Genetic Risk Prediction in Primary Care

初级保健中的遗传风险预测

• 批准号: 8601695
• 负责人: BANU K ARUN
• 金额: $ 8万
• 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 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8601695
• 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（申请人提供）：BRCAPRO 等遗传风险预测模型在携带乳腺癌基因 BRCA1 和 BRCA2 突变的女性的识别和管理中发挥着关键作用。尽管 BRCAPRO 广泛用于遗传咨询，但其在初级保健中使用的一个障碍是，它需要有关咨询者及其家族史的潜在广泛信息来估计 BRCA1/2 基因的携带者概率。另一方面，乳房X光检查中心等初级保健机构非常适合在大量人群中识别乳腺癌和卵巢癌高危女性。由于很大一部分处于高风险的女性通常不知道自己的风险，因此在初级保健中实施风险预测模型可以对识别和管理具有遗传易感性的女性产生巨大影响。为了使 BRCAPRO 达到这个水平，我们需要在所使用的信息量和所达到的准确性之间进行权衡。出于这种动机，我们提出了一个两阶段的方法。在第一阶段，仅收集有限数量的家族史信息，并使用更简单版本的 BRCAPRO 或其他更简单的模型来分析数据。如果此阶段评估的风险足够高，第二阶段将使用完整版的 BRCAPRO 以获得更准确的估计。我们提出了几种第一阶段的工具，这些工具根据所需的信息量而有所不同。在其中一些工具中，我们通过输入缺失的（未询问的）信息（例如未受影响的亲属的当前年龄或未受影响的亲属本身）来增强收集的信息，BRCAPRO 可以利用这些信息来做出更准确的预测。我们将比较第一阶段工具的敏感性、特异性和一系列截止值的其他相关统计数据以及 ROC 曲线下面积 (AUC)。此外，我们开发了一种评估两阶段方法整体性能的方法，该方法考虑到第二阶段结果以第一阶段结果为条件的事实。特别是，我们得出了该方法的总体敏感性、特异性和 AUC。开发该方法后，我们计划在一组独立的数据上对其进行验证。我们的样本总数超过 6,000 个家庭，来源广泛 - 癌症遗传学网络、德克萨斯大学 (UT) MD 安德森癌症中心、德克萨斯大学西南医学中心、牛顿韦尔斯利医院、圣巴拿巴医疗保健系统、耶鲁大学、米德尔塞克斯医院和百慕大癌症遗传学和风险评估计划。我们还将在 BayesMendel 和 HughesRiskApps 软件中实施该方法。