Prediction of interval breast cancer risk using genetic variants

项目摘要/摘要 这是加州大学普通内科医生谢一伟博士的K08奖项申请的重新提交 加州，旧金山(加州大学旧金山分校)。谢医生的长期职业目标是成为一名领先的临床医生- 在精准医学和癌症筛查/预防的交叉点工作的研究员。本次K08大奖 将为谢医生提供必要的支持，以：1)加深对乳腺癌生物学和 肿瘤基因表达谱，2)拓展他在遗传学和预测建模方面的分析技能，3)获得 进行个性化癌症筛查的后期翻译研究所需的技能，以及4)建立一个索引- 悬而未决的研究生涯。为了帮助他实现这些目标，谢博士组建了一支模范团队， 具有互补专业知识的导师。领先的癌症遗传流行病学家Elad Ziv博士将担任 主要指导和监督研究目标和培训目标的完成。共同导师包括：劳拉博士 Van‘t Veer，乳腺癌基因表达分析专家；Mi-ok Kim博士，生物统计学专家 方法和研究设计；以及乳房外科医生、精确度方面的领先创新者劳拉·埃瑟曼博士。 肿瘤学。拟议的工作将受益于加州大学旧金山分校现有的专业知识和丰富的机构环境， 尤其是海伦·迪勒家庭综合癌症中心，这是美国最大的癌症中心之一- 试试看。谢医生将利用上述资源解决癌症控制中的一个重要问题，即 目前乳腺癌筛查战略检测生物侵袭性癌症的能力有限。这些 癌症倾向于在正常的乳房X光检查后出现症状，称为“间歇性癌症”，通常在 高级阶段。谢博士过去的工作重点是利用临床风险预测总体乳腺癌风险 代表多个单核苷酸多核苷酸联合效应的模型和多基因风险分数(PR) 态射(SNP)。谢博士建议采用这种方法来构建间歇性癌症的风险模型 根据初步数据显示，先前发现的SNPs可能对 乳腺癌表型。鉴于基因表达谱能够提供详细的肿瘤表型 信息，谢博士建议识别与快速发展(Ag1)相关的基因表达特征。 渐进性)间歇性癌症(目标1)。然后，谢博士将使用这些基因表达签名来选择SNP 与攻击性有关(目标2)。这些选定的SNP将被用来构建攻击性的PRS 癌症。最后，谢博士将测试这种预测系统单独和联合预测间歇性癌症的能力。 与乳房密度和临床危险因素有关(目标3)。拟议的目标将利用谢博士独特的 获取来自临床试验、肿瘤图谱和乳腺癌筛查队列的数据。结果将直接 引导关于使用区间癌症风险模型进行前瞻性筛查和预调查的后续研究 新的生物标记物在改善高危妇女风险分层中的作用的研究。

Project Summary/Abstract This is a resubmission of a K08 award application for Dr. Yiwey Shieh, a general internist at the University of California, San Francisco (UCSF). Dr. Shieh’s long-term career goal is to become a leading clinician- investigator working at the intersection of precision medicine and cancer screening/prevention. This K08 award will provide Dr. Shieh with the support necessary to: 1) develop an understanding of breast cancer biology and tumor gene expression profiling, 2) expand his analytical skills in genetics and prediction modeling, 3) obtain the skills needed to conduct late-translational studies on personalized cancer screening, and 4) build an inde- pendent research career. To help him achieve these goals, Dr. Shieh has assembled an exemplary team of mentors with complementary expertise. Dr. Elad Ziv, a leading cancer genetic epidemiologist, will serve as the primary mentor and oversee completion of research aims and training goals. Co-mentors include: Dr. Laura van’t Veer, an expert in gene expression analysis of breast cancer; Dr. Mi-Ok Kim, an expert in biostatistical methods and study design; and Dr. Laura Esserman, a breast surgeon and leading innovator in precision on- cology. The proposed work will benefit from the available expertise and rich institutional environment of UCSF, specifically the Helen Diller Family Comprehensive Cancer Center, one of the top cancer centers in the coun- try. Dr. Shieh will leverage the above resources to address an important problem in cancer control, namely the limited ability of current breast cancer screening strategies to detect biologically aggressive cancers. These cancers tend to present symptomatically following a normal mammogram as “interval cancers”, and often at advanced stages. Dr. Shieh’s past work has focused on predicting overall breast cancer risk using clinical risk models and polygenic risk scores (PRS) representing the combined effects of multiple single nucleotide poly- morphisms (SNPs). Dr. Shieh proposes to adopt this approach to construct a risk model for interval cancers based on preliminary data suggesting that previously discovered SNPs may hold additional prediction for breast cancer phenotype. Given the ability of gene expression profiling to provide detailed tumor phenotypic information, Dr. Shieh proposes to identify gene expression signatures correlated with rapidly-developing (ag- gressive) interval cancers (Aim 1). Dr. Shieh will then use these gene expression signatures to select SNPs associated with aggressiveness (Aim 2). These selected SNPs will be used to construct a PRS for aggressive cancer. Finally, Dr. Shieh will test the ability of this PRS to predict interval cancers, both alone and in combina- tion with breast density and clinical risk factors (Aim 3). The proposed aims will leverage Dr. Shieh’s unique access to data from clinical trials, tumor atlases, and breast cancer screening cohorts. The results will directly lead to subsequent studies on using an interval cancer risk model to prospectively inform screening and pre- vention, as well as studies on the role of novel biomarkers in refining risk stratification among high-risk women.