Informatics Tools for Tumor Heterogeneity in Multiplexed Fluorescence Images

多重荧光图像中肿瘤异质性的信息学工具

• 批准号: 9094834
• 负责人: Srinivas Chakra Chennubhotla
• 金额: $ 38.44万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-04 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9094834
• 关键词: Address; Algorithmic Software; Algorithms; Archives; Atypical hyperplasia; Biological Markers; Cell Proliferation; Cell physiology; Cells; Clinical; Communities; Computer software; Computers; DNA; DNA Probes; Data; Dependence; Development; Diagnostic; Diagnostic Neoplasm Staging; Engineering; Evaluation; Fluorescent in Situ Hybridization; Formalin; Genetic; Goals; Heterogeneity; Image; Imagery; Imaging Techniques; Imaging technology; Immunofluorescence Immunologic; Individual; Informatics; Interobserver Variability; Label; Laboratories; Learning; Malignant Neoplasms; Mammaplasty; Mammary Gland Parenchyma; Maps; Measures; Methods; Molecular; Neoplasm Metastasis; Noninfiltrating Intraductal Carcinoma; Nucleic Acids; Operative Surgical Procedures; Outcome; Paraffin Embedding; Pathologist; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Plant Roots; Prognostic Marker; Proteins; RNA; RNA Probes; Research; Risk; Sampling; Scientist; Slide; Spatial Distribution; Staging; Stromal Cells; Testing; Tissue Microarray; Tissue Sample; Tissue imaging; Tissues; Tumor Tissue; Tumor stage; Universities; anticancer research; base; cancer biomarkers; cancer cell; cancer diagnosis; cancer therapy; clinically relevant; cohort; epithelial to mesenchymal transition; flexibility; fluorescence imaging; genetic profiling; indexing; individual patient; infiltrating duct carcinoma; interest; malignant breast neoplasm; non-genetic; open source; prognostic; protein expression; prototype; public health relevance; response; software development; spatial relationship; tool; treatment response; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; user-friendly

 DESCRIPTION (provided by applicant): Informatics Tools for Tumor Heterogeneity in Multiplexed Fluorescence Images Comprehensive genetic profiling has revealed intrinsic molecular variability, or intra-tumor heterogeneity (ITH), in multiple cancers. Heterogeneity is rooted in both genetic and non-genetic factors and evolves within the context of a tumor microenvironment (TME). Not surprisingly, genetic, phenotypic, and TME heterogeneity present major obstacles to optimal cancer diagnosis and treatment; however, the importance of spatial patterning in ITH has been largely overlooked. The spatial distribution of heterogeneity can be critically analyzed with imaging of tissue sections or tumor microarrays (TMAs) using methods such as immunofluorescence (IF) for proteins and fluorescence in situ hybridization (FISH) for DNA and RNA. These fluorescence imaging techniques probe the tumor and surrounding tissue for the expression of proteins, DNA, and RNA in the context of individual cells, sub-cellular domains and clusters of cells within tissue sections. Typically IF/FISH has been restricted to no more than 4-7 proteins/nucleic acids labeled per slide (multiplexed), but new technological advances now allow up to 60 proteins and a few RNA or DNA probes to be labeled on the same multicellular tissue section of up to ca.10 mm (hyperplexed). Larger tumor domains can be analyzed by stitching together images from tissue sections taken from adjacent regions of the tumor. However, the ability to analyze spatial relationships between proteins and nucleic acids at this scale raises several new informatics challenges, such as how to quantitate and characterize spatial ITH and how to interpret ITH data. To address these challenges, a collaborative team of computational biologists, cancer biologists and pathologists at the University of Pittsburgh and engineers and computer scientists at the General Electric Global Research Center (GRC) will develop software for use by cancer biologists and clinicians to quantitate, interpret and visualize spatial ITH in the context of their particular application and s a first step toward constructing diagnostics based on both cancer biomarker expression levels and spatial relationships between cancer and stromal cells.

 描述（由申请人提供）：多重荧光图像中肿瘤异质性的信息学工具综合遗传分析揭示了多种癌症的内在分子变异性或肿瘤内异质性（ITH）。异质性起源于遗传和非遗传因素，并在肿瘤微环境（TME）的背景下演变。毫不奇怪，遗传、表型和TME异质性是最佳癌症诊断和治疗的主要障碍;然而，ITH中空间模式的重要性在很大程度上被忽视了。异质性的空间分布可以通过组织切片或肿瘤微阵列（TMA）的成像来严格分析，所述成像使用诸如用于蛋白质的免疫荧光（IF）和用于DNA和RNA的荧光原位杂交（FISH）的方法。这些荧光成像技术探测肿瘤和周围组织中的单个细胞、亚细胞结构域和组织切片内的细胞簇的蛋白质、DNA和RNA的表达。通常IF/FISH已被限制为每个载玻片标记不超过4 - 7种蛋白质/核酸（多路复用），但新的技术进步现在允许多达60种蛋白质和一些RNA或DNA探针被标记在相同的多细胞组织切片上长达约10 mm（多路复用）。较大的肿瘤区域可以通过将取自肿瘤相邻区域的组织切片的图像拼接在一起来分析。然而，在这种规模下分析蛋白质和核酸之间的空间关系的能力提出了一些新的信息学挑战，如如何定量和表征空间ITH以及如何解释ITH数据。为了应对这些挑战，匹兹堡大学的计算生物学家、癌症生物学家和病理学家以及通用电气全球研究中心（GRC）的工程师和计算机科学家组成的合作团队将开发软件，供癌症生物学家和临床医生使用，在其特定应用程序的上下文中解释和可视化空间ITH，这是基于两者构建诊断的第一步癌症生物标志物表达水平和癌症与基质细胞之间的空间关系。