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（申请人提供）：BRCAPRO等遗传风险预测模型在识别和管理携带乳腺癌基因BRCA1和BRCA2突变的女性中起着至关重要的作用。尽管BRCAPRO在遗传咨询中被广泛使用，但在初级保健中使用它的一个障碍是，它需要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，初级保健机构，如乳房x光检查中心，是在广大人口水平上确定乳腺癌和卵巢癌高风险妇女的理想选择。由于很大一部分高风险妇女通常不知道自己的风险，在初级保健中实施风险预测模型可以对遗传易感妇女的识别和管理产生巨大影响。为了使BRCAPRO达到这个水平，我们需要在使用的信息量和实现的准确性之间进行权衡。基于这一动机，我们提出了一个两阶段的方法。在第一阶段，只收集有限数量的家族史信息，这些数据将使用更简单的BRCAPRO版本或其他更简单的模型进行分析。如果这一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。我们提出了几个第一阶段的工具，这些工具根据它们需要的信息量而变化。在其中一些工具中，我们通过输入缺失的（未询问的）信息（如未受影响亲属的当前年龄或未受影响亲属本身）来增加收集到的信息，BRCAPRO可以利用这些信息进行更准确的预测。我们将比较第一阶段的工具在灵敏度、特异性和其他相关统计数据的截止范围，以及ROC曲线下的面积（AUC）。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的结果以第一阶段的结果为条件。特别是，我们得出了该方法的总体灵敏度、特异性和AUC。在开发了该方法之后，我们计划在一组独立的数据上验证它。我们的样本总数超过6000个家庭，来自广泛的来源-癌症遗传学网络，德克萨斯大学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，圣巴纳巴斯医疗保健系统，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软件中实现该方法。