• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于常见基因变异谱预测乳腺癌风险。

Prediction of breast cancer risk based on profiling with common genetic variants.

作者信息

Mavaddat Nasim, Pharoah Paul D P, Michailidou Kyriaki, Tyrer Jonathan, Brook Mark N, Bolla Manjeet K, Wang Qin, Dennis Joe, Dunning Alison M, Shah Mitul, Luben Robert, Brown Judith, Bojesen Stig E, Nordestgaard Børge G, Nielsen Sune F, Flyger Henrik, Czene Kamila, Darabi Hatef, Eriksson Mikael, Peto Julian, Dos-Santos-Silva Isabel, Dudbridge Frank, Johnson Nichola, Schmidt Marjanka K, Broeks Annegien, Verhoef Senno, Rutgers Emiel J, Swerdlow Anthony, Ashworth Alan, Orr Nick, Schoemaker Minouk J, Figueroa Jonine, Chanock Stephen J, Brinton Louise, Lissowska Jolanta, Couch Fergus J, Olson Janet E, Vachon Celine, Pankratz Vernon S, Lambrechts Diether, Wildiers Hans, Van Ongeval Chantal, van Limbergen Erik, Kristensen Vessela, Grenaker Alnæs Grethe, Nord Silje, Borresen-Dale Anne-Lise, Nevanlinna Heli, Muranen Taru A, Aittomäki Kristiina, Blomqvist Carl, Chang-Claude Jenny, Rudolph Anja, Seibold Petra, Flesch-Janys Dieter, Fasching Peter A, Haeberle Lothar, Ekici Arif B, Beckmann Matthias W, Burwinkel Barbara, Marme Frederik, Schneeweiss Andreas, Sohn Christof, Trentham-Dietz Amy, Newcomb Polly, Titus Linda, Egan Kathleen M, Hunter David J, Lindstrom Sara, Tamimi Rulla M, Kraft Peter, Rahman Nazneen, Turnbull Clare, Renwick Anthony, Seal Sheila, Li Jingmei, Liu Jianjun, Humphreys Keith, Benitez Javier, Pilar Zamora M, Arias Perez Jose Ignacio, Menéndez Primitiva, Jakubowska Anna, Lubinski Jan, Jaworska-Bieniek Katarzyna, Durda Katarzyna, Bogdanova Natalia V, Antonenkova Natalia N, Dörk Thilo, Anton-Culver Hoda, Neuhausen Susan L, Ziogas Argyrios, Bernstein Leslie, Devilee Peter, Tollenaar Robert A E M, Seynaeve Caroline, van Asperen Christi J, Cox Angela, Cross Simon S, Reed Malcolm W R, Khusnutdinova Elza, Bermisheva Marina, Prokofyeva Darya, Takhirova Zalina, Meindl Alfons, Schmutzler Rita K, Sutter Christian, Yang Rongxi, Schürmann Peter, Bremer Michael, Christiansen Hans, Park-Simon Tjoung-Won, Hillemanns Peter, Guénel Pascal, Truong Thérèse, Menegaux Florence, Sanchez Marie, Radice Paolo, Peterlongo Paolo, Manoukian Siranoush, Pensotti Valeria, Hopper John L, Tsimiklis Helen, Apicella Carmel, Southey Melissa C, Brauch Hiltrud, Brüning Thomas, Ko Yon-Dschun, Sigurdson Alice J, Doody Michele M, Hamann Ute, Torres Diana, Ulmer Hans-Ulrich, Försti Asta, Sawyer Elinor J, Tomlinson Ian, Kerin Michael J, Miller Nicola, Andrulis Irene L, Knight Julia A, Glendon Gord, Marie Mulligan Anna, Chenevix-Trench Georgia, Balleine Rosemary, Giles Graham G, Milne Roger L, McLean Catriona, Lindblom Annika, Margolin Sara, Haiman Christopher A, Henderson Brian E, Schumacher Fredrick, Le Marchand Loic, Eilber Ursula, Wang-Gohrke Shan, Hooning Maartje J, Hollestelle Antoinette, van den Ouweland Ans M W, Koppert Linetta B, Carpenter Jane, Clarke Christine, Scott Rodney, Mannermaa Arto, Kataja Vesa, Kosma Veli-Matti, Hartikainen Jaana M, Brenner Hermann, Arndt Volker, Stegmaier Christa, Karina Dieffenbach Aida, Winqvist Robert, Pylkäs Katri, Jukkola-Vuorinen Arja, Grip Mervi, Offit Kenneth, Vijai Joseph, Robson Mark, Rau-Murthy Rohini, Dwek Miriam, Swann Ruth, Annie Perkins Katherine, Goldberg Mark S, Labrèche France, Dumont Martine, Eccles Diana M, Tapper William J, Rafiq Sajjad, John Esther M, Whittemore Alice S, Slager Susan, Yannoukakos Drakoulis, Toland Amanda E, Yao Song, Zheng Wei, Halverson Sandra L, González-Neira Anna, Pita Guillermo, Rosario Alonso M, Álvarez Nuria, Herrero Daniel, Tessier Daniel C, Vincent Daniel, Bacot Francois, Luccarini Craig, Baynes Caroline, Ahmed Shahana, Maranian Mel, Healey Catherine S, Simard Jacques, Hall Per, Easton Douglas F, Garcia-Closas Montserrat

