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Sci Data. 2019 Oct 31;6(1):253. doi: 10.1038/s41597-019-0225-0.

A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors.

Author information

1
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. Satwik.Rajaram@utsouthwestern.edu.
2
Lyda Hill Department of Bioinformatics and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. Satwik.Rajaram@utsouthwestern.edu.
3
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.
4
Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
5
Department of Pathology, University of California San Francisco, San Francisco, CA, USA.
6
Biorepository and Tissue Biomarker Technology Core, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
7
Division of Hematology/Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
8
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. AltschulerAndWu@gmail.com.

Abstract

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.

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