Washington University Human Tumor Atlas Research Center

华盛顿大学人类肿瘤图谱研究中心

基本信息

批准号：
10242181
负责人：
Samuel Achilefu
金额：
$ 177.19万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10242181
关键词：
3-Dimensional Affect Anatomy Archives Area Atlases Biopsy Biopsy Specimen Cancer Biology Cancer Center Cancer Model Cancer Patient Cataloging Cells Clinical Clinical Oncology Clinical Research Clinical Treatment Clinical Trials Clonal Evolution Clonality Collaborations Communities Comprehensive Cancer Center Cytometry DNA Data Data Analyses Data Set Dimensions Discipline Disease Drug resistance Ecosystem Ensure Evolution FAIR principles Fostering Foundations Future Generations Genes Genome Genomics Glioblastoma Goals Histopathology Human Image Immune Immunofluorescence Immunologic Individual Infrastructure Institutes Knowledge Link Location Magnetic Resonance Imaging Malignant Neoplasms Measures Medicine Methodology Minority Groups Modeling Molecular Mutate Mutation Neoplasm Metastasis Organ Pancreatic Ductal Adenocarcinoma Pathology Pathway Analysis Pathway interactions Patient Recruitments Patients Phase Phenotype Physicians Pilot Projects Positron-Emission Tomography Process Prognosis Proteins Proteome Proteomics Recurrence Reporting Research Research Personnel Resistance Sampling Series Site Slice Solid Neoplasm Spatial Distribution Specimen Structure Time Tissues Translational Research United States National Institutes of Health Universities Validation Washington analysis pipeline anticancer research biomarker discovery cancer cell cancer therapy cancer type cell type chemotherapy clinical application clinical care clinical imaging cohesion data integration density diverse data drug development exome sequencing experience genome sequencing imaging informatics improved in vivo innovation liquid chromatography mass spectrometry malignant breast neoplasm medical schools metabolome metabolomics multidisciplinary neoplastic cell new technology new therapeutic target patient population relational database response single-cell RNA sequencing synergism therapeutic development therapy resistant three dimensional structure three-dimensional modeling tool transcriptome sequencing triple-negative invasive breast carcinoma tumor tumor progression whole genome

3-Dimensional Affect Anatomy Archives Area Atlases Biopsy Biopsy Specimen Cancer Biology Cancer Center Cancer Model Cancer Patient Cataloging Cells Clinical Clinical Oncology Clinical Research Clinical Treatment Clinical Trials Clonal Evolution Clonality Collaborations Communities Comprehensive Cancer Center Cytometry DNA Data Data Analyses Data Set Dimensions Discipline Disease Drug resistance Ecosystem Ensure Evolution FAIR principles Fostering Foundations Future Generations Genes Genome Genomics Glioblastoma Goals Histopathology Human Image Immune Immunofluorescence Immunologic Individual Infrastructure Institutes Knowledge Link Location Magnetic Resonance Imaging Malignant Neoplasms Measures Medicine Methodology Minority Groups Modeling Molecular Mutate Mutation Neoplasm Metastasis Organ Pancreatic Ductal Adenocarcinoma Pathology Pathway Analysis Pathway interactions Patient Recruitments Patients Phase Phenotype Physicians Pilot Projects Positron-Emission Tomography Process Prognosis Proteins Proteome Proteomics Recurrence Reporting Research Research Personnel Resistance Sampling Series Site Slice Solid Neoplasm Spatial Distribution Specimen Structure Time Tissues Translational Research United States National Institutes of Health Universities Validation Washington analysis pipeline anticancer research biomarker discovery cancer cell cancer therapy cancer type cell type chemotherapy clinical application clinical care clinical imaging cohesion data integration density diverse data drug development exome sequencing experience genome sequencing imaging informatics improved in vivo innovation liquid chromatography mass spectrometry malignant breast neoplasm medical schools metabolome metabolomics multidisciplinary neoplastic cell new technology new therapeutic target patient population relational database response single-cell RNA sequencing synergism therapeutic development therapy resistant three dimensional structure three-dimensional modeling tool transcriptome sequencing triple-negative invasive breast carcinoma tumor tumor progression whole genome

