Predicting the Breast Cancer Risk for Women Veterans

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，尤其是对女性而言。 年龄在40岁到49岁之间。学术团体和机构在他们关于 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筛查， 关于筛查开始年龄、停止年龄、频率和方式的决定是基于 个人风险最大限度地早期发现侵袭性癌症，并将不必要的危害降至最低 放映。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)分析已被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。对每个女人来说都很重要，最低100 穿透性单核苷酸多态(SNP)具有患乳腺癌的小风险，但会影响 许多女性由于高危等位基因频率所致。癌症易感性的高到中穿透性突变 BRCA和Lynch综合征基因等基因与患乳腺癌的风险较高有关 但只影响少数携带者妇女。百万退伍军人中的女退伍军人 计划(MVP)代表了一组女性，他们的全面基因信息和临床信息 已获得协变量，从而提供了开发、优化和/或验证风险的特殊机会 调整乳腺癌筛查策略。女性患乳房发育的风险预计会增加 癌症预测模型可能受益于在更年轻的年龄和更频繁的乳房开始进行筛查 成像包括结合乳房磁共振成像。预测患乳腺癌风险较低的女性 如果不那么严格地进行筛查，可能会做得很好。因为MVP中的女退伍军人可能有独特的军队和 环境暴露，尚不清楚以前是否开发了乳腺癌风险预测模型 可以适用于这一人群。此外，由于目前MVP队列中28%的女性退伍军人是 非裔美国人后裔，而有助于构建基因预测的遗传标记 模型是从涉及高加索人的研究中开发出来的，目前还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 目前可用的SNPs可以在MVP中的女性退伍军人中得到验证。此外，我们将确定 具有中高外显性的癌症易感基因的突变等位基因是否会产生同样的影响 (乳腺癌)先前确定的癌症风险。这些癌症中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 采用基因图谱为每一位女性建立乳腺癌预测模型。我们计划建造一座 乳腺癌风险预测模型在这个为期两年的试点项目中的最终目标是应用和验证 这些模型在整个MVP人群中。我们的工作，专注于退伍军人，与和 一项即将推出的前瞻性试验将极大地增强我们优化乳房的能力 以个性化方式进行癌症筛查。总而言之，我们将建立一个完全具有分子特征的女性 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是否有可能扩展到其他癌症或疾病类型。 我们的工作将显著提高我们早期发现乳腺癌的能力，并优化和个体化乳腺癌 对所有女性退伍军人和一般女性进行筛查。

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.