Genetic Risk Prediction in Primary Care

初级保健中的遗传风险预测

• 批准号: 8446597
• 负责人: BANU K ARUN
• 金额: $ 8.47万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8446597
• 关键词: Academic Medical Centers; Accounting; Age; Area; BRCA1 Mutation; BRCA1 gene; BRCA2 Mutation; BRCA2 gene; Benefits and Risks; Bermuda; Breast; Cancer Genetics Network; Clinic; Clinical; Clinical Data; Computer software; Counseling; Data; Data Set; Early identification; Equilibrium; Family; Family history of; Gene Mutation; General Population; Genes; Genetic Counseling; Genetic Risk; Goals; Healthcare Systems; High Risk Woman; High-Risk Cancer; Hospitals; Individual; Licensing; Malignant Neoplasms; Malignant neoplasm of ovary; Mammography; Medical center; Methodology; Modeling; Motivation; Mutation; Names; Oncogenes; Patients; Performance; Play; Population; Prevention; Primary Health Care; Probability; Provider; ROC Curve; Recording of previous events; Relative (related person); Resources; Risk; Risk Assessment; Role; Sample Size; Sampling; Sensitivity and Specificity; Site; Source; Staging; Texas; Time; Training; Universities; University of Texas M D Anderson Cancer Center; Validation; Variant; Woman; cancer genetics; cancer risk; cancer therapy; design; high risk; malignant breast neoplasm; population based; prevent; primary care setting; proband; programs; prospective; public health relevance; statistics; time use; tool; user-friendly

DESCRIPTION (provided by applicant): Genetic risk prediction models such as BRCAPRO play a critical role in identification and management of women who carry mutations of breast cancer genes BRCA1 and BRCA2. Although BRCAPRO is widely used in genetic counseling, an impediment to its use in primary care is the fact that it requires potentially extensive information on counselee and her family history to estimate the carrier probabilities of BRCA1/2 genes. On the other hand, primary care settings such as mammography centers are ideal for identifying women at high risk for breast and ovarian cancers at a large population level. As a big proportion of women who are at high risk typically are unaware of their risk, implementing risk prediction models in primary care can make a huge impact in identification and management of genetically pre-disposed women. To bring BRCAPRO to this level, we need to balance the tradeoff between the amount of information used and accuracy achieved. With this motivation, we propose a two-stage approach. In the first stage, only a limited amount of family history information will be collected and that data will be analyzed using a simpler version of BRCAPRO or other simpler models. If the assessed risk at this stage is sufficiently high, full version of BRCAPRO will be used in the second stage to obtain more accurate estimates. We propose several first stage tools that vary by the amount of information they require. In some of these tools, we augment the collected information by imputing the missing (unasked) information such as the current ages of unaffected relatives or the unaffected relatives themselves, which BRCAPRO can utilize to make potentially more accurate prediction. We will compare the first stage tools in terms of their sensitivities, specificities, and other related statistics at a range of cutoffs, and area under the ROC curve (AUC). Further, we develop a methodology to evaluate the overall performance of the two-stage approach that takes into account the fact that the second stage results are conditional on those of the first stage. In particular, we derive the overall sensitivity, specificity, and AUC of the approach. After developing the approach, we plan to validate it on an independent set of data. Our total sample exceeds 6,000 families and come from a wide range of sources - Cancer Genetics Network, University of Texas (UT) MD Anderson Cancer Center, UT Southwestern Medical Center, Newton-Wellesley Hospital, St. Barnabas Health Care system, Yale University, Middlesex Hospital, and Bermuda Cancer Genetics and Risk Assessment Program. We will also implement the approach in BayesMendel and HughesRiskApps software.

描述（由申请人提供）：诸如BRCAPRO之类的遗传风险预测模型在携带乳腺癌基因突变BRCA1和BRCA2突变的女性的识别和管理中起关键作用。尽管BRCAPRO广泛用于遗传咨询，但在初级保健中使用的障碍是，它需要有关顾问和家族史的潜在广泛信息来估算BRCA1/2基因的携带者概率。另一方面，诸如乳房X线摄影中心之类的初级保健环境非常适合在较大的人群水平上识别出乳腺癌和卵巢癌的高风险的理想选择。由于处于高风险的妇女通常没有意识到自己的风险，因此在初级保健中实施风险预测模型会对遗传性预先分配的妇女的识别和管理产生巨大影响。为了将BRCAPRO提高到这个水平，我们需要平衡所用信息的量与所达到的准确性之间的权衡。有了这种动机，我们提出了一种两阶段的方法。在第一阶段，将仅收集大量的家族史信息，并将使用更简单的BRCAPRO或其他更简单的模型来分析数据。如果此阶段的评估风险足够高，则将在第二阶段使用完整版的BRCAPRO，以获得更准确的估计。我们提出了几种第一阶段工具，这些工具因其所需的信息量而异。在其中一些工具中，我们通过推出丢失（未填充的）信息（例如当前未受影响的亲戚的年龄或未受影响的亲戚本身）来增强收集的信息，Brcapro可以利用这些信息来实现更准确的预测。我们将根据其敏感性，特异性和其他相关统计数据在一系列截止范围以及ROC曲线（AUC）下的区域进行比较。此外，我们开发了一种方法来评估两阶段方法的整体性能，该方法考虑到第二阶段结果是在第一阶段的条件下的事实。特别是，我们得出了该方法的总体灵敏度，特异性和AUC。开发方法后，我们计划在一组独立的数据中验证它。我们的总样本超过6,000个家庭，来自广泛的来源 - 癌症遗传网络，德克萨斯大学（UT）MD安德森癌症中心，UT西南医学中心，牛顿 - 韦尔斯利医院，圣巴纳巴斯医疗保健系统，耶鲁大学，米德尔塞克斯医院和伯马达癌症遗传学和风险评估。我们还将在Bayesmendel和Hughesriskapps软件中实施该方法。