Breast Pre-Cancer Atlas Center

乳腺癌前期阿特拉斯中心

• 批准号: 10819754
• 负责人: E.Shelley Hwang
• 金额: $ 13.37万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-04 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10819754
• 关键词: 3-Dimensional; Address; Anxiety; Atlases; Bilateral; Breast; Breast Epithelial Cells; Cell Compartmentation; Cells; Chemotherapy and/or radiation; Clinical; Clinical Trials; Communities; Confusion; Data; Data Analyses; Data Set; Data Storage and Retrieval; Detection; Development; Diagnosis; Dimensions; Disease; Disease Management; Distress; Duct (organ) structure; Ecology; Elements; Enrollment; Epithelium; Evolution; FAIR principles; Future; Gene Expression; Histologic; Image; Immune; Inflammatory; Informatics; Intervention; Intraductal Hyperplasia; Investigation; Life; Longitudinal Studies; Malignant Neoplasms; Mammography; Manuscripts; Mastectomy; Methodology; Methylation; Modeling; Molecular; Morbidity - disease rate; Mutation; Natural History; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Outcome; Parents; Pathologic; Pathway interactions; Patient Care; Patient observation; Patients; Phase; Proteomics; Provider; Publishing; Quality of life; RNA; Radiology Specialty; Research Personnel; Resources; Risk; Sampling; Sum; Testing; Tissues; Toxic effect; United States; Woman; Work; breast lumpectomy; calcification; cancer invasiveness; cell type; clinical diagnosis; cohort; data integration; data visualization; design; disease natural history; evidence base; experience; flexibility; genomic signature; improved; knowledge of results; malignant breast neoplasm; multimodal data; multimodality; multiscale data; nano-string; next generation; novel; pandemic impact; premalignant; prevent; progression marker; progression risk; prospective; risk stratification; sample collection; screening; single-cell RNA sequencing; tool; tumor; 3-Dimensional; Address; Anxiety; Atlases; Bilateral; Breast; Breast Epithelial Cells; Cell Compartmentation; Cells; Chemotherapy and/or radiation; Clinical; Clinical Trials; Communities; Confusion; Data; Data Analyses; Data Set; Data Storage and Retrieval; Detection; Development; Diagnosis; Dimensions; Disease; Disease Management; Distress; Duct (organ) structure; Ecology; Elements; Enrollment; Epithelium; Evolution; FAIR principles; Future; Gene Expression; Histologic; Image; Immune; Inflammatory; Informatics; Intervention; Intraductal Hyperplasia; Investigation; Life; Longitudinal Studies; Malignant Neoplasms; Mammography; Manuscripts; Mastectomy; Methodology; Methylation; Modeling; Molecular; Morbidity - disease rate; Mutation; Natural History; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Outcome; Parents; Pathologic; Pathway interactions; Patient Care; Patient observation; Patients; Phase; Proteomics; Provider; Publishing; Quality of life; RNA; Radiology Specialty; Research Personnel; Resources; Risk; Sampling; Sum; Testing; Tissues; Toxic effect; United States; Woman; Work; breast lumpectomy; calcification; cancer invasiveness; cell type; clinical diagnosis; cohort; data integration; data visualization; design; disease natural history; evidence base; experience; flexibility; genomic signature; improved; knowledge of results; malignant breast neoplasm; multimodal data; multimodality; multiscale data; nano-string; next generation; novel; pandemic impact; premalignant; prevent; progression marker; progression risk; prospective; risk stratification; sample collection; screening; single-cell RNA sequencing; tool; tumor

ABSTRACT Development of invasive breast cancer may frequently progress through a preinvasive precursor called ductal carcinoma in situ (DCIS). DCIS is a common clinical diagnosis that is essentially a disease of screening triggered by the detection of abnormal breast calcifications on mammography. Before the advent of mammography, DCIS was an incidental and relatively uncommon finding. Over 60,000 women in the United States will be presented with this diagnosis each year with relatively weak evidence-based guidance for disease management which ranges from active surveillance to bilateral mastectomy. We propose to compile multi-dimensional and multi-scale data on DCIS to construct a Pre-Cancer Atlas that can be used to better understand the disease but also to better stratify risk of progression. To do this, we have assembled a team of investigators with deep and complementary clinical, experimental, and quantitative expertise and experience with DCIS and breast cancer. Further, we conduct these studies with full consideration of tumor evolution and ecology as it pertains to precancer development and progression. Specific aspects of the proposed Atlas construction include: 1) Several types of DCIS cohorts that will capture spatial and longitudinal information including a prospective clinical trial cohort undergoing active surveillance, 2) Analyses designed to maintain relevant spatial organization of the disease for evolutionary and Atlas building considerations based on 3) Radiologic-histologic-cellular- molecular registration approaches, 4) Characterization at multiple scales including whole tumor, single duct and single cell levels, 5) Characterization of relevant parameters including mutations, copy number changes, methylation, gene expression, and microenvironmental elements including inflammatory cell profiles. 6) Incorporation of the breast cancer intrinsic subtype paradigm into the analytic phase, and 7) Layered, spatial, and longitudinal data visualization. Overall, this work will provide a comprehensive platform to guide the next generation of studies on DCIS and other precancers.

抽象的 侵入性乳腺癌的发展可能经常通过称为侵入性前体进行 导管癌原位（DCIS）。 DCIS是一种常见的临床诊断，本质上是 筛查是由乳腺摄影异常乳腺钙化触发的。之前 乳房X线摄影的出现，DCIS是一个偶然且相对罕见的发现。超过60,000名妇女 在美国，每年将介绍此诊断，基于证据的较弱 疾病管理指南，范围从主动监测到双侧乳房切除术。我们 提议在DCI上编译多维和多尺度数据，以构建一个前癌地图集，该地图集 可以用来更好地理解这种疾病，但也可以更好地分层进展的风险。为此，我们 已经组建了一组研究人员，具有深度和互补的临床，实验性和 DCI和乳腺癌的定量专业知识和经验。此外，我们进行了这些研究 完全考虑肿瘤的进化和生态学，因为它与预科生长有关 进展。提议的地图集结构的特定方面包括：1）几种类型的DCIS 将捕获包括前瞻性临床试验的空间和纵向信息的队列 进行主动监视，2）旨在维持相关空间组织的分析 基于3）放射学 - 遗传学 - 细胞的进化和地图集的疾病 分子登记方法，4）在包括整个肿瘤，单一的多个尺度上表征 管道和单细胞水平，5）相关参数的表征，包括突变，拷贝数 包括炎症细胞在内的变化，甲基化，基因表达和微环境元素 概况。 6）将乳腺癌的内在亚型范式纳入分析阶段，而7） 分层，空间和纵向数据可视化。总体而言，这项工作将提供全面的 指导DCIS和其他挑战者的下一代研究的平台。