DeWard Aaron, Critchley-Thorne Rebecca J
Cernostics, Inc., 235 William Pitt Way, Pittsburgh, PA, 15238, USA.
Methods Mol Biol. 2018;1711:261-273. doi: 10.1007/978-1-4939-7493-1_13.
The complex network of the tissue system, in both pre-neoplastic tissues and tumors, demonstrates the need for a systems biology approach to cancer pathology, in which quantification of key tissue system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making. A systems biology approach to cancer pathology enables integration of key system features that are relevant to diagnoses, patient outcomes, and responses to therapies. Key tissue system features relevant to cancer pathology include molecular and morphologic abnormalities in epithelia, cellular changes in the stroma such as immune infiltrates, and relationships between components of the system, such as interactions and spatial relationships between epithelial and stromal components, and also between specific immune cell subsets. Here, we describe a method for objective quantification of multiple epithelial and stromal biomarkers in the context of tissue architecture to generate a high dimensional tissue profile that can be used to build multivariable predictive models for cancer pathology.
在癌前组织和肿瘤中,组织系统的复杂网络表明癌症病理学需要一种系统生物学方法,即将关键组织系统过程的量化与信息学工具相结合,以生成可用于辅助临床决策的可操作评分。癌症病理学的系统生物学方法能够整合与诊断、患者预后及治疗反应相关的关键系统特征。与癌症病理学相关的关键组织系统特征包括上皮细胞的分子和形态异常、基质中的细胞变化(如免疫浸润)以及系统各组成部分之间的关系,如上皮和基质成分之间的相互作用和空间关系,以及特定免疫细胞亚群之间的关系。在此,我们描述了一种在组织结构背景下客观量化多种上皮和基质生物标志物以生成高维组织图谱的方法,该图谱可用于构建癌症病理学的多变量预测模型。