Heindl Andreas, Nawaz Sidra, Yuan Yinyin
1] Centre for Molecular Pathology, The Institute of Cancer Research, London, UK [2] Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
Lab Invest. 2015 Apr;95(4):377-84. doi: 10.1038/labinvest.2014.155. Epub 2015 Jan 19.
The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data- the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.
采用自动图像分析技术的数字病理学新兴领域，正以临床诊断为核心，给传统病理学带来变革。组织学图像提供了分子谱分析或组学数据中所没有的重要肿瘤特征——单细胞分辨率下肿瘤和基质细胞的空间背景。绘制癌症细胞和正常细胞的空间及形态模式的方法，有助于更全面地理解高度异质性的肿瘤微环境。本综述聚焦于有助于拓展我们对肿瘤微环境瘤内空间异质性的认识的方法，以及它们与分子谱分析技术的潜在协同作用。