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项目摘要

Project Summary/Abstract: Overall Diverse areas of cancer research have progressed to the point that it is now feasible to meaningfully integrate research data and clinical information across the molecular, cellular, and tissue realms into a larger, more detailed picture of the onco-dynamics of cancer, including spatial-temporal details during cancer treatment and progression. Physicians and researchers at Washington University School of Medicine (WUSM) and the Siteman Cancer Center (WUSM-SCC) are longtime leaders in the allied sub-disciplines of cancer, including genomics, proteomics, imaging, functional characterization, pathology, clinical trials, and clinical care. WUSM- SCC is an NCI-designated Comprehensive Cancer Center, which sees ~9,000 new cancer patients annually. Building on our expertise, established infrastructure, large patient population, and extraordinary institutional commitment, we propose to develop the Washington University Human Tumor Atlas Research Center (WU- HTARC) within the NIH Human Tumor Analysis Network (HTAN). We will focus on generating organ-specific human tumor atlases for three high priority cancer types associated with exceptionally poor prognosis: the triple negative breast cancer (TNBC), glioblastoma (GBM), and pancreatic ductal adenocarcinoma (PDAC). Collectively, we will analyze ~1,600-2000 samples collected from spatially separated locations and at different time points along the clinical treatment course from 300-375 patients (selected from ~750 recruited patients) for the duration of the project. In addition to standard histopathological analyses, bulk DNA/RNA sequencing, proteomics, and clinical imaging, etc., we will conduct cutting-edge, comprehensive analyses, including single cell RNA-Seq (scRNA-Seq), multiplexed immunofluorescent protein localization (MxIF), mass cytometry/Cytometry by Time of Flight (CyTOF) cellular characterization, metabolomics analysis, innovative imaging, and 3-D modeling. We have established infrastructure covering the aforementioned areas, from specimen procurement (Biospecimen Unit), to multidisciplinary analyses modules (Characterization Unit), and to analysis pipelines (Data Analysis Unit). Data generated from this study will be valuable for revealing the clonal evolution of the tumor cells from longitudinally collected specimens and to reconstruct the tumor ecosystem involving non- cancer cells and acellular structures. Our atlases will have comprehensive data integration at the 3D level over time, providing unprecedented 4D models for the 3 selected cancer types. Our established infrastructure and continuous efforts in incorporating new technologies in omics, imaging, and informatics, will help ensure our atlases will be the state-of-the-art, taking full advantage of the latest progress in these fields and will continue to evolve beyond the pilot phase to facilitate cancer research and improve clinical care. The proposed atlases target a set of critically important clinical questions, including tumor resistance that has long been a challenge for GBM treatment and also an important clinical problem in BRCA/TNBC and PDAC, in which minority populations are disproportionately affected. Other emphases are BRCA response/resistance to chemotherapy, PDAC metastasis, and GBM local recurrence in conjunction with resistance to therapy. These atlases can cross reference each other for pan-cancer analyses. We will also seek to cooperate with any Pre- Cancer Atlas (PCA) centers studying these disease types to maximize the temporal continuity of research on these cancers. The similarities and differences among the three selected cancer types will provide synergy among the three atlases and will also allow us to accumulate valuable knowledge in atlas building for other cancer types. The data, specimens, and experience gained by our center will be shared with HTAN and the broader research community to foster the next important discoveries in personalized cancer medicine.

项目摘要/摘要：总体癌症研究的各个领域已经发展到现在有意义地整合现在是可行的。整个分子，细胞和组织领域的研究数据和临床信息都变成更大，更大的癌症全动力学的详细图片，包括癌症治疗期间的时空细节和进展。华盛顿大学医学院（WUSM）的医师和研究人员和现场癌症中心（WUSM-SCC）是癌症盟军亚科的长期领导者，包括基因组学，蛋白质组学，成像，功能表征，病理，临床试验和临床护理。 wusm- SCC是NCI指定的综合癌症中心，每年约9,000名新的癌症患者。建立我们的专业知识，建立的基础设施，庞大的患者群体和非凡的机构承诺，我们建议开发华盛顿大学人类肿瘤研究中心（Wu- NIH人类肿瘤分析网络（HTAN）中的HTARC）。我们将专注于生成针对三种相关的三种高优先级类型的器官特异性人肿瘤图谱预后异常不佳：三重阴性乳腺癌（TNBC），胶质母细胞瘤（GBM）和胰腺导管腺癌（PDAC）。总的来说，我们将分析约1,600-2000个样本沿着临床治疗课程的空间分离位置和不同时间点300-375 在该项目期间，患者（从约750名招募患者中选择）。除了标准组织病理学分析，大量DNA/RNA测序，蛋白质组学和临床成像等，我们将进行尖端的，全面的分析，包括单细胞RNA-seq（SCRNA-Seq），多重免疫荧光蛋白定位（MXIF），质量细胞术/细胞术，按飞行时间（Cytof）细胞表征，代谢组学分析，创新成像和3-D建模。我们已经建立了涵盖上述区域的基础设施，从标本采购（生物传播单元），多学科分析模块（表征单元）和分析管道（数据分析单位）。从这项研究产生的数据对于揭示克隆的演变是有价值的来自纵向收集标本的肿瘤细胞，并重建涉及非 - 的肿瘤生态系统癌细胞和细胞结构。我们的地图集将在3D级别具有全面的数据集成时间，为3种选定的癌症类型提供前所未有的4D模型。我们已建立的基础设施和不断努力将新技术纳入OMIC，成像和信息学，将有助于确保我们的地图集将是最先进的，充分利用这些领域的最新进展，并将继续超越试验阶段以促进癌症研究并改善临床护理。提出的地图集针对一组至关重要的临床问题，包括具有长期以来一直是GBM治疗的挑战，也是BRCA/TNBC和PDAC的重要临床问题哪些少数群体受到不成比例的影响。其他重点是BRCA对化学疗法，PDAC转移和GBM局部复发与对治疗的耐药性。这些地图酶可以互相参考以进行泛伴奏分析。我们还将寻求与任何前癌症地图集（PCA）中心研究这些疾病类型，以最大程度地提高研究的时间连续性这些癌症。三种选定癌症类型之间的相似性和差异将提供协同作用在这三个地图集中，还将使我们能够在Atlas建筑中积累宝贵的知识癌症类型。我们中心获得的数据，标本和经验将与HTAN共享更广泛的研究社区，以促进个性化癌症医学中的下一个重要发现。