机构信息

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (NM, PDPP, KM, MKB, QW, JD, RL, JBr, DFE); Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK (PDPP, JT, AMD, MS, CL, CB, SA, MM, CSH, DFE); Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK (MNB, ASw, MJS); Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark (SEB, BGN, SFN); Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (SEB, BGN, SFN); Faculty of Health and Medical Sciences, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (SEB, BGN); Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (HF); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (KC, HD, ME, KH, PHa); Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK (JP, IdSS, FD); Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK (NJ, AA, NO, MGC); Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands (MKS, AB, SV, EJR); Division of Breast Cancer Research, Institute of Cancer Research, London, UK (ASw); Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD (JF, SJC, LB, ASi, MD); Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland (JLis); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN (FJC); Department of Health Sciences Research, Mayo Clinic, Rochester, MN (JEO, CV, VSP, SS); Vesalius Research Center, VIB, Leuven, Belgium (DL); Laboratory for Translational Genetics, Department of Oncology, University of

出版信息

J Natl Cancer Inst. 2015 Apr 8;107(5). doi: 10.1093/jnci/djv036. Print 2015 May.

DOI:10.1093/jnci/djv036
PMID:25855707
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4754625/
Abstract

BACKGROUND

Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.

METHODS

We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.

RESULTS

There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.

CONCLUSIONS

The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

摘要

背景

乳腺癌多种常见易感等位基因的数据可合并起来,以识别处于不同乳腺癌风险水平的女性。这种分层可为预防和筛查策略提供指导。然而,缺乏基因风险分层的实证证据。

方法

在一项针对33673例乳腺癌病例和33381名欧洲裔对照女性的研究中,我们调查了使用77个与乳腺癌相关的单核苷酸多态性(SNP)进行风险分层的价值。我们测试了所有可能的成对相乘相互作用,并构建了一个77-SNP多基因风险评分(PRS),用于总体乳腺癌以及按雌激素受体(ER)状态分层的乳腺癌。PRS对应的乳腺癌绝对风险来自相对风险估计值以及英国的发病率和死亡率。

结果

没有有力证据表明任何SNP对不符合相乘模型。PRS最高的1%女性患乳腺癌的风险比处于五分位数中间的女性增加了两倍(优势比[OR]=3.36,95%置信区间[CI]=2.95至3.83)。ER阳性和ER阴性疾病的OR分别为3.73(95%CI=3.24至4.30)和2.80(95%CI=2.26至3.46)。对于无家族史的女性,PRS最低和最高五分位数的乳腺癌终生风险分别为5.2%和16.6%;对于有乳腺癌一级家族史的女性,分别为8.6%和24.4%。

结论

PRS可对有和没有乳腺癌家族史的女性进行乳腺癌风险分层。观察到的风险区分水平可为有针对性的筛查和预防策略提供依据。通过将PRS与生活方式/环境因素相结合,可能实现进一步的区分,尽管本报告未考虑这些因素。

