Thanthirige L. M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas
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引用次数: 0
摘要
现在,多基因面板检测可以对许多癌症易感基因进行有效检测,从而发现更多的基因突变携带者。他们需要就特定基因突变带来的癌症风险接受咨询。一个重要的癌症易感基因是 PALB2。多项研究报告了 PALB2 致病变异所带来的乳腺癌(BC)风险估计值。由于报告的风险估计值(年龄特异性风险、几率比例、相对风险和标准化发病率比)和效应大小的方式各不相同,因此有必要将这些估计值结合起来进行荟萃分析,以便为该基因突变的患者提供准确的咨询。然而,由于各项研究在研究设计和风险测量方面存在异质性,因此这并非易事。我们采用了最近提出的贝叶斯随机效应荟萃分析方法,该方法可以综合此类异质性研究的估计值。我们采用这种方法综合了 12 项研究对致病性 PALB2 突变携带者 BC 风险的估计值。据估计,到 50 岁时 BC 的总体(基于荟萃分析的)风险为 12.80%(6.11%-22.59%),到 80 岁时为 48.47%(36.05%-61.74%)。PALB2 的致病突变使女性更易患 BC。我们的风险估计值有助于对携带PALB2致病变异的患者进行临床管理。
Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants
Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%−22.59%) by age 50 and 48.47% (36.05%−61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.
期刊介绍:
Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations.
Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.