随着基因科学的发展，医疗检测似乎已经进入基因时代，拿女性来说，通过基因扫描可以获得乳腺癌几率高低的数据。这也许只是理想的状态，现实究竟如何，来自美国国立癌症研究所的科学家在**期刊New England Journal of Medicine上发表评论文章，指出癌基因检测现实与理想的差距。 国立癌症研究所的专家指出，就乳腺癌而言，做DNA检测的效果不比问卷调查（家族病史，乳腺**史等）好。
*近几年，随着基因分析技术的进步与乳腺癌相关的癌基因陆续被发现。*有名的莫过于两个肿瘤抑制基因：BRCA1（breastcancer susceptibility gene 1）和BRCA2（breast cancer susceptibilitygene 1），在英国，有0.3%的人群拥有这2个基因，一旦这2个基因中的任意一个发生恶性的变异，女性患乳腺癌的几率从12%上升至60%。此外，还有18个与乳腺癌相关的基因被找到。
从理论上来说，乳腺癌易感基因的检查可以让女性更了解自己的身体，让她们做出理性的选择，比如说，定期进行乳腺X射线扫描。目前这些基因检测配合一些临床检查以及问卷调查共同进行。问卷调查被称为是Gail model分析，比如，何时行经；何时生育；家族中乳腺癌史。
为了找出基因扫描是否真的对乳腺癌的诊断有利，国立癌症研究所的癌症流行病学专家对这个课题进行了分析研究。他们共开展5个研究，其中4个为群组研究，他们对健康人群进行为期15年的追踪调查，定期检查乳腺癌基因，查看是否发生变异。
研究小组共取样5590名乳腺癌患者，以及5998名健康女性。所有的参与者都定期进行乳腺癌易感基因检测。其后对基因检测模型和问卷调查模型进行对比，对比的结果指数按照以下规律评判。如果这个模型完全不可靠，则调查指数在50%以下，如果这个模型相当**，那么调查指数可达100%。
分析结果发现，基因检测的可靠指数为59.7%，问卷调查的指数为58%，两种方式公用的可靠指数为61.8%。
基因检测技术有一定的指导意义，但在目前的情况下，还有待进一步的完善。
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Performance of Common Genetic Variants inBreast-Cancer Risk Models
Sholom Wacholder, Ph.D., Patricia Hartge,Sc.D., Ross Prentice, Ph.D., Montserrat Garcia-Closas, M.D., Ph.D.,Heather Spencer Feigelson, Ph.D., W. Ryan Diver, M.S.P.H., MichaelJ. Thun, M.D., David G. Cox, Ph.D., Susan E. Hankinson, Ph.D.,Peter Kraft, Ph.D., Bernard Rosner, Ph.D., Christine D. Berg, M.D.,Louise A. Brinton, Ph.D., Jolanta Lissowska, Ph.D., Mark E.Sherman, M.D., Rowan Chlebowski, M.D., Charles Kooperberg, Ph.D.,Rebecca D. Jackson, M.D., Dennis W. Buckman, Ph.D., Peter Hui,B.S., Ruth Pfeiffer, Ph.D., Kevin B. Jacobs, B.S., Gilles D.Thomas, M.D., Robert N. Hoover, M.D., Sc.D., Mitchell H. Gail,M.D., Ph.D., Stephen J. Chanock, M.D., and David J. Hunter, M.B.,B.S., Sc.D.
ABSTRACT
Background Genomewide association studieshave identified multiple genetic variants associated with breastcancer. The extent to which these variants add to existingrisk-assessment models is unknown.
Methods We used information on traditionalrisk factors and 10 common genetic variants associated with breastcancer in 5590 case subjects and 5998 control subjects, 50 to 79years of age, from four U.S. cohort studies and one case–controlstudy from Poland to fit models of the absolute risk of breastcancer. With the use of receiver-operating-characteristic curveanalysis, we calculated the area under the curve (AUC) as a measureof discrimination. By definition, random classification of case andcontrol subjects provides an AUC of 50%; perfect classificationprovides an AUC of 100%. We calculated the fraction of casesubjects in quintiles of estimated absolute risk after the additionof genetic variants to the traditional risk model.
Results The AUC for a risk model with age,study and entry year, and four traditional risk factors was 58.0%;with the addition of 10 genetic variants, the AUC was 61.8%. Abouthalf the case subjects (47.2%) were in the same quintile of risk asin a model without genetic variants; 32.5% were in a higherquintile, and 20.4% were in a lower quintile.
Conclusions The inclusion of newlydiscovered genetic factors modestly improved the performance ofrisk models for breast cancer. The level of predicted breast-cancerrisk among most women changed little after the addition ofcurrently available genetic information.