Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images.
Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images.
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DOI：
10.1038/labinvest.2014.153
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发表时间：
2015-04
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计算、成像和基因组学的技术进步为使用定量方法探索组织学、分子事件和临床结果之间的关系创造了新的机会。载玻片扫描设备现在能够快速产生大量的数字图像档案，以高分辨率捕获组织学细节。计算和图像分析算法的相应进步使档案挖掘能够提取组织学描述，从基本的人类注释到数亿细胞的自动和精确定量形态学表征。这些成像能力代表了基于组织的研究的新维度，当与基因组和临床终点结合时，可用于探索肿瘤微环境的生物学特征，并发现遗传改变和患者结局的新形态学生物标志物。在本文中，我们回顾了定量成像技术的发展，并说明了如何将图像特征与临床和基因组数据相结合，以研究癌症的基本问题。利用胶质母细胞瘤（GBM）研究中的激励性例子，我们展示了癌症基因组图谱（TCGA）的公共数据如何作为一个开放平台来进行基于计算机组织的研究，整合现有的数据资源。我们展示了这些方法如何用于探索肿瘤微环境与基因组改变和基因表达模式的关系，并定义预测遗传改变和临床结果的核形态特征。定量成像和综合分析领域的挑战，限制和新出现的机会也进行了讨论。
Technological advances in computing, imaging and genomics have created new opportunities for exploring relationships between histology, molecular events and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high-resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.