Predicting the Breast Cancer Risk for Women Veterans

预测女性退伍军人患乳腺癌的风险

• 批准号: 9484619
• 负责人: CYNTHIA A. BRANDT
• 金额: --
• 依托单位: PORTLAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9484619
• 关键词: Adoption; Adoption; Affect; Affect; African American; African American; Age; Age; Alleles; Alleles; Breast; Breast Cancer Detection; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Magnetic Resonance Imaging; Cancer-Predisposing Gene; Cancer-Predisposing Gene; Caucasians; Caucasians; Cessation of life; Cessation of life; Clinical; Clinical; Clinical Data; Clinical Data; Clinical Markers; Clinical Markers; Complement; Complement; DNA; DNA; Data; Data; Development; Development; Disease; Disease; Early Diagnosis; Early Diagnosis; Enrollment; Enrollment; Environmental Exposure; Environmental Exposure; Family; Family; Frequencies; Frequencies; Gene Frequency; Gene Frequency; Genes; Genes; Genetic; Genetic; Genetic Markers; Genetic Markers; Genotype; Genotype; Germ Lines; Germ Lines; Goals; Goals; Harm Reduction; Harm Reduction; Hereditary Nonpolyposis Colorectal Neoplasms; Hereditary Nonpolyposis Colorectal Neoplasms; High-Risk Cancer; High-Risk Cancer; Image; Image; Incidence; Incidence; Individual; Individual; Life Style; Life Style; Magnetic Resonance Imaging; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant Neoplasms; Mammographic screening; Mammography; Mammography; Military Personnel; Military Personnel; Minority; Minority; Modality; Modality; Modeling; Modeling; Molecular; Molecular; Monitor; Monitor; Mutation; Mutation; Participant; Participant; Patients; Patients; Penetrance; Penetrance; Performance; Performance; Pilot Projects; Pilot Projects; Population; Population; Population Programs; Population Programs; Predisposition; Predisposition; Recommendation; Recommendation; Recording of previous events; Recording of previous events; Research; Research; Resources; Resources; Risk; Risk; Risk Factors; Risk Factors; Single Nucleotide Polymorphism; Single Nucleotide Polymorphism; Societies; Societies; Sum; Sum; Syndrome; Syndrome; Testing; Testing; Veterans; Veterans; Woman; Woman; Work; Work; base; base; breast density; breast density; breast imaging; breast imaging; cancer risk; cancer risk; cohort; cohort; combat; combat; cost effective; cost effective; demographics; demographics; ethnic diversity; ethnic diversity; experience; experience; follow-up; follow-up; genetic information; genetic information; genetic profiling; genetic profiling; high risk; high risk; instrument; instrument; malignant breast neoplasm; malignant breast neoplasm; mutant; mutant; predictive modeling; predictive modeling; programs; programs; prospective; prospective; risk prediction model; screening; screening; screening guidelines

Despite years of research, optimal breast cancer screening strategies remain elusive, especially for women between the age of 40 and 49. Academic societies and agencies differ in their recommendations regarding the age to begin mammography and the screening intervals. One potential solution is risk-adapted screening, where decisions around the starting age, stopping age, frequency, and modality of screening are based on individual risk to maximize the early detection of aggressive cancers and minimize the harms of unnecessary screening. In addition to demographics, family history, breast density, and other risk factors, single nucleotide polymorphism (SNP) profiling of germ line DNA has been incorporated into breast cancer prediction models that can further guide our clinical recommendations for screening. Of relevance to every woman, the ~100 low penetrant single nucleotide polymorphism (SNP) confers a small risk of breast cancer development but affects many women due to the high risk allele frequency. High to median penetrant mutations of cancer susceptible genes, such as BRCA and Lynch syndrome genes, are associated with a higher risk of breast cancer development but affect only a minority of women who are carriers. Women Veterans in the Million Veteran Program (MVP) represent a cohort of women for whom comprehensive genetic information and clinical covariates have been obtained, providing an exceptional opportunity to develop, optimize and/or validate a risk adapted breast cancer screening strategy. Women predicted to have an elevated risk of developing breast cancer by prediction models may benefit from screening beginning at a younger age and more frequent breast imaging including the incorporation of breast MRI. Women predicted to have a low(er) risk for breast cancer may do well with less intense screening. Because women Veterans in MVP may have unique military and environmental exposures, it is unknown whether previously developed breast cancer risk prediction models can be applied to this population. Moreover, since 28% of women Veterans in the current MVP cohort are of African American descent, while the genetic markers that contribute to the construction of genetic prediction models are developed from studies involving Caucasians, it is not clear if these instruments can be applied to women that are of diverse ethnic backgrounds. Our study will determine if breast cancer prediction models built on currently available SNPs can be validated in women Veterans in the MVP. Moreover, we will determine whether mutant alleles of cancer susceptibility genes with median to high penetrance will confer the same (breast) cancer risks as previously established. A higher cancer risk incurred by mutation in these cancer susceptible genes may make universal testing cost effective, which can further facilitate and motivate the adoption of genetic profiling to build breast cancer prediction models for every woman. We propose to build breast cancer risk prediction models in this two-year pilot project with the ultimate goal to apply and validate these models in the entire MVP population. Our work, focusing on Veteran women, together with and complemented by a prospective trial that is being launched will greatly enhance our ability to optimize breast cancer screening in a personalized manner. In sum, we will build a molecularly full characterized women Veteran cohort in the MVP that we can continue to follow longitudinally. We will focus on building and validating breast cancer risk prediction models with the potential to extend to other cancer or disease types. Our work will significantly enhance our abilities for early detection and optimize and individualize breast cancer screening for all women Veterans and women in general.