相似文献

1
Prediction of breast cancer risk based on profiling with common genetic variants.基于常见基因变异谱预测乳腺癌风险。
J Natl Cancer Inst. 2015 Apr 8;107(5). doi: 10.1093/jnci/djv036. Print 2015 May.
2
Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium.多基因风险评分与乳腺癌环境风险因素在乳腺癌协会联盟中的联合关联。
Int J Epidemiol. 2018 Apr 1;47(2):526-536. doi: 10.1093/ije/dyx242.
3
Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042.雌激素受体阳性和阴性乳腺癌风险与单核苷酸多态性2q35-rs13387042
J Natl Cancer Inst. 2009 Jul 15;101(14):1012-8. doi: 10.1093/jnci/djp167. Epub 2009 Jun 30.
4
A polygenic risk score for breast cancer in women receiving tamoxifen or raloxifene on NSABP P-1 and P-2.在NSABP P-1和P-2研究中接受他莫昔芬或雷洛昔芬治疗的女性乳腺癌多基因风险评分。
Breast Cancer Res Treat. 2015 Jan;149(2):517-23. doi: 10.1007/s10549-014-3175-4. Epub 2015 Jan 10.
5
Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk.调整年龄和体重指数后的乳腺密度与多基因风险评分联合与乳腺癌风险的关联。
Breast Cancer Res. 2019 May 22;21(1):68. doi: 10.1186/s13058-019-1138-8.
6
Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.多基因风险评分在乳腺癌及乳腺癌亚型预测中的应用。
Am J Hum Genet. 2019 Jan 3;104(1):21-34. doi: 10.1016/j.ajhg.2018.11.002. Epub 2018 Dec 13.
7
Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci.个体和多个低外显率遗传易感基因座与乳腺癌及其亚型发病风险的关系。
JAMA. 2010 Jul 28;304(4):426-34. doi: 10.1001/jama.2010.1042.
8
The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study.一组18个单核苷酸多态性对英国一家家族性筛查诊所女性乳腺癌风险的影响:一项病例对照研究。
J Med Genet. 2017 Feb;54(2):111-113. doi: 10.1136/jmedgenet-2016-104125. Epub 2016 Oct 28.
9
Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants.乳腺癌的病理和分期可以通过包括乳腺密度和常见遗传变异在内的风险分层模型更好地预测。
Breast Cancer Res Treat. 2019 Jul;176(1):141-148. doi: 10.1007/s10549-019-05210-2. Epub 2019 Apr 2.
10
Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.基于东亚血统女性常见基因变异预测乳腺癌风险。
Breast Cancer Res. 2016 Dec 8;18(1):124. doi: 10.1186/s13058-016-0786-1.

引用本文的文献

1
Unlocking Clinical Precision Through Polygenic Risk Prediction.通过多基因风险预测实现临床精准性
Nepal J Epidemiol. 2025 Jul 27;14(3):1344-1345. doi: 10.3126/nje.v14i3.82370. eCollection 2024.
2
Integrating breast cancer polygenic risk scores at scale in the WISDOM Study: a national randomized personalized screening trial.在WISDOM研究中大规模整合乳腺癌多基因风险评分:一项全国性随机个性化筛查试验
Genome Med. 2025 Aug 28;17(1):97. doi: 10.1186/s13073-025-01524-7.
3
The Spanish Polygenic Score reference distribution: a resource for personalized medicine.

