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. 2016 Apr 12;7(15):20773-87.
doi: 10.18632/oncotarget.8014.

Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers

James R Bradford  1 , Mark Wappett  2 , Garry Beran  2 , Armelle Logie  2 , Oona Delpuech  2 , Henry Brown  2 , Joanna Boros  2 , Nicola J Camp  3 , Robert McEwen  2 , Anne Marie Mazzola  4 , Celina D'Cruz  4 , Simon T Barry  2
Affiliations

Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers

James R Bradford et al. Oncotarget. .

Abstract

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.

Keywords: RNA-Seq; biomarker discovery; patient-derived xenograft; pre-clinical research; tumor stroma.

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Conflict of interest statement

CONFLICTS OF INTEREST

All authors except NJC have either a current or past affiliation to the commercial funders of this research (AstraZeneca).

Figures

Figure 1
Figure 1. Application of non-negative matrix factorization (NMF) to optimal clustering of human and mouse gene expression
(A) disease representation across the 79 PDX models. (B) consensus matrix at k = 9 for the human transcriptome. (C) contributing cancer types and mean consensus value of each human cluster. “Representative” disease indicates the majority cancer type in the cluster, and numbers of models are given in brackets. Mean consensus value was computed from 200 runs of NMF. (D) consensus matrix at k = 9 for the mouse transcriptome. (E) meta-data breakdown in each mouse cluster and mean consensus value. (F) functional enrichment of the meta-genes driving the mouse clustering. Only clusters with significant functional enrichment (FDR < 0.05) are shown.
Figure 2
Figure 2. Human MIF and mouse Ddx6 are strongly anti-correlated and are identified as cross-species hubs
(A) Cytoscape [59] rendered human (red boxes) and mouse (blue) gene co-expression network where nodes are genes and edges indicate gene pairs achieving r > 0.85 (grey) or r < −0.85 (green). Magnified view shows sub-network of first and second neighbors of human MIF and mouse Ddx6. (B) scatterplot showing anti-correlation (r = −0.90) between human MIF and mouse Ddx6. (C) r-values between all combinations of human/mouse MIF and DDX6 mRNA expression profiles.
Figure 3
Figure 3. Compartment-specific gene expression markers of BTNBC
(A) comparison of ESTIMATE stromal score between PDX, TCGA, CCLE and clinical stroma samples. (B) fold changes achieved by MMP12 between BTNBC and ER+ luminal-B samples across all platforms (*p < 0.01). (C) derivation of tumor specific BTNBC markers exclusive to cell line and PDX datasets. (D) emergence of tumor specific markers in low stroma TCGA samples (*p < 0.05). (E) overlap of stromal [10], EMT [20], CSC [21] and reactive stroma [22] signatures with BTNBC markers derived from human PDX and TCGA samples. (F) comparison of log10 p-values achieved across sample types by overlap with each of the four signatures (*p < 0.05). CSC: cancer stem cell, EMT: epithelial-mesenchymal transition, MMP12: Matrix Metalloproteinase 12.
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