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: 10884208
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10884208
• 关键词: Adoption; Affect; African American; African American population; Age; Alleles; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Magnetic Resonance Imaging; Cancer-Predisposing Gene; Caucasians; Cessation of life; Clinical; Clinical Data; Clinical Markers; Complement; DNA; Data; Development; Disease; Early Diagnosis; Enrollment; Environmental Exposure; Family; Frequencies; Gene Frequency; Genes; Genetic Markers; Germ Lines; Goals; Harm Reduction; Hereditary Nonpolyposis Colorectal Neoplasms; High-Risk Cancer; Image; Incidence; Individual; Life Style; Magnetic Resonance Imaging; Malignant Neoplasms; Mammography; Military Personnel; Minority Women; Modality; Modeling; Molecular; Monitor; Mutation; Participant; Patients; Penetrance; Performance; Pilot Projects; Population; Population Programs; Predictive Cancer Model; Recommendation; Recording of previous events; Research; Resources; Risk; Risk Factors; SNP genotyping; Single Nucleotide Polymorphism; Societies; Syndrome; Testing; Veterans; Woman; Work; brca gene; breast density; breast imaging; cancer predisposition; cancer risk; cohort; combat; cost effective; demographics; ethnic diversity; experience; follow-up; genetic information; genetic predictors; genetic profiling; high risk; instrument; malignant breast neoplasm; military veteran; model building; mutant; predictive modeling; programs; prospective; risk prediction model; 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.

尽管经过多年的研究，最佳的乳腺癌筛查策略仍然难以捉摸，尤其是对于女性而言 年龄在 40 岁至 49 岁之间。学术团体和机构对于 开始乳房X光检查的年龄和筛查间隔。一种潜在的解决方案是风险适应筛查， 关于筛查的起始年龄、停止年龄、频率和方式的决定基于 个人风险，最大限度地早期发现侵袭性癌症并尽量减少不必要的危害 筛选。除了人口统计、家族史、乳腺密度和其他危险因素外，单核苷酸 种系 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筛查建议。与每个女性相关的 ~100 低 渗透性单核苷酸多态性 (SNP) 导致乳腺癌发生的风险很小，但会影响 许多女性由于风险等位基因频率高。癌症易感人群的高到中渗透突变 基因，例如 BRCA 和林奇综合征基因，与较高的乳腺癌风险相关 发展，但仅影响少数携带者女性。百万退伍军人中的女退伍军人 计划（MVP）代表一群女性，她们拥有全面的遗传信息和临床信息 已获得协变量，为开发、优化和/或验证风险提供了绝佳的机会 调整乳腺癌筛查策略。预计女性患乳房的风险较高 癌症预测模型可能受益于从更年轻的年龄开始进行筛查和更频繁的乳房筛查 成像，包括乳腺 MRI 的结合。预计女性患乳腺癌的风险较低 不太严格的筛查可能会效果很好。因为 MVP 中的女性退伍军人可能拥有独特的军事和 环境暴露，尚不清楚先前是否开发了乳腺癌风险预测模型 可以应用到这个人群。此外，由于当前 MVP 群体中 28% 的女性退伍军人是 非裔美国人血统，而有助于构建遗传预测的遗传标记 模型是根据涉及白人的研究开发的，目前尚不清楚这些工具是否可以应用于 具有不同种族背景的妇女。我们的研究将确定乳腺癌预测模型是否建立 目前可用的 SNP 可以在 MVP 中的女性退伍军人中得到验证。此外，我们将确定 具有中等到高外显率的癌症易感性基因的突变等位基因是否会赋予相同的效果 先前确定的（乳腺癌）癌症风险。这些癌症的突变导致更高的癌症风险 易感基因可能使普遍检测具有成本效益，这可以进一步促进和激励 采用基因分析为每位女性建立乳腺癌预测模型。我们建议建造 这个为期两年的试点项目的乳腺癌风险预测模型的最终目标是应用和验证 这些模型存在于整个 MVP 群体中。我们的工作重点是退伍军人妇女以及 辅之以正在启动的前瞻性试验，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。总之，我们将构建一个分子特征完整的女性 我们可以继续纵向追踪 MVP 中的老兵群体。我们将重点建设和 验证乳腺癌风险预测模型是否有可能扩展到其他癌症或疾病类型。 我们的工作将显着提高我们早期检测、优化和个体化乳腺癌的能力 对所有女性退伍军人和一般女性进行筛查。