Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.
Lyda Hill Department of Bioinformatics and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Sci Data. 2019 Oct 31;6(1):253. doi: 10.1038/s41597-019-0225-0.
Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models.
患者来源异种移植物（PDX）是研究肿瘤生物学的重要临床前资源。然而，PDX 肿瘤内和跨肿瘤的细胞异质性会强烈影响 PDX 研究的解释。在这里，我们生成了一个多模态、大规模的数据集，以研究转移性结直肠癌（CRC）在肿瘤模型、空间尺度以及基因组、转录组、蛋白质组和成像分析模式上的 PDX 异质性。为了展示这个数据集，我们进行了分析以评估 PDX 变异的来源，包括植入肿瘤内的解剖方位、小鼠贡献以及重复 PDX 肿瘤之间的差异。我们数据集的一个独特方面是通过免疫荧光成像对肿瘤内异质性进行深度特征分析，这使得能够从亚细胞到整个肿瘤水平等多个空间尺度上研究变异。我们的研究为研究转移性 CRC 的 PDX 模型提供了基准数据资源，并为未来对 PDX 肿瘤模型内和跨肿瘤的空间异质性进行定量研究提供了模板。
