Multiscale Framework for Molecular Heterogeneity Analysis

描述（由申请人提供）：基因组分析已成为选择许多疾病治疗方法的常规做法，使患者能够分类到与特定治疗改善结果相关的类别。这种方法的一个潜在的缺点是用于分析的组织的巨大异质性。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目标，

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.