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.
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.
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。
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