瘤医院的研究报告，针对筛查预测癌症（PROCAS）大型前瞻队列研究的亚组女性，探讨了18个SNP（SNP18）结合经典风险因素和钼靶密度能否用于预测乳腺癌。结果发现，对于该大型癌症筛查队列其中9363例女性，无论是否考虑钼靶密度和经典风险因素，SNP18的预测作用相似。因此，SNP风险组大大提高了乳腺癌风险预测模型的能力，可以精准确定通过预防治疗或其他筛查形式获益最多的女性。
PROCAS研究于2009年10月～2015年6月从英国大曼彻斯特地区多个人群筛查中心入组正在参加全国乳腺钼靶筛查的46～73岁既往未被诊断为乳腺癌女性共5万7902例，通过自填问卷，调查其乳腺癌风险因素。本研究邀请其中住在南曼彻斯特威辛顿地区的亚组女性9363例（平均年龄59岁，范围46～73岁）参加进一步的乳腺癌风险评定。所有被诊断为乳腺癌的患者自填问卷后，均提供唾液进行SNP18基因分型（唾液采集于2009年10月～2013年12月）并作为病例患者参与。通过全国乳腺钼靶筛查系统，对入组筛查时或之后被诊断为乳腺癌（浸润癌或导管原位癌）进行每月随访，直至2017年1月5日。SNP18、根据目测百分比对钼靶密度进行评定，根据队列入组时的自填问卷数据输入国际乳腺癌干预研究（IBIS）泰尔-库齐克风险模型对经典风险进行评定。主要结局衡量指标为SNP18对乳腺癌诊断（浸润癌和导管原位癌）的预测能力，根据预测风险四分位距逻辑回归比值比进行定量。
结果，共诊断乳腺癌466例，其中患病271例（首次筛查诊断271例）、发病195例（后续筛查诊断112例、筛查间期诊断83例）。
无论是否考虑钼靶密度和经典风险因素，SNP18的预测作用相似（四分位距比值比：1.56、1.53，95%：1.38～1.77、1.35～1.74），所见风险非常接近预测（校正比值比：0.98，95%：0.69～1.28）。
根据10年风险联合评分：
≥5%：占整个亚组的18%、所有癌的30%、筛查间期癌的35%、分期≥2期癌的42%
＜2%：占整个亚组的33%、所有癌的18%、筛查间期癌的17%、分期≥2期癌的15%
因此，SNP18为钼靶密度和根据泰尔-库齐克风险模型评定的乳腺癌风险增添了实质信息，乳腺癌风险联合评分可能有助于根据不同风险进行分层筛查并制定预防对策。
相关阅读
JAMA Oncol. 2018 Jan 18. [Epub ahead of print]
Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction.
Elke M. van Veen; Adam R. Brentnall; Helen Byers; Elaine F. Harkness; Susan M. Astley; Sarah Sampson; Anthony Howell; William G. Newman; Jack Cuzick; D. Gareth R. Evans.
University of Manchester, Manchester, England; Queen Mary University of London, London, England; Manchester University Hospital Foundation Trust, Manchester, England; The Christie NHS Foundation Trust, Manchester, United Kingdom; Manchester University NHS Foundation Trust, Manchester, United Kingdom.
This study of a subcohort of women in the PROCAS study evaluates a panel of 18 single-nucleotide polymorphisms in combination with mammographic density for ability to improve classic breast cancer risk prediction.
QUESTION: Can panels of single-nucleotide polymorphisms (SNPs) be combined with measurement of mammographic density and classic risk factors to improve breast cancer risk assessment?
FINDINGS: In a general screening cohort of 9363 women, a panel of 18 SNPs was similarly predictive whether unadjusted or adjusted for both mammographic density and classic risk factors.
MEANING: SNP risk panels substantially improve the ability of breast cancer risk prediction models to accurately identify women who may benefit most from preventive therapy or additional screening modalities.
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.
DOI: 10.1001/jamaoncol.2017.4881
联系客服
