Data Analysis Unit

数据分析单元

基本信息

批准号：
10259733
负责人：
GAD A GETZ
金额：
$ 49.88万
依托单位：
DANA-FARBER CANCER INST
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：
2018-09-24 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10259733
关键词：
Adoption Affect Algorithms Architecture Atlases Awareness Biological Breast Cell Communication Cell Nucleus Cells Cellular Structures Clinical Collaborations Colon Communities Complex Computer Analysis Computer Models Computing Methodologies Data Data Analyses Data Science Development Disseminated Malignant Neoplasm Drug resistance Ecosystem Ensure Experimental Designs Genetic Geography Goals Histologic Human Immune Immunotherapy Infrastructure Knowledge Learning Malignant - descriptor Malignant Neoplasms Metadata Methodology Methods Modality Modeling Molecular Molecular Structure Morphology Neighborhoods Non-Malignant Patients Pharmaceutical Preparations Property Recurrence Reproducibility Research Personnel Resistance Resolution Sampling Software Tools Specimen Statistical Models Stromal Cells Structure System Techniques Testing Therapeutic Tissues Tumor Subtype Validation Visualization Visualization software Work cell type clinical application clinically relevant computerized data processing deep learning design innovation melanoma neoplastic cell next generation novel open source patient response patient stratification personalized diagnostics power analysis precision medicine predictive modeling programs prospective query tools response scaffold sound statistical learning targeted treatment therapy resistant transcriptome sequencing tumor

项目摘要

Abstract Tumor drug response and resistance is driven by the tumor ecosystem, which includes an intricate combination of tumor cell properties and complex interactions among those cells organized in histological structures with surrounding immune and stromal cells. However, we lack a systematic framework of this ecosystem across tumor subtypes and patients upon which we can predict, study, and understand drug response in order to enable more precise diagnostics and better therapeutics. Tumor atlases at high spatial, cellular and genetic resolution provide an extraordinary opportunity to make these discoveries but require overcoming key methodological challenges. The Data Analysis Unit (DAU) will take advantage of this opportunity in the context of three metastatic cancers (melanoma, colon, and breast) and their resistance to immunotherapy or targeted therapy. The DAU will develop the next generation of computational methods to reconstruct these atlases from complex, massive, diverse and multidimensional spatial and cellular data, and ensure their immediate impact by formulating specific predictive models about drug effects and patient response. To do this, we will design adaptive power analyses for experimental design methods to drive the choice of samples, data modalities, and experimental parameters in a systematic way. We will develop approaches to quantify features from each data modality and across modalities. We will create an infrastructure to identify the scaffold of shared cellular, histological and clinical features across samples to build a tumor atlas, and illustrate the value of these atlases in discovering the mechanisms of drug resistance. Finally, we will design methods for querying, visualizing, and sharing atlas knowledge at scale to enable immediate access, partner with others in the Human Tumor Atlas Network (HTAN) and impact researchers and clinicians. Overall, the DAU will create the methodological framework to create the tumor atlases herein and for others developed in HTAN and the broader community.

抽象的肿瘤药物反应和耐药性由肿瘤生态系统驱动，其中包括复杂的组合在组织学结构中组织的肿瘤细胞特性和复杂相互作用与与围绕免疫和基质细胞。但是，我们缺乏整个生态系统的系统框架我们可以预测，研究和理解药物反应以实现的肿瘤亚型和患者更精确的诊断和更好的治疗学。高空间，细胞和遗传分辨率的肿瘤图谱提供了一个非凡的机会来进行这些发现，但需要克服关键方法论挑战。数据分析单元（DAU）将在三个转移性的背景下利用这一机会癌症（黑色素瘤，结肠和乳腺癌）及其对免疫疗法或靶向疗法的抗性。 dau 将开发下一代计算方法，以从复杂，大型，多样化和多维的空间和细胞数据，并通过制定特定有关药物影响和患者反应的预测模型。为此，我们将设计自适应能力分析用于实验设计方法，以驱动样品，数据方式和实验参数的选择以系统的方式。我们将开发方法来量化每种数据模式以及跨越的功能方式。我们将创建一个基础设施来识别共享的细胞，组织学和临床的支架跨样品建立肿瘤地图集的特征，并说明这些图书馆的价值耐药性机制。最后，我们将设计用于查询，可视化和共享地图集的方法大规模的知识以使能够立即访问，与人类肿瘤网络（HTAN）中的其他人合作并影响研究人员和临床医生。总体而言，DAU将创建方法论框架以创建肿瘤地图集在此处以及在HTAN和更广泛的社区中开发的其他肿瘤地图集。