Prediction of interval breast cancer risk using genetic variants

项目概要/摘要 这是一个K 08奖申请的重新提交博士Yiwey Shieh，在大学的普通内科医生， 加州，旧金山弗朗西斯科（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人员。K 08获奖 将为Shieh博士提供必要的支持：1）了解乳腺癌生物学， 肿瘤基因表达谱分析，2）扩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后期转化研究所需的技能，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，Shieh博士组建了一个模范团队， 具有互补专业知识的导师。Elad Ziv博士是一位领先的癌症遗传流行病学家，他将担任 主要指导和监督研究目标和培训目标的完成。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Laura Esserman博士，一位乳腺外科医生，在精确治疗方面的领先创新者， 学。拟议的工作将受益于UCSF的现有专业知识和丰富的制度环境， 特别是海伦·迪勒家庭综合癌症中心，这是美国最好的癌症中心之一， 试试Shieh博士将利用上述资源解决癌症控制中的一个重要问题，即 目前的乳腺癌筛查策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光检查后表现为“间隔癌”，并且通常在 晚期Shieh博士过去的工作主要集中在使用临床风险预测整体乳腺癌风险 模型和多基因风险评分（PRS）表示多个单核苷酸多聚核苷酸的组合效应， 态射（SNP）。Shieh博士建议采用这种方法来构建间隔癌症的风险模型 基于初步数据表明，先前发现的SNP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，Shieh博士建议确定与快速发育相关的基因表达特征（ag- 退行性）间隔癌（Aim 1）。然后Shieh博士将使用这些基因表达特征来选择SNP 与侵略性相关（目标2）。这些选择的SNP将用于构建针对侵袭性的PRS。 癌最后，Shieh博士将测试这种PRS预测间隔期癌症的能力，无论是单独还是组合， 与乳腺密度和临床危险因素的关系（目的3）。拟议的目标将利用谢博士独特的 访问来自临床试验、肿瘤图谱和乳腺癌筛查队列的数据。结果将直接 导致随后的研究使用间隔癌症风险模型前瞻性地告知筛查和预 预防，以及新的生物标志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.