Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers
Metrics: PDF 2891 views | HTML 3802 views | ?
James R. Bradford1, Mark Wappett2, Garry Beran2, Armelle Logie2, Oona Delpuech2, Henry Brown2, Joanna Boros2, Nicola J. Camp3, Robert McEwen2, Anne Marie Mazzola4, Celina D’Cruz4, Simon T. Barry2
1Department of Oncology and Metabolism, University of Sheffield, Sheffield, South Yorkshire, UK
2Oncology iMED, AstraZeneca Pharmaceuticals, Alderley Park, Cheshire, UK
3Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
4Oncology iMED, AstraZeneca Pharmaceuticals, Gatehouse Park, Massachusetts, USA
James R. Bradford, e-mail: [email protected]
Keywords: patient-derived xenograft, RNA-Seq, tumor stroma, biomarker discovery, pre-clinical research
Received: November 09, 2015 Accepted: February 18, 2016 Published: March 09, 2016
The tumor microenvironment is emerging as a key regulator of cancer growth and progression, however the exact mechanisms of interaction with the tumor are poorly understood. Whilst the majority of genomic profiling efforts thus far have focused on the tumor, here we investigate RNA-Seq as a hypothesis-free tool to generate independent tumor and stromal biomarkers, and explore tumor-stroma interactions by exploiting the human-murine compartment specificity of patient-derived xenografts (PDX).
Across a pan-cancer cohort of 79 PDX models, we determine that mouse stroma can be separated into distinct clusters, each corresponding to a specific stromal cell type. This implies heterogeneous recruitment of mouse stroma to the xenograft independent of tumor type. We then generate cross-species expression networks to recapitulate a known association between tumor epithelial cells and fibroblast activation, and propose a potentially novel relationship between two hypoxia-associated genes, human MIF and mouse Ddx6. Assessment of disease subtype also reveals MMP12 as a putative stromal marker of triple-negative breast cancer. Finally, we establish that our ability to dissect recruited stroma from trans-differentiated tumor cells is crucial to identifying stem-like poor-prognosis signatures in the tumor compartment.
In conclusion, RNA-Seq is a powerful, cost-effective solution to global analysis of human tumor and mouse stroma simultaneously, providing new insights into mouse stromal heterogeneity and compartment-specific disease markers that are otherwise overlooked by alternative technologies. The study represents the first comprehensive analysis of its kind across multiple PDX models, and supports adoption of the approach in pre-clinical drug efficacy studies, and compartment-specific biomarker discovery.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.