尽管进行了多年的研究，但最佳的乳腺癌筛查策略仍然难以捉摸，尤其是对于女性 在40至49岁之间。学术社会和机构在他们关于 开始乳房X线摄影和筛查间隔的年龄。一个潜在的解决方案是适应风险的筛查， 在开始年龄，停止年龄，频率和筛选方式的决定基于 个人风险以最大化侵略性癌症的早期发现并最大程度地减少不必要的危害 筛选。除了人口统计学，家族史，乳房密度和其他危险因素外，单核苷酸 生殖系DNA的多态性（SNP）分析已纳入乳腺癌预测模型 这可以进一步指导我们的临床建议进行筛查。与每个女人的相关性，约100低 渗透剂单核苷酸多态性（SNP）赋予乳腺癌发育风险很小，但影响 许多女性由于高风险等位基因频率。癌症易感的高度至中位渗透突变 基因，例如BRCA和Lynch综合征基因，与乳腺癌的风险更高有关 发展，但只影响少数妇女是载体。百万退伍军人的老兵 计划（MVP）代表了一群妇女，这些妇女为她们全面的遗传信息和临床 已经获得了协变量，为开发，优化和/或验证风险提供了非凡的机会 适应乳腺癌筛查策略。妇女预测患乳房的风险升高 预测模型的癌症可能会受益于从年轻开始的筛查和更频繁的乳房 成像，包括掺入乳房MRI。妇女预测乳腺癌的风险很低 筛查较少的筛查可能会很好。因为MVP的女退伍军人可能有独特的军事力量，并且 环境暴露，尚不清楚先前开发的乳腺癌风险预测模型 可以应用于这个人群。此外，由于当前MVP队列中有28％的女退伍军人是 非裔美国人的血统，而有助于遗传预测的遗传标记 模型是根据涉及高加索人的研究开发的，尚不清楚这些工具是否可以应用于 具有不同种族背景的女性。我们的研究将确定乳腺癌预测模型是否建立 当前可用的SNP可以在MVP中的女退伍军人中得到验证。而且，我们将确定 癌症易感性基因中位数的突变等位基因是否会赋予相同 （乳房）癌症的风险如前所述。这些癌症突变引起的癌症风险更高 易感基因可能使普遍测试成本效益，这可以进一步促进和激励 采用基因分析来为每个女人建立乳腺癌预测模型。我们建议建造 在这个为期两年的试点项目中，乳腺癌风险预测模型的最终目标是申请和验证 这些模型在整个MVP人群中。我们的工作，专注于资深妇女，以及 通过前瞻性试验的补充，正在发起的试验将大大增强我们优化乳房的能力 以个性化的方式进行癌症筛查。总而言之，我们将建立一个分子表征的女性 在MVP中，我们可以继续纵向跟随。我们将专注于建造和 验证乳腺癌风险预测模型，可能会扩展到其他癌症或疾病类型。 我们的工作将大大提高我们的早期检测能力，并优化和个性化乳腺癌 对所有退伍军人和一般妇女进行筛查。