Multiscale Framework for Molecular Heterogeneity Analysis

描述(由申请人提供)：基因组图谱已成为选择许多疾病治疗方法的常规做法，能够将患者分类为与特定治疗结果改善相关的类别。这一方法的一个潜在批评者是用于侧写的组织中的巨大异质性。从相对较小的活检中获得的基因组分类受到受影响组织中广泛的区域差异的影响。细胞尺度上的异质性也会模糊治疗的目标， 因为具有不同分子图谱的细胞在基因组图谱中是均质的。实现更好的治疗将在很大程度上取决于了解个体内部分子异质性的能力，这一挑战需要新的方法来组织、分析和整合来自多个空间和分子尺度的数据。这项建议描述了一个信息学框架，以表征基于组织的研究的异质性。该框架将结合成像信息学和基因组学来描述多个空间和分子尺度上的分子异质性。成像组件将利用一种新的量子点技术，该技术能够对单个样本中的多个蛋白质表达路径进行详细映射。荧光原位杂交成像将用于测量DNA含量。全幻灯片数字化将使计算机算法能够捕获数亿个细胞的分子图谱，计算定量特征来描述它们的表达模式和DNA含量。将使用一种新型的主动机器学习分类器来使用描述分子生物学和细胞解剖学的本体来注释细胞，从而能够在生物学背景下分析幻灯片，从而生成对每个细胞具有生物学意义的描述。细胞边界、特征和注释将通过病理分析成像标准(PAIS)数据库进行整合，为数据挖掘分析提供支持。将开发挖掘方法来发现细胞表型的丰富，并分析细胞相对于血管等结构的空间布局，以发现组织微环境对周围细胞关键表达途径的影响。这些工具将应用于脑胶质母细胞瘤的研究，但也适用于其他实体组织疾病的研究。这项科学研究将使用在一项新的临床试验中切除的组织，该试验准确地定义了侵袭的肿瘤边缘、体积和富含坏死的核心。将对组织进行基因表达和成像分析，以生成每个区域的配对基因组成像图谱。挖掘这些区域的成像和基因表达谱将识别肿瘤内细胞表型的差异 并说明了基因组分类的变异程度。配对的成像和基因表达谱也将被挖掘，以确定特定表达类别和成像观察之间的关系，以说明异质性的完整图景。将部署一个项目储存库来传播图像、分析管道和分析结果。该存储库将为脑肿瘤研究和获取开源工具提供公共资源。

DESCRIPTION (provided by applicant): Genomic profiling has become a routine practice in selecting treatments for many diseases, enabling the classification of patients into categories that associate with improved outcomes for specific treatments. One potential detractor to this approach is the tremendous heterogeneity in tissues used for profiling. Genomic classifications, obtained from a relatively small biopsy, are subject to influence from broad, regional variations in the affected tissue. Heterogeneity on a cellular scale can also obscure the target of treatment, as cells with distinct molecular profiles are homogenized in genomic profiling. Realizing better therapies will depend greatly on the ability to understand molecular heterogeneity within an individual, a challenge that necessitates new approaches to organize, analyze and integrate data from multiple spatial and molecular scales. This proposal describes an informatics framework to characterizing heterogeneity for tissue based studies. The framework will combine imaging informatics with genomics to describe molecular heterogeneity at multiple spatial and molecular scales. The imaging component will leverage a novel quantum dot technology that enables detailed mapping of multiple protein expression pathways within a single sample. Fluorescence in situ hybridization imaging will be used to measure DNA content. Whole-slide digitization will enable computer algorithms to capture molecular profiles of hundreds of millions of cells, calculating quantitative features to describe their expression patterns and DNA content. Biologically meaningful descriptions of each cell will be generated using a novel active machine learning classifier to annotate cells with an ontology describing molecular biology and cell anatomy, enabling slides to be analyzed in a biological context. Cell boundaries, features, and annotations will be integrated through the Pathology Analytic Imaging Standards (PAIS) database to provide support for data mining analysis. Mining methods will be developed to find the enrichment of cellular phenotypes, and to analyze the spatial layout of cells with respect to structures like blood vessels to discover the influence of the tissue microenvironment on key expression pathways in surrounding cells. These tools will be applied to studies of glioblastoma brain tumors, but are relevant for studies of other solid tissue diseases. The scientific study wil use tissues resected in a novel clinical trial that accurately defines the invading tumor margin, bulk and necrosis-rich core. Tissues will be analyzed for gene expression and imaging to generate a paired genomic-imaging profile for each region. Mining the imaging and gene expression profiles of these regions will identify intra-tumoral differences in cellular phenotypes and illustrate the extent of variation in genomic classifications. The paired imaging and gene expression profiles will also be mined to determine relationships between specific expression classes and the imaging observations to illustrate a complete picture of heterogeneity. A project repository will be deployed to disseminate images, analysis pipelines and analytic results. This repository will provide a public resource for brain tumor research and access to open source tools.