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

Satwik Rajaram, Maike A. Roth, Julia Malato, Scott VandenBerg, Byron Hann, Chloe E. Atreya, Steven J. Altschuler, Lani F. Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Number of pages1
JournalScientific data
Volume6
Issue number1
DOIs
StatePublished - Oct 31 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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