Data Analysis Unit

数据分析单元

• 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
• 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

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.

摘要 肿瘤药物反应和耐药性是由肿瘤生态系统驱动的，其中包括一个复杂的组合 肿瘤细胞的特性和组织结构中的细胞之间的复杂相互作用， 周围的免疫细胞和基质细胞。然而，我们缺乏一个系统的框架， 肿瘤亚型和患者，我们可以预测，研究和了解药物反应， 更精确的诊断和更好的治疗。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，使这些发现，但需要克服关键的方法论 挑战数据分析部门（DAU）将在三个转移性肿瘤的背景下利用这一机会。 癌症（黑色素瘤、结肠癌和乳腺癌）及其对免疫疗法或靶向疗法的抗性。的DAU 将开发下一代的计算方法，从复杂的，巨大的， 多样化和多维的空间和细胞数据，并通过制定具体的 关于药物效果和患者反应的预测模型。为此，我们将设计自适应功耗分析 用于实验设计方法，以驱动样本、数据模态和实验参数的选择 以系统的方式。我们将开发方法来量化每个数据模态的特征， 方式。我们将建立一个基础设施，以确定共享的细胞，组织学和临床的支架， 特征，以建立肿瘤图谱，并说明这些图谱在发现肿瘤中的价值。 耐药机制。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，以实现即时访问，与人类肿瘤图谱网络（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将创建方法框架， 本文中的肿瘤图谱以及在HTAN和更广泛的社区中开发的其他肿瘤图谱。