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.

尽管经过多年的研究，最佳的乳腺癌筛查策略仍然难以捉摸，特别是对女性来说 年龄在40到49岁之间。学术团体和机构在关于 开始乳房X光检查的年龄和筛查间隔。一个潜在的解决方案是风险适应性筛查， 关于开始年龄、停止年龄、频率和筛查方式的决定是基于 个人风险，以最大限度地早期发现侵袭性癌症，并尽量减少不必要的伤害 筛选除了人口统计学、家族史、乳腺密度和其他风险因素外，单核苷酸 生殖系DNA的多态性（SNP）分析已被纳入乳腺癌预测模型 可以进一步指导我们的临床筛查建议。与每一个女人相关的，100低 渗透性单核苷酸多态性（SNP）赋予乳腺癌发展的小风险，但影响 许多妇女由于高风险等位基因频率。高至中位渗透突变的癌症易感性 BRCA和Lynch综合征基因等基因与乳腺癌的高风险相关 发展，但只影响少数妇女谁是携带者。百万退伍军人中的女性退伍军人 MVP计划代表了一组女性，她们的综合遗传信息和临床 已获得协变量，提供了开发、优化和/或验证风险的绝佳机会 调整乳腺癌筛查策略。女性预测有一个高风险的发展乳房 癌症预测模型可能受益于从更年轻开始的筛查， 包括乳腺MRI的结合。预测患乳腺癌风险较低的女性 可以通过低强度的筛选做得很好。因为MVP中的女性退伍军人可能有独特的军事和 环境暴露，目前尚不清楚先前开发的乳腺癌风险预测模型是否 可以应用于这个群体。此外，由于目前MVP队列中28%的女性退伍军人是 非洲裔美国人的血统，而遗传标记，有助于遗传预测的建设 模型是从涉及高加索人的研究中开发的，尚不清楚这些工具是否可以应用于 不同种族背景的女性。我们的研究将确定是否建立乳腺癌预测模型 目前可用的SNP可以在MVP中的女性退伍军人中验证。此外，我们将确定 具有中至高突变率的癌症易感性基因的突变等位基因是否会赋予相同的 （乳腺癌）的风险，如以前建立的。这些癌症中的突变引起的更高的癌症风险 易感基因可以使通用检测具有成本效益，这可以进一步促进和激励 采用基因图谱，为每一位妇女建立乳腺癌预测模型。我们提出建设 乳腺癌风险预测模型在这个为期两年的试点项目的最终目标是应用和验证 在整个MVP人群中使用这些模型。我们的工作重点是退伍军人妇女， 辅以一项正在启动的前瞻性试验，将大大提高我们优化乳腺癌的能力。 以个性化的方式进行癌症筛查。总之，我们将建立一个分子充分表征妇女 MVP中的老将队列，我们可以继续纵向跟踪。我们将专注于建设和 验证乳腺癌风险预测模型，并有可能扩展到其他癌症或疾病类型。 我们的工作将大大提高我们早期发现的能力，并优化和个性化乳腺癌 为所有女性退伍军人和一般女性进行筛查。