Queensland Institute of Medical Research, Brisbane, Australia.
Hum Mutat. 2012 Jan;33(1):2-7. doi: 10.1002/humu.21628. Epub 2011 Nov 3.
As genetic testing for predisposition to human diseases has become an increasingly common practice in medicine, the need for clear interpretation of the test results is apparent. However, for many disease genes, including the breast cancer susceptibility genes BRCA1 and BRCA2, a significant fraction of tests results in the detection of a genetic variant for which disease association is not known. The finding of an "unclassified" variant (UV)/variant of uncertain significance (VUS) complicates genetic test reporting and counseling. As these variants are individually rare, a large collaboration of researchers and clinicians will facilitate studies to assess their association with cancer predisposition. It was with this in mind that the ENIGMA consortium (www.enigmaconsortium.org) was initiated in 2009. The membership is both international and interdisciplinary, and currently includes more than 100 research scientists and clinicians from 19 countries. Within ENIGMA, there are presently six working groups focused on the following topics: analysis, clinical, database, functional, tumor histopathology, and mRNA splicing. ENIGMA provides a mechanism to pool resources, exchange methods and data, and coordinately develop and apply algorithms for classification of variants in BRCA1 and BRCA2. It is envisaged that the research and clinical application of models developed by ENIGMA will be relevant to the interpretation of sequence variants in other disease genes.
随着针对人类疾病易感性的基因检测在医学中变得越来越普遍,对测试结果进行清晰解释的需求变得显而易见。然而,对于许多疾病基因,包括乳腺癌易感性基因 BRCA1 和 BRCA2,相当一部分测试结果检测到一种与疾病关联未知的遗传变异。发现“未分类”(UV)/意义不确定的变异(VUS)使基因检测报告和咨询变得复杂。由于这些变体在个体中较为罕见,因此需要研究人员和临床医生的大量合作,以评估它们与癌症易感性的关联。正是出于这个原因,ENIGMA 联盟(www.enigmaconsortium.org)于 2009 年成立。该联盟的成员既有国际性的,也有跨学科的,目前包括来自 19 个国家的 100 多名研究科学家和临床医生。在 ENIGMA 内部,目前有六个工作组专注于以下主题:分析、临床、数据库、功能、肿瘤组织病理学和 mRNA 剪接。ENIGMA 提供了一种汇集资源、交流方法和数据的机制,并协调开发和应用用于分类 BRCA1 和 BRCA2 中变异的算法。预计 ENIGMA 开发的模型的研究和临床应用将与其他疾病基因中序列变异的解释相关。