Li Jun, Meeks Huong, Feng Bing-Jian, Healey Sue, Thorne Heather, Makunin Igor, Ellis Jonathan, Campbell Ian, Southey Melissa, Mitchell Gillian, Clouston David, Kirk Judy, Goldgar David, Chenevix-Trench Georgia
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, USA.
J Med Genet. 2016 Jan;53(1):34-42. doi: 10.1136/jmedgenet-2015-103452. Epub 2015 Nov 3.
Gene panel testing for breast cancer susceptibility has become relatively cheap and accessible. However, the breast cancer risks associated with mutations in many genes included in these panels are unknown.
We performed custom-designed targeted sequencing covering the coding exons of 17 known and putative breast cancer susceptibility genes in 660 non-BRCA1/2 women with familial breast cancer. Putative deleterious mutations were genotyped in relevant family members to assess co-segregation of each variant with disease. We used maximum likelihood models to estimate the breast cancer risks associated with mutations in each of the genes.
We found 31 putative deleterious mutations in 7 known breast cancer susceptibility genes (TP53, PALB2, ATM, CHEK2, CDH1, PTEN and STK11) in 45 cases, and 22 potential deleterious mutations in 31 cases in 8 other genes (BARD1, BRIP1, MRE11, NBN, RAD50, RAD51C, RAD51D and CDK4). The relevant variants were then genotyped in 558 family members. Assuming a constant relative risk of breast cancer across age groups, only variants in CDH1, CHEK2, PALB2 and TP53 showed evidence of a significantly increased risk of breast cancer, with some supportive evidence that mutations in ATM confer moderate risk.
Panel testing for these breast cancer families provided additional relevant clinical information for <2% of families. We demonstrated that segregation analysis has some potential to help estimate the breast cancer risks associated with mutations in breast cancer susceptibility genes, but very large case-control sequencing studies and/or larger family-based studies will be needed to define the risks more accurately.
用于检测乳腺癌易感性的基因组合检测已变得相对便宜且易于获得。然而,这些基因组合中许多基因的突变所关联的乳腺癌风险尚不清楚。
我们对660名患有家族性乳腺癌的非BRCA1/2女性进行了定制设计的靶向测序,覆盖17个已知和推定的乳腺癌易感基因的编码外显子。在相关家庭成员中对推定的有害突变进行基因分型,以评估每个变异与疾病的共分离情况。我们使用最大似然模型来估计每个基因中突变所关联的乳腺癌风险。
我们在45例患者中发现7个已知乳腺癌易感基因(TP53、PALB2、ATM、CHEK2、CDH1、PTEN和STK11)中有31个推定的有害突变,在另外8个基因(BARD1、BRIP1、MRE11、NBN、RAD50、RAD51C、RAD51D和CDK4)的31例患者中发现22个潜在的有害突变。然后在558名家庭成员中对相关变异进行基因分型。假设各年龄组乳腺癌的相对风险恒定,只有CDH1、CHEK2、PALB2和TP53中的变异显示出乳腺癌风险显著增加的证据,有一些支持性证据表明ATM突变会带来中度风险。
对这些乳腺癌家族进行基因组合检测为不到2%的家族提供了额外的相关临床信息。我们证明,分离分析在帮助估计乳腺癌易感基因突变所关联的乳腺癌风险方面具有一定潜力,但需要非常大规模的病例对照测序研究和/或更大规模的基于家族的研究来更准确地确定风险。