生物谷报道：英国科学家最近进行的一项大规模遗传研究，确定出了4种显著影响女性患乳腺癌风险的新的基因。该研究成果5月27日在线发表于《自然》杂志上。
乳腺癌是女性健康的巨大“杀手”，全球每年有50万妇女死于这种癌症。长期以来，科学家知道遗传因素对乳腺癌有很大的影响，但到目前为止，所知的基因只占整个风险因素的四分之一。
在新的研究中，英国剑桥大学的Douglas Easton和同事对4400位患有乳腺癌女性和4300位健康女性的基因组进行了对比分析，发现了单DNA碱基对上的30个不同之处。通过进一步研究，科学家确定出了4个与乳腺癌显著相关的基因。
在4个新发现的基因中，有3个参与调控细胞的生长，其中关联最强的是成纤维细胞生长因子受体II（fibroblast growth factor receptor 2，简称FGFR2）。
Easton和同事发现，大约有16%的女性拥有FGFR2基因的两个高风险副本，这使得她们患乳腺癌的风险比其他人高出了60%。
目前新发现的癌症基因还只是“冰山一角”。Easton说，“即使最终确定出数百种风险基因，我也丝毫不会感到惊讶。”不过，随着越来越多的遗传风险因素被确定，人类最终将会看到“全景”，而相应的风险检测和治疗也会变得更加准确。
原始出处:
Nature advance online publication 27 May 2007 | doi:10.1038/nature05887; Received 9 February 2007; Accepted 30 April 2007; Published online 27 May 2007
Genome-wide association study identifies novel breast cancer susceptibility loci
Douglas F. Easton1, Karen A. Pooley2, Alison M. Dunning2, Paul D. P. Pharoah2, Deborah Thompson1, Dennis G. Ballinger3, Jeffery P. Struewing4, Jonathan Morrison2, Helen Field2, Robert Luben5, Nicholas Wareham5, Shahana Ahmed2, Catherine S. Healey2, Richard Bowman6The SEARCH collaborators and , Kerstin B. Meyer7, Christopher A. Haiman8, Laurence K. Kolonel9, Brian E. Henderson8, Loic Le Marchand9, Paul Brennan10, Suleeporn Sangrajrang11, Valerie Gaborieau10, Fabrice Odefrey10, Chen-Yang Shen12, Pei-Ei Wu12, Hui-Chun Wang12, Diana Eccles13, D. Gareth Evans14, Julian Peto15, Olivia Fletcher16, Nichola Johnson16, Sheila Seal17, Michael R. Stratton17,18, Nazneen Rahman17, Georgia Chenevix-Trench19, Stig E. Bojesen20, Børge G. Nordestgaard20, Christen K. Axelsson21, Montserrat Garcia-Closas22, Louise Brinton22, Stephen Chanock23, Jolanta Lissowska24, Beata Peplonska25, Heli Nevanlinna26, Rainer Fagerholm26, Hannaleena Eerola26,27, Daehee Kang28, Keun-Young Yoo28,29, Dong-Young Noh28, Sei-Hyun Ahn30, David J. Hunter31,32, Susan E. Hankinson32, David G. Cox31, Per Hall33, Sara Wedren33, Jianjun Liu34, Yen-Ling Low34, Natalia Bogdanova35,36, Peter Schürmann36, Thilo Dörk36, Rob A. E. M. Tollenaar37, Catharina E. Jacobi38, Peter Devilee39, Jan G. M. Klijn40, Alice J. Sigurdson41, Michele M. Doody41, Bruce H. Alexander42, Jinghui Zhang4, Angela Cox43, Ian W. Brock43, Gordon MacPherson43, Malcolm W. R. Reed44, Fergus J. Couch45, Ellen L. Goode45, Janet E. Olson45, Hanne Meijers-Heijboer46,47, Ans van den Ouweland47, André Uitterlinden48, Fernando Rivadeneira48, Roger L. Milne49, Gloria Ribas49, Anna Gonzalez-Neira49, Javier Benitez49, John L. Hopper50, Margaret McCredie51, Melissa Southey50, Graham G. Giles52, Chris Schroen53, Christina Justenhoven54, Hiltrud Brauch54, Ute Hamann55, Yon-Dschun Ko56, Amanda B. Spurdle19, Jonathan Beesley19, Xiaoqing Chen19kConFab and AOCS Management Group and , Arto Mannermaa118,119, Veli-Matti Kosma118,119, Vesa Kataja118,120, Jaana Hartikainen118,119, Nicholas E. Day65, David R. Cox63 & Bruce A. J. Ponder62,67
Correspondence to: Douglas F. Easton1 Correspondence and requests for materials should be addressed to D.F.E. (Email: d.easton@srl.cam.ac.uk).
Abstract
Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2 > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10-7). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