本文引用的文献

1
Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer.遗传和环境风险因素的联合关联:对乳腺癌预防的启示
J Natl Cancer Inst. 2014 Nov 12;106(11). doi: 10.1093/jnci/dju305. Print 2014 Nov.
2
A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.利用来自乳腺癌协会联盟的46450例病例和42461例对照,对乳腺癌易感性中的双向单核苷酸多态性相互作用进行大规模评估。
Hum Mol Genet. 2014 Apr 1;23(7):1934-46. doi: 10.1093/hmg/ddt581. Epub 2013 Nov 15.
3
Breast cancer screening: time to target women at risk.
西班牙多基因评分参考分布:个性化医疗的一种资源。
Eur J Hum Genet. 2025 Apr 24. doi: 10.1038/s41431-025-01850-9.
4
Guidance for the Clinical Use of the Breast Cancer Polygenic Risk Scores.乳腺癌多基因风险评分的临床应用指南。
Cancers (Basel). 2025 Mar 21;17(7):1056. doi: 10.3390/cancers17071056.
5
Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.人工智能在赋能和重新定义肿瘤护理格局中的作用:来自一个发展中国家的视角。
Front Digit Health. 2025 Mar 4;7:1550407. doi: 10.3389/fdgth.2025.1550407. eCollection 2025.
6
Overlap of high-risk individuals across family history, genetic & non-genetic breast cancer risk models: Analysis of 180,398 women from European & Asian ancestries.家族史、遗传和非遗传乳腺癌风险模型中高危个体的重叠:对180398名欧洲和亚洲血统女性的分析。
medRxiv. 2025 Mar 3:2025.02.27.25323002. doi: 10.1101/2025.02.27.25323002.
7
Association of Inherited Genetic Variants with Multiple Primary Melanoma.遗传性基因变异与多发性原发性黑色素瘤的关联
Cancer Epidemiol Biomarkers Prev. 2025 May 2;34(5):805-814. doi: 10.1158/1055-9965.EPI-24-1442.
8
Association of dietary carbohydrate ratio, caloric restriction, and genetic factors with breast cancer risk in a cohort study.一项队列研究中饮食碳水化合物比例、热量限制及遗传因素与乳腺癌风险的关联
Sci Rep. 2025 Feb 20;15(1):6263. doi: 10.1038/s41598-025-90844-0.
9
Clinical significance of ALDH1A1 and Ki67 expression in women with breast carcinoma.醛脱氢酶1A1(ALDH1A1)和Ki67表达在乳腺癌女性中的临床意义
Histol Histopathol. 2025 Sep;40(9):1479-1486. doi: 10.14670/HH-18-880. Epub 2025 Jan 28.
10
Double CHEK2 Pathogenic and Low-Risk Variants and Associated Cancer Phenotypes.双重CHEK2致病和低风险变异及相关癌症表型
JAMA Netw Open. 2025 Jan 2;8(1):e2451361. doi: 10.1001/jamanetworkopen.2024.51361.
乳腺癌筛查:是时候针对高危女性了。
Br J Cancer. 2013 Jun 11;108(11):2202-4. doi: 10.1038/bjc.2013.257. Epub 2013 Jun 6.
4
Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data.选择性雌激素受体调节剂在乳腺癌预防中的应用:一项个体参与者数据的更新荟萃分析。
Lancet. 2013 May 25;381(9880):1827-34. doi: 10.1016/S0140-6736(13)60140-3. Epub 2013 Apr 30.
5
Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors.常见乳腺癌易感基因座与已确定环境风险因素之间的基因-环境相互作用的证据。
PLoS Genet. 2013;9(3):e1003284. doi: 10.1371/journal.pgen.1003284. Epub 2013 Mar 27.
6
Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers.11q13 乳腺癌风险位点的功能变体通过长距离增强子调控细胞周期蛋白 D1 的表达。
Am J Hum Genet. 2013 Apr 4;92(4):489-503. doi: 10.1016/j.ajhg.2013.01.002. Epub 2013 Mar 27.
7
Genome-wide association studies identify four ER negative-specific breast cancer risk loci.全基因组关联研究确定了四个 ER 阴性特异性乳腺癌风险位点。
Nat Genet. 2013 Apr;45(4):392-8, 398e1-2. doi: 10.1038/ng.2561.
8
Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer.多个位于 TERT 基因座的独立变体与端粒长度和乳腺癌及卵巢癌的风险相关。
Nat Genet. 2013 Apr;45(4):371-84, 384e1-2. doi: 10.1038/ng.2566.
9
Large-scale genotyping identifies 41 new loci associated with breast cancer risk.大规模基因分型鉴定出 41 个与乳腺癌风险相关的新位点。
Nat Genet. 2013 Apr;45(4):353-61, 361e1-2. doi: 10.1038/ng.2563.
10
Public health implications from COGS and potential for risk stratification and screening.从 COGS 角度看公共卫生影响,以及风险分层和筛查的可能性。
Nat Genet. 2013 Apr;45(4):349-51. doi: 10.1038/ng.2582.