Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Mayo Clinic, Rochester, MN, USA.
Genet Med. 2019 Jul;21(7):1497-1506. doi: 10.1038/s41436-018-0361-5. Epub 2018 Dec 3.
Several genes on hereditary breast and ovarian cancer susceptibility test panels have not been systematically examined for strength of association with disease. We employed the Clinical Genome Resource (ClinGen) clinical validity framework to assess the strength of evidence between selected genes and breast or ovarian cancer.
Thirty-one genes offered on cancer panel testing were selected for evaluation. The strength of gene-disease relationship was systematically evaluated and a clinical validity classification of either Definitive, Strong, Moderate, Limited, Refuted, Disputed, or No Reported Evidence was assigned.
Definitive clinical validity classifications were made for 10/31 and 10/32 gene-disease pairs for breast and ovarian cancer respectively. Two genes had a Moderate classification whereas, 6/31 and 6/32 genes had Limited classifications for breast and ovarian cancer respectively. Contradictory evidence resulted in Disputed or Refuted assertions for 9/31 genes for breast and 4/32 genes for ovarian cancer. No Reported Evidence of disease association was asserted for 5/31 genes for breast and 11/32 for ovarian cancer.
Evaluation of gene-disease association using the ClinGen clinical validity framework revealed a wide range of classifications. This information should aid laboratories in tailoring appropriate gene panels and assist health-care providers in interpreting results from panel testing.
遗传性乳腺癌和卵巢癌易感基因检测面板上的一些基因尚未系统地进行疾病关联强度的检查。我们采用临床基因组资源(ClinGen)临床有效性框架来评估选定基因与乳腺癌或卵巢癌之间的证据强度。
选择了 31 个在癌症面板检测中提供的基因进行评估。系统地评估了基因-疾病关系的强度,并分配了明确的、强有力的、中等的、有限的、反驳的、有争议的或无报告证据的临床有效性分类。
分别对 10/31 和 10/32 对乳腺癌和卵巢癌的基因-疾病对做出了明确的临床有效性分类。有两个基因被归类为中度,而 6/31 和 6/32 个基因对乳腺癌和卵巢癌分别归类为有限。9/31 个基因对乳腺癌和 4/32 个基因对卵巢癌的证据相互矛盾,导致有争议或反驳的断言。对 5/31 个基因对乳腺癌和 11/32 个基因对卵巢癌没有报告疾病相关性的证据。
使用 ClinGen 临床有效性框架评估基因-疾病关联揭示了广泛的分类。这些信息应有助于实验室定制适当的基因面板,并帮助医疗保健提供者解释面板检测的结果。